Merge branch 'develop' into threaded_nms

src/targets/fpga/subgraph.cpp

@@ -113,8 +113,7 @@ void subgraph::apply(module_pass_manager& mpm) const
     // TODO(varunsh): this code may be replaceable by code in the fuse_pointwise pass
 
     // assuming all FPGA instructions are in one contiguous range
-    pm->insert_instructions(pm->end(), first, last, {});
-
+    pm->insert_instructions(pm->end(), first, std::next(last), {});
     migraphx::instruction_ref placeholder_ins;
     for(auto it : iterator_for(mod))
     {

src/targets/fpga/vitis_ai_adapter.cpp

@@ -33,7 +33,7 @@ migraphx::shape x_model::get_shape() const { return shape; };
 
 void x_model::set_shape(migraphx::shape s) { shape = s; }
 
-x_model create_xmodel(const migraphx::module_ref mod)
+x_model create_xmodel(migraphx::const_module_ref mod)
 {
     std::cout << "Calling an external function: create_xmodel!\n";
     x_model xmodel;

src/targets/gpu/CMakeLists.txt

@@ -33,6 +33,11 @@ if(NOT TARGET MIOpen)
     message(SEND_ERROR "Cant find miopen")
 endif()
 
+if(NOT WIN32)
+    # TODO: re-enable when CK is ported to Windows
+    find_package(composable_kernel 1.0.0 REQUIRED COMPONENTS jit_library)
+endif()
+
 if(BUILD_DEV)
     set(MIGRAPHX_USE_HIPRTC OFF CACHE BOOL "Use hipRTC APIs")
 else()
@@ -40,12 +45,12 @@ else()
 endif()
 
 include(Embed)
-file(GLOB KERNEL_FILES ${CONFIGURE_DEPENDS}
+file(GLOB KERNEL_FILES CONFIGURE_DEPENDS
     ${CMAKE_CURRENT_SOURCE_DIR}/kernels/include/migraphx/kernels/*.hpp)
 message(STATUS "KERNEL_FILES: ${KERNEL_FILES}")
-add_embed_library(migraphx_kernels ${KERNEL_FILES})
+add_embed_library(migraphx_kernels ${KERNEL_FILES} RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}/kernels/include/)
 
-file(GLOB DEVICE_GPU_SRCS ${CONFIGURE_DEPENDS} ${CMAKE_CURRENT_SOURCE_DIR}/device/*.cpp)
+file(GLOB DEVICE_GPU_SRCS CONFIGURE_DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/device/*.cpp)
 add_library(migraphx_device ${DEVICE_GPU_SRCS})
 
 add_library(compile_for_gpu INTERFACE)
@@ -65,6 +70,8 @@ target_link_libraries(migraphx_device PUBLIC migraphx)
 target_link_libraries(migraphx_device PRIVATE compile_for_gpu)
 target_include_directories(migraphx_device PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>)
 target_include_directories(migraphx_device PRIVATE $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/device/include>)
+target_compile_options(migraphx_device PRIVATE -Wno-ignored-attributes)
+migraphx_generate_export_header(migraphx_device DIRECTORY migraphx/gpu/device)
 
 add_library(kernel_file_check EXCLUDE_FROM_ALL)
 
@@ -80,7 +87,13 @@ target_link_libraries(kernel_file_check compile_for_gpu)
 
 rocm_clang_tidy_check(kernel_file_check)
 
-file(GLOB JIT_GPU_SRCS ${CONFIGURE_DEPENDS} ${CMAKE_CURRENT_SOURCE_DIR}/jit/*.cpp)
+file(GLOB JIT_GPU_SRCS CONFIGURE_DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/jit/*.cpp)
+
+if(WIN32)
+    # TODO: re-enable when CK is ported to Windows
+    list(REMOVE_ITEM JIT_GPU_SRCS ${CMAKE_CURRENT_SOURCE_DIR}/jit/ck_gemm.cpp)
+endif()
+
 add_library(migraphx_gpu
     abs.cpp
     analyze_streams.cpp
@@ -95,6 +108,7 @@ add_library(migraphx_gpu
     compile_miopen.cpp
     compiler.cpp
     device_name.cpp
+    fuse_ck.cpp
     fuse_mlir.cpp
     fuse_ops.cpp
     gather.cpp
@@ -123,11 +137,14 @@ add_library(migraphx_gpu
     schedule_model.cpp
     sync_device.cpp
     target.cpp
+    time_op.cpp
     topk.cpp
     write_literals.cpp
     ${JIT_GPU_SRCS}
 )
+
 set_target_properties(migraphx_gpu PROPERTIES EXPORT_NAME gpu)
+migraphx_generate_export_header(migraphx_gpu)
 
 function(register_migraphx_gpu_ops PREFIX)
     foreach(OP ${ARGN})
@@ -169,7 +186,7 @@ register_op(migraphx_gpu
     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>
+    OPERATORS gpu::miopen_convolution<op::convolution> gpu::miopen_convolution<op::convolution_backwards> 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)
@@ -181,7 +198,9 @@ if(MIGRAPHX_ENABLE_MLIR)
     find_package(rocMLIR 1.0.0 CONFIG REQUIRED)
     message(STATUS "Build with rocMLIR::rockCompiler ${rocMLIR_VERSION}")
     target_compile_definitions(migraphx_gpu PRIVATE "-DMIGRAPHX_MLIR")
-    target_link_libraries(migraphx_gpu PUBLIC rocMLIR::rockCompiler)
+    # Make this private to avoid multiple inclusions of LLVM symbols.
+    # TODO: Fix rocMLIR's library to hide LLVM internals.
+    target_link_libraries(migraphx_gpu PRIVATE rocMLIR::rockCompiler)
 endif()
 
 if(MIGRAPHX_USE_HIPRTC)
@@ -227,7 +246,12 @@ check_library_exists(MIOpen "miopenFindSolutions" "${MIOPEN_LOCATION}" HAS_FIND_
 set(MIGRAPHX_USE_FIND_2_API "${HAS_FIND_2_API}" CACHE BOOL "")
 
 if(MIGRAPHX_USE_FIND_2_API)
-    target_compile_definitions(migraphx_gpu PUBLIC -DMIGRAPHX_HAS_FIND_2_API)
+    check_library_exists(MIOpen "miopenSetFindOptionPreallocatedTensor" "${MIOPEN_LOCATION}" HAS_PREALLOCATION_API)
+    if(HAS_PREALLOCATION_API)
+        target_compile_definitions(migraphx_gpu PUBLIC -DMIGRAPHX_HAS_FIND_2_API -DMIGRAPHX_PREALLOCATE_MIOPEN_BUFFERS)
+    else()
+        target_compile_definitions(migraphx_gpu PUBLIC -DMIGRAPHX_HAS_FIND_2_API)
+    endif()
     message(STATUS "MIGraphx is using Find-2.0 API of MIOpen")
 else()
     message(STATUS "MIGraphx is using legacy Find API in MIOpen")
@@ -242,6 +266,10 @@ endif()
 
 target_link_libraries(migraphx_gpu PUBLIC migraphx MIOpen roc::rocblas)
 target_link_libraries(migraphx_gpu PRIVATE migraphx_device migraphx_kernels)
+if(NOT WIN32)
+    # TODO: re-enable when CK is ported to Windows
+    target_link_libraries(migraphx_gpu PRIVATE composable_kernel::jit_library)
+endif()
 
 add_subdirectory(driver)
 add_subdirectory(hiprtc)

src/targets/gpu/compile_gen.cpp

@@ -29,6 +29,7 @@
 #include <migraphx/module.hpp>
 #include <migraphx/dead_code_elimination.hpp>
 #include <migraphx/eliminate_common_subexpression.hpp>
+#include <migraphx/rewrite_quantization.hpp>
 #include <migraphx/cpp_generator.hpp>
 #include <migraphx/pass_manager.hpp>
 #include <migraphx/instruction.hpp>
@@ -171,7 +172,8 @@ std::string make_transformer_args(std::vector<std::string> transformers)
 void generate_pointwise(cpp_generator& gg, const module& pm, const std::string& name)
 {
     module m = pm;
-    run_passes(m, {eliminate_common_subexpression{}, dead_code_elimination{}});
+    run_passes(m,
+               {rewrite_quantization{}, eliminate_common_subexpression{}, dead_code_elimination{}});
     cpp_generator g;
     g.fmap([](const std::string& fname) { return "migraphx::" + fname; });
     g.add_point_op("where", "${function:where}(${0}, ${1}, ${2})");
@@ -280,6 +282,14 @@ std::string generate_reduce(const module& m, const std::string& name)
                                     not input->get_shape().broadcasted();
                          });
             auto inner_names = names;
+            for(auto input : ins->inputs())
+            {
+                if(input->name() != "@param")
+                    continue;
+                if(contains(tensors, input))
+                    continue;
+                inner_names[input] += "[out_idx]";
+            }
             for(auto input : tensors)
                 inner_names[input] += "_lambda_param";
             auto call_function =
@@ -308,6 +318,8 @@ std::string generate_reduce(const module& m, const std::string& name)
     });
     f.set_attributes({"__device__", "__attribute__((const))"}).set_generic_types(m).set_name(name);
     f.add_generic_param("r");
+    f.add_generic_param("out_idx");
+    f.unused_param("out_idx");
     g.create_function(f);
     return g.str();
 }

src/targets/gpu/compile_hip.cpp

@@ -56,9 +56,6 @@ MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_GPU_DUMP_SRC);
 
 #ifdef MIGRAPHX_USE_HIPRTC
 
-MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_TRACE_HIPRTC);
-MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_ENABLE_HIPRTC_WORKAROUNDS);
-
 std::string hiprtc_error(hiprtcResult err, const std::string& msg)
 {
     return "hiprtc: " + (hiprtcGetErrorString(err) + (": " + msg));
@@ -194,6 +191,7 @@ std::vector<std::vector<char>> compile_hip_src_with_hiprtc(std::vector<hiprtc_sr
         options.push_back("-DMIGRAPHX_HAS_DPP=0");
         options.push_back("-DMIGRAPHX_ENABLE_HIPRTC_WORKAROUNDS=1");
         options.push_back("-Wno-reserved-identifier");
+        options.push_back("-Wno-unused-parameter");
         options.push_back("-Wno-gnu-line-marker");
         options.push_back("-Wno-old-style-cast");
     }
@@ -216,6 +214,15 @@ std::vector<std::vector<char>>
 compile_hip_src(const std::vector<src_file>& srcs, std::string params, const std::string& arch)
 {
     std::vector<hiprtc_src_file> hsrcs{srcs.begin(), srcs.end()};
+    if(enabled(MIGRAPHX_GPU_DUMP_SRC{}))
+    {
+        for(const auto& src : srcs)
+        {
+            if(src.path.extension() != ".cpp")
+                continue;
+            std::cout << std::string(src.content.first, src.len()) << std::endl;
+        }
+    }
     auto p      = dynamic_loader::path(&compile_hip_src_with_hiprtc);
     auto driver = p.parent_path().parent_path() / "bin" / "migraphx-hiprtc-driver";
 

src/targets/gpu/compile_hip_code_object.cpp

@@ -135,10 +135,14 @@ compute_global_for(context& ctx, std::size_t n, std::size_t over)
     std::size_t max_global = ctx.get_current_device().get_cu_count() *
                              ctx.get_current_device().get_max_workitems_per_cu();
     return [n, over, max_global](std::size_t local) {
-        std::size_t groups     = (n + local - 1) / local;
-        std::size_t max_blocks = max_global / local;
-        std::size_t nglobal    = std::min(max_blocks * over, groups) * local;
-        return std::min(nglobal, n);
+        // hip require global workitems multiple of local workitems. It may degrade performance.
+        // [TODO]: consider adding "fno-hip-uniform-block" flag when it becomes available.
+        // https://reviews.llvm.org/D155213
+        std::size_t num_elements = ((n + local - 1) / local) * local;
+        std::size_t groups       = (num_elements + local - 1) / local;
+        std::size_t max_blocks   = max_global / local;
+        std::size_t nglobal      = std::min(max_blocks * over, groups) * local;
+        return std::min(nglobal, num_elements);
     };
 }
 
@@ -156,14 +160,14 @@ operation compile_hip_code_object(const std::string& content, hip_compile_option
     assert(not options.inputs.empty());
     assert(options.inputs.size() == options.virtual_inputs.size() or
            options.virtual_inputs.empty());
-    std::vector<src_file> srcs;
+    std::vector<src_file> srcs = options.additional_src_files;
     std::transform(migraphx_kernels().begin(),
                    migraphx_kernels().end(),
                    std::back_inserter(srcs),
                    [](auto&& p) {
                        auto&& name = p.first;
                        auto&& c    = p.second;
-                       auto path   = fs::path{"migraphx"} / "kernels" / name;
+                       auto path   = name;
                        return src_file{path, c};
                    });
     srcs.push_back(src_file{fs::path{"main.cpp"},

src/targets/gpu/compile_miopen.cpp

@@ -79,7 +79,7 @@ void compile_miopen::apply(module& m) const
         std::size_t ws = 0;
         try
         {
-            // for the regular convolution and deconvolution, this try would always succeed
+            // for the regular convolution and convolution_backwards, this try would always succeed
             ws = compile(op, ins, int8_x4_format);
         }
         catch(migraphx::exception&)

src/targets/gpu/compile_ops.cpp

@@ -30,6 +30,7 @@
 #include <migraphx/register_op.hpp>
 #include <migraphx/op/identity.hpp>
 #include <migraphx/gpu/compiler.hpp>
+#include <migraphx/gpu/time_op.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -76,33 +77,201 @@ struct compiled_result
     instruction_ref ins;
 };
 
+struct problem_cache
+{
+    bool has(const std::string& name, const value& problem) const
+    {
+        return contains(cache, create_key(name, problem));
+    }
+    void insert(const std::string& name, const value& problem, const value& solution)
+    {
+        assert(not solution.is_null());
+        cache[create_key(name, problem)] = solution;
+    }
+    void mark(const std::string& name, const value& problem)
+    {
+        cache.insert(std::make_pair(create_key(name, problem), value{}));
+    }
+    optional<value> get(const std::string& name, const value& problem) const
+    {
+        auto it = cache.find(create_key(name, problem));
+        if(it == cache.end())
+            return nullopt;
+        return it->second;
+    }
+    static value create_key(const std::string& name, const value& problem)
+    {
+        return {{"name", name}, {"problem", problem}};
+    }
+    std::unordered_map<value, value> cache;
+};
+
+struct compile_plan
+{
+    context* ctx;
+    operation preop;
+    instruction_ref ins;
+    optional<tuning_config> config                 = nullopt;
+    std::vector<optional<compiled_result>> results = {};
+    void update_config(bool exhaustive)
+    {
+        config = get_tuning_config(*ctx, ins, preop, exhaustive);
+    }
+    template <class Vector>
+    void insert_compiles(Vector& compiles, const value& solution, std::size_t i)
+    {
+        compiles.emplace_back([=] {
+            try
+            {
+                results[i] = compiled_result{compile(*ctx, ins, preop, solution), ins};
+            }
+            catch(...)
+            {
+                results[i] = nullopt;
+            }
+        });
+    }
+
+    template <class Vector>
+    void add_compiles(Vector& compiles, problem_cache& pc)
+    {
+        if(config.has_value())
+        {
+            const auto& problem = config->problem;
+            if(auto sol = pc.get(preop.name(), problem))
+            {
+                auto solution = sol.value();
+                // No solution yet until benchmarked so skip for now
+                if(solution.is_null())
+                    return;
+                results.resize(1);
+                insert_compiles(compiles, solution, 0);
+            }
+            else
+            {
+                pc.mark(preop.name(), problem);
+                const auto& solutions = config->solutions;
+                results.resize(solutions.size());
+                for(auto i : range(solutions.size()))
+                {
+                    auto solution = solutions[i];
+                    insert_compiles(compiles, solution, i);
+                }
+            }
+        }
+        else
+        {
+            results.resize(1);
+            insert_compiles(compiles, value{}, 0);
+        }
+    }
+    const compiled_result& benchmark(problem_cache& pc) const
+    {
+        if(results.empty())
+            MIGRAPHX_THROW("No configs to tune");
+        if(results.size() == 1)
+        {
+            if(not results.front().has_value())
+                MIGRAPHX_THROW("No configs to tune");
+            return *results.front();
+        }
+        if(not config)
+            MIGRAPHX_THROW("Multiple kernels without config");
+        std::cout << "Benchmarking " << preop.name() << ": " << results.size() << " configs"
+                  << std::endl;
+        std::vector<double> times;
+        times.reserve(results.size());
+        std::transform(
+            results.begin(), results.end(), std::back_inserter(times), [&](const auto& cr) {
+                if(not cr.has_value())
+                    return std::numeric_limits<double>::max();
+                return time_op(*ctx, cr->replace.code_object, to_shapes(cr->ins->inputs()), 20)
+                    .first;
+            });
+        auto i = std::distance(times.begin(), std::min_element(times.begin(), times.end()));
+        std::cout << "Fastest solution: " << config->solutions.at(i) << std::endl;
+        pc.insert(preop.name(), config->problem, config->solutions.at(i));
+        if(not results[i].has_value())
+            MIGRAPHX_THROW("No valid tuned compilation.");
+        return *results[i];
+    }
+    void replace(module& m, problem_cache& pc) const
+    {
+        const auto& cr = benchmark(pc);
+        cr.replace.replace(m, cr.ins);
+    }
+};
+
 template <class F>
 void par_compile(std::size_t n, F f)
 {
     if(n == 0)
         return;
-    par_for(n, n / value_of(MIGRAPHX_GPU_COMPILE_PARALLEL{}, n), f);
+    auto d = value_of(MIGRAPHX_GPU_COMPILE_PARALLEL{});
+    if(d == 0)
+        d = n;
+    par_for(n, n / d, f);
 }
 
-void compile_ops::apply(module& m) const
+struct compile_manager
 {
-    std::vector<std::function<compiled_result()>> compiles;
+    problem_cache pc;
+    std::vector<compile_plan> cps;
+    bool exhaustive = false;
+
+    template <class... Ts>
+    void add_plan(Ts&&... xs)
+    {
+        cps.push_back({std::forward<Ts>(xs)...});
+    }
+
+    void update_configs()
+    {
+        par_compile(cps.size(), [&](auto i) { cps[i].update_config(exhaustive); });
+    }
 
+    void compile(module& m)
+    {
+        std::vector<std::function<void()>> compiles;
+        for(auto& cp : cps)
+        {
+            cp.add_compiles(compiles, pc);
+        }
+        par_compile(compiles.size(), [&](auto i) { compiles[i](); });
+
+        // Replace and/or benchmark
+        for(const auto& cp : cps)
+        {
+            if(cp.results.empty())
+                continue;
+            cp.replace(m, pc);
+        }
+
+        // Remove compile_plan already executed
+        cps.erase(std::remove_if(cps.begin(),
+                                 cps.end(),
+                                 [](const auto& cp) { return not cp.results.empty(); }),
+                  cps.end());
+    }
+};
+
+void compile_ops::apply(module& m) const
+{
+    compile_manager cm;
+    cm.exhaustive = exhaustive_tune;
+    // Find all precompile opes
     for(auto ins : iterator_for(m))
     {
         if(ins->name() != "gpu::precompile_op")
             continue;
         operation preop = any_cast<precompile_op>(ins->get_operator()).op;
-        compiles.emplace_back([=]() -> compiled_result {
-            return {compile(*ctx, ins, preop), ins};
-        });
-    }
-    std::vector<compiled_result> results(compiles.size());
-    par_compile(compiles.size(), [&](auto i) { results[i] = compiles[i](); });
-    for(const auto& cr : results)
-    {
-        cr.replace(m, cr.ins);
+        cm.add_plan(ctx, preop, ins);
     }
+    cm.update_configs();
+    cm.compile(m);
+    // Compile already tuned configs
+    cm.compile(m);
+    assert(cm.cps.empty());
 }
 
 } // namespace gpu

src/targets/gpu/compiler.cpp

@@ -28,33 +28,45 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 
-auto& compiler_map()
+namespace {
+struct compiler_handle
 {
-    static std::unordered_map<std::string, compiler_compile> m; // NOLINT
-    return m;
-}
+    compiler_compile compile;
+    compiler_compile_op compile_op;
+    compiler_tuning_config get_tuning_config;
+};
+} // namespace
 
-auto& compiler_op_map()
+auto& compiler_map()
 {
-    static std::unordered_map<std::string, compiler_compile_op> m; // NOLINT
+    static std::unordered_map<std::string, compiler_handle> m; // NOLINT
     return m;
 }
 
-void register_compiler(const std::string& name, compiler_compile c, compiler_compile_op cop)
+void register_compiler(const std::string& name,
+                       compiler_compile c,
+                       compiler_compile_op cop,
+                       compiler_tuning_config ctg)
 {
-    compiler_map()[name]    = std::move(c);
-    compiler_op_map()[name] = std::move(cop);
+    compiler_map()[name] = {std::move(c), std::move(cop), std::move(ctg)};
 }
 
 bool has_compiler_for(const std::string& name) { return compiler_map().count(name) > 0; }
-compiler_replace compile(context& ctx, instruction_ref ins, const operation& op)
+compiler_replace
+compile(context& ctx, instruction_ref ins, const operation& op, const value& solution)
 {
-    return compiler_map().at(op.name())(ctx, ins, op);
+    return compiler_map().at(op.name()).compile(ctx, ins, op, solution);
 }
 operation
 compile_op(const std::string& name, context& ctx, const std::vector<shape>& inputs, const value& v)
 {
-    return compiler_op_map().at(name)(ctx, inputs, v);
+    return compiler_map().at(name).compile_op(ctx, inputs, v);
+}
+
+optional<tuning_config>
+get_tuning_config(context& ctx, instruction_ref ins, const operation& op, bool exhaustive)
+{
+    return compiler_map().at(op.name()).get_tuning_config(ctx, ins, op, exhaustive);
 }
 
 } // namespace gpu

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

@@ -94,6 +94,10 @@ template <>
 struct is_hip_type<std::uint8_t> : std::true_type
 {
 };
+template <>
+struct is_hip_type<std::int32_t> : std::true_type
+{
+};
 
 template <class T, class V, MIGRAPHX_REQUIRES(is_hip_type<typename T::type>{})>
 void hip_visitor_invoke(T as, V&& v)
@@ -120,12 +124,10 @@ void hip_visit_all_impl(const shape& s, F f, V&& v, Ts&&... xs)
     if(not std::all_of(
            types.begin(), types.end(), [&](migraphx::shape::type_t t) { return t == s.type(); }))
         MIGRAPHX_THROW("Types must be the same");
-    std::initializer_list<index_int> ranks = {
-        static_cast<index_int>(get_shape(xs).lens().size())...};
-    if(not std::all_of(
-           ranks.begin(), ranks.end(), [&](index_int r) { return r == s.lens().size(); }))
+    std::initializer_list<index_int> ranks = {static_cast<index_int>(get_shape(xs).ndim())...};
+    if(not std::all_of(ranks.begin(), ranks.end(), [&](index_int r) { return r == s.ndim(); }))
         MIGRAPHX_THROW("Ranks must be the same");
-    visit_tensor_size(s.lens().size(), [&](auto ndim) {
+    visit_tensor_size(s.ndim(), [&](auto ndim) {
         s.visit_type(hip_visitor([&](auto as) { v(f(xs, ndim, as)...); }));
     });
 }
@@ -133,12 +135,10 @@ void hip_visit_all_impl(const shape& s, F f, V&& v, Ts&&... xs)
 template <class V, class F, class... Ts>
 void hip_visit_views_impl(const shape& s, F f, V&& v, Ts&&... xs)
 {
-    std::initializer_list<index_int> ranks = {
-        static_cast<index_int>(get_shape(xs).lens().size())...};
-    if(not std::all_of(
-           ranks.begin(), ranks.end(), [&](index_int r) { return r == s.lens().size(); }))
+    std::initializer_list<index_int> ranks = {static_cast<index_int>(get_shape(xs).ndim())...};
+    if(not std::all_of(ranks.begin(), ranks.end(), [&](index_int r) { return r == s.ndim(); }))
         MIGRAPHX_THROW("Ranks must be the same");
-    visit_tensor_size(s.lens().size(), [&](auto ndim) { v(f(xs, ndim)...); });
+    visit_tensor_size(s.ndim(), [&](auto ndim) { v(f(xs, ndim)...); });
 }
 
 template <class F>

src/targets/gpu/device/multinomial.cpp

@@ -67,18 +67,19 @@ void multinomial(hipStream_t stream,
     size_t class_size  = arg0.get_shape().lens().back();
     size_t sample_size = result.get_shape().lens().back();
 
-    hip_visit_all(arg0, arg1)([&](auto cdf, auto dist) {
-        result.visit([&](auto out) {
-            hip_visit_views(out)([&](auto output) {
-                gs_launch(stream, batch_size * sample_size)([=](auto i) __device__ {
-                    auto idx       = output.get_shape().multi(i);
-                    auto cdf_begin = cdf.begin() + (idx.front() * class_size);
-                    auto cdf_end   = cdf_begin + class_size;
-                    auto sample_iter =
-                        upper_bound(cdf_begin, cdf_end, dist[i] * *(std::prev(cdf_end)));
-                    output[i] = std::distance(cdf_begin, sample_iter);
+    visit_all(arg0, arg1)([&](auto cdf_host, auto dist_host) {
+        result.visit([&](auto output_host) {
+            hip_visit_views(cdf_host, dist_host, output_host)(
+                [&](auto cdf, auto dist, auto output) {
+                    gs_launch(stream, batch_size * sample_size)([=](auto i) __device__ {
+                        auto idx       = output.get_shape().multi(i);
+                        auto cdf_begin = cdf.begin() + (idx.front() * class_size);
+                        auto cdf_end   = cdf_begin + class_size;
+                        auto* sample_iter =
+                            upper_bound(cdf_begin, cdf_end, dist[i] * *(std::prev(cdf_end)));
+                        output[i] = std::distance(cdf_begin, sample_iter);
+                    });
                 });
-            });
         });
     });
 }

src/targets/gpu/device/scatter.cpp

@@ -37,22 +37,26 @@ argument scatter(
     hipStream_t stream, argument result, argument arg0, argument arg1, argument arg2, int64_t axis)
 {
     auto ds            = arg0.get_shape();
-    auto inds          = arg1.get_shape();
+    auto s1            = arg1.get_shape();
     auto axis_dim_size = ds.lens()[axis];
-    hip_visit_all(result, arg0, inds)([&](auto output, auto data, auto s1) {
+    hip_visit_all(result, arg0, arg2)([&](auto output, auto data, auto update) {
         auto* output_ptr     = device_cast(output.data());
         const auto* data_ptr = device_cast(data.data());
         gs_launch(stream, ds.elements())([=](auto i) __device__ { output_ptr[i] = data_ptr[i]; });
-        hip_visit_all(arg1, arg2)([&](auto indices, auto update) {
-            const auto* upd_ptr     = device_cast(update.data());
-            const auto* indices_ptr = device_cast(indices.data());
-            gs_launch(stream, inds.elements())([=](auto i) __device__ {
-                auto out_idx    = s1.multi(i);
-                auto index      = indices_ptr[i];
-                index           = index < 0 ? index + axis_dim_size : index;
-                out_idx[axis]   = index;
-                output[out_idx] = upd_ptr[i];
-            });
+
+        hip_visit_all(arg1)([&](auto indices) {
+            if constexpr(indices.get_shape().lens.size() == output.get_shape().lens.size())
+            {
+                const auto* upd_ptr     = device_cast(update.data());
+                const auto* indices_ptr = device_cast(indices.data());
+                gs_launch(stream, s1.elements())([=](auto i) __device__ {
+                    auto out_idx    = indices.get_shape().multi(i);
+                    auto index      = indices_ptr[i];
+                    index           = index < 0 ? index + axis_dim_size : index;
+                    out_idx[axis]   = index;
+                    output[out_idx] = upd_ptr[i];
+                });
+            }
         });
     });
 

src/targets/gpu/device_name.cpp

@@ -43,6 +43,8 @@ auto get_arch_name(rank<1>, const HipDeviceProp& props) -> decltype(std::string(
     return std::string(props.gcnArchName);
 }
 
+std::string get_arch_name(const hipDeviceProp_t& props) { return get_arch_name(rank<1>{}, props); }
+
 int get_device_id()
 {
     int device;
@@ -58,7 +60,7 @@ std::string get_device_name()
     auto status = hipGetDeviceProperties(&props, get_device_id());
     if(status != hipSuccess)
         MIGRAPHX_THROW("Failed to get device properties");
-    return get_arch_name(rank<1>{}, props);
+    return get_arch_name(props);
 }
 
 } // namespace gpu

src/targets/gpu/driver/CMakeLists.txt

@@ -22,7 +22,7 @@
 # THE SOFTWARE.
 #####################################################################################
 
-file(GLOB GPU_DRIVER_SRCS ${CONFIGURE_DEPENDS} ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+file(GLOB GPU_DRIVER_SRCS CONFIGURE_DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 add_executable(gpu-driver
     ${GPU_DRIVER_SRCS}
 )

src/targets/gpu/driver/compile_op.cpp

@@ -22,7 +22,7 @@
  * THE SOFTWARE.
  */
 #include <migraphx/gpu/driver/action.hpp>
-#include <migraphx/gpu/driver/perf.hpp>
+#include <migraphx/gpu/time_op.hpp>
 #include <migraphx/gpu/compiler.hpp>
 #include <migraphx/gpu/context.hpp>
 

src/targets/gpu/driver/run_op.cpp

@@ -22,7 +22,7 @@
  * THE SOFTWARE.
  */
 #include <migraphx/gpu/driver/action.hpp>
-#include <migraphx/gpu/driver/perf.hpp>
+#include <migraphx/gpu/time_op.hpp>
 #include <migraphx/gpu/context.hpp>
 #include <migraphx/make_op.hpp>
 

src/targets/gpu/fuse_ck.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_ck.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 {
+
+struct ck_gemm
+{
+    operation op = make_op("dot");
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.op, "op"));
+    }
+
+    std::string name() const { return "gpu::ck_gemm"; }
+
+    void check_gemm_shape(const shape& s) const
+    {
+        if(not contains(range(s.strides().rbegin(), s.strides().rbegin() + 3), 1))
+            MIGRAPHX_THROW("Invalid shape for ck_gemm");
+    }
+
+    shape compute_shape(std::vector<shape> inputs, const std::vector<module_ref>& mods) const
+    {
+        check_shapes{inputs, *this}.same_ndims();
+        if(inputs.size() < 2)
+            MIGRAPHX_THROW("should have at least two inputs.");
+        auto a = inputs[0];
+        auto b = inputs[1];
+        for(const auto& input : inputs)
+            check_gemm_shape(input);
+        auto r = op.compute_shape({a, b});
+        if(mods.empty())
+            return r;
+        return r.with_type(mods.front()->get_output_shapes().front().type());
+    }
+};
+MIGRAPHX_REGISTER_OP(ck_gemm);
+
+namespace {
+
+bool is_ck_supported_type(shape::type_t t)
+{
+    return contains({shape::half_type, shape::int8_type, shape::int32_type}, t);
+}
+
+MIGRAPHX_PRED_MATCHER(is_ck_gemm, instruction_ref ins)
+{
+    if(ins->name() != "dot" and ins->name() != "quant_dot")
+        return false;
+    if(not is_ck_supported_type(ins->get_shape().type()))
+        return false;
+    auto a = ins->inputs().front()->get_shape();
+    auto b = ins->inputs().back()->get_shape();
+    auto m = a.lens()[a.lens().size() - 2];
+    auto n = b.lens().back();
+    auto k = a.lens().back();
+    // Integer gemms must be divisible by 4 in ck
+    if(contains({shape::int8_type, shape::int32_type}, ins->get_shape().type()))
+    {
+        if(m % 4 != 0)
+            return false;
+        if(n % 4 != 0)
+            return false;
+        if(k % 4 != 0)
+            return false;
+    }
+    // Skipping GEMMs with a K dimension greater than 2048 is a course-grained strategy
+    // to avoid poor-performing GEMM kernels from CK
+    // To-do: Investigate a more precise strategy
+    return k <= 2048;
+}
+
+struct find_ck_gemm_pointwise
+{
+    // Find a gemm followed by a pointwise operation.
+    auto matcher() const
+    {
+        auto gemm = match::skip(match::name("contiguous"))(
+            match::name("dot", "quant_dot")(is_ck_gemm().bind("gemm")));
+        return match::name("pointwise")(match::any_of[match::inputs()](gemm.bind("x")));
+    }
+
+    void apply(module_pass_manager& mpm, const match::matcher_result& r) const
+    {
+        auto ins      = r.result;
+        auto gemm_ins = r.instructions["gemm"];
+        auto x_ins    = r.instructions["x"]; // input after contiguous
+        auto* pm      = ins->module_inputs().front();
+        auto names    = pm->get_parameter_names();
+        std::sort(names.begin(), names.end());
+        auto inputs   = ins->inputs();
+        auto gemm_it  = std::find(inputs.begin(), inputs.end(), x_ins);
+        auto gemm_idx = gemm_it - inputs.begin();
+        if(gemm_ins->get_shape().type() != shape::int32_type and
+           ins->get_shape().type() != gemm_ins->get_shape().type())
+            return;
+        if(std::any_of(ins->inputs().begin(), ins->inputs().end(), [](auto input) {
+               return not is_ck_supported_type(input->get_shape().type());
+           }))
+            return;
+        assert(gemm_it != inputs.end());
+        if(gemm_idx != 0)
+        {
+            auto first_param    = pm->get_parameter(names[0]);
+            auto gemm_param     = pm->get_parameter(names[gemm_idx]);
+            auto new_gemm_param = pm->add_parameter(names[0] + "_0", gemm_param->get_shape());
+            auto new_first_param =
+                pm->add_parameter(names[gemm_idx] + "_0", first_param->get_shape());
+            pm->replace_instruction(gemm_param, new_gemm_param);
+            pm->replace_instruction(first_param, new_first_param);
+            pm->remove_instruction(first_param);
+            pm->remove_instruction(gemm_param);
+        }
+        inputs.erase(gemm_it);
+        inputs.insert(inputs.begin(), gemm_ins->inputs().begin(), gemm_ins->inputs().end());
+
+        mpm.get_module().replace_instruction(ins, ck_gemm{gemm_ins->get_operator()}, inputs, {pm});
+    }
+};
+
+struct find_ck_gemm
+{
+    auto matcher() const { return match::name("dot")(is_ck_gemm().bind("gemm")); }
+
+    void apply(module_pass_manager& mpm, const match::matcher_result& r) const
+    {
+        auto ins = r.result;
+        mpm.get_module().replace_instruction(ins, ck_gemm{ins->get_operator()}, ins->inputs());
+    }
+};
+
+} // namespace
+
+void fuse_ck::apply(module_pass_manager& mpm) const
+{
+    match::find_matches(mpm, find_ck_gemm_pointwise{});
+    match::find_matches(mpm, find_ck_gemm{});
+}
+
+} // namespace gpu
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/fuse_mlir.cpp

@@ -38,6 +38,27 @@ namespace gpu {
 
 MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_ENABLE_MLIR);
 
+bool mlir_enabled()
+{
+#ifdef MIGRAPHX_MLIR
+    const bool mlir_enabled = enabled(MIGRAPHX_ENABLE_MLIR{});
+    if(mlir_enabled)
+    {
+        return true;
+    }
+    else
+    {
+
+        std::cerr << "WARNING: MIGraphX built with MLIR but it is not enabled. Please set the env "
+                     "var MIGRAPHX_ENABLE_MLIR to use MLIR kernel generator."
+                  << std::endl;
+        return false;
+    }
+#else
+    return false;
+#endif
+}
+
 #ifdef MIGRAPHX_MLIR
 
 struct mlir_op
@@ -58,8 +79,41 @@ struct mlir_op
             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]});
+
+        module_ref mod = mods[0];
+        auto type      = mod->get_output_shapes().front().type();
+        std::unordered_map<instruction_ref, shape> ins_shapes;
+        size_t param_cnt               = 0;
+        std::vector<std::string> names = mod->get_parameter_names();
+        std::sort(names.begin(), names.end());
+        for(std::string param_name : names)
+        {
+            ins_shapes[mod->get_parameter(param_name)] = inputs[param_cnt++];
+        }
+        for(auto ins : iterator_for(*mod))
+        {
+            if(ins->name() == "@param")
+            {
+                continue;
+            }
+            if(ins->name() == "@literal")
+            {
+                ins_shapes[ins] = ins->get_shape();
+                continue;
+            }
+            if(ins->name() == "@return")
+            {
+                return ins_shapes[ins->inputs().at(0)].with_type(type);
+            }
+            std::vector<shape> input_shapes;
+            input_shapes.resize(ins->inputs().size());
+            std::transform(ins->inputs().begin(),
+                           ins->inputs().end(),
+                           input_shapes.begin(),
+                           [&](auto in) { return ins_shapes[in]; });
+            ins_shapes[ins] = ins->get_operator().compute_shape(input_shapes);
+        }
+        MIGRAPHX_THROW("No return found in the submodule");
     }
 };
 MIGRAPHX_REGISTER_OP(mlir_op);
@@ -68,7 +122,7 @@ namespace {
 
 MIGRAPHX_PRED_MATCHER(is_mlir_conv, instruction_ref ins)
 {
-    if(ins->name() != "convolution")
+    if(ins->name() != "convolution" and ins->name() != "quant_convolution")
         return false;
     value v    = ins->get_operator().to_value();
     auto group = v.at("group").to<int>();
@@ -85,10 +139,121 @@ struct find_mlir_op
     auto matcher() const
     {
         auto dot_or_conv = match::skip(match::name("contiguous"))(
-            match::any_of(match::name("dot"), is_mlir_conv()).bind("gemm_based_op"));
+            match::any_of(match::name("dot"), match::name("quant_dot"), is_mlir_conv())
+                .bind("gemm_based_op"));
         return match::name("pointwise")(match::any_of[match::inputs()](dot_or_conv.bind("x")));
     }
 
+    std::unordered_map<instruction_ref, instruction_ref>
+    create_param_map_with_literals(module_ref mm, const module* pm, const shape& shape) const
+    {
+        std::unordered_map<instruction_ref, instruction_ref> ins_map;
+        for(auto ins : iterator_for(*pm))
+        {
+            if(ins->name() != "@literal")
+            {
+                continue;
+            }
+            literal r               = ins->get_literal();
+            instruction_ref literal = mm->add_literal(r);
+            instruction_ref mbcast  = mm->add_instruction(
+                make_op("multibroadcast", {{"out_lens", shape.lens()}}), literal);
+            ins_map[ins] = mbcast;
+        }
+        return ins_map;
+    }
+
+    std::tuple<instruction_ref, std::vector<instruction_ref>>
+    fuse_input_ops_and_gemm_based_op(module_ref mm, instruction_ref gemm_based_op) const
+    {
+        std::vector<instruction_ref> top_inputs;
+        std::vector<instruction_ref> imm_inputs;
+        size_t input_cnt = 0;
+        for(instruction_ref input : gemm_based_op->inputs())
+        {
+            std::vector<operation> op_stream;
+            while(contains({"slice", "transpose", "contiguous", "reshape"}, input->name()))
+            {
+                op_stream.push_back(input->get_operator());
+                input = input->inputs().at(0);
+            }
+            top_inputs.push_back(input);
+            instruction_ref prev_input =
+                mm->add_parameter("y" + std::to_string(input_cnt++), input->get_shape());
+            for(const auto& op : reverse(op_stream))
+            {
+                prev_input = mm->add_instruction(op, {prev_input});
+            }
+            imm_inputs.push_back(prev_input);
+        }
+        instruction_ref new_gemm_based_op =
+            mm->add_instruction(gemm_based_op->get_operator(), imm_inputs);
+        return {new_gemm_based_op, top_inputs};
+    }
+
+    // Whitelist supported fusion options, including imposing type constraints
+    // for cases where MLIR only supports an operation (usually a pointwise function)
+    // on particular types.
+    bool is_pointwise_op_supported_by_mlir(const instruction& i) const
+    {
+        using type_t                                      = shape::type_t;
+        const auto& name                                  = i.name();
+        const auto result_type                            = i.get_shape().type();
+        const std::initializer_list<type_t> allowed_types = {type_t::float_type,
+                                                             type_t::half_type,
+                                                             type_t::int8_type,
+                                                             type_t::int32_type,
+                                                             type_t::bool_type};
+        // Preliminary type check.
+        if(not contains(allowed_types, result_type))
+        {
+            return false;
+        }
+        const std::initializer_list<std::string> any_type_ops = {"@literal", "@param", "@return"};
+        const std::initializer_list<std::string> no_bool_ops  = {"convolution",
+                                                                "quant_convolution",
+                                                                "dot",
+                                                                "quant_dot",
+                                                                "add",
+                                                                "clip",
+                                                                "relu",
+                                                                "sub",
+                                                                "mul",
+                                                                "div",
+                                                                "pow",
+                                                                "where",
+                                                                "quantizelinear",
+                                                                "dequantizelinear",
+                                                                "abs",
+                                                                "neg"};
+        const std::initializer_list<std::string> fp_only_ops  = {"ceil",
+                                                                "erf",
+                                                                "exp",
+                                                                "floor",
+                                                                "log",
+                                                                "recip",
+                                                                "rsqrt",
+                                                                "sigmoid"
+                                                                "softmax",
+                                                                "tanh"};
+        bool is_float = contains({type_t::float_type, type_t::half_type}, result_type);
+        if(contains(any_type_ops, name))
+            return true;
+        if(result_type != type_t::bool_type && contains(no_bool_ops, name))
+            return true;
+        if(is_float && contains(fp_only_ops, name))
+            return true;
+        // Only conversions between floating types are known to be unambigiously
+        // supported.
+        if(is_float && name == "convert")
+        {
+            return std::all_of(i.inputs().begin(), i.inputs().end(), [](const auto& arg) {
+                return contains({type_t::float_type, type_t::half_type}, arg->get_shape().type());
+            });
+        }
+        return false;
+    }
+
     void apply(module_pass_manager& mpm, const match::matcher_result& r) const
     {
         auto ins           = r.result;
@@ -96,35 +261,25 @@ struct find_mlir_op
         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", "dot", "add", "relu"},
-                   i.name());
-           }))
-            return;
-        // Only fuse with fp32/fp16
-        if(std::any_of(ins->inputs().begin(), ins->inputs().end(), [&](auto i) {
-               return not contains({shape::type_t::float_type, shape::type_t::half_type},
-                                   i->get_shape().type());
+        // Whitelist pointwise operators.
+        if(std::any_of(pm->begin(), pm->end(), [&](const auto& i) {
+               return not is_pointwise_op_supported_by_mlir(i);
            }))
             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()),
-                                   gemm_based_op->inputs().at(0)->get_shape());
-        auto w    = mm->add_parameter("x" + std::to_string(names.size() + 1),
-                                   gemm_based_op->inputs().at(1)->get_shape());
-        auto conv = mm->add_instruction(gemm_based_op->get_operator(), {x, w});
+        std::unordered_map<instruction_ref, instruction_ref> param_map =
+            create_param_map_with_literals(mm, pm, gemm_based_op->get_shape());
+        auto [anchor_op, top_inputs] = fuse_input_ops_and_gemm_based_op(mm, gemm_based_op);
         std::transform(names.begin(),
                        names.end(),
                        ins->inputs().begin(),
                        std::inserter(param_map, param_map.end()),
-                       [&](auto name, auto input) {
+                       [&, &anchor_op = anchor_op](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), anchor_op);
                            return std::make_pair(pm->get_parameter(name),
                                                  mm->add_parameter(name, input->get_shape()));
                        });
@@ -135,7 +290,7 @@ struct find_mlir_op
                      ins->inputs().end(),
                      std::back_inserter(inputs),
                      [&](auto input) { return input != gemm_based_op; });
-        inputs.insert(inputs.end(), gemm_based_op->inputs().begin(), gemm_based_op->inputs().end());
+        inputs.insert(inputs.end(), top_inputs.begin(), top_inputs.end());
         mpm.get_module().replace_instruction(
             ins, mlir_op{gemm_based_op->get_operator()}, inputs, {mm});
     }
@@ -148,17 +303,7 @@ struct find_mlir_op
 void fuse_mlir::apply(module_pass_manager& mpm) const
 {
 #ifdef MIGRAPHX_MLIR
-    const bool mlir_enabled = enabled(MIGRAPHX_ENABLE_MLIR{});
-    if(mlir_enabled)
-    {
-        match::find_matches(mpm, find_mlir_op{});
-    }
-    else
-    {
-        std::cerr << "WARNING: MIGraphX built with MLIR but it is not enabled. Please set the env "
-                     "var MIGRAPHX_ENABLE_MLIR to use MLIR kernel generator."
-                  << std::endl;
-    }
+    match::find_matches(mpm, find_mlir_op{});
 #else
     (void)mpm;
 #endif

src/targets/gpu/fuse_ops.cpp

@@ -165,7 +165,8 @@ struct fusion
 
 const std::unordered_set<std::string>& get_supported_archs()
 {
-    static std::unordered_set<std::string> supported_archs{"gfx900", "gfx906", "gfx908", "gfx1030"};
+    static std::unordered_set<std::string> supported_archs{
+        "gfx900", "gfx906", "gfx908", "gfx1030", "gfx940"};
     return supported_archs;
 }
 

src/targets/gpu/gemm_impl.cpp

@@ -140,12 +140,10 @@ void gemm_impl(context& ctx,
             compute_type = rocblas_datatype_f32_r;
     }
 
-#if ROCBLAS_VERSION_MAJOR >= 2 && ROCBLAS_VERSION_MINOR >= 38
-    rocblas_gemm_flags flag =
-        int8_x4_format ? rocblas_gemm_flags_pack_int8x4 : rocblas_gemm_flags_none;
-#else
-    (void)int8_x4_format;
-    int flag = 0;
+    rocblas_gemm_flags flag = rocblas_gemm_flags_none;
+#if ROCBLAS_VERSION_MAJOR < 3
+    if(int8_x4_format)
+        flag = rocblas_gemm_flags_pack_int8x4;
 #endif
 
     auto a_lens = args[0].get_shape().lens();