Merge branch 'develop' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dyn_nms

Dockerfile

@@ -86,7 +86,7 @@ RUN git clone --single-branch --branch ${ONNXRUNTIME_BRANCH} --recursive ${ONNXR
 
 ADD tools/build_and_test_onnxrt.sh /onnxruntime/build_and_test_onnxrt.sh
 
-RUN PATH=/opt/cmake/bin:$PATH cget -p /usr/local install ROCmSoftwarePlatform/llvm-project-mlir@02078ce236ad90e3aec04c0c770ef5bfc99e49c2
+RUN cget -p /usr/local install ROCmSoftwarePlatform/llvm-project-mlir@26a4b3cfc0a1a15181490f24ae461608fef1b04e -DBUILD_MIXR_TARGET=On
 
 ENV MIOPEN_FIND_DB_PATH=/tmp/miopen/find-db
 ENV MIOPEN_USER_DB_PATH=/tmp/miopen/user-db

src/eliminate_contiguous.cpp

@@ -93,9 +93,11 @@ static bool try_compute_shape(instruction_ref ins,
     return try_compute_shape(ins, inputs, mods);
 }
 
-void eliminate_contiguous::apply(module& m) const
+template <class F>
+static void remove_contiguous(const std::string& op_name, module& m, F f)
 {
-    std::vector<instruction_ref> const_instruction;
+    auto last = std::prev(m.end());
+    std::vector<instruction_ref> const_instructions;
 
     for(auto ins : iterator_for(m))
     {
@@ -103,6 +105,12 @@ void eliminate_contiguous::apply(module& m) const
         if(ins->name() == "@return")
             continue;
 
+        if(ins != last and ins->outputs().empty())
+            continue;
+
+        if(not f(ins))
+            continue;
+
         // Make a copy so we can modify it while we iterate
         auto args     = ins->inputs();
         auto new_args = args;
@@ -110,36 +118,46 @@ void eliminate_contiguous::apply(module& m) const
 
         for(auto arg : ins->inputs())
         {
-            if(arg->name() == op_name)
+            if(arg->name() != op_name)
+                continue;
+            auto prev = arg->inputs().front();
+            replace(new_args, arg, prev);
+            if(try_compute_shape(ins, new_args, mod_args))
+            {
+                instruction::replace_argument(ins, arg, prev);
+            }
+            else if(prev->can_eval())
             {
-                auto prev = arg->inputs().front();
-                replace(new_args, arg, prev);
-                if(try_compute_shape(ins, new_args, mod_args))
-                {
-                    instruction::replace_argument(ins, arg, prev);
-                }
-                else if(prev->can_eval())
-                {
-                    const_instruction.push_back(arg);
-                }
+                const_instructions.push_back(arg);
             }
         }
     }
 
     // Perform evaluations in parallel
-    std::vector<argument> literals(const_instruction.size());
-    par_for(const_instruction.size(), 1, [&](const auto i) {
+    std::vector<argument> literals(const_instructions.size());
+    par_for(const_instructions.size(), 1, [&](const auto i) {
         auto c      = op::contiguous{};
-        auto prev   = const_instruction[i]->inputs().front();
+        auto prev   = const_instructions[i]->inputs().front();
         literals[i] = c.compute(c.compute_shape({prev->get_shape()}), {prev->eval()});
     });
 
-    for(size_t i = 0; i < const_instruction.size(); i++)
+    for(size_t i = 0; i < const_instructions.size(); i++)
     {
         auto l = m.add_literal(literals[i].get_shape(), literals[i].data());
-        m.replace_instruction(const_instruction[i], l);
+        m.replace_instruction(const_instructions[i], l);
     }
 }
 
+void eliminate_contiguous::apply(module& m) const
+{
+    // Skip contiguous from splits first
+    remove_contiguous(op_name, m, [](auto ins) {
+        if(ins->name() != "slice")
+            return true;
+        return (ins->inputs().front()->outputs().size() == 1);
+    });
+    remove_contiguous(op_name, m, [](auto) { return true; });
+}
+
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/include/migraphx/matcher.hpp

@@ -349,25 +349,27 @@ match::matcher_result find_match(module& modl, M&& m)
 MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_TRACE_MATCHES)
 
 /// Find matches for an instruction in the module
-template <class... Ms>
-void find_matches(module& mod, instruction_ref ins, Ms&&... ms)
+template <class Mod, class... Ms>
+void find_matches(Mod& mod, instruction_ref ins, Ms&&... ms)
 {
 #if !defined(__GNUC__) || defined(__clang__) || __GNUC__ > 5
     const
 #endif
-        bool trace = enabled(MIGRAPHX_TRACE_MATCHES{});
-    bool match     = false;
+        int trace = value_of(MIGRAPHX_TRACE_MATCHES{});
+    bool match    = false;
     each_args(
         [&](auto&& m) {
             if(match)
                 return;
-            auto r = match_instruction(mod, ins, m.matcher());
-            if(r.result == mod.end())
+            if(trace > 1)
+                std::cout << "Match: " << get_type_name(m) << std::endl;
+            auto r = match_instruction(get_module(mod), ins, m.matcher());
+            if(r.result == get_module(mod).end())
                 return;
-            if(trace)
+            if(trace > 0)
             {
                 std::cout << "Matched by " << get_type_name(m) << std::endl;
-                mod.debug_print(ins);
+                get_module(mod).debug_print(ins);
             }
             m.apply(mod, r);
             match = true;
@@ -376,10 +378,10 @@ void find_matches(module& mod, instruction_ref ins, Ms&&... ms)
 }
 
 /// Find matches in a module
-template <class... Ms>
-void find_matches(module& mod, Ms&&... ms)
+template <class Mod, class... Ms>
+void find_matches(Mod& mod, Ms&&... ms)
 {
-    for(auto ins : iterator_for(mod))
+    for(auto ins : iterator_for(get_module(mod)))
     {
         find_matches(mod, ins, ms...);
     }

src/include/migraphx/module.hpp

@@ -124,7 +124,7 @@ struct module
                      std::unordered_map<instruction_ref, instruction_ref> map_ins = {});
 
     std::vector<instruction_ref>
-    add_instructions(module_ref m,
+    add_instructions(const_module_ref m,
                      std::unordered_map<instruction_ref, instruction_ref> map_ins = {});
 
     std::vector<instruction_ref>
@@ -139,7 +139,7 @@ struct module
 
     std::vector<instruction_ref>
     insert_instructions(instruction_ref ins,
-                        module_ref m,
+                        const_module_ref m,
                         std::unordered_map<instruction_ref, instruction_ref> map_ins = {});
 
     std::vector<instruction_ref>
@@ -164,6 +164,10 @@ struct module
 
     instruction_ref replace_return(std::vector<instruction_ref> args);
 
+    instruction_ref insert_literal(instruction_ref ins, literal l);
+
+    instruction_ref insert_parameter(instruction_ref ins, std::string name, shape s);
+
     std::vector<std::string> get_parameter_names() const;
 
     shape get_parameter_shape(std::string name) const;
@@ -227,6 +231,8 @@ struct module
     std::unique_ptr<module_impl> impl;
 };
 
+inline module& get_module(module& m) { return m; }
+
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx
 

src/include/migraphx/module_ref.hpp

@@ -32,7 +32,8 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
 struct module;
-using module_ref = module*;
+using module_ref       = module*;
+using const_module_ref = const module*;
 
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/include/migraphx/ranges.hpp

@@ -198,6 +198,12 @@ void transform(Range&& r, Iterator it, F f)
     std::transform(r.begin(), r.end(), it, f);
 }
 
+template <class Range1, class Range2, class Iterator, class F>
+void transform(Range1&& r1, Range2&& r2, Iterator it, F f)
+{
+    std::transform(r1.begin(), r1.end(), r2.begin(), it, f);
+}
+
 template <class Range>
 auto reverse(Range& r)
 {

src/include/migraphx/shape.hpp

@@ -256,6 +256,10 @@ struct shape
 
         std::size_t size(std::size_t n = 1) const { return sizeof(type) * n; }
 
+        auto is_integral() const { return std::is_integral<type>{}; }
+        auto is_signed() const { return std::is_signed<type>{}; }
+        auto is_unsigned() const { return std::is_unsigned<type>{}; }
+
         template <class U>
         type* from(U* buffer, std::size_t n = 0) const
         {

src/module.cpp

@@ -399,7 +399,8 @@ module::add_instructions(const std::vector<instruction_ref>& instructions,
 }
 
 std::vector<instruction_ref>
-module::add_instructions(module_ref m, std::unordered_map<instruction_ref, instruction_ref> map_ins)
+module::add_instructions(const_module_ref m,
+                         std::unordered_map<instruction_ref, instruction_ref> map_ins)
 {
     return this->insert_instructions(this->end(), m, std::move(map_ins));
 }
@@ -420,8 +421,10 @@ module::insert_instructions(instruction_ref ins,
     return insert_generic_instructions(*this, ins, instructions, std::move(map_ins));
 }
 
-std::vector<instruction_ref> module::insert_instructions(
-    instruction_ref ins, module_ref m, std::unordered_map<instruction_ref, instruction_ref> map_ins)
+std::vector<instruction_ref>
+module::insert_instructions(instruction_ref ins,
+                            const_module_ref m,
+                            std::unordered_map<instruction_ref, instruction_ref> map_ins)
 {
     return insert_generic_instructions(*this, ins, iterator_for(*m), std::move(map_ins));
 }
@@ -436,11 +439,7 @@ module::insert_instructions(instruction_ref ins,
     return insert_generic_instructions(*this, ins, iterator_for(r), std::move(map_ins));
 }
 
-instruction_ref module::add_literal(literal l)
-{
-    impl->emplace_front(std::move(l));
-    return impl->instructions.begin();
-}
+instruction_ref module::add_literal(literal l) { return insert_literal(begin(), std::move(l)); }
 
 instruction_ref module::add_outline(const shape& s)
 {
@@ -450,10 +449,7 @@ instruction_ref module::add_outline(const shape& s)
 
 instruction_ref module::add_parameter(std::string name, shape s)
 {
-    assert(get_parameter_shape(name) == shape{});
-    impl->push_front({builtin::param{std::move(name), impl->nparams}, std::move(s), {}});
-    impl->nparams++;
-    return impl->instructions.begin();
+    return insert_parameter(begin(), std::move(name), std::move(s));
 }
 
 instruction_ref module::add_return(std::vector<instruction_ref> args)
@@ -466,6 +462,20 @@ instruction_ref module::add_return(std::vector<instruction_ref> args)
     return result;
 }
 
+instruction_ref module::insert_literal(instruction_ref ins, literal l)
+{
+    impl->emplace(ins, std::move(l));
+    return std::prev(ins);
+}
+
+instruction_ref module::insert_parameter(instruction_ref ins, std::string name, shape s)
+{
+    assert(get_parameter_shape(name) == shape{});
+    impl->insert(ins, {builtin::param{std::move(name), impl->nparams}, std::move(s), {}});
+    impl->nparams++;
+    return std::prev(ins);
+}
+
 instruction_ref module::replace_return(std::vector<instruction_ref> args)
 {
     auto last = std::prev(this->end());

src/program.cpp

@@ -542,12 +542,14 @@ static void mod_from_val(module_ref mod,
 
         if(name == "@param")
         {
-            output = mod->add_parameter(fields["parameter"].to<std::string>(),
-                                        migraphx::from_value<shape>(node.at("shape")));
+            output = mod->insert_parameter(mod->end(),
+                                           fields["parameter"].to<std::string>(),
+                                           migraphx::from_value<shape>(node.at("shape")));
         }
         else if(name == "@literal")
         {
-            output = mod->add_literal(migraphx::from_value<literal>(node.at("literal")));
+            output =
+                mod->insert_literal(mod->end(), migraphx::from_value<literal>(node.at("literal")));
         }
         else
         {
@@ -582,11 +584,11 @@ static void mod_from_val(module_ref mod,
             }
             else if(module_inputs.empty())
             {
-                output = mod->add_instruction(op, inputs);
+                output = mod->insert_instruction(mod->end(), op, inputs);
             }
             else
             {
-                output = mod->add_instruction(op, inputs, module_inputs);
+                output = mod->insert_instruction(mod->end(), op, inputs, module_inputs);
             }
         }
         output->set_normalized(normalized);

src/serialize.cpp

@@ -36,7 +36,7 @@ void raw_data_to_value(value& v, const RawData& rd)
     result["shape"] = migraphx::to_value(rd.get_shape());
     if(rd.get_shape().type() == shape::tuple_type)
         result["sub"] = migraphx::to_value(rd.get_sub_objects());
-    else
+    else if(not rd.empty())
         result["data"] = migraphx::value::binary(rd.data(), rd.get_shape().bytes());
     v = result;
 }
@@ -56,7 +56,7 @@ void migraphx_from_value(const value& v, argument& a)
         literal l = migraphx::from_value<literal>(v);
         a         = l.get_argument();
     }
-    else
+    else if(v.contains("sub"))
     {
         a = migraphx::from_value<std::vector<argument>>(v.at("sub"));
     }

src/simplify_reshapes.cpp

@@ -601,6 +601,69 @@ struct find_transpose_contiguous_reshaper_unary
     }
 };
 
+struct find_slice_transpose
+{
+    auto matcher() const
+    {
+        return match::any(match::any_of[match::outputs()](
+            match::name("slice")(match::output(match::name("transpose")))));
+    }
+
+    static std::vector<int64_t> find_common_perm(const std::vector<instruction_ref>& transposes)
+    {
+        std::map<std::vector<int64_t>, int64_t> count;
+        for(auto t : transposes)
+        {
+            auto perm = t->get_operator().to_value()["permutation"].to_vector<int64_t>();
+            count[perm]++;
+        }
+        return std::max_element(
+                   count.begin(), count.end(), by(std::less<>{}, [](auto&& p) { return p.second; }))
+            ->first;
+    }
+
+    void apply(module& m, const match::matcher_result& r) const
+    {
+        auto ins = r.result;
+        std::vector<instruction_ref> splits;
+        std::copy_if(ins->outputs().begin(),
+                     ins->outputs().end(),
+                     std::back_inserter(splits),
+                     [&](instruction_ref out) {
+                         return out->name() == "slice" and out->outputs().size() == 1 and
+                                out->outputs().front()->name() == "transpose";
+                     });
+        if(splits.size() < 2)
+            return;
+        std::vector<instruction_ref> transposes;
+        std::transform(splits.begin(),
+                       splits.end(),
+                       std::back_inserter(transposes),
+                       [](auto split) { return split->outputs().front(); });
+        auto perm  = find_common_perm(transposes);
+        auto iperm = invert_permutation(perm);
+        auto pre   = m.insert_instruction(
+            std::next(ins), make_op("transpose", {{"permutation", perm}}), ins);
+        for(auto i : range(transposes.size()))
+        {
+            auto split = splits[i];
+            auto t     = transposes[i];
+            auto op    = any_cast<op::slice>(split->get_operator());
+            std::transform(op.axes.begin(), op.axes.end(), op.axes.begin(), [&](auto axis) {
+                return iperm[axis];
+            });
+            auto new_ins = m.insert_instruction(t, op, pre);
+            if(t->get_operator() != pre->get_operator())
+            {
+                auto curr = t->get_operator().to_value()["permutation"].to_vector<int64_t>();
+                new_ins   = m.insert_instruction(
+                    t, make_op("transpose", {{"permutation", reorder_dims(iperm, curr)}}), new_ins);
+            }
+            m.replace_instruction(t, new_ins);
+        }
+    }
+};
+
 void simplify_reshapes::apply(module& m) const
 {
     for(int i = 0; i < 2; i++)
@@ -616,6 +679,7 @@ void simplify_reshapes::apply(module& m) const
                             find_nested_convert{},
                             find_nested_slice{},
                             find_nested_concat{},
+                            find_slice_transpose{},
                             find_transpose_contiguous_reshaper_unary{});
         dead_code_elimination{}.apply(m);
     }

src/targets/cpu/write_literals.cpp

@@ -25,6 +25,7 @@
 #include <migraphx/module.hpp>
 #include <migraphx/instruction.hpp>
 #include <migraphx/iterator_for.hpp>
+#include <migraphx/register_op.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -52,6 +53,7 @@ struct cpu_literal
         return os;
     }
 };
+MIGRAPHX_REGISTER_OP(cpu_literal);
 
 void write_literals::apply(module& m) const
 {

src/targets/gpu/CMakeLists.txt

@@ -164,6 +164,7 @@ add_library(migraphx_gpu
     deconvolution.cpp
     device_name.cpp
     elu.cpp
+    fuse_mlir.cpp
     fuse_ops.cpp
     gather.cpp
     gemm_impl.cpp
@@ -176,7 +177,7 @@ add_library(migraphx_gpu
     loop.cpp
     lrn.cpp
     leaky_relu.cpp
-    mlir_conv.cpp
+    mlir.cpp
     multinomial.cpp
     nonzero.cpp
     pack_args.cpp
@@ -320,16 +321,26 @@ message(STATUS "extractkernel: ${MIGRAPHX_EXTRACT_KERNEL}")
 
 set(MIGRAPHX_ENABLE_MLIR OFF CACHE BOOL "")
 if(MIGRAPHX_ENABLE_MLIR)
-    find_library(LIBMLIRMIOPEN MLIRMIOpenThin REQUIRED)
+    find_library(MLIRAPI_LIBRARY MLIRMIOpen 
+        PATH_SUFFIXES
+        # Workaournd broken mlir install
+        lib/ lib/lib)
     # REQUIRED is not supported before cmake 3.18
-    if(NOT LIBMLIRMIOPEN)
-        message(FATAL_ERROR "libMLIRMIOpenThin not found")
+    if(NOT MLIRAPI_LIBRARY)
+        message(FATAL_ERROR "libMLIRMIOpen not found")
     else()
-        message(STATUS "Build with libMLIRMIOpenThin: " ${LIBMLIRMIOPEN})
+        message(STATUS "Build with libMLIRMIOpen: " ${MLIRAPI_LIBRARY})
     endif()
 
-    target_compile_definitions(migraphx_gpu PRIVATE "-DMIGRAPHX_MLIR_MIOPEN_SUPPORT")
-    target_link_libraries(migraphx_gpu PUBLIC ${LIBMLIRMIOPEN})
+    find_path(MLIRAPI_HEADERS NAMES mlir-c/Dialect/MIGraphX.h)
+    # Workaround MLIR broken installation
+    find_path(MLIRAPI_HEADERS2 NAMES mlir-c/Registration.h
+        PATH_SUFFIXES 
+        include/external/include external/include)
+
+    target_compile_definitions(migraphx_gpu PRIVATE "-DMIGRAPHX_MLIR")
+    target_include_directories(migraphx_gpu SYSTEM PRIVATE ${MLIRAPI_HEADERS} ${MLIRAPI_HEADERS2})
+    target_link_libraries(migraphx_gpu PUBLIC ${MLIRAPI_LIBRARY})
 endif()
 
 set(MIGRAPHX_USE_HIPRTC OFF CACHE BOOL "")

src/targets/gpu/code_object_op.cpp

@@ -52,7 +52,7 @@ code_object_op::compute(context& ctx, const shape&, const std::vector<argument>&
     std::transform(
         args.begin(), args.end(), kargs.begin(), [](const argument& a) { return a.data(); });
     k.launch(ctx.get_stream().get(), global, local, std::move(kargs));
-    return args.back();
+    return args[get_output_arg(args.size())];
 }
 void code_object_op::finalize(context&, const shape&, const std::vector<shape>&)
 {

src/targets/gpu/compile_gen.cpp

@@ -43,6 +43,9 @@ static std::vector<std::size_t> vector_sizes(const std::vector<shape>& inputs)
 
 vectorize vectorize::elements(std::size_t axis, const std::vector<shape>& inputs)
 {
+    if(std::all_of(
+           inputs.begin(), inputs.end(), [&](const auto& s) { return s.lens()[axis] == 1; }))
+        return {1, axis};
     auto sizes = vector_sizes(inputs);
     std::vector<std::size_t> max_vec_size;
     std::transform(inputs.begin(),

src/targets/gpu/deconvolution.cpp

@@ -59,31 +59,30 @@ argument miopen_deconvolution::compute(context& ctx,
     auto w_desc = make_tensor(reshape_if_1d(args[1].get_shape()));
     auto y_desc = make_tensor(reshape_if_1d(output_shape));
 
-    float alpha = 1;
-    float beta  = 0;
-    auto status = miopenConvolutionForward(ctx.get_stream().get_miopen(),
-                                           &alpha,
-                                           x_desc.get(),
-                                           args[0].implicit(),
-                                           w_desc.get(),
-                                           args[1].implicit(),
-                                           cd.get(),
-                                           algo,
-                                           &beta,
-                                           y_desc.get(),
-                                           args[3].implicit(),
-                                           args[2].implicit(),
-                                           args[2].get_shape().bytes());
+    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("Running deconvolution failed");
+        MIGRAPHX_THROW("MIOpen Deconvolution: running convolution failed");
     return args[3];
 }
 
-shape miopen_deconvolution::compile(context& ctx,
-                                    const shape& output_shape,
-                                    std::vector<shape> inputs)
+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));
@@ -119,9 +118,35 @@ shape miopen_deconvolution::compile(context& ctx,
                                                         workspace_size,
                                                         false);
     if(status != miopenStatusSuccess)
-        MIGRAPHX_THROW("Find deconvolution failed");
-    handle = ctx.get_stream().get_miopen();
-    algo   = perf.fwd_algo;
+        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}};
 }
 
@@ -129,13 +154,29 @@ void miopen_deconvolution::finalize(context& ctx,
                                     const shape& output_shape,
                                     std::vector<shape> inputs)
 {
-    if(handle == ctx.get_stream().get_miopen())
-        return;
-    // Check that workspace hasn't changed
-    auto size = inputs.at(2).bytes();
-    auto ws   = compile(ctx, output_shape, std::move(inputs));
-    if(ws.bytes() > size)
-        MIGRAPHX_THROW("Workspace has changed during finalization.");
+    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

src/targets/gpu/fuse_mlir.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/fuse_mlir.hpp>
+#include <migraphx/gpu/mlir.hpp>
+#include <migraphx/matcher.hpp>
+#include <migraphx/pass_manager.hpp>
+#include <migraphx/make_op.hpp>
+#include <migraphx/register_op.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+struct module;
+
+namespace gpu {
+
+#ifdef MIGRAPHX_MLIR
+struct mlir_conv
+{
+    operation op = make_op("convolution");
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.op, "op"));
+    }
+
+    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();
+        if(mods.size() != 1)
+            MIGRAPHX_THROW("should have one submodule.");
+        if(inputs.size() < 2)
+            MIGRAPHX_THROW("should have at least two inputs.");
+        auto n = inputs.size();
+        return op.compute_shape({inputs[n - 2], inputs[n - 1]});
+    }
+};
+MIGRAPHX_REGISTER_OP(mlir_conv);
+
+namespace {
+struct find_conv_pointwise
+{
+    // Find a convolution followed by a pointwise operation.
+    auto matcher() const
+    {
+        auto convolution =
+            match::skip(match::name("contiguous"))(match::name("convolution").bind("convolution"));
+        return match::name("pointwise")(match::any_of[match::inputs()](convolution.bind("x")));
+    }
+
+    void apply(module_pass_manager& mpm, const match::matcher_result& r) const
+    {
+        auto ins      = r.result;
+        auto conv_ins = r.instructions["convolution"];
+        auto x_ins    = r.instructions["x"]; // input after contiguous
+        auto* pm      = ins->module_inputs().front();
+        auto names    = pm->get_parameter_names();
+        // Whitelist pointwise operators
+        if(std::any_of(pm->begin(), pm->end(), [](const auto& i) {
+               return not contains({"@literal", "@param", "@return", "convolution", "add", "relu"},
+                                   i.name());
+           }))
+            return;
+        // Only fuse with fp32 for now
+        if(std::any_of(ins->inputs().begin(), ins->inputs().end(), [&](auto i) {
+               return i->get_shape().type() != shape::type_t::float_type;
+           }))
+            return;
+        std::sort(names.begin(), names.end());
+        module_ref mm = mpm.create_module("mlir_" + pm->name());
+        mm->set_bypass();
+        std::unordered_map<instruction_ref, instruction_ref> param_map;
+        auto x    = mm->add_parameter("x" + std::to_string(names.size()),
+                                   conv_ins->inputs().at(0)->get_shape());
+        auto w    = mm->add_parameter("x" + std::to_string(names.size() + 1),
+                                   conv_ins->inputs().at(1)->get_shape());
+        auto conv = mm->add_instruction(conv_ins->get_operator(), {x, w});
+        std::transform(names.begin(),
+                       names.end(),
+                       ins->inputs().begin(),
+                       std::inserter(param_map, param_map.end()),
+                       [&](auto name, auto input) {
+                           if(input == x_ins)
+                               return std::make_pair(pm->get_parameter(name), conv);
+                           return std::make_pair(pm->get_parameter(name),
+                                                 mm->add_parameter(name, input->get_shape()));
+                       });
+        mm->add_return(mm->insert_instructions(mm->end(), pm, param_map));
+
+        std::vector<instruction_ref> inputs;
+        std::copy_if(ins->inputs().begin(),
+                     ins->inputs().end(),
+                     std::back_inserter(inputs),
+                     [&](auto input) { return input != conv_ins; });
+        inputs.insert(inputs.end(), conv_ins->inputs().begin(), conv_ins->inputs().end());
+        mpm.get_module().replace_instruction(
+            ins, mlir_conv{conv_ins->get_operator()}, inputs, {mm});
+    }
+};
+} // namespace
+
+#endif
+
+void fuse_mlir::apply(module_pass_manager& mpm) const
+{
+#ifdef MIGRAPHX_MLIR
+    match::find_matches(mpm, find_conv_pointwise{});
+#else
+    (void)mpm;
+#endif
+}
+
+} // namespace gpu
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/fuse_ops.cpp

@@ -336,6 +336,7 @@ void move_standard_front(std::vector<instruction_ref>& args)
 
 auto gpu_name(const std::string& s) { return match::name("gpu::" + s); }
 
+namespace {
 struct find_layernorm
 {
     auto matcher() const { return match::layernorm(&gpu_name); }
@@ -836,15 +837,6 @@ inline auto precompile_name(std::string s) // NOLINT
     });
 }
 
-template <class... Ms>
-auto conv_bias_pointwise(Ms... ms)
-{
-    return precompile_name("pointwise")(
-        match::either_arg(0, 1)(bias_shape(match::used_once()).bind("bias"),
-                                fusable_conv(match::used_once()).bind("conv")),
-        ms...);
-}
-
 struct find_conv_bias
 {
     context* ctx = nullptr;
@@ -1013,6 +1005,7 @@ struct find_commutative_broadcast
         m.replace_instruction(ins, ins->get_operator(), args);
     }
 };
+} // namespace
 
 struct find_contiguous
 {

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

@@ -38,12 +38,13 @@ struct context;
 
 struct code_object_op
 {
-    value::binary code_object;
-    std::string symbol_name;
-    std::size_t global;
-    std::size_t local;
-    std::vector<shape> expected_inputs;
-    shape output;
+    value::binary code_object{};
+    std::string symbol_name = "";
+    std::size_t global      = 0;
+    std::size_t local       = 0;
+    std::vector<shape> expected_inputs{};
+    shape output{};
+    std::int64_t output_arg = -1;
     kernel k{};
 
     template <class Self, class F>
@@ -66,9 +67,13 @@ struct code_object_op
     argument
     compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
     void finalize(context&, const shape&, const std::vector<shape>&);
+    std::int64_t get_output_arg(std::size_t n) const
+    {
+        return output_arg < 0 ? n + output_arg : output_arg;
+    }
     std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
     {
-        return shapes.size() - 1;
+        return get_output_arg(shapes.size());
     }
 
     friend std::ostream& operator<<(std::ostream& os, const code_object_op& op)

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

@@ -39,20 +39,20 @@ struct miopen_deconvolution
     op::deconvolution op;
     shared<convolution_descriptor> cd;
     miopenConvFwdAlgorithm_t algo{};
-    miopenHandle_t handle = nullptr;
+    uint64_t solution_id = 0;
 
     template <class Self, class F>
     static auto reflect(Self& self, F f)
     {
-        // TODO: Add algo
-        return op::convolution::reflect(self.op, f);
+        return pack_join(op::deconvolution::reflect(self.op, f),
+                         pack(f(self.solution_id, "solution_id")));
     }
 
     std::string name() const { return "gpu::deconv"; }
     shape compute_shape(const std::vector<shape>& inputs) const;
     argument
     compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
-    shape compile(context& ctx, const shape& output_shape, std::vector<shape> inputs);
+    shape find(context& ctx, const shape& output_shape, std::vector<shape> inputs);
     void finalize(context& ctx, const shape& output_shape, std::vector<shape> inputs);
     std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
     {