Merge from develop

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

+#ifndef MIGRAPHX_GUARD_RTGLIB_UNARY_HPP
+#define MIGRAPHX_GUARD_RTGLIB_UNARY_HPP
+
+#include <migraphx/gpu/hip.hpp>
+#include <migraphx/gpu/context.hpp>
+#include <migraphx/shape.hpp>
+#include <migraphx/argument.hpp>
+#include <migraphx/config.hpp>
+#include <migraphx/type_name.hpp>
+#include <utility>
+#include <iostream>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+template <class Derived>
+struct oper
+{
+    std::string name() const
+    {
+        const std::string& name = get_type_name<Derived>();
+        // search the namespace gpu (::gpu::)
+        auto pos_ns = name.find("::gpu::");
+        if(pos_ns != std::string::npos)
+        {
+            auto pos_name = name.find("hip_", pos_ns + std::string("::gpu::").length());
+            if(pos_name != std::string::npos)
+            {
+                return std::string("gpu::") + name.substr(pos_name + 4);
+            }
+            else
+            {
+                return name.substr(pos_ns + 2);
+            }
+        }
+
+        return "unknown";
+    }
+};
+
+template <class Derived, void (*F)(hipStream_t, const argument&, const argument&)>
+struct unary_device : oper<Derived>
+{
+    shape compute_shape(const std::vector<shape>& inputs) const
+    {
+        check_shapes{inputs, *this}.has(2);
+        return inputs.at(0);
+    }
+
+    argument compute(context& ctx, const shape&, const std::vector<argument>& args) const
+    {
+        F(ctx.get_stream().get(), args[1], args[0]);
+        return args[1];
+    }
+
+    int output_alias(const std::vector<shape>& shapes) const { return shapes.size() - 1; }
+};
+
+template <class Derived, void (*F)(hipStream_t, const argument&, const argument&, const argument&)>
+struct binary_device : oper<Derived>
+{
+    shape compute_shape(const std::vector<shape>& inputs) const
+    {
+        check_shapes{inputs, *this}.has(3);
+        return inputs.at(0);
+    }
+
+    argument compute(context& ctx, const shape&, const std::vector<argument>& args) const
+    {
+        F(ctx.get_stream().get(), args[2], args[1], args[0]);
+        return args[2];
+    }
+
+    int output_alias(const std::vector<shape>& shapes) const { return shapes.size() - 1; }
+};
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

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

+#ifndef MIGRAPHX_GUARD_RTGLIB_PAD_HPP
+#define MIGRAPHX_GUARD_RTGLIB_PAD_HPP
+
+#include <migraphx/shape.hpp>
+#include <migraphx/operators.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+struct context;
+
+struct hip_pad
+{
+    op::pad op;
+
+    std::string name() const { return "gpu::pad"; }
+    shape compute_shape(std::vector<shape> inputs) const;
+    argument
+    compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
+    int output_alias(const std::vector<shape>& shapes) const { return shapes.size() - 1; }
+};
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

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

 #ifndef MIGRAPHX_GUARD_RTGLIB_POOLING_HPP
 #define MIGRAPHX_GUARD_RTGLIB_POOLING_HPP
 
-#include <migraphx/gpu/lowering.hpp>
-#include <migraphx/manage_ptr.hpp>
-#include <migraphx/instruction.hpp>
+#include <migraphx/shape.hpp>
 #include <migraphx/operators.hpp>
-#include <migraphx/generate.hpp>
-#include <migraphx/shape_for_each.hpp>
-#include <migraphx/config.hpp>
 #include <migraphx/gpu/miopen.hpp>
-#include <migraphx/gpu/hip.hpp>
-#include <migraphx/dfor.hpp>
-#include <migraphx/gpu/device/contiguous.hpp>
-#include <migraphx/gpu/device/add.hpp>
-#include <migraphx/iterator_for.hpp>
-#include <migraphx/gpu/rocblas.hpp>
-#include <migraphx/gpu/context.hpp>
-#include <utility>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 
+struct context;
+
 struct miopen_pooling
 {
     op::pooling op;

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

 #ifndef MIGRAPHX_GUARD_RTGLIB_RELU_HPP
 #define MIGRAPHX_GUARD_RTGLIB_RELU_HPP
 
-#include <migraphx/gpu/lowering.hpp>
-#include <migraphx/manage_ptr.hpp>
-#include <migraphx/instruction.hpp>
-#include <migraphx/operators.hpp>
-#include <migraphx/generate.hpp>
-#include <migraphx/shape_for_each.hpp>
-#include <migraphx/config.hpp>
+#include <migraphx/shape.hpp>
 #include <migraphx/gpu/miopen.hpp>
-#include <migraphx/gpu/hip.hpp>
-#include <migraphx/dfor.hpp>
-#include <migraphx/gpu/device/contiguous.hpp>
-#include <migraphx/gpu/device/add.hpp>
-#include <migraphx/iterator_for.hpp>
-#include <migraphx/gpu/rocblas.hpp>
-#include <migraphx/gpu/context.hpp>
-#include <utility>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 
+struct context;
+
 struct miopen_relu
 {
     shared<activation_descriptor> ad;

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

@@ -2,7 +2,6 @@
 #define MIGRAPHX_GUARD_MIGRAPHLIB_ROCBLAS_HPP
 
 #include <migraphx/manage_ptr.hpp>
-#include <migraphx/operators.hpp>
 #include <migraphx/config.hpp>
 #include <rocblas.h>
 

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

+#ifndef MIGRAPHX_GUARD_RTGLIB_SIGMOID_HPP
+#define MIGRAPHX_GUARD_RTGLIB_SIGMOID_HPP
+
+#include <migraphx/shape.hpp>
+#include <migraphx/gpu/miopen.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+struct context;
+
+struct miopen_sigmoid
+{
+    shared<activation_descriptor> ad;
+    std::string name() const { return "gpu::sigmoid"; }
+    shape compute_shape(const std::vector<shape>& inputs) const;
+    argument
+    compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
+    int output_alias(const std::vector<shape>& shapes) const { return shapes.size() - 1; }
+};
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

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

 #ifndef MIGRAPHX_GUARD_RTGLIB_SIN_HPP
 #define MIGRAPHX_GUARD_RTGLIB_SIN_HPP
 
-#include <migraphx/gpu/lowering.hpp>
-#include <migraphx/manage_ptr.hpp>
-#include <migraphx/instruction.hpp>
-#include <migraphx/operators.hpp>
-#include <migraphx/generate.hpp>
-#include <migraphx/shape_for_each.hpp>
-#include <migraphx/gpu/miopen.hpp>
-#include <migraphx/gpu/hip.hpp>
-#include <migraphx/dfor.hpp>
-#include <migraphx/gpu/device/contiguous.hpp>
+#include <migraphx/gpu/oper.hpp>
 #include <migraphx/gpu/device/sin.hpp>
-#include <migraphx/iterator_for.hpp>
-#include <migraphx/gpu/rocblas.hpp>
-#include <migraphx/gpu/context.hpp>
-#include <migraphx/config.hpp>
-#include <utility>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 
-struct hip_sin
+struct hip_sin : unary_device<hip_sin, device::sin>
 {
-    std::string name() const { return "gpu::sin"; }
-    shape compute_shape(const std::vector<shape>& inputs) const;
-    argument compute(context&, const shape&, const std::vector<argument>& args) const;
-    int output_alias(const std::vector<shape>& shapes) const { return shapes.size() - 1; }
 };
 
 } // namespace gpu

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

+#ifndef MIGRAPHX_GUARD_RTGLIB_SINH_HPP
+#define MIGRAPHX_GUARD_RTGLIB_SINH_HPP
+
+#include <migraphx/gpu/oper.hpp>
+#include <migraphx/gpu/device/sinh.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+struct hip_sinh : unary_device<hip_sinh, device::sinh>
+{
+};
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

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

 #ifndef MIGRAPHX_GUARD_RTGLIB_SOFTMAX_HPP
 #define MIGRAPHX_GUARD_RTGLIB_SOFTMAX_HPP
 
-#include <migraphx/gpu/lowering.hpp>
-#include <migraphx/manage_ptr.hpp>
-#include <migraphx/instruction.hpp>
+#include <migraphx/shape.hpp>
 #include <migraphx/operators.hpp>
-#include <migraphx/generate.hpp>
-#include <migraphx/shape_for_each.hpp>
-#include <migraphx/config.hpp>
-#include <migraphx/gpu/miopen.hpp>
-#include <migraphx/gpu/hip.hpp>
-#include <migraphx/dfor.hpp>
-#include <migraphx/gpu/device/contiguous.hpp>
-#include <migraphx/gpu/device/add.hpp>
-#include <migraphx/iterator_for.hpp>
-#include <migraphx/gpu/rocblas.hpp>
-#include <migraphx/gpu/context.hpp>
-#include <utility>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 
+struct context;
+
 struct miopen_softmax
 {
     op::softmax op;

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

+#ifndef MIGRAPHX_GUARD_RTGLIB_SUB_HPP
+#define MIGRAPHX_GUARD_RTGLIB_SUB_HPP
+
+#include <migraphx/gpu/oper.hpp>
+#include <migraphx/gpu/device/sub.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+struct hip_sub : binary_device<hip_sub, device::sub>
+{
+};
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

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

+#ifndef MIGRAPHX_GUARD_RTGLIB_TAN_HPP
+#define MIGRAPHX_GUARD_RTGLIB_TAN_HPP
+
+#include <migraphx/gpu/oper.hpp>
+#include <migraphx/gpu/device/tan.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+struct hip_tan : unary_device<hip_tan, device::tan>
+{
+};
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

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

+#ifndef MIGRAPHX_GUARD_RTGLIB_TANH_HPP
+#define MIGRAPHX_GUARD_RTGLIB_TANH_HPP
+
+#include <migraphx/shape.hpp>
+#include <migraphx/gpu/miopen.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+struct context;
+
+struct miopen_tanh
+{
+    shared<activation_descriptor> ad;
+    std::string name() const { return "gpu::tanh"; }
+    shape compute_shape(const std::vector<shape>& inputs) const;
+    argument
+    compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
+    int output_alias(const std::vector<shape>& shapes) const { return shapes.size() - 1; }
+};
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/targets/gpu/leaky_relu.cpp

 #include <migraphx/gpu/leaky_relu.hpp>
-#include <migraphx/operators.hpp>
-#include <migraphx/manage_ptr.hpp>
-#include <migraphx/gpu/miopen.hpp>
-#include <utility>
+#include <migraphx/gpu/context.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -18,7 +15,8 @@ argument miopen_leaky_relu::compute(context& ctx,
                                     const shape& output_shape,
                                     const std::vector<argument>& args) const
 {
-    float alpha = 1, beta = 0;
+    float alpha = 1;
+    float beta  = 0;
     auto x_desc = make_tensor(args[0].get_shape());
     auto y_desc = make_tensor(output_shape);
     miopenActivationForward(ctx.get_stream().get_miopen(),

src/targets/gpu/lowering.cpp

@@ -16,16 +16,37 @@
 #include <migraphx/gpu/convolution.hpp>
 #include <migraphx/gpu/contiguous.hpp>
 #include <migraphx/gpu/relu.hpp>
+#include <migraphx/gpu/sigmoid.hpp>
+#include <migraphx/gpu/abs.hpp>
 #include <migraphx/gpu/leaky_relu.hpp>
+#include <migraphx/gpu/elu.hpp>
 #include <migraphx/gpu/softmax.hpp>
 #include <migraphx/gpu/add.hpp>
+#include <migraphx/gpu/sub.hpp>
+#include <migraphx/gpu/exp.hpp>
+#include <migraphx/gpu/log.hpp>
 #include <migraphx/gpu/sin.hpp>
+#include <migraphx/gpu/cos.hpp>
+#include <migraphx/gpu/tan.hpp>
+#include <migraphx/gpu/sinh.hpp>
+#include <migraphx/gpu/cosh.hpp>
+#include <migraphx/gpu/tanh.hpp>
+#include <migraphx/gpu/asin.hpp>
+#include <migraphx/gpu/acos.hpp>
+#include <migraphx/gpu/atan.hpp>
 #include <migraphx/gpu/mul.hpp>
+#include <migraphx/gpu/max.hpp>
+#include <migraphx/gpu/min.hpp>
 #include <migraphx/gpu/batchnorm.hpp>
 #include <migraphx/gpu/pooling.hpp>
 #include <migraphx/gpu/gemm.hpp>
 #include <migraphx/gpu/concat.hpp>
+#include <migraphx/gpu/pad.hpp>
+#include <migraphx/gpu/gather.hpp>
+#include <migraphx/gpu/lrn.hpp>
 #include <utility>
+#include <functional>
+#include <algorithm>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -35,6 +56,8 @@ struct miopen_apply
 {
     program* prog = nullptr;
     context ctx{};
+    std::unordered_map<std::string, std::function<instruction_ref(instruction_ref)>> apply_map{};
+    instruction_ref last{};
 
     void check_shape(shape x, instruction_ref i)
     {
@@ -43,190 +66,180 @@ struct miopen_apply
         (void)i;
     }
 
+    void init()
+    {
+        this->last = instruction::get_output_alias(std::prev(prog->end()));
+        add_miopen_simple_op<miopen_relu>("relu", make_relu);
+        add_miopen_simple_op<miopen_sigmoid>("sigmoid", make_sigmoid);
+        add_miopen_simple_op<miopen_abs>("abs", make_abs);
+        add_miopen_simple_op<miopen_tanh>("tanh", make_tanh);
+
+        add_miopen_extend_op<miopen_leaky_relu, op::leaky_relu>("leaky_relu", make_leaky_relu);
+        add_miopen_extend_op<miopen_elu, op::elu>("elu", make_elu);
+
+        add_generic_op<hip_add>("add");
+        add_generic_op<hip_sub>("sub");
+        add_generic_op<hip_exp>("exp");
+        add_generic_op<hip_log>("log");
+        add_generic_op<hip_sin>("sin");
+        add_generic_op<hip_cos>("cos");
+        add_generic_op<hip_tan>("tan");
+        add_generic_op<hip_sinh>("sinh");
+        add_generic_op<hip_cosh>("cosh");
+        add_generic_op<hip_asin>("asin");
+        add_generic_op<hip_acos>("acos");
+        add_generic_op<hip_atan>("atan");
+        add_generic_op<hip_mul>("mul");
+        add_generic_op<hip_max>("max");
+        add_generic_op<hip_min>("min");
+
+        add_extend_op<miopen_gemm, op::dot>("dot");
+        add_extend_op<miopen_contiguous, op::contiguous>("contiguous");
+        add_extend_op<hip_concat, op::concat>("concat");
+        add_extend_op<miopen_softmax, op::softmax>("softmax");
+        add_extend_op<hip_gather, op::gather>("gather");
+        add_extend_op<hip_pad, op::pad>("pad");
+
+        add_lrn_op();
+        add_convolution_op();
+        add_pooling_op();
+        add_batch_norm_inference_op();
+    }
+
     void apply()
     {
+        init();
         for(auto it = prog->begin(); it != prog->end(); it++)
         {
             auto s = it->get_shape();
-            if(it->name() == "convolution")
-            {
-                check_shape(s, apply_convolution(it));
-            }
-            else if(it->name() == "relu")
-            {
-                check_shape(s, apply_relu(it));
-            }
-            else if(it->name() == "leaky_relu")
-            {
-                check_shape(s, apply_leaky_relu(it));
-            }
-            else if(it->name() == "pooling")
-            {
-                check_shape(s, apply_pooling(it));
-            }
-            else if(it->name() == "add")
-            {
-                check_shape(s, apply_add(it));
-            }
-            else if(it->name() == "sin")
-            {
-                check_shape(s, apply_sin(it));
-            }
-            else if(it->name() == "mul")
-            {
-                check_shape(s, apply_mul(it));
-            }
-            else if(it->name() == "dot")
-            {
-                check_shape(s, apply_gemm(it));
-            }
-            else if(it->name() == "contiguous")
+            if(apply_map.count(it->name()) > 0)
             {
-                check_shape(s, apply_contiguous(it));
-            }
-            else if(it->name() == "concat")
-            {
-                check_shape(s, apply_concat(it));
-            }
-            else if(it->name() == "batch_norm_inference")
-            {
-                check_shape(s, apply_batch_norm_inference(it));
-            }
-            else if(it->name() == "softmax")
-            {
-                check_shape(s, apply_softmax(it));
+                check_shape(s, apply_map.at(it->name())(it));
             }
         }
     }
 
     instruction_ref insert_allocation(instruction_ref ins, const shape& s, std::string tag = "")
     {
-        if(ins == --prog->end() and tag.empty())
+        if(ins == last and tag.empty())
         {
             return prog->add_parameter("output", s);
         }
         else
         {
-            auto is     = prog->add_outline(s);
-            auto result = prog->insert_instruction(ins, hip_allocate{std::move(tag)}, is);
+            auto result = prog->insert_instruction(ins, hip_allocate{s, std::move(tag)});
             return result;
         }
     }
 
-    instruction_ref apply_convolution(instruction_ref ins)
+    void add_convolution_op()
     {
-        auto&& op = any_cast<op::convolution>(ins->get_operator());
+        apply_map.emplace("convolution", [=](instruction_ref ins) {
+            auto&& op = any_cast<op::convolution>(ins->get_operator());
 
-        auto conv = miopen_convolution{op, make_conv(op)};
-        auto ws   = conv.compile(ctx, ins->get_shape(), ins->inputs());
+            auto conv = miopen_convolution{op, make_conv(op)};
+            auto ws   = conv.compile(ctx, ins->get_shape(), to_shapes(ins->inputs()));
 
-        auto workspace = insert_allocation(ins, ws, "workspace");
-        auto output    = insert_allocation(ins, ins->get_shape());
+            auto workspace = insert_allocation(ins, ws, "workspace");
+            auto output    = insert_allocation(ins, ins->get_shape());
 
-        return prog->replace_instruction(
-            ins, conv, ins->inputs().at(0), ins->inputs().at(1), workspace, output);
+            return prog->replace_instruction(
+                ins, conv, ins->inputs().at(0), ins->inputs().at(1), workspace, output);
+        });
     }
 
-    instruction_ref apply_pooling(instruction_ref ins)
+    void add_pooling_op()
     {
-        auto&& op   = any_cast<op::pooling>(ins->get_operator());
-        auto pd     = make_pooling(op);
-        auto output = insert_allocation(ins, ins->get_shape());
-
-        return prog->replace_instruction(
-            ins, miopen_pooling{op, std::move(pd)}, ins->inputs().at(0), output);
+        apply_map.emplace("pooling", [=](instruction_ref ins) {
+            auto&& op   = any_cast<op::pooling>(ins->get_operator());
+            auto pd     = make_pooling(op);
+            auto output = insert_allocation(ins, ins->get_shape());
+
+            return prog->replace_instruction(
+                ins, miopen_pooling{op, std::move(pd)}, ins->inputs().at(0), output);
+        });
     }
 
-    instruction_ref apply_relu(instruction_ref ins)
+    void add_lrn_op()
     {
-        auto ad = make_relu();
-
-        auto output = insert_allocation(ins, ins->get_shape());
-        return prog->replace_instruction(
-            ins, miopen_relu{std::move(ad)}, ins->inputs().at(0), output);
+        apply_map.emplace("lrn", [=](instruction_ref ins) {
+            auto&& op   = any_cast<op::lrn>(ins->get_operator());
+            auto ldesc  = make_lrn(op);
+            auto output = insert_allocation(ins, ins->get_shape());
+            return prog->replace_instruction(
+                ins, miopen_lrn{std::move(ldesc)}, ins->inputs().at(0), output);
+        });
     }
 
-    instruction_ref apply_leaky_relu(instruction_ref ins)
+    template <class T>
+    void add_generic_op(std::string name)
     {
-        auto&& op = any_cast<op::leaky_relu>(ins->get_operator());
-        auto ad   = make_leaky_relu(op.alpha);
+        apply_map.emplace(name, [=](instruction_ref ins) {
+            auto output                       = insert_allocation(ins, ins->get_shape());
+            std::vector<instruction_ref> refs = ins->inputs();
+            refs.push_back(output);
 
-        auto output = insert_allocation(ins, ins->get_shape());
-        return prog->replace_instruction(
-            ins, miopen_leaky_relu{std::move(ad)}, ins->inputs().at(0), output);
+            return prog->replace_instruction(ins, T{}, refs);
+        });
     }
 
-    instruction_ref apply_softmax(instruction_ref ins)
+    template <class T, class Op>
+    void add_extend_op(std::string name)
     {
-        auto&& op   = any_cast<op::softmax>(ins->get_operator());
-        auto output = insert_allocation(ins, ins->get_shape());
-        return prog->replace_instruction(ins, miopen_softmax{op}, ins->inputs().at(0), output);
+        apply_map.emplace(name, [=](instruction_ref ins) {
+            auto&& op                         = any_cast<Op>(ins->get_operator());
+            auto output                       = insert_allocation(ins, ins->get_shape());
+            std::vector<instruction_ref> refs = ins->inputs();
+            refs.push_back(output);
+
+            return prog->replace_instruction(ins, T{op}, refs);
+        });
     }
 
-    instruction_ref apply_add(instruction_ref ins)
+    template <class T, class Op, class F>
+    void add_miopen_extend_op(std::string name, F f)
     {
-        auto output = insert_allocation(ins, ins->get_shape());
-        return prog->replace_instruction(
-            ins, hip_add{}, ins->inputs().at(0), ins->inputs().at(1), output);
-    }
+        apply_map.emplace(name, [=](instruction_ref ins) {
+            auto&& op = any_cast<Op>(ins->get_operator());
+            auto ad   = f(op.alpha);
 
-    instruction_ref apply_sin(instruction_ref ins)
-    {
-        auto output = insert_allocation(ins, ins->get_shape());
-        return prog->replace_instruction(ins, hip_sin{}, ins->inputs().at(0), output);
-    }
-
-    instruction_ref apply_mul(instruction_ref ins)
-    {
-        auto output = insert_allocation(ins, ins->get_shape());
-        return prog->replace_instruction(
-            ins, hip_mul{}, ins->inputs().at(0), ins->inputs().at(1), output);
-    }
-
-    instruction_ref apply_gemm(instruction_ref ins)
-    {
-        auto&& op   = any_cast<op::dot>(ins->get_operator());
-        auto output = insert_allocation(ins, ins->get_shape());
-        return prog->replace_instruction(
-            ins, miopen_gemm{op}, ins->inputs().at(0), ins->inputs().at(1), output);
-    }
-
-    instruction_ref apply_contiguous(instruction_ref ins)
-    {
-        auto&& op   = any_cast<op::contiguous>(ins->get_operator());
-        auto output = insert_allocation(ins, ins->get_shape());
-        return prog->replace_instruction(ins, miopen_contiguous{op}, ins->inputs().at(0), output);
+            auto output = insert_allocation(ins, ins->get_shape());
+            return prog->replace_instruction(ins, T{std::move(ad)}, ins->inputs().at(0), output);
+        });
     }
 
-    instruction_ref apply_concat(instruction_ref ins)
+    template <class T, class F>
+    void add_miopen_simple_op(std::string name, F f)
     {
-        auto&& op                         = any_cast<op::concat>(ins->get_operator());
-        auto output                       = insert_allocation(ins, ins->get_shape());
-        std::vector<instruction_ref> refs = ins->inputs();
-        refs.push_back(output);
-        return prog->replace_instruction(ins, hip_concat{op}, refs);
+        apply_map.emplace(name, [=](instruction_ref ins) {
+            auto ad     = f();
+            auto output = insert_allocation(ins, ins->get_shape());
+            return prog->replace_instruction(ins, T{std::move(ad)}, ins->inputs().at(0), output);
+        });
     }
 
-    instruction_ref apply_batch_norm_inference(instruction_ref ins)
+    void add_batch_norm_inference_op()
     {
-        auto&& op       = any_cast<op::batch_norm_inference>(ins->get_operator());
-        auto output     = insert_allocation(ins, ins->get_shape());
-        shape old_shape = ins->inputs().at(1)->get_shape();
-        std::vector<int64_t> new_shape{1, static_cast<int64_t>(old_shape.elements()), 1, 1};
-        auto reshape_op = op::reshape{new_shape};
-        std::vector<instruction_ref> reshapes;
-        std::transform(ins->inputs().begin() + 1,
-                       ins->inputs().end(),
-                       std::back_inserter(reshapes),
-                       [&](auto i) { return prog->insert_instruction(ins, reshape_op, i); });
-        return prog->replace_instruction(ins,
-                                         miopen_batch_norm_inference{op},
-                                         ins->inputs().at(0),
-                                         reshapes[0],
-                                         reshapes[1],
-                                         reshapes[2],
-                                         reshapes[3],
-                                         output);
+        apply_map.emplace("batch_norm_inference", [=](instruction_ref ins) {
+            auto&& op       = any_cast<op::batch_norm_inference>(ins->get_operator());
+            auto output     = insert_allocation(ins, ins->get_shape());
+            shape old_shape = ins->inputs().at(1)->get_shape();
+            std::vector<int64_t> new_shape{1, static_cast<int64_t>(old_shape.elements()), 1, 1};
+            auto reshape_op = op::reshape{new_shape};
+            std::vector<instruction_ref> reshapes;
+            std::transform(ins->inputs().begin() + 1,
+                           ins->inputs().end(),
+                           std::back_inserter(reshapes),
+                           [&](auto i) { return prog->insert_instruction(ins, reshape_op, i); });
+            return prog->replace_instruction(ins,
+                                             miopen_batch_norm_inference{op},
+                                             ins->inputs().at(0),
+                                             reshapes[0],
+                                             reshapes[1],
+                                             reshapes[2],
+                                             reshapes[3],
+                                             output);
+        });
     }
 };
 

src/targets/gpu/lrn.cpp

+#include <migraphx/gpu/lrn.hpp>
+#include <migraphx/gpu/context.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+shape miopen_lrn::compute_shape(const std::vector<shape>& inputs) const
+{
+    check_shapes{inputs, *this}.has(2).not_broadcasted();
+    return inputs.at(1);
+}
+
+argument miopen_lrn::compute(context& ctx,
+                             const shape& output_shape,
+                             const std::vector<argument>& args) const
+{
+    float alpha = 1;
+    float beta  = 0;
+    auto x_desc = make_tensor(args[0].get_shape());
+    auto y_desc = make_tensor(output_shape);
+    miopenLRNForward(ctx.get_stream().get_miopen(),
+                     ldesc.get(),
+                     &alpha,
+                     x_desc.get(),
+                     args[0].implicit(),
+                     &beta,
+                     y_desc.get(),
+                     args[1].implicit(),
+                     false,
+                     nullptr);
+
+    return args[1];
+}
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/mul.cpp

-#include <migraphx/gpu/mul.hpp>
-#include <migraphx/operators.hpp>
-#include <migraphx/manage_ptr.hpp>
-#include <migraphx/gpu/miopen.hpp>
-#include <utility>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-shape hip_mul::compute_shape(const std::vector<shape>& inputs) const
-{
-    // check_shapes{inputs, *this}.has(3).standard();
-    check_shapes{inputs, *this}.has(3);
-    return inputs.at(0);
-}
-
-argument hip_mul::compute(context& ctx, const shape&, const std::vector<argument>& args) const
-{
-    device::mul(ctx.get_stream().get(), args[2], args[0], args[1]);
-    return args[2];
-}
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx

src/targets/gpu/pad.cpp

+#include <migraphx/gpu/pad.hpp>
+#include <migraphx/gpu/context.hpp>
+#include <migraphx/gpu/device/pad.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+shape hip_pad::compute_shape(std::vector<shape> inputs) const
+{
+    inputs.pop_back();
+    return op.compute_shape(inputs);
+}
+
+argument hip_pad::compute(context& ctx, const shape&, const std::vector<argument>& args) const
+{
+    return device::pad(ctx.get_stream().get(), args.back(), args.front(), op.value, op.pads);
+}
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/pooling.cpp

 #include <migraphx/gpu/pooling.hpp>
-#include <migraphx/operators.hpp>
-#include <migraphx/manage_ptr.hpp>
-#include <migraphx/gpu/miopen.hpp>
-#include <utility>
+#include <migraphx/gpu/context.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -20,7 +17,8 @@ argument miopen_pooling::compute(context& ctx,
     auto x_desc = make_tensor(args[0].get_shape());
     auto y_desc = make_tensor(output_shape);
 
-    float alpha = 1, beta = 0;
+    float alpha = 1;
+    float beta  = 0;
 
     miopenPoolingForward(ctx.get_stream().get_miopen(),
                          pd.get(),

src/targets/gpu/relu.cpp

 #include <migraphx/gpu/relu.hpp>
-#include <migraphx/operators.hpp>
-#include <migraphx/manage_ptr.hpp>
-#include <migraphx/gpu/miopen.hpp>
-#include <utility>
+#include <migraphx/gpu/context.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -18,7 +15,8 @@ argument miopen_relu::compute(context& ctx,
                               const shape& output_shape,
                               const std::vector<argument>& args) const
 {
-    float alpha = 1, beta = 0;
+    float alpha = 1;
+    float beta  = 0;
     auto x_desc = make_tensor(args[0].get_shape());
     auto y_desc = make_tensor(output_shape);
     miopenActivationForward(ctx.get_stream().get_miopen(),

src/targets/gpu/sigmoid.cpp

+#include <migraphx/gpu/sigmoid.hpp>
+#include <migraphx/gpu/context.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+shape miopen_sigmoid::compute_shape(const std::vector<shape>& inputs) const
+{
+    check_shapes{inputs, *this}.has(2).not_broadcasted();
+    return inputs.at(1);
+}
+
+argument miopen_sigmoid::compute(context& ctx,
+                                 const shape& output_shape,
+                                 const std::vector<argument>& args) const
+{
+    float alpha = 1;
+    float beta  = 0;
+    auto x_desc = make_tensor(args[0].get_shape());
+    auto y_desc = make_tensor(output_shape);
+    miopenActivationForward(ctx.get_stream().get_miopen(),
+                            ad.get(),
+                            &alpha,
+                            x_desc.get(),
+                            args[0].implicit(),
+                            &beta,
+                            y_desc.get(),
+                            args[1].implicit());
+
+    return args[1];
+}
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx