Ensure reflect methods for all operators

src/include/migraphx/op/abnormal_ops.hpp

@@ -39,6 +39,11 @@ struct undefined
 struct unknown
 {
     std::string op;
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.op, "op"));
+    }
     std::string name() const { return "unknown:" + op; }
     shape compute_shape(std::vector<shape> input) const
     {

src/include/migraphx/op/concat.hpp

@@ -19,6 +19,13 @@ namespace op {
 struct concat
 {
     std::size_t axis = 0;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.axis, "axis"));
+    }
+    
     std::string name() const { return "concat"; }
     std::vector<std::size_t> compute_offsets(const shape& output_shape,
                                              const std::vector<argument>& args) const

src/include/migraphx/op/leaky_relu.hpp

@@ -18,19 +18,21 @@ namespace op {
 
 struct leaky_relu
 {
-    std::string name() const { return "leaky_relu"; }
     float alpha;
-    shape compute_shape(std::vector<shape> inputs) const
-    {
-        check_shapes{inputs, *this}.has(1);
-        return inputs.front();
-    }
     
     template <class Self, class F>
     static auto reflect(Self& self, F f)
     {
         return pack(f(self.alpha, "alpha"));
     }
+    
+    std::string name() const { return "leaky_relu"; }
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs, *this}.has(1);
+        return inputs.front();
+    }
+
 };
 
 } // namespace op

src/include/migraphx/operation.hpp

@@ -87,6 +87,8 @@ namespace operation_equal {
 template <class T, class U>
 auto operator==(const T& x, const U& y) -> decltype(x.name() == y.name())
 {
+    static_assert(is_reflectable<T>{} or sizeof(T) <= 1,
+                  "Missing equality operator or reflect method.");
     if(x.name() != y.name())
         return false;
     const auto& yy = any_cast<T>(y);
@@ -175,7 +177,7 @@ auto is_context_free_op(const T& x) -> decltype(is_context_free_op(
 }
 
 template <class T>
-std::ptrdiff_t output_alias_op(rank<0>, const T&, const std::vector<shape>&)
+int output_alias_op(rank<0>, const T&, const std::vector<shape>&)
 {
     return -1;
 }
@@ -188,7 +190,7 @@ auto output_alias_op(rank<1>, const T& x, const std::vector<shape>& shapes)
 }
 
 template <class T>
-std::ptrdiff_t output_alias_op(const T& x, const std::vector<shape>& shapes)
+int output_alias_op(const T& x, const std::vector<shape>& shapes)
 {
     return output_alias_op(rank<1>{}, x, shapes);
 }
@@ -239,7 +241,7 @@ auto has_finalize_op(const T&) -> decltype(has_finalize_op(rank<1>{},
  *      std::string name() const;
  *      bool is_context_free() const;
  *      bool has_finalize() const;
- *      std::ptrdiff_t output_alias(const std::vector<shape>& input) const;
+ *      int output_alias(const std::vector<shape>& input) const;
  *      void finalize(context& ctx,const shape& output,const std::vector<shape>& input) ;
  *      shape compute_shape(const std::vector<shape>& input) const;
  *      argument compute(context& ctx,const shape& output,const std::vector<argument>& input) const;
@@ -325,7 +327,7 @@ struct operation
         return (*this).private_detail_te_get_handle().has_finalize();
     }
 
-    std::ptrdiff_t output_alias(const std::vector<shape>& input) const
+    int output_alias(const std::vector<shape>& input) const
     {
         assert((*this).private_detail_te_handle_mem_var);
         return (*this).private_detail_te_get_handle().output_alias(input);
@@ -383,7 +385,7 @@ struct operation
         virtual std::string name() const                                = 0;
         virtual bool is_context_free() const                            = 0;
         virtual bool has_finalize() const                               = 0;
-        virtual std::ptrdiff_t output_alias(const std::vector<shape>& input) const = 0;
+        virtual int output_alias(const std::vector<shape>& input) const = 0;
         virtual void
         finalize(context& ctx, const shape& output, const std::vector<shape>& input) = 0;
         virtual shape compute_shape(const std::vector<shape>& input) const           = 0;
@@ -432,7 +434,7 @@ struct operation
 
         bool has_finalize() const override { return has_finalize_op(private_detail_te_value); }
 
-        std::ptrdiff_t output_alias(const std::vector<shape>& input) const override
+        int output_alias(const std::vector<shape>& input) const override
         {
 
             return output_alias_op(private_detail_te_value, input);

src/include/migraphx/reflect.hpp

@@ -11,6 +11,15 @@ inline namespace MIGRAPHX_INLINE_NS {
 
 namespace detail {
 
+struct reflect_placeholder
+{
+    template<class... Ts>
+    int operator()(Ts&&...) const
+    {
+        return 0;
+    }
+};
+
 template <class T, class Selector>
 auto reflect_impl(rank<1>, T& x, Selector f) -> decltype(T::reflect(x, f))
 {
@@ -23,8 +32,17 @@ auto reflect_impl(rank<0>, T&, Selector)
     return pack();
 }
 
+template <class T>
+auto reflectable_impl(rank<1>, T&& x) -> decltype(T::reflect(x, reflect_placeholder{}), std::true_type{});
+
+template <class T>
+auto reflectable_impl(rank<0>, T&&) -> decltype(std::false_type{});
+
 } // namespace detail
 
+template<class T>
+using is_reflectable = decltype(detail::reflectable_impl(rank<1>{}, std::declval<T>()));
+
 template <class T, class Selector>
 auto reflect(T& x, Selector f)
 {

src/targets/cpu/lowering.cpp

@@ -48,6 +48,12 @@ struct cpu_batch_norm_inference
 {
     op::batch_norm_inference op;
 
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
+
     std::string name() const { return "cpu::batch_norm_inference"; }
 
     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
@@ -107,6 +113,12 @@ struct cpu_lrn
 {
     op::lrn op;
 
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
+
     std::string name() const { return "cpu::lrn"; }
     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
     argument compute(context&, shape output_shape, std::vector<argument> args) const
@@ -144,6 +156,12 @@ struct cpu_convolution
 {
     op::convolution op;
 
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
+
     std::string name() const { return "cpu::convolution"; }
     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
     argument compute(context&, shape output_shape, std::vector<argument> args) const
@@ -190,6 +208,12 @@ struct cpu_im2col
 {
     op::im2col op;
 
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
+
     static std::string name() { return "cpu::im2col"; }
     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
 
@@ -271,6 +295,12 @@ struct cpu_pooling
 {
     op::pooling op;
 
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
+
     std::string name() const { return "cpu::pooling_" + Op::name(); }
     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
     argument compute(context&, const shape& output_shape, std::vector<argument> args) const
@@ -315,20 +345,53 @@ struct cpu_pooling
     }
 };
 
-struct cpu_contiguous
+struct cpu_op
 {
-    op::contiguous op;
-    std::string name() const { return "cpu::contiguous"; }
+    operation op;
+    std::string name() const { return "cpu::" + op.name(); }
     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
     argument compute(context&, const shape& output_shape, std::vector<argument> args) const
     {
         return op.compute(output_shape, std::move(args));
     }
+    friend bool operator==(const cpu_op& x, const cpu_op& y)
+    {
+        return x.op == y.op;
+    }
+    friend bool operator==(const cpu_op& x, const operation& y)
+    {
+        if(x.name() != y.name())
+            return false;
+        return x == any_cast<cpu_op>(y);
+    }
+    friend bool operator==(const operation& x, const cpu_op& y)
+    {
+        return y == x;
+    }
+
 };
 
+// struct cpu_contiguous
+// {
+//     op::contiguous op;
+//     std::string name() const { return "cpu::contiguous"; }
+//     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
+//     argument compute(context&, const shape& output_shape, std::vector<argument> args) const
+//     {
+//         return op.compute(output_shape, std::move(args));
+//     }
+// };
+
 struct cpu_pad
 {
     op::pad op;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
+
     std::string name() const { return "cpu::contiguous"; }
     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
     argument compute(context&, const shape& output_shape, std::vector<argument> args) const
@@ -352,20 +415,26 @@ struct cpu_pad
     }
 };
 
-struct cpu_concat
-{
-    op::concat op;
-    std::string name() const { return "cpu::concat"; }
-    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
-    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
-    {
-        return op.compute(output_shape, std::move(args));
-    }
-};
+// struct cpu_concat
+// {
+//     op::concat op;
+//     std::string name() const { return "cpu::concat"; }
+//     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
+//     argument compute(context&, const shape& output_shape, std::vector<argument> args) const
+//     {
+//         return op.compute(output_shape, std::move(args));
+//     }
+// };
 
 struct cpu_gemm
 {
     op::dot op;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
     std::string name() const { return "cpu::dot"; }
     shape compute_shape(const std::vector<shape>& inputs) const
     {
@@ -408,161 +477,161 @@ struct cpu_gemm
     }
 };
 
-struct cpu_gather
-{
-    op::gather op;
-    std::string name() const { return "cpu::gather"; }
-    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
-
-    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
-    {
-        return op.compute(output_shape, std::move(args));
-    }
-};
-
-struct identity_op
-{
-    std::string name() const { return "cpu::identity"; }
-    auto fcn() const
-    {
-        return [](auto x) { return x; };
-    }
-};
-
-struct abs_op
-{
-    std::string name() const { return "cpu::abs"; }
-    auto fcn() const
-    {
-        return [](auto x) { return std::abs(make_signed(x)); };
-    }
-};
-
-struct exp_op
-{
-    std::string name() const { return "cpu::exp"; }
-    auto fcn() const
-    {
-        return [](auto x) { return std::exp(x); };
-    }
-};
-
-struct log_op
-{
-    std::string name() const { return "cpu::log"; }
-    auto fcn() const
-    {
-        return [](auto x) { return std::log(x); };
-    }
-};
-
-struct sin_op
-{
-    std::string name() const { return "cpu::sin"; }
-    auto fcn() const
-    {
-        return [](auto x) { return std::sin(x); };
-    }
-};
-
-struct cos_op
-{
-    std::string name() const { return "cpu::cos"; }
-    auto fcn() const
-    {
-        return [](auto x) { return std::cos(x); };
-    }
-};
-
-struct tan_op
-{
-    std::string name() const { return "cpu::tan"; }
-    auto fcn() const
-    {
-        return [](auto x) { return std::tan(x); };
-    }
-};
-
-struct asin_op
-{
-    std::string name() const { return "cpu::asin"; }
-    auto fcn() const
-    {
-        return [](auto x) { return std::asin(x); };
-    }
-};
-
-struct acos_op
-{
-    std::string name() const { return "cpu::acos"; }
-    auto fcn() const
-    {
-        return [](auto x) { return std::acos(x); };
-    }
-};
-
-struct atan_op
-{
-    std::string name() const { return "cpu::atan"; }
-    auto fcn() const
-    {
-        return [](auto x) { return std::atan(x); };
-    }
-};
-
-struct sinh_op
-{
-    std::string name() const { return "cpu::sinh"; }
-    auto fcn() const
-    {
-        return [](auto x) { return std::sinh(x); };
-    }
-};
-
-struct cosh_op
-{
-    std::string name() const { return "cpu::cosh"; }
-    auto fcn() const
-    {
-        return [](auto x) { return std::cosh(x); };
-    }
-};
-
-struct tanh_op
-{
-    std::string name() const { return "cpu::tanh"; }
-    auto fcn() const
-    {
-        return [](auto x) { return std::tanh(x); };
-    }
-};
-
-struct sigmoid_op
-{
-    std::string name() const { return "cpu::sigmoid"; }
-    auto fcn() const
-    {
-        return [](auto x) { return 1.f / (1.f + std::exp(-x)); };
-    }
-};
-
-struct neg_op
-{
-    std::string name() const { return "cpu::neg"; }
-    auto fcn() const
-    {
-        return [](auto x) { return -x; };
-    }
-};
-
-struct relu_op
-{
-    std::string name() const { return "cpu::relu"; }
-    auto fcn() const
-    {
-        return [](auto x) { return std::max(decltype(x){0}, x); };
-    }
-};
+// struct cpu_gather
+// {
+//     op::gather op;
+//     std::string name() const { return "cpu::gather"; }
+//     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
+
+//     argument compute(context&, const shape& output_shape, std::vector<argument> args) const
+//     {
+//         return op.compute(output_shape, std::move(args));
+//     }
+// };
+
+// struct identity_op
+// {
+//     std::string name() const { return "cpu::identity"; }
+//     auto fcn() const
+//     {
+//         return [](auto x) { return x; };
+//     }
+// };
+
+// struct abs_op
+// {
+//     std::string name() const { return "cpu::abs"; }
+//     auto fcn() const
+//     {
+//         return [](auto x) { return std::abs(make_signed(x)); };
+//     }
+// };
+
+// struct exp_op
+// {
+//     std::string name() const { return "cpu::exp"; }
+//     auto fcn() const
+//     {
+//         return [](auto x) { return std::exp(x); };
+//     }
+// };
+
+// struct log_op
+// {
+//     std::string name() const { return "cpu::log"; }
+//     auto fcn() const
+//     {
+//         return [](auto x) { return std::log(x); };
+//     }
+// };
+
+// struct sin_op
+// {
+//     std::string name() const { return "cpu::sin"; }
+//     auto fcn() const
+//     {
+//         return [](auto x) { return std::sin(x); };
+//     }
+// };
+
+// struct cos_op
+// {
+//     std::string name() const { return "cpu::cos"; }
+//     auto fcn() const
+//     {
+//         return [](auto x) { return std::cos(x); };
+//     }
+// };
+
+// struct tan_op
+// {
+//     std::string name() const { return "cpu::tan"; }
+//     auto fcn() const
+//     {
+//         return [](auto x) { return std::tan(x); };
+//     }
+// };
+
+// struct asin_op
+// {
+//     std::string name() const { return "cpu::asin"; }
+//     auto fcn() const
+//     {
+//         return [](auto x) { return std::asin(x); };
+//     }
+// };
+
+// struct acos_op
+// {
+//     std::string name() const { return "cpu::acos"; }
+//     auto fcn() const
+//     {
+//         return [](auto x) { return std::acos(x); };
+//     }
+// };
+
+// struct atan_op
+// {
+//     std::string name() const { return "cpu::atan"; }
+//     auto fcn() const
+//     {
+//         return [](auto x) { return std::atan(x); };
+//     }
+// };
+
+// struct sinh_op
+// {
+//     std::string name() const { return "cpu::sinh"; }
+//     auto fcn() const
+//     {
+//         return [](auto x) { return std::sinh(x); };
+//     }
+// };
+
+// struct cosh_op
+// {
+//     std::string name() const { return "cpu::cosh"; }
+//     auto fcn() const
+//     {
+//         return [](auto x) { return std::cosh(x); };
+//     }
+// };
+
+// struct tanh_op
+// {
+//     std::string name() const { return "cpu::tanh"; }
+//     auto fcn() const
+//     {
+//         return [](auto x) { return std::tanh(x); };
+//     }
+// };
+
+// struct sigmoid_op
+// {
+//     std::string name() const { return "cpu::sigmoid"; }
+//     auto fcn() const
+//     {
+//         return [](auto x) { return 1.f / (1.f + std::exp(-x)); };
+//     }
+// };
+
+// struct neg_op
+// {
+//     std::string name() const { return "cpu::neg"; }
+//     auto fcn() const
+//     {
+//         return [](auto x) { return -x; };
+//     }
+// };
+
+// struct relu_op
+// {
+//     std::string name() const { return "cpu::relu"; }
+//     auto fcn() const
+//     {
+//         return [](auto x) { return std::max(decltype(x){0}, x); };
+//     }
+// };
 
 struct leaky_relu_op
 {
@@ -590,6 +659,12 @@ template <typename Op>
 struct cpu_unary
 {
     Op op;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op.op, f);
+    }
     std::string name() const { return op.name(); }
     shape compute_shape(const std::vector<shape>& inputs) const { return inputs.front(); }
     argument compute(context&, const shape& output_shape, std::vector<argument> args) const
@@ -646,6 +721,13 @@ struct softmax2d
 struct cpu_logsoftmax
 {
     op::logsoftmax op;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
+
     std::string name() const { return "cpu::logsoftmax"; }
     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
 
@@ -712,86 +794,86 @@ struct cpu_logsoftmax
     }
 };
 
-struct add_op
-{
-    std::string name() const { return "add"; }
-    auto fcn() const
-    {
-        return [](auto x, auto y) { return x + y; };
-    }
-};
-
-struct sub_op
-{
-    std::string name() const { return "sub"; }
-    auto fcn() const
-    {
-        return [](auto x, auto y) { return x - y; };
-    }
-};
-
-struct mul_op
-{
-    std::string name() const { return "mul"; }
-    auto fcn() const
-    {
-        return [](auto x, auto y) { return x * y; };
-    }
-};
-
-struct div_op
-{
-    std::string name() const { return "div"; }
-    auto fcn() const
-    {
-        return [](auto x, auto y) { return x / y; };
-    }
-};
-
-struct max_op
-{
-    std::string name() const { return "max"; }
-    auto fcn() const
-    {
-        return [](auto x, auto y) { return std::max(x, y); };
-    }
-};
-
-struct min_op
-{
-    std::string name() const { return "min"; }
-    auto fcn() const
-    {
-        return [](auto x, auto y) { return std::min(x, y); };
-    }
-};
-
-template <typename Op>
-struct cpu_binary
-{
-    Op op;
-    std::string name() const { return "cpu::" + op.name(); }
-    shape compute_shape(const std::vector<shape>& inputs) const { return inputs.front(); }
-    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
-    {
-        argument result{output_shape};
-        visit_all(result, args[0], args[1])([&](auto output, auto input1, auto input2) {
-            if(input1.get_shape().packed() and input2.get_shape().packed())
-            {
-                std::transform(
-                    input1.begin(), input1.end(), input2.begin(), output.begin(), op.fcn());
-            }
-            else
-            {
-                shape_for_each(output.get_shape(), [&](const auto& idx) {
-                    output(idx.begin(), idx.end()) =
-                        op.fcn()(input1(idx.begin(), idx.end()), input2(idx.begin(), idx.end()));
-                });
-            }
-        });
-        return result;
-    }
-};
+// struct add_op
+// {
+//     std::string name() const { return "add"; }
+//     auto fcn() const
+//     {
+//         return [](auto x, auto y) { return x + y; };
+//     }
+// };
+
+// struct sub_op
+// {
+//     std::string name() const { return "sub"; }
+//     auto fcn() const
+//     {
+//         return [](auto x, auto y) { return x - y; };
+//     }
+// };
+
+// struct mul_op
+// {
+//     std::string name() const { return "mul"; }
+//     auto fcn() const
+//     {
+//         return [](auto x, auto y) { return x * y; };
+//     }
+// };
+
+// struct div_op
+// {
+//     std::string name() const { return "div"; }
+//     auto fcn() const
+//     {
+//         return [](auto x, auto y) { return x / y; };
+//     }
+// };
+
+// struct max_op
+// {
+//     std::string name() const { return "max"; }
+//     auto fcn() const
+//     {
+//         return [](auto x, auto y) { return std::max(x, y); };
+//     }
+// };
+
+// struct min_op
+// {
+//     std::string name() const { return "min"; }
+//     auto fcn() const
+//     {
+//         return [](auto x, auto y) { return std::min(x, y); };
+//     }
+// };
+
+// template <typename Op>
+// struct cpu_binary
+// {
+//     Op op;
+//     std::string name() const { return "cpu::" + op.name(); }
+//     shape compute_shape(const std::vector<shape>& inputs) const { return inputs.front(); }
+//     argument compute(context&, const shape& output_shape, std::vector<argument> args) const
+//     {
+//         argument result{output_shape};
+//         visit_all(result, args[0], args[1])([&](auto output, auto input1, auto input2) {
+//             if(input1.get_shape().packed() and input2.get_shape().packed())
+//             {
+//                 std::transform(
+//                     input1.begin(), input1.end(), input2.begin(), output.begin(), op.fcn());
+//             }
+//             else
+//             {
+//                 shape_for_each(output.get_shape(), [&](const auto& idx) {
+//                     output(idx.begin(), idx.end()) =
+//                         op.fcn()(input1(idx.begin(), idx.end()), input2(idx.begin(), idx.end()));
+//                 });
+//             }
+//         });
+//         return result;
+//     }
+// };
 
 struct cpu_apply
 {
@@ -818,37 +900,37 @@ struct cpu_apply
         apply_map["batch_norm_inference"] =
             extend_op<cpu_batch_norm_inference, op::batch_norm_inference>();
         apply_map["lrn"]        = extend_op<cpu_lrn, op::lrn>();
-        apply_map["contiguous"] = extend_op<cpu_contiguous, op::contiguous>();
-        apply_map["pad"]        = extend_op<cpu_pad, op::pad>();
-        apply_map["concat"]     = extend_op<cpu_concat, op::concat>();
-        apply_map["gather"]     = extend_op<cpu_gather, op::gather>();
-        apply_map["logsoftmax"] = extend_op<cpu_logsoftmax, op::logsoftmax>();
         apply_map["leaky_relu"] = extend_op<cpu_unary<leaky_relu_op>, op::leaky_relu>();
+        apply_map["logsoftmax"] = extend_op<cpu_logsoftmax, op::logsoftmax>();
         apply_map["elu"]        = extend_op<cpu_unary<elu_op>, op::elu>();
-        apply_map["identity"]   = simple_op<cpu_unary<identity_op>>();
-        apply_map["abs"]        = simple_op<cpu_unary<abs_op>>();
-        apply_map["sinh"]       = simple_op<cpu_unary<sinh_op>>();
-        apply_map["cosh"]       = simple_op<cpu_unary<cosh_op>>();
-        apply_map["tanh"]       = simple_op<cpu_unary<tanh_op>>();
-        apply_map["sigmoid"]    = simple_op<cpu_unary<sigmoid_op>>();
-        apply_map["exp"]        = simple_op<cpu_unary<exp_op>>();
-        apply_map["log"]        = simple_op<cpu_unary<log_op>>();
-        apply_map["neg"]        = simple_op<cpu_unary<neg_op>>();
-        apply_map["sin"]        = simple_op<cpu_unary<sin_op>>();
-        apply_map["cos"]        = simple_op<cpu_unary<cos_op>>();
-        apply_map["tan"]        = simple_op<cpu_unary<tan_op>>();
-        apply_map["asin"]       = simple_op<cpu_unary<asin_op>>();
-        apply_map["acos"]       = simple_op<cpu_unary<acos_op>>();
-        apply_map["atan"]       = simple_op<cpu_unary<atan_op>>();
-        apply_map["relu"]       = simple_op<cpu_unary<relu_op>>();
-        apply_map["add"]        = simple_op<cpu_binary<add_op>>();
-        apply_map["sub"]        = simple_op<cpu_binary<sub_op>>();
-        apply_map["mul"]        = simple_op<cpu_binary<mul_op>>();
-        apply_map["div"]        = simple_op<cpu_binary<div_op>>();
-        apply_map["max"]        = simple_op<cpu_binary<max_op>>();
-        apply_map["min"]        = simple_op<cpu_binary<min_op>>();
-
         apply_map["softmax"] = simple_op<softmax2d>();
+        apply_map["pad"]        = extend_op<cpu_pad, op::pad>();
+        // apply_map["contiguous"] = extend_op<cpu_contiguous, op::contiguous>();
+        // apply_map["concat"]     = extend_op<cpu_concat, op::concat>();
+        // apply_map["gather"]     = extend_op<cpu_gather, op::gather>();
+        // apply_map["identity"]   = simple_op<cpu_unary<identity_op>>();
+        // apply_map["abs"]        = simple_op<cpu_unary<abs_op>>();
+        // apply_map["sinh"]       = simple_op<cpu_unary<sinh_op>>();
+        // apply_map["cosh"]       = simple_op<cpu_unary<cosh_op>>();
+        // apply_map["tanh"]       = simple_op<cpu_unary<tanh_op>>();
+        // apply_map["sigmoid"]    = simple_op<cpu_unary<sigmoid_op>>();
+        // apply_map["exp"]        = simple_op<cpu_unary<exp_op>>();
+        // apply_map["log"]        = simple_op<cpu_unary<log_op>>();
+        // apply_map["neg"]        = simple_op<cpu_unary<neg_op>>();
+        // apply_map["sin"]        = simple_op<cpu_unary<sin_op>>();
+        // apply_map["cos"]        = simple_op<cpu_unary<cos_op>>();
+        // apply_map["tan"]        = simple_op<cpu_unary<tan_op>>();
+        // apply_map["asin"]       = simple_op<cpu_unary<asin_op>>();
+        // apply_map["acos"]       = simple_op<cpu_unary<acos_op>>();
+        // apply_map["atan"]       = simple_op<cpu_unary<atan_op>>();
+        // apply_map["relu"]       = simple_op<cpu_unary<relu_op>>();
+        // apply_map["add"]        = simple_op<cpu_binary<add_op>>();
+        // apply_map["sub"]        = simple_op<cpu_binary<sub_op>>();
+        // apply_map["mul"]        = simple_op<cpu_binary<mul_op>>();
+        // apply_map["div"]        = simple_op<cpu_binary<div_op>>();
+        // apply_map["max"]        = simple_op<cpu_binary<max_op>>();
+        // apply_map["min"]        = simple_op<cpu_binary<min_op>>();
+
     }
 
     void apply()
@@ -864,9 +946,18 @@ struct cpu_apply
             {
                 apply_map.at(it->name())(it);
             }
+            else if (is_context_free(it->get_operator()))
+            {
+                apply_cpu_op(it);
+            }
         }
     }
 
+    void apply_cpu_op(instruction_ref ins)
+    {
+        prog->replace_instruction(ins, cpu_op{ins->get_operator()}, ins->inputs());
+    }
+
     template <class T>
     void apply_simple_op(instruction_ref ins)
     {

src/targets/gpu/include/migraphx/gpu/abs.hpp

@@ -13,6 +13,13 @@ struct context;
 struct miopen_abs
 {
     shared<activation_descriptor> ad;
+    
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return gpu::reflect(self.ad.get(), f);
+    }
+
     std::string name() const { return "gpu::abs"; }
     shape compute_shape(const std::vector<shape>& inputs) const;
     argument

src/targets/gpu/include/migraphx/gpu/batchnorm.hpp

@@ -13,6 +13,13 @@ struct context;
 struct miopen_batch_norm_inference
 {
     op::batch_norm_inference op;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
+    
     std::string name() const { return "gpu::batch_norm_inference"; }
     shape compute_shape(const std::vector<shape>& inputs) const;
     argument

src/targets/gpu/include/migraphx/gpu/concat.hpp

@@ -14,6 +14,12 @@ struct hip_concat
 {
     op::concat op;
 
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
+    
     std::string name() const { return "gpu::concat"; }
     shape compute_shape(std::vector<shape> inputs) const;
     argument

src/targets/gpu/include/migraphx/gpu/contiguous.hpp

@@ -13,6 +13,13 @@ struct context;
 struct miopen_contiguous
 {
     op::contiguous op;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
+    
     std::string name() const { return "gpu::contiguous"; }
     shape compute_shape(const std::vector<shape>& inputs) const;
     argument compute(context&, shape output_shape, const std::vector<argument>& args) const;

src/targets/gpu/include/migraphx/gpu/elu.hpp

@@ -13,6 +13,13 @@ struct context;
 struct miopen_elu
 {
     shared<activation_descriptor> ad;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return gpu::reflect(self.ad.get(), f);
+    }
+    
     std::string name() const { return "gpu::elu"; }
     shape compute_shape(const std::vector<shape>& inputs) const;
     argument

src/targets/gpu/include/migraphx/gpu/gather.hpp

@@ -14,6 +14,13 @@ struct context;
 struct hip_gather
 {
     op::gather op;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
+    
     std::string name() const { return "gpu::gather"; }
     shape compute_shape(std::vector<shape> inputs) const;
     argument

src/targets/gpu/include/migraphx/gpu/gemm.hpp

@@ -13,6 +13,13 @@ struct context;
 struct miopen_gemm
 {
     op::dot op;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
+    
     std::string name() const { return "gpu::gemm"; }
     shape compute_shape(const std::vector<shape>& inputs) const;
     argument

src/targets/gpu/include/migraphx/gpu/hip.hpp

@@ -28,6 +28,13 @@ struct hip_allocate
 {
     shape s;
     std::string tag{};
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.s, "shape"), f(self.tag, "tag"));
+    }
+
     std::string name() const { return "hip::allocate"; }
     shape compute_shape(const std::vector<shape>& inputs) const
     {
@@ -43,6 +50,13 @@ struct hip_allocate
 struct hip_sync
 {
     std::string tag{};
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.tag, "tag"));
+    }
+
     std::string name() const { return "hip::sync"; }
     shape compute_shape(const std::vector<shape>& inputs) const
     {

src/targets/gpu/include/migraphx/gpu/leaky_relu.hpp

@@ -13,6 +13,13 @@ struct context;
 struct miopen_leaky_relu
 {
     shared<activation_descriptor> ad;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return gpu::reflect(self.ad.get(), f);
+    }
+    
     std::string name() const { return "gpu::leaky_relu"; }
     shape compute_shape(const std::vector<shape>& inputs) const;
     argument

src/targets/gpu/include/migraphx/gpu/logsoftmax.hpp

@@ -25,6 +25,13 @@ namespace gpu {
 struct hip_logsoftmax
 {
     op::logsoftmax op;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
+    
     std::string name() const { return "gpu::logsoftmax"; }
     shape compute_shape(const std::vector<shape>& inputs) const;
     argument

src/targets/gpu/include/migraphx/gpu/lrn.hpp

@@ -13,6 +13,13 @@ struct context;
 struct miopen_lrn
 {
     shared<lrn_descriptor> ldesc;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return gpu::reflect(self.ldesc.get(), f);
+    }
+    
     std::string name() const { return "gpu::lrn"; }
     shape compute_shape(const std::vector<shape>& inputs) const;
     argument

src/targets/gpu/include/migraphx/gpu/miopen.hpp

@@ -162,6 +162,29 @@ inline fused_operator_args make_fused_args()
     return make_obj<fused_operator_args>(&miopenCreateOperatorArgs);
 }
 
+template<class F>
+auto reflect(miopenActivationDescriptor_t ad, F f)
+{
+    miopenActivationMode_t mode;
+    double alpha;
+    double beta;
+    double gamma;
+    miopenGetActivationDescriptor(ad, &mode, &alpha, &beta, &gamma);
+    return pack(f(mode, "mode"), f(alpha, "alpha"), f(beta, "beta"), f(gamma, "gamma"));
+}
+
+template<class F>
+auto reflect(miopenLRNDescriptor_t lrnd, F f)
+{
+    miopenLRNMode_t mode;;
+    unsigned int n;
+    double alpha;
+    double beta;
+    double k;
+    miopenGetLRNDescriptor(lrnd,&mode, &n, &alpha, &beta, &k);
+    return pack(f(mode, "mode"), f(n, "n"), f(alpha, "alpha"), f(beta, "beta"), f(k, "k"));
+}
+
 } // namespace gpu
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/targets/gpu/include/migraphx/gpu/pad.hpp

@@ -14,6 +14,12 @@ struct hip_pad
 {
     op::pad op;
 
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
+
     std::string name() const { return "gpu::pad"; }
     shape compute_shape(std::vector<shape> inputs) const;
     argument

src/targets/gpu/include/migraphx/gpu/pooling.hpp

@@ -16,6 +16,12 @@ struct miopen_pooling
     op::pooling op;
     shared<pooling_descriptor> pd;
 
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
+
     std::string name() const { return "gpu::pooling"; }
     shape compute_shape(const std::vector<shape>& inputs) const;
     argument