Merge branch 'develop' into fix_parse_if

src/include/migraphx/op/quant_convolution.hpp

@@ -63,8 +63,8 @@ struct quant_convolution
 
     void check_attribute_size() const
     {
-        if(not((padding.size() == stride.size() or (padding.size() / 2) == stride.size()) and
-               stride.size() == dilation.size()))
+        if((padding.size() != stride.size() and (padding.size() / 2) != stride.size()) or
+           stride.size() != dilation.size())
         {
             MIGRAPHX_THROW("QUANT_CONVOLUTION: inconsistent attribute sizes");
         }

src/include/migraphx/op/unary.hpp

@@ -30,6 +30,7 @@
 #include <migraphx/argument.hpp>
 #include <migraphx/stringutils.hpp>
 #include <migraphx/value.hpp>
+#include <migraphx/dyn_output.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -62,9 +63,9 @@ struct unary : op_name<Derived>
     value attributes() const { return base_attributes(); }
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, static_cast<const Derived&>(*this)}.has(1);
+        check_shapes{inputs, static_cast<const Derived&>(*this), true}.has(1);
         auto s = inputs.at(0);
-        if(s.scalar())
+        if(s.dynamic() or s.scalar())
         {
             return s;
         }
@@ -78,9 +79,9 @@ struct unary : op_name<Derived>
         }
     }
 
-    argument compute(const shape& output_shape, std::vector<argument> args) const
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        argument result{output_shape};
+        argument result{dyn_out.computed_shape};
         result.visit([&](auto output) {
             args[0].visit([&](auto input) {
                 std::transform(input.begin(),

src/include/migraphx/operation.hpp

@@ -32,6 +32,8 @@
 #include <utility>
 #include <unordered_map>
 #include <migraphx/reflect.hpp>
+#include <migraphx/dyn_output.hpp>
+#include <migraphx/functional.hpp>
 #include <migraphx/streamutils.hpp>
 #include <migraphx/normalize_attributes.hpp>
 #include <migraphx/argument.hpp>
@@ -199,9 +201,12 @@ auto compute_op(rank<1>,
                 context& ctx,
                 const shape& output_shape,
                 const std::vector<argument>& input)
-    -> decltype(x.compute(auto_any_cast(ctx), output_shape, input))
+    -> decltype(x.compute(auto_any_cast(ctx),
+                          make_compute_output_shape(pack(x, output_shape, input)),
+                          input))
 {
-    return x.compute(auto_any_cast(ctx), output_shape, input);
+    return x.compute(
+        auto_any_cast(ctx), make_compute_output_shape(pack(x, output_shape, input)), input);
 }
 
 template <class T>
@@ -220,9 +225,9 @@ compute_op(const T& x, context& ctx, const shape& output_shape, const std::vecto
 
 template <class T>
 auto compute_op(rank<1>, const T& x, const shape& output_shape, const std::vector<argument>& input)
-    -> decltype(x.compute(output_shape, input))
+    -> decltype(x.compute(make_compute_output_shape(pack(x, output_shape, input)), input))
 {
-    return x.compute(output_shape, input);
+    return x.compute(make_compute_output_shape(pack(x, output_shape, input)), input);
 }
 
 template <class T>
@@ -244,9 +249,11 @@ auto compute_op(rank<1>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f) -> decltype(x.compute(output, inputs, module_args, f))
+                F f)
+    -> decltype(
+        x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f))
 {
-    return x.compute(output, inputs, module_args, f);
+    return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f);
 }
 
 template <class T, class F>
@@ -278,9 +285,17 @@ auto compute_op(rank<4>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f) -> decltype(x.compute(auto_any_cast(ctx), output, inputs, module_args, f))
+                F f) -> decltype(x.compute(auto_any_cast(ctx),
+                                           make_compute_output_shape(pack(x, output, inputs)),
+                                           inputs,
+                                           module_args,
+                                           f))
 {
-    return x.compute(auto_any_cast(ctx), output, inputs, module_args, f);
+    return x.compute(auto_any_cast(ctx),
+                     make_compute_output_shape(pack(x, output, inputs)),
+                     inputs,
+                     module_args,
+                     f);
 }
 
 template <class T, class F>
@@ -290,9 +305,11 @@ auto compute_op(rank<3>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f) -> decltype(x.compute(output, inputs, module_args, f))
+                F f)
+    -> decltype(
+        x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f))
 {
-    return x.compute(output, inputs, module_args, f);
+    return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f);
 }
 
 template <class T, class F>
@@ -302,9 +319,10 @@ auto compute_op(rank<2>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>&,
-                F) -> decltype(x.compute(output, inputs))
+                F)
+    -> decltype(x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs))
 {
-    return x.compute(output, inputs);
+    return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs);
 }
 
 template <class T, class F>
@@ -314,9 +332,12 @@ auto compute_op(rank<1>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>&,
-                F) -> decltype(x.compute(auto_any_cast(ctx), output, inputs))
+                F) -> decltype(x.compute(auto_any_cast(ctx),
+                                         make_compute_output_shape(pack(x, output, inputs)),
+                                         inputs))
 {
-    return x.compute(auto_any_cast(ctx), output, inputs);
+    return x.compute(
+        auto_any_cast(ctx), make_compute_output_shape(pack(x, output, inputs)), inputs);
 }
 
 template <class T, class F>
@@ -348,7 +369,8 @@ auto is_context_free_op(rank<1>,
                         const T& x,
                         const shape& output_shape,
                         const std::vector<argument>& input)
-    -> decltype(x.compute(output_shape, input), std::true_type{});
+    -> decltype(x.compute(make_compute_output_shape(pack(x, output_shape, input)), input),
+                std::true_type{});
 
 template <class T>
 auto is_context_free_op(rank<0>, const T&, const shape&, const std::vector<argument>&)

src/include/migraphx/reflect.hpp

@@ -56,11 +56,11 @@ auto reflect_impl(rank<0>, T&, Selector)
 }
 
 template <class T>
-auto reflectable_impl(rank<1>, T&& x)
+auto reflectable_impl(rank<1>, const T& x)
     -> decltype(T::reflect(x, reflect_placeholder{}), std::true_type{});
 
 template <class T>
-auto reflectable_impl(rank<0>, T &&) -> decltype(std::false_type{});
+auto reflectable_impl(rank<0>, const T&) -> decltype(std::false_type{});
 
 template <class T>
 struct remove_rvalue_reference
@@ -111,8 +111,18 @@ auto reflect(T& x, Selector f)
 template <class T>
 auto reflect_tie(T& x)
 {
-    return reflect(x, [](auto&& y, auto&&...) { return detail::wrap<decltype(y)>(y); })(
-        [](auto&&... xs) { return detail::auto_tuple(xs.get()...); });
+    return reflect(x, [](auto&& y, auto&&...) {
+        // cppcheck-suppress UnnecessaryElseStatement
+        if constexpr(is_reflectable<decltype(y)>{})
+        {
+            auto t = reflect_tie(y);
+            return detail::wrap<decltype(t)>(t);
+        }
+        else
+        {
+            return detail::wrap<decltype(y)>(y);
+        }
+    })([](auto&&... xs) { return detail::auto_tuple(xs.get()...); });
 }
 
 template <class T, class F>

src/include/migraphx/streamutils.hpp

@@ -26,7 +26,9 @@
 
 #include <ostream>
 #include <algorithm>
+#include <migraphx/reflect.hpp>
 #include <migraphx/rank.hpp>
+#include <migraphx/requires.hpp>
 #include <migraphx/config.hpp>
 #include <vector>
 
@@ -83,6 +85,20 @@ auto stream_write_value_impl(rank<0>, std::ostream& os, const Range& r)
     os << "}";
 }
 
+template <class T, MIGRAPHX_REQUIRES(is_reflectable<T>{})>
+void stream_write_value_impl(rank<0>, std::ostream& os, const T& x)
+{
+    char delim = '{';
+    reflect_each(x, [&](auto&& y, auto name) {
+        os << delim;
+        os << name << "=";
+        stream_write_value_impl(rank<2>{}, os, y);
+        delim = ',';
+    });
+    if(delim == ',')
+        os << "}";
+}
+
 } // namespace detail
 
 template <class T>

src/load_save.cpp

@@ -25,7 +25,6 @@
 #include <migraphx/file_buffer.hpp>
 #include <migraphx/json.hpp>
 #include <migraphx/msgpack.hpp>
-#include <migraphx/file_buffer.hpp>
 #include <fstream>
 
 namespace migraphx {

src/module.cpp

@@ -34,7 +34,6 @@
 #include <migraphx/pass_manager.hpp>
 #include <migraphx/make_op.hpp>
 #include <migraphx/register_target.hpp>
-#include <migraphx/make_op.hpp>
 #include <migraphx/json.hpp>
 #include <iostream>
 #include <sstream>

src/onnx/conv.cpp

@@ -30,7 +30,7 @@ namespace onnx {
 
 void recalc_conv_attributes(value& v, size_t kdims)
 {
-    if(not(v["padding"].size() == kdims or v["padding"].size() == kdims * 2))
+    if(v["padding"].size() != kdims and v["padding"].size() != kdims * 2)
     {
         v["padding"].resize(kdims);
         std::fill_n(v["padding"].begin(), kdims, 0);

src/rewrite_rnn.cpp

@@ -46,9 +46,6 @@
 #include <migraphx/iterator_for.hpp>
 #include <migraphx/dfor.hpp>
 #include <migraphx/ranges.hpp>
-#include <migraphx/op/common.hpp>
-#include <migraphx/op/rnn_var_sl_last_output.hpp>
-#include <migraphx/op/rnn_variable_seq_lens.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {

src/simplify_algebra.cpp

@@ -827,7 +827,7 @@ MIGRAPHX_PRED_MATCHER(horiz_conv_dot, instruction_ref ins)
     };
     auto dots  = std::count_if(ins->outputs().begin(), ins->outputs().end(), pred("dot"));
     auto convs = std::count_if(ins->outputs().begin(), ins->outputs().end(), pred("convolution"));
-    return not(dots < 2 and convs < 2);
+    return (dots >= 2 or convs >= 2);
 }
 
 struct find_conv_dot_horiz_fusion

src/targets/cpu/lowering.cpp

@@ -215,55 +215,6 @@ struct cpu_pad
 };
 MIGRAPHX_REGISTER_OP(cpu_pad)
 
-struct leaky_relu_op
-{
-    op::leaky_relu op;
-    std::string name() const { return "cpu::leaky_relu"; }
-    auto fcn() const
-    {
-        auto a = op.alpha;
-        return [a](auto x) { return x > 0 ? x : x * a; };
-    }
-};
-
-template <typename Op>
-struct cpu_unary2 : auto_register_op<cpu_unary2<Op>>
-{
-    cpu_unary2() = default;
-
-    template <class T>
-    cpu_unary2(T pop) : op(Op{std::move(pop)})
-    {
-    }
-
-    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
-    {
-        check_shapes{inputs, *this}.has(1);
-        const auto& s = inputs.at(0);
-        return {s.type(), s.lens()};
-    }
-
-    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
-    {
-        argument result{output_shape};
-        visit_all(result, args[0])([&](auto output, auto input) {
-            assert(input.get_shape().standard());
-            std::transform(input.begin(), input.end(), output.begin(), op.fcn());
-        });
-
-        return result;
-    }
-};
-template struct cpu_unary2<leaky_relu_op>;
-
 struct cpu_rnn_var_sl_last_output
 {
     op::rnn_var_sl_last_output op;

src/targets/cpu/target.cpp

@@ -41,7 +41,6 @@
 #include <migraphx/rewrite_quantization.hpp>
 #include <migraphx/rewrite_rnn.hpp>
 #include <migraphx/schedule.hpp>
-#include <migraphx/memory_coloring.hpp>
 #include <migraphx/simplify_algebra.hpp>
 #include <migraphx/simplify_qdq.hpp>
 #include <migraphx/simplify_reshapes.hpp>

src/targets/gpu/CMakeLists.txt

@@ -84,10 +84,7 @@ add_library(migraphx_gpu
     compile_hip.cpp
     compile_hip_code_object.cpp
     compiler.cpp
-    convolution.cpp
-    deconvolution.cpp
     device_name.cpp
-    elu.cpp
     fuse_mlir.cpp
     fuse_ops.cpp
     gather.cpp
@@ -100,7 +97,6 @@ add_library(migraphx_gpu
     logsoftmax.cpp
     loop.cpp
     lrn.cpp
-    leaky_relu.cpp
     mlir.cpp
     multinomial.cpp
     nonzero.cpp
@@ -110,7 +106,6 @@ add_library(migraphx_gpu
     pad.cpp
     perfdb.cpp
     pooling.cpp
-    quant_convolution.cpp
     reverse.cpp
     rnn_variable_seq_lens.cpp
     rocblas.cpp
@@ -146,14 +141,9 @@ register_migraphx_gpu_ops(hip_
 register_migraphx_gpu_ops(miopen_
     abs
     contiguous
-    convolution
-    deconvolution
-    elu
     int8_conv_pack
-    leaky_relu
     lrn
     pooling
-    quant_convolution
 )
 register_op(migraphx_gpu 
     HEADER migraphx/gpu/rnn_variable_seq_lens.hpp 
@@ -167,6 +157,9 @@ register_op(migraphx_gpu
     HEADER migraphx/gpu/gemm.hpp 
     OPERATORS gpu::rocblas_gemm<op::dot> gpu::rocblas_gemm<op::quant_dot>
     INCLUDES migraphx/gpu/context.hpp)
+register_op(migraphx_gpu HEADER migraphx/gpu/convolution.hpp 
+    OPERATORS gpu::miopen_convolution<op::convolution> gpu::miopen_convolution<op::deconvolution> gpu::miopen_convolution<op::quant_convolution>
+    INCLUDES migraphx/gpu/context.hpp)
 rocm_set_soversion(migraphx_gpu ${MIGRAPHX_SO_VERSION})
 rocm_clang_tidy_check(migraphx_gpu)
 

src/targets/gpu/convolution.cpp

-/*
- * The MIT License (MIT)
- *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
- * THE SOFTWARE.
- */
-#include <migraphx/gpu/convolution.hpp>
-#include <migraphx/gpu/context.hpp>
-#include <migraphx/generate.hpp>
-#include <miopen/miopen.h>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-shape miopen_convolution::compute_shape(const std::vector<shape>& inputs) const
-{
-    check_shapes{inputs, *this}.has(4).standard();
-    std::vector<shape> conv_inputs(inputs.begin(), inputs.begin() + 2);
-    check_shapes{conv_inputs, *this}.max_ndims(5);
-    return op.normalize_compute_shape(conv_inputs);
-}
-
-inline shape reshape_if_1d(const shape& input)
-{
-    shape new_shape{input};
-    auto dims = new_shape.lens();
-
-    if(dims.size() == 3)
-    {
-        std::vector<size_t> new_dims = dims;
-        new_dims.insert(new_dims.begin() + 2, 1);
-        new_shape = shape{input.type(), new_dims};
-    }
-    return new_shape;
-}
-
-argument miopen_convolution::compute(context& ctx,
-                                     const shape& output_shape,
-                                     const std::vector<argument>& args) const
-{
-    auto x_desc                = make_tensor(reshape_if_1d(args[0].get_shape()));
-    auto w_desc                = make_tensor(reshape_if_1d(args[1].get_shape()));
-    auto y_desc                = make_tensor(reshape_if_1d(output_shape));
-    auto* miopen_stream_handle = ctx.get_stream().get_miopen();
-    auto workspace_size        = args[2].get_shape().bytes();
-
-#ifdef MIGRAPHX_HAS_FIND_2_API
-    {
-        const miopenTensorArgument_t tensor_args[3] = {
-            {miopenTensorConvolutionX, nullptr, args[0].implicit()},
-            {miopenTensorConvolutionW, nullptr, args[1].implicit()},
-            {miopenTensorConvolutionY, nullptr, args[3].implicit()},
-        };
-
-        if(solution_ptr.get() == nullptr)
-            MIGRAPHX_THROW("MIOpen Convolution : Load MIOpen Solution before running it");
-
-        auto status = miopenRunSolution(miopen_stream_handle,
-                                        solution_ptr.get(),
-                                        3,
-                                        tensor_args,
-                                        args[2].implicit(),
-                                        workspace_size);
-        if(status != miopenStatusSuccess)
-            MIGRAPHX_THROW("MIOpen Convolution: running convolution using find_2.0 failed");
-
-        return args[3];
-    }
-#else
-    // else use immediate mode
-    if(solution_id == 0)
-        MIGRAPHX_THROW("MIOpen Convolution: invalid solution ID");
-
-    auto status = miopenConvolutionForwardImmediate(miopen_stream_handle,
-                                                    w_desc.get(),
-                                                    args[1].implicit(),
-                                                    x_desc.get(),
-                                                    args[0].implicit(),
-                                                    cd.get(),
-                                                    y_desc.get(),
-                                                    args[3].implicit(),
-                                                    args[2].implicit(),
-                                                    workspace_size,
-                                                    solution_id);
-
-    if(status != miopenStatusSuccess)
-        MIGRAPHX_THROW("MIOpen Convolution: running convolution failed");
-    return args[3];
-#endif
-}
-
-shape miopen_convolution::find(context& ctx, const shape& output_shape, std::vector<shape> inputs)
-{
-    shape workspace_shape{};
-    auto x_desc                = make_tensor(reshape_if_1d(inputs[0]));
-    auto w_desc                = make_tensor(reshape_if_1d(inputs[1]));
-    auto y_desc                = make_tensor(reshape_if_1d(output_shape));
-    std::size_t workspace_size = 0;
-
-#ifdef MIGRAPHX_HAS_FIND_2_API
-    {
-        auto conv_problem = make_obj<miopen_problem>(
-            &miopenCreateConvProblem, cd.get(), miopenProblemDirectionForward);
-
-        set_tensor_descriptor(miopenTensorConvolutionX, x_desc, conv_problem);
-        set_tensor_descriptor(miopenTensorConvolutionW, w_desc, conv_problem);
-        set_tensor_descriptor(miopenTensorConvolutionY, y_desc, conv_problem);
-
-        auto* miopen_stream_handle = ctx.get_stream().get_miopen();
-
-        solution_ptr = find_solution(miopen_stream_handle, conv_problem.get());
-
-        auto status = miopenGetSolutionWorkspaceSize(solution_ptr.get(), &workspace_size);
-        if(status != miopenStatusSuccess)
-            MIGRAPHX_THROW("MIOpen Convolution : failed to get solution's workspace size");
-
-        std::size_t solution_size;
-        status = miopenGetSolutionSize(solution_ptr.get(), &solution_size);
-        if(status != miopenStatusSuccess)
-            MIGRAPHX_THROW("MIOpen Convolution: Failed to fetch solution size");
-
-        auto solution_binary = std::vector<char>{};
-        solution_binary.resize(solution_size);
-
-        status = miopenSaveSolution(solution_ptr.get(), solution_binary.data());
-        if(status != miopenStatusSuccess)
-            MIGRAPHX_THROW("MIOpen Convolution: Saving solution failed");
-        solution_object = value::binary{solution_binary.data(), solution_size};
-
-        return shape{shape::int8_type, {workspace_size}};
-    }
-#else
-    // else use immediate find mode
-    auto status = miopenConvolutionForwardGetWorkSpaceSize(ctx.get_stream().get_miopen(),
-                                                           w_desc.get(),
-                                                           x_desc.get(),
-                                                           cd.get(),
-                                                           y_desc.get(),
-                                                           &workspace_size);
-    if(status != miopenStatusSuccess)
-        MIGRAPHX_THROW("MIOpen Convolution: Failed to get forward workspace size");
-
-    workspace_shape = shape{shape::int8_type, {workspace_size}};
-
-    auto x         = to_gpu(generate_argument(inputs[0]));
-    auto w         = to_gpu(generate_argument(inputs[1]));
-    auto y         = allocate_gpu(output_shape);
-    auto workspace = allocate_gpu(workspace_shape);
-
-    int algo_count = 1;
-    miopenConvAlgoPerf_t perf;
-    status = miopenFindConvolutionForwardAlgorithm(ctx.get_stream().get_miopen(),
-                                                   x_desc.get(),
-                                                   x.implicit(),
-                                                   w_desc.get(),
-                                                   w.implicit(),
-                                                   cd.get(),
-                                                   y_desc.get(),
-                                                   y.implicit(),
-                                                   1,
-                                                   &algo_count,
-                                                   &perf,
-                                                   workspace.implicit(),
-                                                   workspace_size,
-                                                   false);
-    if(status != miopenStatusSuccess)
-        MIGRAPHX_THROW("MIOpen Convolution: find convolution failed");
-    algo = perf.fwd_algo;
-
-    size_t solution_count;
-
-    status = miopenConvolutionForwardGetSolutionCount(ctx.get_stream().get_miopen(),
-                                                      w_desc.get(),
-                                                      x_desc.get(),
-                                                      cd.get(),
-                                                      y_desc.get(),
-                                                      &solution_count);
-    if(status != miopenStatusSuccess)
-        MIGRAPHX_THROW("MIOpen Convolution: get solution count failed");
-
-    std::vector<miopenConvSolution_t> solutions(solution_count);
-
-    status = miopenConvolutionForwardGetSolution(ctx.get_stream().get_miopen(),
-                                                 w_desc.get(),
-                                                 x_desc.get(),
-                                                 cd.get(),
-                                                 y_desc.get(),
-                                                 solution_count,
-                                                 &solution_count,
-                                                 solutions.data());
-    if(status != miopenStatusSuccess)
-        MIGRAPHX_THROW("MIOpen Convolution: get solution failed");
-
-    solution_id = solutions.front().solution_id;
-
-    return shape{shape::int8_type, {perf.memory}};
-#endif
-}
-
-void miopen_convolution::finalize(context& ctx,
-                                  const shape& output_shape,
-                                  const std::vector<shape>& inputs)
-{
-#ifdef MIGRAPHX_HAS_FIND_2_API
-    {
-        (void)(ctx); // avoid warnings
-        (void)(output_shape);
-        (void)(inputs);
-        // load solution
-        if(solution_ptr == nullptr)
-        {
-            miopenSolution_t ptr;
-            auto status  = miopenLoadSolution(&ptr,
-                                             reinterpret_cast<const char*>(solution_object.data()),
-                                             solution_object.size());
-            solution_ptr = miopen_solution{ptr};
-            if(status != miopenStatusSuccess)
-                MIGRAPHX_THROW("MIOpen Convolution: loading convolution solution failed");
-        }
-    }
-#else
-    // Use immediate mode API
-    {
-        if(cd == nullptr)
-            cd = make_conv(op);
-        if(solution_id == 0)
-        {
-            // Check that workspace hasn't changed
-            auto size = inputs.at(2).bytes();
-            auto ws   = find(ctx, output_shape, inputs);
-            if(ws.bytes() > size)
-                MIGRAPHX_THROW("MIOpen Convolution: workspace has changed during finalization.");
-        }
-
-        auto x_desc = make_tensor(reshape_if_1d(inputs[0]));
-        auto w_desc = make_tensor(reshape_if_1d(inputs[1]));
-        auto y_desc = make_tensor(reshape_if_1d(output_shape));
-
-        auto status = miopenConvolutionForwardCompileSolution(ctx.get_stream().get_miopen(),
-                                                              w_desc.get(),
-                                                              x_desc.get(),
-                                                              cd.get(),
-                                                              y_desc.get(),
-                                                              solution_id);
-        if(status != miopenStatusSuccess)
-            MIGRAPHX_THROW("MIOpen Convolution: compile solution failed");
-    }
-#endif
-}
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx

src/targets/gpu/deconvolution.cpp

-/*
- * The MIT License (MIT)
- *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
- * THE SOFTWARE.
- */
-#include <migraphx/gpu/deconvolution.hpp>
-#include <migraphx/gpu/context.hpp>
-#include <migraphx/generate.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-shape miopen_deconvolution::compute_shape(const std::vector<shape>& inputs) const
-{
-    check_shapes{inputs, *this}.has(4).standard();
-    std::vector<shape> conv_inputs(inputs.begin(), inputs.begin() + 2);
-    check_shapes{conv_inputs, *this}.max_ndims(5);
-    return op.compute_shape(conv_inputs);
-}
-
-inline shape reshape_if_1d(const shape& input)
-{
-    shape new_shape{input};
-    auto dims = new_shape.lens();
-
-    if(dims.size() == 3)
-    {
-        std::vector<size_t> new_dims = dims;
-        new_dims.insert(new_dims.begin() + 2, 1);
-        new_shape = shape{input.type(), new_dims};
-    }
-    return new_shape;
-}
-
-argument miopen_deconvolution::compute(context& ctx,
-                                       const shape& output_shape,
-                                       const std::vector<argument>& args) const
-{
-    auto x_desc = make_tensor(reshape_if_1d(args[0].get_shape()));
-    auto w_desc = make_tensor(reshape_if_1d(args[1].get_shape()));
-    auto y_desc = make_tensor(reshape_if_1d(output_shape));
-
-    if(solution_id == 0)
-        MIGRAPHX_THROW("MIOpen Deconvolution: invalid solution ID");
-
-    auto status = miopenConvolutionForwardImmediate(ctx.get_stream().get_miopen(),
-                                                    w_desc.get(),
-                                                    args[1].implicit(),
-                                                    x_desc.get(),
-                                                    args[0].implicit(),
-                                                    cd.get(),
-                                                    y_desc.get(),
-                                                    args[3].implicit(),
-                                                    args[2].implicit(),
-                                                    args[2].get_shape().bytes(),
-                                                    solution_id);
-
-    if(status != miopenStatusSuccess)
-        MIGRAPHX_THROW("MIOpen Deconvolution: running convolution failed");
-    return args[3];
-}
-
-shape miopen_deconvolution::find(context& ctx, const shape& output_shape, std::vector<shape> inputs)
-{
-    shape workspace_shape{};
-
-    auto x_desc = make_tensor(reshape_if_1d(inputs[0]));
-    auto w_desc = make_tensor(reshape_if_1d(inputs[1]));
-    auto y_desc = make_tensor(reshape_if_1d(output_shape));
-
-    std::size_t workspace_size = 0;
-    miopenConvolutionForwardGetWorkSpaceSize(ctx.get_stream().get_miopen(),
-                                             w_desc.get(),
-                                             x_desc.get(),
-                                             cd.get(),
-                                             y_desc.get(),
-                                             &workspace_size);
-    workspace_shape = shape{shape::int8_type, {workspace_size}};
-
-    auto x         = to_gpu(generate_argument(inputs[0]));
-    auto w         = to_gpu(generate_argument(inputs[1]));
-    auto y         = allocate_gpu(output_shape);
-    auto workspace = allocate_gpu(workspace_shape);
-
-    int algo_count = 1;
-    miopenConvAlgoPerf_t perf;
-    auto status = miopenFindConvolutionForwardAlgorithm(ctx.get_stream().get_miopen(),
-                                                        x_desc.get(),
-                                                        x.implicit(),
-                                                        w_desc.get(),
-                                                        w.implicit(),
-                                                        cd.get(),
-                                                        y_desc.get(),
-                                                        y.implicit(),
-                                                        1,
-                                                        &algo_count,
-                                                        &perf,
-                                                        workspace.implicit(),
-                                                        workspace_size,
-                                                        false);
-    if(status != miopenStatusSuccess)
-        MIGRAPHX_THROW("MIOpen Deconvolution: find convolution failed");
-    algo = perf.fwd_algo;
-
-    size_t solution_count;
-
-    status = miopenConvolutionForwardGetSolutionCount(ctx.get_stream().get_miopen(),
-                                                      w_desc.get(),
-                                                      x_desc.get(),
-                                                      cd.get(),
-                                                      y_desc.get(),
-                                                      &solution_count);
-    if(status != miopenStatusSuccess)
-        MIGRAPHX_THROW("MIOpen Deconvolution: get solution count failed");
-
-    std::vector<miopenConvSolution_t> solutions(solution_count);
-
-    status = miopenConvolutionForwardGetSolution(ctx.get_stream().get_miopen(),
-                                                 w_desc.get(),
-                                                 x_desc.get(),
-                                                 cd.get(),
-                                                 y_desc.get(),
-                                                 solution_count,
-                                                 &solution_count,
-                                                 solutions.data());
-    if(status != miopenStatusSuccess)
-        MIGRAPHX_THROW("MIOpen Deconvolution: get solution failed");
-
-    solution_id = solutions.front().solution_id;
-
-    return shape{shape::int8_type, {perf.memory}};
-}
-
-void miopen_deconvolution::finalize(context& ctx,
-                                    const shape& output_shape,
-                                    std::vector<shape> inputs)
-{
-    if(cd == nullptr)
-        cd = make_deconv(op);
-    if(solution_id == 0)
-    {
-        // Check that workspace hasn't changed
-        auto size = inputs.at(2).bytes();
-        auto ws   = find(ctx, output_shape, inputs);
-        if(ws.bytes() > size)
-            MIGRAPHX_THROW("MIOpen Deconvolution: workspace has changed during finalization.");
-    }
-
-    auto x_desc = make_tensor(reshape_if_1d(inputs[0]));
-    auto w_desc = make_tensor(reshape_if_1d(inputs[1]));
-    auto y_desc = make_tensor(reshape_if_1d(output_shape));
-
-    auto status = miopenConvolutionForwardCompileSolution(ctx.get_stream().get_miopen(),
-                                                          w_desc.get(),
-                                                          x_desc.get(),
-                                                          cd.get(),
-                                                          y_desc.get(),
-                                                          solution_id);
-    if(status != miopenStatusSuccess)
-        MIGRAPHX_THROW("MIOpen Deconvolution: compile solution failed");
-}
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx

src/targets/gpu/elu.cpp

-/*
- * The MIT License (MIT)
- *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
- * THE SOFTWARE.
- */
-#include <migraphx/gpu/elu.hpp>
-#include <migraphx/gpu/context.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-shape miopen_elu::compute_shape(const std::vector<shape>& inputs) const
-{
-    check_shapes{inputs, *this}.has(2).not_broadcasted();
-    return inputs.at(1);
-}
-
-argument miopen_elu::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];
-}
-
-void miopen_elu::finalize(context&, const shape&, const std::vector<shape>&)
-{
-    ad = make_elu(op.alpha);
-}
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx

src/targets/gpu/fuse_mlir.cpp

@@ -49,7 +49,7 @@ struct mlir_conv
     std::string name() const { return "gpu::mlir_conv"; }
     shape compute_shape(std::vector<shape> inputs, const std::vector<module_ref>& mods) const
     {
-        check_shapes{inputs, *this}.standard();
+        check_shapes{inputs, *this}.packed_or_broadcasted();
         if(mods.size() != 1)
             MIGRAPHX_THROW("should have one submodule.");
         if(inputs.size() < 2)
@@ -70,6 +70,9 @@ MIGRAPHX_PRED_MATCHER(is_mlir_conv, instruction_ref ins)
     auto group = v.at("group").to<int>();
     if(group != 1)
         return false;
+    // Avoid MLIR assertion: Index < Length && "Invalid index!"
+    if(ins->get_shape().lens().size() != 4)
+        return false;
     return true;
 }
 
@@ -96,9 +99,10 @@ struct find_conv_pointwise
                                    i.name());
            }))
             return;
-        // Only fuse with fp32 for now
+        // Only fuse with fp32/fp16
         if(std::any_of(ins->inputs().begin(), ins->inputs().end(), [&](auto i) {
-               return i->get_shape().type() != shape::type_t::float_type;
+               return not contains({shape::type_t::float_type, shape::type_t::half_type},
+                                   i->get_shape().type());
            }))
             return;
         std::sort(names.begin(), names.end());

src/targets/gpu/fuse_ops.cpp

@@ -26,7 +26,6 @@
 #include <migraphx/gpu/fuse_ops.hpp>
 #include <migraphx/matcher.hpp>
 #include <migraphx/gpu/miopen.hpp>
-#include <migraphx/gpu/convolution.hpp>
 #include <migraphx/gpu/device_name.hpp>
 #include <migraphx/gpu/oper.hpp>
 #include <migraphx/gpu/gemm.hpp>
@@ -190,10 +189,12 @@ MIGRAPHX_PRED_MATCHER(fusable_conv, instruction_ref ins)
         return false;
     auto wei = ins->inputs().at(1)->get_shape();
     assert(wei.lens().size() == 4);
-    auto conv = any_cast<miopen_convolution>(ins->get_operator());
-    if(conv.op.group > 1)
+    auto miopen_conv_op = ins->get_operator().to_value();
+    auto algo           = miopen_conv_op.at("algo").to<miopenConvFwdAlgorithm_t>();
+    auto conv_op        = from_value<op::convolution>(miopen_conv_op["op"]);
+    if(conv_op.group > 1)
         return false;
-    if(wei.lens()[1] > 512 and conv.algo != miopenConvolutionFwdAlgoWinograd)
+    if(wei.lens()[1] > 512 and algo != miopenConvolutionFwdAlgoWinograd)
         return false;
 
     // Do not fuse non-symmetric input
@@ -201,13 +202,12 @@ MIGRAPHX_PRED_MATCHER(fusable_conv, instruction_ref ins)
     if(input_lens[2] != input_lens[3] or wei.lens()[2] != wei.lens()[3])
         return false;
 
-    auto op = conv.op;
     // Dont fuse winograd for non-3x3s since there is no fused windograd for those configs
-    if(conv.algo == miopenConvolutionFwdAlgoWinograd and wei.lens()[2] != 3 and
-       wei.lens()[3] != 3 and contains({{1, 1}}, op.stride))
+    if(algo == miopenConvolutionFwdAlgoWinograd and wei.lens()[2] != 3 and wei.lens()[3] != 3 and
+       contains({{1, 1}}, conv_op.stride))
         return false;
-    return contains({{0, 0, 0, 0}, {1, 1, 1, 1}, {2, 2, 2, 2}}, op.padding) and
-           contains({{0, 0}, {1, 1}}, op.stride) and contains({{1, 1}}, op.dilation);
+    return contains({{0, 0, 0, 0}, {1, 1, 1, 1}, {2, 2, 2, 2}}, conv_op.padding) and
+           contains({{0, 0}, {1, 1}}, conv_op.stride) and contains({{1, 1}}, conv_op.dilation);
 }
 
 void move_broadcasted_back(std::vector<instruction_ref>& args)
@@ -462,7 +462,7 @@ void apply_conv_bias(context& ctx, module& m, const match::matcher_result& r)
     auto ins         = r.result;
     auto input_ins   = conv_ins->inputs().at(0);
     auto weights_ins = conv_ins->inputs().at(1);
-    auto conv_op     = any_cast<miopen_convolution>(conv_ins->get_operator()).op;
+    auto conv_op     = from_value<op::convolution>((conv_ins->get_operator()).to_value()["op"]);
     auto alloc_ins   = ins->inputs().back();
     auto old_ws_ins  = conv_ins->inputs().at(2);
 
@@ -528,7 +528,7 @@ struct find_conv_pointwise
         auto ins         = r.result;
         auto input_ins   = conv_ins->inputs().at(0);
         auto weights_ins = conv_ins->inputs().at(1);
-        auto conv_op     = any_cast<miopen_convolution>(conv_ins->get_operator()).op;
+        auto conv_op     = from_value<op::convolution>(conv_ins->get_operator().to_value()["op"]);
         auto alloc_ins   = ins->inputs().back();
 
         module_ref pm = ins->module_inputs().front();

src/targets/gpu/hip.cpp

@@ -183,8 +183,8 @@ argument register_on_gpu(const argument& arg)
 {
     auto arg_shared = arg.share();
     auto p          = register_on_gpu(arg_shared.data(), arg_shared.get_shape().bytes());
-    return {arg_shared.get_shape(),
-            [p, a = std::move(arg_shared)]() mutable { return get_device_ptr(p.get()); }};
+    auto s          = arg_shared.get_shape();
+    return {s, [p, a = std::move(arg_shared)]() mutable { return get_device_ptr(p.get()); }};
 }
 
 argument to_gpu(const argument& arg, bool host)

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

@@ -25,18 +25,40 @@
 #define MIGRAPHX_GUARD_RTGLIB_CONVOLUTION_HPP
 
 #include <migraphx/shape.hpp>
-#include <migraphx/op/convolution.hpp>
+#include <migraphx/generate.hpp>
+#include <migraphx/operation.hpp>
+#include <migraphx/register_op.hpp>
 #include <migraphx/gpu/miopen.hpp>
-
+#include <migraphx/op/identity.hpp>
+#include <migraphx/op/convolution.hpp>
+#include <migraphx/op/quant_convolution.hpp>
+#include <migraphx/op/deconvolution.hpp>
+#include <unordered_map>
+#include <migraphx/reflect.hpp>
+#include <migraphx/gpu/context.hpp>
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 
-struct context;
+inline shape reshape_if_1d(const shape& input)
+{
+    shape new_shape{input};
+    auto dims = new_shape.lens();
+
+    if(dims.size() == 3)
+    {
+        std::vector<size_t> new_dims = dims;
+        new_dims.insert(new_dims.begin() + 2, 1);
+        new_shape = shape{input.type(), new_dims};
+    }
+    return new_shape;
+}
 
+template <class Op>
 struct miopen_convolution
 {
-    op::convolution op;
+    Op op;
+    bool int8_x4_format               = false;
     shared<convolution_descriptor> cd = nullptr;
     miopenConvFwdAlgorithm_t algo{};
 #ifdef MIGRAPHX_HAS_FIND_2_API
@@ -48,29 +70,276 @@ struct miopen_convolution
     template <class Self, class F>
     static auto reflect(Self& self, F f)
     {
-        return pack(f(self.op.padding, "padding"),
-                    f(self.op.stride, "stride"),
-                    f(self.op.dilation, "dilation"),
-                    f(self.op.group, "group"),
-                    f(self.op.padding_mode, "padding_mode"),
+        return pack(f(self.op, "op"),
 #ifdef MIGRAPHX_HAS_FIND_2_API
                     f(self.solution_object, "solution_object"),
 #endif
+                    f(self.algo, "algo"),
+                    f(self.int8_x4_format, "int8_x4_format"),
                     f(self.solution_id, "solution_id"));
     }
 
-    std::string name() const { return "gpu::convolution"; }
-    shape compute_shape(const std::vector<shape>& inputs) const;
+    std::string name() const { return "gpu::" + op.name(); }
+
+    inline shape compute_shape(const std::vector<shape>& inputs) const
+    {
+        check_shapes{inputs, op}.has(4).standard();
+        std::vector<shape> conv_inputs(inputs.begin(), inputs.begin() + 2);
+        check_shapes{conv_inputs, op}.max_ndims(5);
+        return migraphx::compute_shape<Op>(op, conv_inputs);
+    }
+
     argument
-    compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
-    shape find(context& ctx, const shape& output_shape, std::vector<shape> inputs);
-    void finalize(context& ctx, const shape& output_shape, const std::vector<shape>& inputs);
-    std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
+    compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const
+    {
+        auto x_desc = make_tensor(reshape_if_1d(args[0].get_shape()), int8_x4_format);
+        auto w_desc = make_tensor(reshape_if_1d(args[1].get_shape()), int8_x4_format);
+        auto y_desc = make_tensor(reshape_if_1d(output_shape));
+        auto* miopen_stream_handle = ctx.get_stream().get_miopen();
+        auto workspace_size        = args[2].get_shape().bytes();
+
+#ifdef MIGRAPHX_HAS_FIND_2_API
+        {
+            const miopenTensorArgument_t tensor_args[3] = {
+                {miopenTensorConvolutionX, nullptr, args[0].implicit()},
+                {miopenTensorConvolutionW, nullptr, args[1].implicit()},
+                {miopenTensorConvolutionY, nullptr, args[3].implicit()},
+            };
+
+            if(solution_ptr.get() == nullptr)
+                MIGRAPHX_THROW("MIOpen " + op.name() + " : Load MIOpen Solution before running it");
+
+            auto status = miopenRunSolution(miopen_stream_handle,
+                                            solution_ptr.get(),
+                                            3,
+                                            tensor_args,
+                                            args[2].implicit(),
+                                            workspace_size);
+            if(status != miopenStatusSuccess)
+                MIGRAPHX_THROW("MIOpen " + op.name() +
+                               " : running convolution using find_2.0 failed");
+
+            return args[3];
+        }
+#else
+        // else use immediate mode
+        if(solution_id == 0)
+            MIGRAPHX_THROW("MIOpen " + op.name() + " : invalid solution ID");
+
+        auto status = miopenConvolutionForwardImmediate(miopen_stream_handle,
+                                                        w_desc.get(),
+                                                        args[1].implicit(),
+                                                        x_desc.get(),
+                                                        args[0].implicit(),
+                                                        cd.get(),
+                                                        y_desc.get(),
+                                                        args[3].implicit(),
+                                                        args[2].implicit(),
+                                                        workspace_size,
+                                                        solution_id);
+
+        if(status != miopenStatusSuccess)
+            MIGRAPHX_THROW("MIOpen " + op.name() + ": running convolution failed");
+        return args[3];
+#endif
+    }
+
+    inline void set_conv_descriptor()
+    {
+        if(cd == nullptr)
+        {
+            cd = (op.name() == "deconvolution") ? make_deconv(op) : make_conv(op);
+        }
+    }
+
+    value compile(migraphx::context& ctx, const shape& output, const std::vector<shape>& input)
+    {
+        set_conv_descriptor();
+        auto ws = find(any_cast<migraphx::gpu::context>(ctx), output, input);
+        return {{"workspace", ws.bytes()}};
+    }
+
+    shape find(context& ctx, const shape& output_shape, const std::vector<shape>& inputs)
+    {
+        shape workspace_shape{};
+        auto x_desc                = make_tensor(reshape_if_1d(inputs[0]), int8_x4_format);
+        auto w_desc                = make_tensor(reshape_if_1d(inputs[1]), int8_x4_format);
+        auto y_desc                = make_tensor(reshape_if_1d(output_shape));
+        std::size_t workspace_size = 0;
+#ifdef MIGRAPHX_HAS_FIND_2_API
+        {
+            auto conv_problem = make_obj<miopen_problem>(
+                &miopenCreateConvProblem, cd.get(), miopenProblemDirectionForward);
+
+            set_tensor_descriptor(miopenTensorConvolutionX, x_desc, conv_problem);
+            set_tensor_descriptor(miopenTensorConvolutionW, w_desc, conv_problem);
+            set_tensor_descriptor(miopenTensorConvolutionY, y_desc, conv_problem);
+
+            auto* miopen_stream_handle = ctx.get_stream().get_miopen();
+
+            solution_ptr = find_solution(miopen_stream_handle, conv_problem.get());
+            auto status  = miopenGetSolutionWorkspaceSize(solution_ptr.get(), &workspace_size);
+            if(status != miopenStatusSuccess)
+                MIGRAPHX_THROW("MIOpen" + op.name() + " : failed to get solution's workspace size");
+
+            std::size_t solution_size;
+            status = miopenGetSolutionSize(solution_ptr.get(), &solution_size);
+            if(status != miopenStatusSuccess)
+                MIGRAPHX_THROW("MIOpen" + op.name() + ": Failed to fetch solution size");
+
+            auto solution_binary = std::vector<char>{};
+            solution_binary.resize(solution_size);
+
+            status = miopenSaveSolution(solution_ptr.get(), solution_binary.data());
+            if(status != miopenStatusSuccess)
+                MIGRAPHX_THROW("MIOpen" + op.name() + ": Saving solution failed");
+            solution_object = value::binary{solution_binary.data(), solution_size};
+            return shape{shape::int8_type, {workspace_size}};
+        }
+#else
+        auto status = miopenConvolutionForwardGetWorkSpaceSize(ctx.get_stream().get_miopen(),
+                                                               w_desc.get(),
+                                                               x_desc.get(),
+                                                               cd.get(),
+                                                               y_desc.get(),
+                                                               &workspace_size);
+        if(status != miopenStatusSuccess)
+            MIGRAPHX_THROW("MIOpen" + op.name() + " : Failed to get forward workspace size");
+
+        workspace_shape = shape{shape::int8_type, {workspace_size}};
+
+        auto x_shape = inputs[0];
+        auto w_shape = inputs[1];
+        if(int8_x4_format)
+        {
+            x_shape = pack_int8_shape(x_shape);
+            w_shape = pack_int8_shape(w_shape);
+        }
+        auto x         = to_gpu(generate_argument(x_shape));
+        auto w         = to_gpu(generate_argument(w_shape));
+        auto y         = allocate_gpu(output_shape);
+        auto workspace = allocate_gpu(workspace_shape);
+
+        int algo_count = 1;
+        miopenConvAlgoPerf_t perf;
+        status = miopenFindConvolutionForwardAlgorithm(ctx.get_stream().get_miopen(),
+                                                       x_desc.get(),
+                                                       x.implicit(),
+                                                       w_desc.get(),
+                                                       w.implicit(),
+                                                       cd.get(),
+                                                       y_desc.get(),
+                                                       y.implicit(),
+                                                       1,
+                                                       &algo_count,
+                                                       &perf,
+                                                       workspace.implicit(),
+                                                       workspace_size,
+                                                       false);
+        if(status != miopenStatusSuccess)
+            MIGRAPHX_THROW("MIOpen " + op.name() + " : find convolution failed");
+        algo = perf.fwd_algo;
+
+        size_t solution_count;
+
+        status = miopenConvolutionForwardGetSolutionCount(ctx.get_stream().get_miopen(),
+                                                          w_desc.get(),
+                                                          x_desc.get(),
+                                                          cd.get(),
+                                                          y_desc.get(),
+                                                          &solution_count);
+        if(status != miopenStatusSuccess)
+            MIGRAPHX_THROW("MIOpen " + op.name() + ": get solution count failed");
+
+        std::vector<miopenConvSolution_t> solutions(solution_count);
+
+        status = miopenConvolutionForwardGetSolution(ctx.get_stream().get_miopen(),
+                                                     w_desc.get(),
+                                                     x_desc.get(),
+                                                     cd.get(),
+                                                     y_desc.get(),
+                                                     solution_count,
+                                                     &solution_count,
+                                                     solutions.data());
+        if(status != miopenStatusSuccess)
+            MIGRAPHX_THROW("MIOpen " + op.name() + ": get solution failed");
+
+        solution_id = solutions.front().solution_id;
+
+        return shape{shape::int8_type, {perf.memory}};
+#endif
+    }
+
+    void finalize(context& ctx, const shape& output_shape, const std::vector<shape>& inputs)
+    {
+#ifdef MIGRAPHX_HAS_FIND_2_API
+        {
+            (void)(ctx); // avoid warnings
+            (void)(output_shape);
+            (void)(inputs);
+            // load solution
+            if(solution_ptr == nullptr)
+            {
+                miopenSolution_t ptr;
+                auto status =
+                    miopenLoadSolution(&ptr,
+                                       reinterpret_cast<const char*>(solution_object.data()),
+                                       solution_object.size());
+                solution_ptr = miopen_solution{ptr};
+                if(status != miopenStatusSuccess)
+                    MIGRAPHX_THROW("MIOpen " + op.name() + ": loading convolution solution failed");
+            }
+        }
+#else
+        // Use immediate mode API
+        {
+            set_conv_descriptor();
+            if(solution_id == 0)
+            {
+                // Check that workspace hasn't changed
+                auto size = inputs.at(2).bytes();
+                auto ws   = find(ctx, output_shape, inputs);
+                if(ws.bytes() > size)
+                    MIGRAPHX_THROW("MIOpen " + op.name() +
+                                   ": workspace has changed during finalization.");
+            }
+
+            auto x_desc = make_tensor(reshape_if_1d(inputs[0]), int8_x4_format);
+            auto w_desc = make_tensor(reshape_if_1d(inputs[1]), int8_x4_format);
+            auto y_desc = make_tensor(reshape_if_1d(output_shape));
+
+            auto status = miopenConvolutionForwardCompileSolution(ctx.get_stream().get_miopen(),
+                                                                  w_desc.get(),
+                                                                  x_desc.get(),
+                                                                  cd.get(),
+                                                                  y_desc.get(),
+                                                                  solution_id);
+            if(status != miopenStatusSuccess)
+                MIGRAPHX_THROW("MIOpen Convolution: compile solution failed");
+        }
+#endif
+    }
+
+    inline std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
     {
         return shapes.size() - 1;
     }
-};
 
+    inline shape pack_int8_shape(const shape& s) const
+    {
+        if(s.type() != shape::int8_type)
+        {
+            return s;
+        }
+
+        auto lens    = s.lens();
+        auto strides = s.strides();
+        lens[1]      = (lens[1] + 3) / 4 * 4;
+        strides[0]   = strides[1] * lens[1];
+
+        return {s.type(), lens, strides};
+    }
+};
 } // namespace gpu
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx