Merge branch 'simplify_1_mul_div_ops' into divide_by_zero_check

src/targets/gpu/include/migraphx/gpu/int8_gemm_pack.hpp

@@ -24,6 +24,7 @@
 #ifndef MIGRAPHX_GUARD_RTGLIB_INT8_GEMM_PACK_HPP
 #define MIGRAPHX_GUARD_RTGLIB_INT8_GEMM_PACK_HPP
 
+#include <migraphx/argument.hpp>
 #include <migraphx/op/quant_dot.hpp>
 #include <migraphx/config.hpp>
 #include <utility>

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

+/*
+ * 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.
+ */
+#ifndef MIGRAPHX_GUARD_RTGLIB_GPU_MLIR_HPP
+#define MIGRAPHX_GUARD_RTGLIB_GPU_MLIR_HPP
+
+#include <string>
+#include <vector>
+#include <migraphx/config.hpp>
+#include <migraphx/gpu/code_object_op.hpp>
+#include <migraphx/instruction_ref.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+struct module;
+namespace gpu {
+
+std::string dump_mlir(const module& m);
+code_object_op compile_mlir(const context& ctx, const module& m);
+
+instruction_ref insert_mlir(module& m,
+                            instruction_ref ins,
+                            code_object_op co,
+                            const std::vector<instruction_ref>& inputs);
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

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

@@ -41,7 +41,7 @@ struct miopen_quant_convolution
     bool int8_x4_format = false;
     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)
@@ -55,7 +55,7 @@ struct miopen_quant_convolution
     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
     {

src/targets/gpu/jit/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/compiler.hpp>
+#include <migraphx/make_op.hpp>
+#include <migraphx/gpu/context.hpp>
+
+#include <migraphx/gpu/mlir.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+struct mlir_compiler : compiler<mlir_compiler>
+{
+    std::vector<std::string> names() const { return {"gpu::mlir_conv"}; }
+
+    operation compile_op(context&, const std::vector<shape>&, const value&) const { return {}; }
+
+    compiler_replace compile(context& ctx, instruction_ref ins, const operation&) const
+    {
+        auto* smod = ins->module_inputs().front();
+        assert(smod->get_parameter_names().size() == ins->inputs().size() - 1);
+        return insert(compile_mlir(ctx, *smod));
+    }
+
+    compiler_replace insert(code_object_op co) const
+    {
+        return [co = std::move(co)](module& m, instruction_ref ins) {
+            auto mlir = insert_mlir(m, ins, co, ins->inputs());
+            m.replace_instruction(ins, mlir);
+        };
+    }
+};
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/jit/softmax.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/compiler.hpp>
+#include <migraphx/gpu/context.hpp>
+#include <migraphx/gpu/compile_hip_code_object.hpp>
+#include <migraphx/gpu/compile_hip.hpp>
+#include <migraphx/gpu/compile_gen.hpp>
+
+#include <migraphx/cpp_generator.hpp>
+#include <migraphx/ranges.hpp>
+#include <migraphx/reduce_dims.hpp>
+#include <migraphx/stringutils.hpp>
+#include <migraphx/dead_code_elimination.hpp>
+#include <migraphx/eliminate_common_subexpression.hpp>
+#include <migraphx/module.hpp>
+#include <migraphx/pass_manager.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+using namespace migraphx::gpu::gen; // NOLINT
+
+static const char* const softmax_kernel = R"__migraphx__(
+#include <migraphx/kernels/index.hpp>
+#include <migraphx/kernels/softmax.hpp>
+#include <migraphx/kernels/vectorize.hpp>
+#include <args.hpp>
+
+namespace migraphx {
+
+extern "C" {
+__global__ void softmax_kernel(void* input_p, void* output_p) 
+{
+    transform_args(make_tensors(), ${transformers})(input_p, output_p)([](auto input, auto output) {
+        softmax<${axis}>(input, output);
+    });
+}
+    
+}
+
+} // namespace migraphx
+
+)__migraphx__";
+
+struct softmax_compiler : compiler<softmax_compiler>
+{
+    std::vector<std::string> names() const { return {"softmax"}; }
+
+    operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
+    {
+        // TODO: Use reduce_dims
+        auto axis  = v.at("axis").to<int64_t>();
+        auto faxis = find_fast_axis({inputs.front()});
+        vectorize vec{};
+        // Vectorize if the axis is a reduction axis
+        if(faxis == axis)
+        {
+            vec = vectorize::elements(faxis, inputs);
+        }
+        auto relements  = inputs[0].lens()[axis] / vec.size;
+        auto nelements  = (inputs.back().elements() / inputs[0].lens()[axis]);
+        auto block_size = compute_block_size(relements, 256);
+        hip_compile_options options;
+        options.set_launch_params(
+            v, compute_global_for(ctx, nelements * block_size, 256), block_size);
+        options.output      = inputs.back();
+        options.inputs      = inputs;
+        options.kernel_name = "softmax_kernel";
+
+        auto src = interpolate_string(
+            softmax_kernel,
+            {{"transformers", make_transformer_args(vec)}, {"axis", to_string(axis)}});
+
+        return compile_hip_code_object(src, options);
+    }
+
+    compiler_replace compile(context& ctx, instruction_ref ins, const operation& op) const
+    {
+        return replace(compile_op(ctx, to_shapes(ins->inputs()), op.to_value()));
+    }
+};
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/kernels/include/migraphx/kernels/array.hpp

@@ -27,6 +27,7 @@
 #include <migraphx/kernels/types.hpp>
 #include <migraphx/kernels/type_traits.hpp>
 #include <migraphx/kernels/integral_constant.hpp>
+#include <migraphx/kernels/functional.hpp>
 #include <migraphx/kernels/debug.hpp>
 
 namespace migraphx {
@@ -213,6 +214,13 @@ constexpr auto transform(integral_const_array<T, Xs...>, F f)
     return integral_const_array<T, f(Xs)...>{};
 }
 
+template <class T, T... Xs, class F>
+constexpr auto transform_i(integral_const_array<T, Xs...>, F f)
+{
+    return sequence_c<sizeof...(Xs)>(
+        [=](auto... is) { return integral_const_array<T, f(Xs, is)...>{}; });
+}
+
 template <class T, T... Xs, class U, U... Ys, class F>
 constexpr auto transform(integral_const_array<T, Xs...>, integral_const_array<U, Ys...>, F f)
 {

src/targets/gpu/kernels/include/migraphx/kernels/functional.hpp

@@ -24,7 +24,7 @@
 #ifndef MIGRAPHX_GUARD_KERNELS_FUNCTIONAL_HPP
 #define MIGRAPHX_GUARD_KERNELS_FUNCTIONAL_HPP
 
-#include <migraphx/kernels/array.hpp>
+#include <migraphx/kernels/integral_constant.hpp>
 
 // NOLINTNEXTLINE
 #define MIGRAPHX_RETURNS(...) \

src/targets/gpu/kernels/include/migraphx/kernels/index.hpp

@@ -27,6 +27,7 @@
 #include <migraphx/kernels/hip.hpp>
 #include <migraphx/kernels/types.hpp>
 #include <migraphx/kernels/integral_constant.hpp>
+#include <migraphx/kernels/type_traits.hpp>
 
 namespace migraphx {
 
@@ -53,29 +54,51 @@ struct index
         return blockDim.x; // NOLINT
     }
 #endif
+    template <class N, class Stride>
+    static constexpr auto max_stride_iterations(N n, Stride stride)
+    {
+        return (n - _c<1>) / stride + _c<1>;
+    }
 
-    template <class F>
-    __device__ void global_stride(index_int n, F f) const
+    template <class F, class N, class Stride>
+    static constexpr void for_stride(index_int start, N n, Stride stride, F f)
     {
-        const auto stride = nglobal();
-        for(index_int i = global; i < n; i += stride)
+        if constexpr(not is_integral<N>{} and not is_integral<Stride>{} and
+                     max_stride_iterations(n, stride) == 1)
         {
-            f(i);
+            if constexpr(stride > n)
+            {
+                if(start < n)
+                    f(start);
+            }
+            else
+            {
+                f(start);
+            }
+        }
+        else
+        {
+            for(index_int i = start; i < n; i += stride)
+            {
+                f(i);
+            }
         }
     }
 
-    template <class F>
-    __device__ void local_stride(index_int n, F f) const
+    template <class F, class N>
+    __device__ void global_stride(N n, F f) const
     {
-        const auto stride = nlocal();
-        for(index_int i = local; i < n; i += stride)
-        {
-            f(i);
-        }
+        for_stride(global, n, nglobal(), f);
+    }
+
+    template <class F, class N>
+    __device__ void local_stride(N n, F f) const
+    {
+        for_stride(local, n, nlocal(), f);
     }
 };
 
-inline __device__ index make_index()
+inline __device__ __attribute__((const)) index make_index()
 {
     return index{blockIdx.x * blockDim.x + threadIdx.x, threadIdx.x, blockIdx.x}; // NOLINT
 }

src/targets/gpu/kernels/include/migraphx/kernels/preload.hpp

@@ -186,7 +186,8 @@ __device__ auto auto_preload(index idx)
 {
     return make_transform([=](auto f, auto... xs) {
         auto invoke = [=](auto... ys) {
-            __syncthreads();
+            if constexpr((Bs or ...))
+                __syncthreads();
             f(ys...);
         };
         join(invoke, preload_copy<Bs>(idx, xs)...);

src/targets/gpu/kernels/include/migraphx/kernels/reduce.hpp

@@ -175,6 +175,21 @@ constexpr auto sliced(Slicer slicer, F f)
     };
 }
 
+template <class Input, index_int Axis>
+constexpr auto compute_reduce_axis()
+{
+    constexpr auto lens =
+        transform_i(get_shape_c<Input>{}.lens, [](index_int x, index_int i) -> index_int {
+            if(i == Axis)
+                return 1;
+            return x;
+        });
+    return make_shape(lens, get_shape_c<Input>{}.strides);
+}
+
+template <class Input, index_int Axis>
+using with_axis = decltype(compute_reduce_axis<Input, Axis>());
+
 struct block
 {
     template <class Slicer>
@@ -201,6 +216,14 @@ struct block
             if(idx.local == 0)
                 f();
         }
+
+        template <class F>
+        __device__ auto inner(F f) const
+        {
+            return sliced(slicer, [=](auto x, auto... xs) {
+                idx.local_stride(x.get_shape().elements(), [&](auto j) { f(x[j], xs[j]...); });
+            });
+        }
     };
 
     template <class Slicer>
@@ -247,6 +270,17 @@ struct lane
         {
             f();
         }
+
+        template <class F>
+        __device__ auto inner(F f) const
+        {
+            return sliced(slicer, [=](auto x, auto... xs) {
+                for(index_int j = 0; j < x.get_shape().elements(); j++)
+                {
+                    f(x[j], xs[j]...);
+                }
+            });
+        }
     };
 
     template <class Slicer>

src/targets/gpu/kernels/include/migraphx/kernels/shape.hpp

@@ -32,6 +32,7 @@ namespace migraphx {
 template <class Lens, class Strides>
 struct shape
 {
+    using shape_type  = shape;
     using index_array = typename Lens::base_array;
     Lens lens         = {};
     Strides strides   = {};

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

+/*
+ * 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.
+ */
+#ifndef MIGRAPHX_GUARD_KERNELS_SOFTMAX_HPP
+#define MIGRAPHX_GUARD_KERNELS_SOFTMAX_HPP
+
+#include <migraphx/kernels/reduce.hpp>
+#include <migraphx/kernels/ops.hpp>
+
+namespace migraphx {
+
+template <index_int Axis, class Input, class Output>
+__device__ void softmax(Input input, Output output)
+{
+    reduce::block::run<reduce::with_axis<Input, Axis>>([&](auto, auto r) {
+        auto batch_max = r.reduce(op::max{}, lowest{}, op::id{})(input);
+        auto batch_sum =
+            r.reduce(op::sum{}, 0, [&](auto x) { return migraphx::exp(x - batch_max); })(input);
+        r.inner([&](auto& y, auto x) { y = migraphx::exp(x - batch_max) / batch_sum; })(output,
+                                                                                        input);
+    });
+}
+
+} // namespace migraphx
+#endif // MIGRAPHX_GUARD_KERNELS_SOFTMAX_HPP

src/targets/gpu/kernels/include/migraphx/kernels/vec.hpp

@@ -27,6 +27,8 @@
 #include <migraphx/kernels/types.hpp>
 #include <migraphx/kernels/integral_constant.hpp>
 #include <migraphx/kernels/functional.hpp>
+#include <migraphx/kernels/type_traits.hpp>
+#include <migraphx/kernels/debug.hpp>
 
 namespace migraphx {
 

src/targets/gpu/lowering.cpp

@@ -186,7 +186,6 @@ struct miopen_apply
         add_extend_op("rnn_var_sl_shift_output");
         add_extend_op("rnn_var_sl_shift_sequence");
         add_extend_op("scatter_none");
-        add_extend_op("softmax");
         add_extend_op("topk");
 
         add_batch_norm_inference_op();
@@ -301,7 +300,7 @@ struct miopen_apply
             auto&& op = any_cast<op::deconvolution>(ins->get_operator());
 
             auto conv = miopen_deconvolution{op, make_deconv(op)};
-            auto ws   = conv.compile(get_context(), ins->get_shape(), to_shapes(ins->inputs()));
+            auto ws   = conv.find(get_context(), ins->get_shape(), to_shapes(ins->inputs()));
 
             auto workspace = insert_allocation(ins, ws);
             auto output    = insert_allocation(ins, ins->get_shape());
@@ -332,7 +331,7 @@ struct miopen_apply
             miopen_quant_convolution conv;
             auto compile_quant_conv_with_format = [&](bool format) {
                 conv = miopen_quant_convolution{op, format, make_conv(op)};
-                ws   = conv.compile(get_context(), ins->get_shape(), to_shapes(ins->inputs()));
+                ws   = conv.find(get_context(), ins->get_shape(), to_shapes(ins->inputs()));
             };
 
             try

src/targets/gpu/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/mlir.hpp>
+
+#ifdef MIGRAPHX_MLIR
+#include <mlir-c/IR.h>
+#include <mlir-c/BuiltinAttributes.h>
+#include <mlir-c/BuiltinTypes.h>
+#include <mlir-c/Diagnostics.h>
+#include <mlir-c/Dialect/MIGraphX.h>
+#include <mlir-c/IntegerSet.h>
+#include <mlir-c/Pass.h>
+#include <mlir-c/Registration.h>
+#endif
+
+#include <migraphx/env.hpp>
+#include <migraphx/manage_ptr.hpp>
+#include <migraphx/module.hpp>
+#include <migraphx/instruction.hpp>
+#include <migraphx/config.hpp>
+#include <migraphx/ranges.hpp>
+#include <migraphx/gpu/code_object_op.hpp>
+#include <migraphx/gpu/context.hpp>
+#include <migraphx/gpu/device_name.hpp>
+#include <migraphx/iterator_for.hpp>
+#include <deque>
+#include <variant>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_TRACE_MLIR);
+
+#ifdef MIGRAPHX_MLIR
+template <class T, class F, F f> // NOLINT
+struct mlir_handle
+{
+    struct ptr
+    {
+        ptr() = default;
+        ptr(std::nullptr_t) {}
+        ptr(T x) : obj(x) {}
+
+        std::intptr_t get_value() const
+        {
+            static_assert(sizeof(T) == sizeof(std::intptr_t), "MLIR Handle different size");
+            return reinterpret_cast<const std::intptr_t&>(obj);
+        }
+
+        T get() const { return obj; }
+
+        friend bool operator==(ptr x, ptr y) { return x.get_value() == y.get_value(); }
+
+        friend bool operator!=(ptr x, ptr y) { return !(x == y); }
+        T obj{};
+    };
+
+    struct deleter
+    {
+        using pointer = ptr;
+
+        void operator()(pointer x) const
+        {
+            if(x != nullptr)
+            {
+                (void)f(x.obj);
+            }
+        }
+    };
+
+    mlir_handle() : handle(nullptr) {}
+
+    mlir_handle(T p) : handle(ptr{p}) {}
+
+    T get() const { return handle.get().get(); }
+
+    T release() { return handle.release().get(); }
+
+    private:
+    std::unique_ptr<ptr, deleter> handle;
+};
+
+#define MIGRAPHX_MANAGE_MLIR_HANDLE(T, F) migraphx::gpu::mlir_handle<T, decltype(&F), &F> // NOLINT
+
+using mlir_context           = MIGRAPHX_MANAGE_MLIR_HANDLE(MlirContext, mlirContextDestroy);
+using mlir_module            = MIGRAPHX_MANAGE_MLIR_HANDLE(MlirModule, mlirModuleDestroy);
+using mlir_operation         = MIGRAPHX_MANAGE_MLIR_HANDLE(MlirOperation, mlirOperationDestroy);
+using mlir_op_printing_flags = MIGRAPHX_MANAGE_MLIR_HANDLE(MlirOpPrintingFlags,
+                                                           mlirOpPrintingFlagsDestroy);
+using mlir_region            = MIGRAPHX_MANAGE_MLIR_HANDLE(MlirRegion, mlirRegionDestroy);
+using mlir_block             = MIGRAPHX_MANAGE_MLIR_HANDLE(MlirBlock, mlirBlockDestroy);
+using mlir_pass_manager      = MIGRAPHX_MANAGE_MLIR_HANDLE(MlirPassManager, mlirPassManagerDestroy);
+
+std::string_view to_string_view(MlirStringRef s) { return {s.data, s.length}; }
+
+MlirStringRef make_mlir_string_ref(const std::string_view& s)
+{
+    return mlirStringRefCreate(s.data(), s.size());
+}
+
+template <class F, class T, class Printer>
+void mlir_print(F f, T x, Printer printer)
+{
+    f(
+        x,
+        +[](MlirStringRef s, void* data) {
+            (*reinterpret_cast<Printer*>(data))(to_string_view(s));
+        },
+        &printer);
+}
+
+template <class F, class T>
+void mlir_print(F f, T x, std::ostream& os)
+{
+    mlir_print(f, x, [&](auto s) { os << s; });
+}
+
+template <class F, class T>
+std::string mlir_print(F f, T x)
+{
+    std::stringstream ss;
+    mlir_print(f, x, [&](auto s) { ss << s; });
+    return ss.str();
+}
+
+struct mlir_program
+{
+    mlir_program()
+        : ctx(mlirContextCreate()),
+          location(mlirLocationUnknownGet(ctx.get())),
+          mmodule(mlirModuleCreateEmpty(location))
+    {
+        MlirDialectHandle mixr_handle = mlirGetDialectHandle__migraphx__();
+        mlirDialectHandleRegisterDialect(mixr_handle, ctx.get());
+        mlirRegisterAllDialects(ctx.get());
+        mlirContextSetAllowUnregisteredDialects(ctx.get(), true /*allow*/);
+    }
+
+    MlirType make_type(shape::type_t t) const
+    {
+        MlirType result;
+        shape::visit(t, [&](auto as) {
+            if(as.type_enum() == shape::float_type)
+                result = mlirF32TypeGet(ctx.get());
+            else if(as.type_enum() == shape::half_type)
+                result = mlirF16TypeGet(ctx.get());
+            else if(as.type_enum() == shape::double_type)
+                result = mlirF64TypeGet(ctx.get());
+            else if(as.is_integral())
+            {
+                if(as.is_signed())
+                    result = mlirIntegerTypeSignedGet(ctx.get(), as.size() * 8);
+                else
+                    result = mlirIntegerTypeGet(ctx.get(), as.size() * 8);
+            }
+            else
+                MIGRAPHX_THROW("Unsupported type: " + std::to_string(as.type_enum()));
+        });
+        return result;
+    }
+
+    MlirType make_tensor(const shape& s) const
+    {
+        assert(s.standard());
+        std::vector<int64_t> lens(s.lens().begin(), s.lens().end());
+        return mlirRankedTensorTypeGet(
+            lens.size(), lens.data(), make_type(s.type()), mlirAttributeGetNull());
+    }
+
+    template <class Range>
+    std::vector<MlirType> make_tensors(const Range& r)
+    {
+        std::vector<MlirType> result;
+        std::transform(r.begin(), r.end(), std::back_inserter(result), [&](const auto& s) {
+            return make_tensor(s);
+        });
+        return result;
+    }
+
+    MlirType make_function_type(const std::vector<shape>& inputs, const std::vector<shape>& outputs)
+    {
+        auto in  = make_tensors(inputs);
+        auto out = make_tensors(outputs);
+        return mlirFunctionTypeGet(ctx.get(), in.size(), in.data(), out.size(), out.data());
+    }
+
+    MlirIdentifier id(const std::string_view& s) const
+    {
+        return mlirIdentifierGet(ctx.get(), make_mlir_string_ref(s));
+    }
+
+    MlirAttribute attribute(std::int64_t i) const
+    {
+        if(i < 0)
+            MIGRAPHX_THROW("MLIR cant handle negative values since they are ambiguous");
+        return mlirIntegerAttrGet(mlirIntegerTypeGet(ctx.get(), 64), i);
+    }
+    MlirAttribute attribute(std::uint64_t i) const
+    {
+        if(i > (std::numeric_limits<std::uint64_t>::max() / 2))
+            MIGRAPHX_THROW("MLIR cant handle large integer values since they are ambiguous");
+        return mlirIntegerAttrGet(mlirIntegerTypeGet(ctx.get(), 64), i);
+    }
+    MlirAttribute attribute(unsigned char i) const { return attribute(std::uint64_t(i)); }
+    MlirAttribute attribute(bool b) const { return mlirBoolAttrGet(ctx.get(), b ? 1 : 0); }
+    MlirAttribute attribute(double d) const
+    {
+        return mlirFloatAttrDoubleGet(ctx.get(), mlirF64TypeGet(ctx.get()), d);
+    }
+    MlirAttribute attribute(const std::string& s) const
+    {
+        return mlirStringAttrGet(ctx.get(), make_mlir_string_ref(s));
+    }
+    MlirAttribute attribute(std::nullptr_t) const { return {}; }
+    template <class T>
+    MlirAttribute attribute(const std::vector<T>& v) const
+    {
+        std::vector<MlirAttribute> attributes;
+        attributes.reserve(v.size());
+        std::transform(v.begin(), v.end(), std::back_inserter(attributes), [&](auto&& x) {
+            return attribute(x);
+        });
+        return mlirArrayAttrGet(ctx.get(), attributes.size(), attributes.data());
+    }
+    MlirAttribute attribute(const value& v) const
+    {
+        MlirAttribute attr;
+        v.visit_value([&](auto&& x) { attr = attribute(x); });
+        return attr;
+    }
+    MlirAttribute attribute(const std::vector<value>& v) const
+    {
+        if(v.empty())
+        {
+            return mlirArrayAttrGet(ctx.get(), 0, nullptr);
+        }
+        if(not v.front().get_key().empty())
+        {
+            std::vector<MlirNamedAttribute> attributes = name_attributes(v);
+            return mlirDictionaryAttrGet(ctx.get(), attributes.size(), attributes.data());
+        }
+        else
+        {
+            std::vector<MlirAttribute> attributes;
+            attributes.reserve(v.size());
+            std::transform(v.begin(), v.end(), std::back_inserter(attributes), [&](auto&& x) {
+                return attribute(x);
+            });
+            return mlirArrayAttrGet(ctx.get(), attributes.size(), attributes.data());
+        }
+    }
+
+    MlirAttribute attribute(MlirType t) const { return mlirTypeAttrGet(t); }
+
+    MlirAttribute attribute(MlirAttribute a) const { return a; }
+
+    template <class T>
+    MlirNamedAttribute name_attribute(const std::string_view& key, const T& x) const
+    {
+        MlirNamedAttribute attr;
+        attr.name      = id(key);
+        attr.attribute = attribute(x);
+        return attr;
+    }
+
+    using attribute_t       = std::variant<std::nullptr_t,
+                                     std::uint64_t,
+                                     unsigned char,
+                                     bool,
+                                     double,
+                                     std::string,
+                                     value,
+                                     std::vector<value>,
+                                     MlirType>;
+    using named_attribute_t = std::pair<std::string_view, attribute_t>;
+
+    MlirNamedAttribute name_attribute(const named_attribute_t& na) const
+    {
+        return name_attribute(na.first,
+                              std::visit([&](const auto& x) { return attribute(x); }, na.second));
+    }
+
+    std::vector<MlirNamedAttribute>
+    name_attributes(const std::vector<named_attribute_t>& named_attrs) const
+    {
+        std::vector<MlirNamedAttribute> attributes;
+        attributes.reserve(named_attrs.size());
+        std::transform(named_attrs.begin(),
+                       named_attrs.end(),
+                       std::back_inserter(attributes),
+                       [&](const named_attribute_t& a) { return name_attribute(a); });
+        return attributes;
+    }
+
+    std::vector<MlirNamedAttribute> name_attributes(const value& v) const
+    {
+        std::vector<MlirNamedAttribute> attributes;
+        attributes.reserve(v.size());
+        std::transform(v.begin(), v.end(), std::back_inserter(attributes), [&](const value& x) {
+            return name_attribute(x.get_key(), x.without_key());
+        });
+        return attributes;
+    }
+
+    struct mlir_operation_state
+    {
+        mlir_operation_state(mlir_program& p, const std::string_view& name)
+            : prog(&p), op_state(mlirOperationStateGet(make_mlir_string_ref(name), p.location))
+        {
+        }
+
+        mlir_operation_state& add_attributes(const std::vector<named_attribute_t>& named_attrs)
+        {
+            auto attributes = prog->name_attributes(named_attrs);
+            mlirOperationStateAddAttributes(&op_state, attributes.size(), attributes.data());
+            return *this;
+        }
+
+        mlir_operation_state& add_attribute_value(const value& v)
+        {
+            auto attributes = prog->name_attributes(v);
+            mlirOperationStateAddAttributes(&op_state, attributes.size(), attributes.data());
+            return *this;
+        }
+
+        mlir_operation_state& add_regions(std::vector<mlir_region> rs)
+        {
+            regions = std::move(rs);
+            return *this;
+        }
+
+        mlir_operation_state& add_region(mlir_region r)
+        {
+            regions.emplace_back(std::move(r));
+            return *this;
+        }
+
+        mlir_operation_state& add_results(const std::vector<shape>& outputs)
+        {
+            auto x = prog->make_tensors(outputs);
+            mlirOperationStateAddResults(&op_state, x.size(), x.data());
+            return *this;
+        }
+
+        mlir_operation_state& add_operands(const std::vector<MlirValue>& inputs)
+        {
+            mlirOperationStateAddOperands(&op_state, inputs.size(), inputs.data());
+            return *this;
+        }
+
+        mlir_operation create_operation()
+        {
+            std::vector<MlirRegion> mregions(regions.size());
+            std::transform(regions.begin(), regions.end(), mregions.begin(), [](const auto& r) {
+                return r.get();
+            });
+            mlirOperationStateAddOwnedRegions(&op_state, mregions.size(), mregions.data());
+            mlir_operation op(mlirOperationCreate(&op_state));
+            // Release memory since mlir_operation owns it
+            for(auto& r : regions)
+                r.release();
+            regions.clear();
+            return op;
+        }
+
+        mlir_program* prog;
+        MlirOperationState op_state;
+        std::vector<mlir_region> regions = {};
+    };
+
+    mlir_operation_state create_operation_state(const std::string_view& name)
+    {
+        return {*this, name};
+    }
+
+    std::vector<MlirValue> insert(MlirBlock body, mlir_operation_state ops)
+    {
+        std::vector<MlirValue> result;
+        mlir_operation op = ops.create_operation();
+        auto weak_op      = op.get();
+        mlirBlockAppendOwnedOperation(body, op.release());
+
+        auto n = mlirOperationGetNumResults(weak_op);
+        result.reserve(n);
+        transform(range(n), std::back_inserter(result), [&](auto i) {
+            return mlirOperationGetResult(weak_op, i);
+        });
+        return result;
+    }
+
+    MlirBlock
+    insert(MlirBlock body, const module& m, std::unordered_map<instruction_ref, MlirValue>& ins_map)
+    {
+        auto names = m.get_parameter_names();
+        std::sort(names.begin(), names.end());
+        std::vector<shape> inputs;
+        std::transform(names.begin(),
+                       names.end(),
+                       std::back_inserter(inputs),
+                       [&](const std::string& name) { return m.get_parameter_shape(name); });
+        std::vector<shape> outputs = m.get_output_shapes();
+
+        std::vector<MlirLocation> arg_locs(inputs.size(), location);
+        auto body_inputs   = make_tensors(inputs);
+        mlir_region region = mlirRegionCreate();
+        mlir_block fbody = mlirBlockCreate(body_inputs.size(), body_inputs.data(), arg_locs.data());
+        MlirBlock result = fbody.get();
+        mlirRegionAppendOwnedBlock(region.get(), fbody.release());
+
+        auto ops = create_operation_state("func.func");
+        ops.add_attributes({{"function_type", make_function_type(inputs, outputs)},
+                            {"sym_name", std::string("main")},
+                            {"kernel", std::string("mixr")}});
+        ops.add_region(std::move(region));
+        insert(body, std::move(ops));
+
+        for(auto i : range(names.size()))
+            ins_map[m.get_parameter(names[i])] = mlirBlockGetArgument(result, i);
+        return result;
+    }
+
+    static std::string get_name(instruction_ref ins)
+    {
+        if(ins->name() == "@return")
+            return "func.return";
+        return "migraphx." + ins->name();
+    }
+
+    static value get_operator_value(const operation& op)
+    {
+        auto v = op.to_value();
+        if(op.name() == "convolution")
+        {
+            // Adjust symetrical padding
+            if(v.at("padding").size() == v.at("stride").size())
+            {
+                auto padding = v.at("padding");
+                std::copy(padding.begin(), padding.end(), std::back_inserter(v.at("padding")));
+            }
+        }
+        return v;
+    }
+
+    static shape get_shape(instruction_ref ins)
+    {
+        if(ins->name() == "@return")
+        {
+            assert(ins->inputs().size() == 1);
+            return ins->inputs().front()->get_shape();
+        }
+        return ins->get_shape();
+    }
+
+    void parse(const module& m)
+    {
+        auto mbody = mlirModuleGetBody(mmodule.get());
+        std::unordered_map<instruction_ref, MlirValue> ins_map;
+        auto fbody = insert(mbody, m, ins_map);
+        for(auto ins : iterator_for(m))
+        {
+            if(ins->name() == "@param")
+                continue;
+            auto name = get_name(ins);
+            auto ops  = create_operation_state(name);
+            ops.add_attribute_value(get_operator_value(ins->get_operator()));
+            if(ins->name() != "@return")
+                ops.add_results({get_shape(ins)});
+
+            std::vector<MlirValue> inputs;
+            transform(
+                ins->inputs(), std::back_inserter(inputs), [&](auto i) { return ins_map.at(i); });
+            ops.add_operands(inputs);
+
+            auto outputs = insert(fbody, std::move(ops));
+            if(ins->name() != "@return")
+            {
+                assert(outputs.size() == 1);
+                ins_map[ins] = outputs.front();
+            }
+        }
+    }
+
+    code_object_op compile() MIGRAPHX_TIDY_CONST
+    {
+        mlir_pass_manager pm{mlirPassManagerCreate(ctx.get())};
+        // 1st pipeline to call
+        mlirMIGraphXAddHighLevelPipeline(pm.get());
+        // 2nd pipeline to call
+        std::string tname = get_device_name();
+        // HACK: Since MLIR can't handle the full target name
+        auto hacked_tname = tname.substr(0, tname.find(':'));
+        if(tname.size() != hacked_tname.size())
+            std::cout
+                << "*************** WARNING: MLIR may not compile the correct target features for: "
+                << tname << std::endl;
+        mlirMIGraphXAddBackendPipeline(pm.get(), hacked_tname.c_str(), "amdgcn-amd-amdhsa", "");
+        mlirPassManagerRun(pm.get(), mmodule.get());
+
+        code_object_op op{};
+        op.symbol_name                = "main";
+        op.code_object                = get_binary();
+        std::tie(op.global, op.local) = get_launch_params();
+        return op;
+    }
+
+    std::pair<std::size_t, std::size_t> get_launch_params() const
+    {
+        uint32_t attrs[2];
+        // returns block and grid sizes
+        mlirGetKernelAttrs(mmodule.get(), attrs);
+        std::size_t local  = attrs[0];
+        std::size_t global = local * attrs[1];
+        return {global, local};
+    }
+
+    value::binary get_binary() const
+    {
+        int size = 0;
+        mlirGetBinary(mmodule.get(), &size, nullptr);
+        value::binary result(size);
+        if(mlirGetBinary(mmodule.get(), &size, reinterpret_cast<char*>(result.data())))
+            return result;
+        MIGRAPHX_THROW("Failed to compile mlir program");
+    }
+
+    mlir_context ctx;
+    MlirLocation location;
+    mlir_module mmodule;
+    std::deque<std::string> strings{};
+};
+
+std::string dump_mlir(const module& m)
+{
+    mlir_program mp;
+    mp.parse(m);
+    auto mod_op = mlirModuleGetOperation(mp.mmodule.get());
+    return mlir_print(&mlirOperationPrint, mod_op);
+}
+
+code_object_op compile_mlir(const context&, const module& m)
+{
+    const bool trace = enabled(MIGRAPHX_TRACE_MLIR{});
+    if(trace)
+        std::cout << m << std::endl;
+    mlir_program mp;
+    mp.parse(m);
+    auto mod_op = mlirModuleGetOperation(mp.mmodule.get());
+    if(trace)
+        std::cout << mlir_print(&mlirOperationPrint, mod_op) << std::endl;
+    auto co   = mp.compile();
+    co.output = m.get_output_shapes().front();
+    return co;
+}
+
+instruction_ref insert_mlir(module& m,
+                            instruction_ref ins,
+                            code_object_op co,
+                            const std::vector<instruction_ref>& inputs)
+{
+    std::vector<instruction_ref> refs;
+    refs.reserve(inputs.size() * 15);
+
+    std::unordered_map<uint64_t, instruction_ref> literal_map{};
+    auto get_literal = [&](uint64_t value) {
+        auto fi = literal_map.find(value);
+        if(fi != literal_map.end())
+            return fi->second;
+        auto lit = m.add_literal(value);
+        literal_map.emplace(value, lit);
+        return lit;
+    };
+
+    std::size_t last = 0;
+    for(auto input : inputs)
+    {
+        const size_t offset = 0;
+        auto s              = input->get_shape();
+        last                = refs.size();
+        refs.push_back(input);
+        refs.push_back(input);
+        refs.push_back(get_literal(offset)); // offset
+
+        // dim sizes
+        std::transform(s.lens().begin(),
+                       s.lens().end(),
+                       std::back_inserter(refs),
+                       [&](const auto& lval) { return get_literal(lval); });
+        // refs.push_back(get_literal(1)); // G
+
+        // dim strides
+        std::transform(s.strides().begin(),
+                       s.strides().end(),
+                       std::back_inserter(refs),
+                       [&](const auto& lval) { return get_literal(lval); });
+        // refs.push_back(get_literal(1)); // G
+    }
+    co.expected_inputs = to_shapes(refs);
+    co.output_arg      = last;
+    return m.insert_instruction(ins, co, refs);
+}
+
+#else
+
+std::string dump_mlir(const module&) { return {}; }
+
+code_object_op compile_mlir(const context&, const module&) { return {}; }
+
+template <class T>
+void use(T&)
+{
+}
+
+instruction_ref
+// cppcheck-suppress funcArgNamesDifferent
+insert_mlir(module& m, instruction_ref, code_object_op co, const std::vector<instruction_ref>&)
+{
+    use(co);
+    return m.end();
+}
+
+#endif
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/mlir_conv.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/mlir_conv.hpp>
-#include <migraphx/manage_ptr.hpp>
-#include <migraphx/instruction.hpp>
-#include <migraphx/make_op.hpp>
-
-#include <migraphx/op/convolution.hpp>
-
-#include <migraphx/gpu/context.hpp>
-#include <migraphx/gpu/convolution.hpp>
-#include <migraphx/iterator_for.hpp>
-#include <migraphx/program.hpp>
-
-#include <migraphx/ranges.hpp>
-#include <migraphx/make_op.hpp>
-#include <migraphx/generate.hpp>
-#include <migraphx/program.hpp>
-#include <migraphx/gpu/kernel.hpp>
-#include <migraphx/gpu/target.hpp>
-#include <migraphx/gpu/hip.hpp>
-#include <migraphx/gpu/compile_hip.hpp>
-
-#include <utility>
-#include <functional>
-#include <algorithm>
-
-#ifdef MIGRAPHX_MLIR_MIOPEN_SUPPORT
-#include <Miir.h>
-#endif // MIGRAPHX_MLIR_MIOPEN_SUPPORT
-
-#include <cstdio>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-struct mlir_apply
-{
-    module* mod           = nullptr;
-    const mlir_conv* pass = nullptr;
-
-    const char* mlir_kernel_name = "migraphx_conv2d";
-
-    std::unordered_map<uint64_t, instruction_ref> literal_map{};
-
-    struct execution_spec
-    {
-        migraphx::value::binary binary;
-        size_t global_size;
-        size_t local_size;
-        execution_spec(migraphx::value::binary&& binary_m, size_t global_s, size_t local_s)
-            : binary(std::move(binary_m)), global_size(global_s), local_size(local_s)
-        {
-        }
-    };
-
-    std::unordered_map<std::string, std::shared_ptr<execution_spec>> binary_map{};
-
-    context& get_context() const
-    {
-        assert(pass != nullptr);
-        assert(pass->ctx != nullptr);
-        return *pass->ctx;
-    }
-
-    void init() const
-    {
-        assert(mod != nullptr);
-        assert(pass != nullptr);
-    }
-
-    std::shared_ptr<execution_spec> make_mlir_binary(instruction_ref op_r)
-    {
-        std::shared_ptr<execution_spec> result;
-
-#ifdef MIGRAPHX_MLIR_MIOPEN_SUPPORT
-        auto conv  = any_cast<op::convolution>(op_r->get_operator());
-        auto inp_t = op_r->inputs().at(0)->get_shape();
-        auto flt_t = op_r->inputs().at(1)->get_shape();
-        auto out_t = op_r->get_shape();
-
-        auto get_type_str = [](const shape& s) -> const char* {
-            switch(s.type())
-            {
-            case shape::float_type: return "f32";
-            case shape::half_type: return "f16";
-            case shape::bool_type:
-            case shape::double_type:
-            case shape::uint8_type:
-            case shape::int8_type:
-            case shape::uint16_type:
-            case shape::int16_type:
-            case shape::int32_type:
-            case shape::int64_type:
-            case shape::uint32_type:
-            case shape::uint64_type:
-            case shape::tuple_type: break;
-            }
-            return nullptr;
-        };
-
-        const auto* inp_t_s = get_type_str(inp_t);
-        const auto* flt_t_s = get_type_str(flt_t);
-        const auto* out_t_s = get_type_str(out_t);
-
-        if(out_t_s == nullptr || inp_t_s == nullptr || flt_t_s == nullptr)
-            return result;
-
-        std::string mlir_options = "--kernel_name " + std::string(mlir_kernel_name);
-
-        // platform spec
-        auto& device = get_context().get_current_device();
-        char dev_name[64];
-        sprintf(dev_name, "gfx%lu%02lu", device.get_device_major(), device.get_device_minor());
-        mlir_options += " --arch " + std::string(dev_name) + " --num_cu " +
-                        std::to_string(device.get_cu_count()); // ???
-
-        // Conv spec
-        mlir_options +=
-            " --operation "
-            "conv2d"
-            " --batchsize " +
-            std::to_string(conv.group) + " --groupsize " + std::to_string(1) + " --padding_h " +
-            std::to_string(conv.padding[0]) + " --padding_w " + std::to_string(conv.padding[1]) +
-            " --conv_stride_h " + std::to_string(conv.stride[0]) + " --conv_stride_w " +
-            std::to_string(conv.stride[1]) + " --dilation_h " + std::to_string(conv.dilation[0]) +
-            " --dilation_w " + std::to_string(conv.dilation[1]);
-
-        // Input spec
-        mlir_options += " --in_layout "
-                        "NCHWG"
-                        " --in_type " +
-                        std::string(inp_t_s) + " --in_channels " + std::to_string(inp_t.lens()[1]) +
-                        " --in_h " + std::to_string(inp_t.lens()[2]) + " --in_w " +
-                        std::to_string(inp_t.lens()[3]);
-
-        // Filter spec
-        mlir_options += " --fil_layout "
-                        "NCHWG"
-                        " --fil_type " +
-                        std::string(flt_t_s) + " --fil_h " + std::to_string(flt_t.lens()[2]) +
-                        " --fil_w " + std::to_string(flt_t.lens()[3]);
-
-        // Output spec
-        mlir_options += " --out_layout "
-                        "NCHWG"
-                        " --out_type " +
-                        std::string(out_t_s) + " --out_channels " +
-                        std::to_string(out_t.lens()[1]) + " --out_h " +
-                        std::to_string(out_t.lens()[2]) + " --out_w " +
-                        std::to_string(out_t.lens()[3]);
-
-        auto bin_i = binary_map.find(mlir_options);
-        if(bin_i == binary_map.end())
-        {
-            size_t bin_size = 0;
-
-            using mlir_handle = MIGRAPHX_MANAGE_PTR(MiirHandle, miirDestroyHandle);
-            auto handle       = mlir_handle(miirCreateHandle(mlir_options.c_str()));
-
-            if(miirLowerBin(handle.get()) == MIIR_SUCCESS &&
-               miirBufferGet(handle.get(), nullptr, &bin_size) == MIIR_SUCCESS)
-            {
-                migraphx::value::binary bin(bin_size);
-                if(miirBufferGet(handle.get(), reinterpret_cast<char*>(bin.data()), &bin_size) ==
-                   MIIR_SUCCESS)
-                {
-                    size_t global_size;
-                    size_t block_size;
-                    if(miirGetExecutionDims(handle.get(), &global_size, &block_size) ==
-                       MIIR_SUCCESS)
-                    {
-                        result = std::make_shared<execution_spec>(
-                            std::move(bin), global_size, block_size);
-                    }
-                }
-            }
-
-            binary_map[mlir_options] = result;
-        }
-        else
-        {
-            result = bin_i->second;
-        }
-#else  // MIGRAPHX_MLIR_MIOPEN_SUPPORT
-        (void)op_r;
-#endif // MIGRAPHX_MLIR_MIOPEN_SUPPORT
-        return result;
-    }
-
-    instruction_ref get_literal(uint64_t value)
-    {
-        auto fi = literal_map.find(value);
-        if(fi != literal_map.end())
-            return fi->second;
-        auto lit = mod->add_literal(value);
-        literal_map.emplace(value, lit);
-        return lit;
-    }
-
-    operation make_code_object_op(instruction_ref op_r, const std::shared_ptr<execution_spec>& spec)
-    {
-        // each pointer is expanded out to a MemRefDescriptor
-        auto inp_t = op_r->inputs().at(0)->get_shape();
-        auto flt_t = op_r->inputs().at(1)->get_shape();
-        auto out_t = op_r->get_shape();
-
-        auto i64 = shape(shape::uint64_type);
-
-        std::vector<shape> expected_inputs = {
-            flt_t, flt_t, i64, i64, i64, i64, i64, i64, i64, i64, i64, i64,  i64,   inp_t,
-            inp_t, i64,   i64, i64, i64, i64, i64, i64, i64, i64, i64, i64,  out_t, out_t,
-            i64,   i64,   i64, i64, i64, i64, i64, i64, i64, i64, i64, out_t};
-
-        return migraphx::make_op("gpu::code_object",
-                                 {
-                                     {"code_object", spec->binary},
-                                     {"symbol_name", mlir_kernel_name},
-                                     {"global", spec->global_size},
-                                     {"local", spec->local_size},
-                                     {"expected_inputs", migraphx::to_value(expected_inputs)},
-                                     {"output", migraphx::to_value(out_t)},
-                                 });
-    }
-
-    void add_memref_descriptor(std::vector<instruction_ref>& refs, instruction_ref inst)
-    {
-        const size_t offset = 0;
-        auto inst_t         = inst->get_shape();
-        refs.push_back(inst);
-        refs.push_back(inst);
-        refs.push_back(get_literal(offset)); // offset
-
-        // dim sizes
-        std::transform(inst_t.lens().begin(),
-                       inst_t.lens().end(),
-                       std::back_inserter(refs),
-                       [&](const auto& lval) { return get_literal(lval); });
-        refs.push_back(get_literal(1)); // G
-
-        // dim strides
-        std::transform(inst_t.strides().begin(),
-                       inst_t.strides().end(),
-                       std::back_inserter(refs),
-                       [&](const auto& lval) { return get_literal(lval); });
-        refs.push_back(get_literal(1)); // G
-    }
-
-    instruction_ref insert_allocation(instruction_ref ins, const shape& s) const
-    {
-        return mod->insert_instruction(ins, hip_allocate{s});
-    }
-
-    void replace_conv_op(instruction_ref ins)
-    {
-        auto conv_bin = make_mlir_binary(ins);
-        if(conv_bin)
-        {
-            auto conv = make_code_object_op(ins, conv_bin);
-
-            auto inp = ins->inputs().at(0);
-            auto flt = ins->inputs().at(1);
-            auto out = insert_allocation(ins, ins->get_shape());
-
-            std::vector<instruction_ref> refs;
-            refs.reserve(3 * 13 + 1);
-            add_memref_descriptor(refs, flt);
-            add_memref_descriptor(refs, inp);
-            add_memref_descriptor(refs, out);
-            refs.push_back(out);
-
-            mod->replace_instruction(ins, conv, refs);
-        }
-    }
-
-    void apply()
-    {
-        init();
-        for(auto it : iterator_for(*mod))
-        {
-            if(it->name() == "convolution")
-            {
-                replace_conv_op(it);
-            }
-        }
-    }
-};
-
-void mlir_conv::apply(module& m) const { mlir_apply{&m, this}.apply(); }
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx

src/targets/gpu/quant_convolution.cpp

@@ -67,9 +67,9 @@ argument miopen_quant_convolution::compute(context& ctx,
     return args[3];
 }
 
-shape miopen_quant_convolution::compile(context& ctx,
-                                        const shape& output_shape,
-                                        std::vector<shape> inputs)
+shape miopen_quant_convolution::find(context& ctx,
+                                     const shape& output_shape,
+                                     std::vector<shape> inputs)
 {
     shape workspace_shape{};
     auto x_desc = make_tensor(inputs[0], int8_x4_format);
@@ -92,18 +92,18 @@ shape miopen_quant_convolution::compile(context& ctx,
         x_shape = pack_int8_shape(x_shape);
         w_shape = pack_int8_shape(w_shape);
     }
-    auto arg_vec4_x = to_gpu(generate_argument(x_shape));
-    auto arg_vec4_w = to_gpu(generate_argument(w_shape));
-    auto y          = allocate_gpu(output_shape);
-    auto workspace  = allocate_gpu(workspace_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;
     auto status = miopenFindConvolutionForwardAlgorithm(ctx.get_stream().get_miopen(),
                                                         x_desc.get(),
-                                                        arg_vec4_x.implicit(),
+                                                        x.implicit(),
                                                         w_desc.get(),
-                                                        arg_vec4_w.implicit(),
+                                                        w.implicit(),
                                                         cd.get(),
                                                         y_desc.get(),
                                                         y.implicit(),
@@ -114,11 +114,35 @@ shape miopen_quant_convolution::compile(context& ctx,
                                                         workspace_size,
                                                         false);
     if(status != miopenStatusSuccess)
-    {
-        MIGRAPHX_THROW("QUANT_CONVOLUTION: find convolution failed");
-    }
-    handle = ctx.get_stream().get_miopen();
-    algo   = perf.fwd_algo;
+        MIGRAPHX_THROW("MIOpen Quant 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 Quant 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 Quant Convolution: get solution failed");
+
+    solution_id = solutions.front().solution_id;
+
     return shape{shape::int8_type, {perf.memory}};
 }
 
@@ -126,13 +150,29 @@ void miopen_quant_convolution::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_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 Quant Convolution: workspace has changed during finalization.");
+    }
+
+    auto x_desc = make_tensor(inputs[0], int8_x4_format);
+    auto w_desc = make_tensor(inputs[1], int8_x4_format);
+    auto y_desc = make_tensor(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 Quant Convolution: compile solution failed");
 }
 
 shape miopen_quant_convolution::pack_int8_shape(const shape& s) const

src/targets/gpu/target.cpp

@@ -53,10 +53,11 @@
 #include <migraphx/gpu/compile_ops.hpp>
 #include <migraphx/gpu/concat_gpu_opt.hpp>
 #include <migraphx/gpu/context.hpp>
+#include <migraphx/gpu/device_name.hpp>
+#include <migraphx/gpu/fuse_mlir.hpp>
 #include <migraphx/gpu/fuse_ops.hpp>
 #include <migraphx/gpu/prefuse_ops.hpp>
 #include <migraphx/gpu/lowering.hpp>
-#include <migraphx/gpu/mlir_conv.hpp>
 #include <migraphx/gpu/pack_int8_args.hpp>
 #include <migraphx/gpu/schedule_model.hpp>
 #include <migraphx/gpu/sync_device.hpp>
@@ -128,7 +129,8 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
         dead_code_elimination{},
         enable_pass(not enabled(MIGRAPHX_DISABLE_POINTWISE_FUSION{}), fuse_pointwise{}),
         dead_code_elimination{},
-        mlir_conv{&ctx},
+        fuse_mlir{&ctx},
+        dead_code_elimination{},
         lowering{&ctx, options.offload_copy},
         eliminate_contiguous{"gpu::contiguous"},
         dead_code_elimination{},
@@ -161,7 +163,7 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
 
 std::string target::name() const { return "gpu"; }
 
-migraphx::context target::get_context() const { return context{}; }
+migraphx::context target::get_context() const { return context(gpu::get_device_id()); }
 
 argument target::copy_to(const argument& arg) const { return gpu::to_gpu(arg); }
 

src/tf/tf_parser.cpp

@@ -216,7 +216,7 @@ static std::vector<T> get_data_vals(const google::protobuf::RepeatedField<T>& da
         std::fill(data_vals.begin(), data_vals.end(), data[0]);
     }
     else
-        copy(data.begin(), data.end(), std::back_inserter(data_vals));
+        copy(data.begin(), data.end(), data_vals.begin());
     return data_vals;
 }
 
@@ -329,33 +329,37 @@ void tf_parser::parse_node(const std::string& name)
         auto&& node = nodes.at(name);
         if(not is_valid_op(node))
             return;
-
         std::vector<instruction_ref> args;
-
         for(auto&& input : node.input())
         {
             // control dependencies (signified by ^ before the name) are ignored
             if(contains(input, "^"))
                 continue;
-            if(nodes.count(input) > 0)
+            std::string input_name = input;
+            // if input has trailing `:0` index then remove it
+            auto multi_out_idx = input.find(':');
+            if(multi_out_idx != std::string::npos && input.substr(multi_out_idx + 1) == "0")
+            {
+                input_name = input.substr(0, multi_out_idx);
+            }
+            if(nodes.count(input_name) > 0)
             {
-                std::string iname;
                 // input was from a node with multiple outputs
-                if(contains(input, ':'))
+                if(contains(input_name, ':'))
                 {
-                    iname = input.substr(0, input.find(':'));
+                    input_name = input_name.substr(0, input.find(':'));
                 }
                 else
                 {
-                    iname = get_name(nodes.at(input));
+                    input_name = get_name(nodes.at(input_name));
                 }
-                assert(name != iname);
-                this->parse_node(iname);
-                args.push_back(instructions.at(input));
+                assert(name != input_name);
+                this->parse_node(input_name);
+                args.push_back(instructions.at(input_name));
             }
             else
             {
-                args.push_back(instructions.at(input));
+                args.push_back(instructions.at(input_name));
             }
         }
         std::vector<instruction_ref> result;

test/CMakeLists.txt

@@ -137,6 +137,22 @@ if(MIGRAPHX_ENABLE_GPU)
     endforeach()
 endif()
 
+if(MIGRAPHX_ENABLE_FPGA)
+    # fpga tests
+    file(GLOB FPGA_TESTS ${CONFIGURE_DEPENDS} fpga/*.cpp)
+
+    foreach(TEST ${FPGA_TESTS})
+        get_filename_component(BASE_NAME ${TEST} NAME_WE)
+        add_test_executable(test_fpga_${BASE_NAME} ${TEST})
+        rocm_clang_tidy_check(test_fpga_${BASE_NAME})
+        set_tests_properties(test_fpga_${BASE_NAME} PROPERTIES 
+            COST 10 
+            RESOURCE_LOCK fpga
+        )
+        target_link_libraries(test_fpga_${BASE_NAME} migraphx_fpga)
+    endforeach()
+endif()
+
 # Onnx test
 set(TEST_ONNX_DIR ${CMAKE_CURRENT_SOURCE_DIR}/onnx)
 file (GLOB ONNX_TESTS ${TEST_ONNX_DIR}/*.cpp)