Merge branch 'develop' into fastsoftmax

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

@@ -27,7 +27,7 @@
 #include <migraphx/argument.hpp>
 #include <migraphx/reflect.hpp>
 #include <migraphx/op/reverse.hpp>
-#include <migraphx/gpu/miopen.hpp>
+#include <migraphx/gpu/context.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {

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

@@ -24,22 +24,10 @@
 #ifndef MIGRAPHX_GUARD_RTGLIB_SOFTMAX_HPP
 #define MIGRAPHX_GUARD_RTGLIB_SOFTMAX_HPP
 
-#include <migraphx/gpu/lowering.hpp>
-#include <migraphx/manage_ptr.hpp>
-#include <migraphx/instruction.hpp>
 #include <migraphx/op/softmax.hpp>
-#include <migraphx/generate.hpp>
-#include <migraphx/shape_for_each.hpp>
-#include <migraphx/config.hpp>
-#include <migraphx/gpu/miopen.hpp>
-#include <migraphx/gpu/hip.hpp>
-#include <migraphx/dfor.hpp>
-#include <migraphx/gpu/device/contiguous.hpp>
-#include <migraphx/gpu/device/add.hpp>
-#include <migraphx/iterator_for.hpp>
-#include <migraphx/gpu/rocblas.hpp>
+#include <migraphx/shape.hpp>
+#include <migraphx/reflect.hpp>
 #include <migraphx/gpu/context.hpp>
-#include <utility>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {

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

@@ -25,8 +25,6 @@
 #define MIGRAPHX_GUARD_RTGLIB_GPU_SYNC_DEVICE_HPP
 
 #include <string>
-#include <migraphx/instruction_ref.hpp>
-#include <migraphx/gpu/context.hpp>
 #include <migraphx/config.hpp>
 
 namespace migraphx {

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

@@ -27,7 +27,7 @@
 #include <migraphx/argument.hpp>
 #include <migraphx/reflect.hpp>
 #include <migraphx/op/topk.hpp>
-#include <migraphx/gpu/miopen.hpp>
+#include <migraphx/gpu/context.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {

src/targets/gpu/jit/concat.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/reduce_dims.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+using namespace migraphx::gpu::gen; // NOLINT
+
+// NOLINTNEXTLINE
+static const char* const concat_kernel = R"__migraphx__(
+#include <migraphx/kernels/concat.hpp>
+#include <migraphx/kernels/vectorize.hpp>
+#include <args.hpp>
+
+namespace migraphx {
+
+extern "C" {
+
+__global__ void ${kernel}(${params}) 
+{
+    transform_args(make_tensors(), rotate_last(), ${transformers})(${args})([](auto y, auto... xs) {
+        concat<${axis}>(y, xs...);
+    });
+}
+
+}
+
+} // namespace migraphx
+
+)__migraphx__";
+
+struct concat_compiler : compiler<concat_compiler>
+{
+    std::vector<std::string> names() const { return {"concat"}; }
+
+    static std::size_t get_concat_elements(const std::vector<shape>& inputs)
+    {
+        return inputs.back().elements() / (inputs.size() - 1);
+    }
+
+    operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
+    {
+        // TODO: Use reduce_dims
+        hip_compile_options options;
+        options.inputs      = inputs;
+        options.output      = inputs.back();
+        options.params      = "-Wno-float-equal";
+        auto axis           = find_fast_axis(options.inputs);
+        auto vec            = vectorize::elements(axis, options.inputs);
+        options.kernel_name = v.get("kernel", "concat_kernel");
+        options.set_launch_params(
+            v, compute_global_for(ctx, get_concat_elements(options.inputs) / vec.size, 256));
+        auto src = interpolate_string(concat_kernel,
+                                      {{"kernel", options.kernel_name},
+                                       {"params", enum_params(inputs.size(), "void * private_p")},
+                                       {"args", enum_params(inputs.size(), "private_p")},
+                                       {"transformers", make_transformer_args(vec)},
+                                       {"axis", v.at("axis").to<std::string>()}});
+        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/jit/gathernd.cpp

@@ -27,13 +27,6 @@
 
 #include <migraphx/gpu/compile_hip_code_object.hpp>
 #include <migraphx/gpu/compile_hip.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 {

src/targets/gpu/jit/layernorm.cpp

@@ -27,8 +27,6 @@
 #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>
 

src/targets/gpu/jit/pointwise.cpp

@@ -26,16 +26,7 @@
 #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/permutation.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 {

src/targets/gpu/jit/reduce.cpp

@@ -26,15 +26,7 @@
 #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 {

src/targets/gpu/jit/roialign.cpp

@@ -24,16 +24,7 @@
 #include <migraphx/gpu/compiler.hpp>
 #include <migraphx/gpu/compile_hip_code_object.hpp>
 #include <migraphx/gpu/context.hpp>
-
 #include <migraphx/gpu/compile_hip.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 {

src/targets/gpu/jit/scatternd.cpp

@@ -24,16 +24,8 @@
 #include <migraphx/gpu/compiler.hpp>
 #include <migraphx/make_op.hpp>
 #include <migraphx/gpu/context.hpp>
-
 #include <migraphx/gpu/compile_hip_code_object.hpp>
 #include <migraphx/gpu/compile_hip.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 {

src/targets/gpu/jit/softmax.cpp

@@ -26,15 +26,7 @@
 #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 {

src/targets/gpu/kernel.cpp

@@ -80,7 +80,9 @@ void launch_kernel(hipFunction_t fun,
                    std::size_t global,
                    std::size_t local,
                    void* kernargs,
-                   std::size_t size)
+                   std::size_t size,
+                   hipEvent_t start,
+                   hipEvent_t stop)
 {
     assert(global > 0);
     assert(local > 0);
@@ -97,34 +99,55 @@ void launch_kernel(hipFunction_t fun,
 #endif
     };
 
-    auto status = hipExtModuleLaunchKernel(
-        fun, global, 1, 1, local, 1, 1, 0, stream, nullptr, reinterpret_cast<void**>(&config));
+    auto status = hipExtModuleLaunchKernel(fun,
+                                           global,
+                                           1,
+                                           1,
+                                           local,
+                                           1,
+                                           1,
+                                           0,
+                                           stream,
+                                           nullptr,
+                                           reinterpret_cast<void**>(&config),
+                                           start,
+                                           stop);
     if(status != hipSuccess)
         MIGRAPHX_THROW("Failed to launch kernel: " + hip_error(status));
+    if(stop != nullptr)
+    {
+        status = hipEventSynchronize(stop);
+        if(status != hipSuccess)
+            MIGRAPHX_THROW("Failed to sync event: " + hip_error(status));
+    }
 }
 
 void kernel::launch(hipStream_t stream,
                     std::size_t global,
                     std::size_t local,
-                    std::vector<void*> args) const
+                    std::vector<void*> args,
+                    hipEvent_t start,
+                    hipEvent_t stop) const
 {
     assert(impl != nullptr);
     void* kernargs   = args.data();
     std::size_t size = args.size() * sizeof(void*);
 
-    launch_kernel(impl->fun, stream, global, local, kernargs, size);
+    launch_kernel(impl->fun, stream, global, local, kernargs, size, start, stop);
 }
 
 void kernel::launch(hipStream_t stream,
                     std::size_t global,
                     std::size_t local,
-                    const std::vector<kernel_argument>& args) const
+                    const std::vector<kernel_argument>& args,
+                    hipEvent_t start,
+                    hipEvent_t stop) const
 {
     assert(impl != nullptr);
     std::vector<char> kernargs = pack_args(args);
     std::size_t size           = kernargs.size();
 
-    launch_kernel(impl->fun, stream, global, local, kernargs.data(), size);
+    launch_kernel(impl->fun, stream, global, local, kernargs.data(), size, start, stop);
 }
 
 } // namespace gpu

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

@@ -163,7 +163,7 @@ constexpr Iterator1 search(Iterator1 first, Iterator1 last, Iterator2 s_first, I
             {
                 return last;
             }
-            if(!(*it == *s_it))
+            if(not(*it == *s_it))
             {
                 break;
             }

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

@@ -33,49 +33,95 @@
 namespace migraphx {
 
 // NOLINTNEXTLINE
-#define MIGRAPHX_DEVICE_ARRAY_OP(op, binary_op)                                    \
-    template <class U>                                                             \
-    constexpr array& operator op(const array<U, N>& x)                             \
-    {                                                                              \
-        for(index_int i = 0; i < N; i++)                                           \
-            d[i] op x[i];                                                          \
-        return *this;                                                              \
-    }                                                                              \
-    template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})>                  \
-    constexpr array& operator op(const U& x)                                       \
-    {                                                                              \
-        for(index_int i = 0; i < N; i++)                                           \
-            d[i] op x;                                                             \
-        return *this;                                                              \
-    }                                                                              \
-    template <class U>                                                             \
-    friend constexpr auto operator binary_op(const array& x, const array<U, N>& y) \
-    {                                                                              \
-        array<decltype(T {} binary_op U{}), N> z{};                                \
-        for(index_int i = 0; i < N; i++)                                           \
-            z[i] = x[i] binary_op y[i];                                            \
-        return z;                                                                  \
-    }                                                                              \
-    template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})>                  \
-    friend constexpr auto operator binary_op(const array& x, const U& y)           \
-    {                                                                              \
-        array<decltype(T {} binary_op U{}), N> z{};                                \
-        for(index_int i = 0; i < N; i++)                                           \
-            z[i] = x[i] binary_op y;                                               \
-        return z;                                                                  \
-    }                                                                              \
-    template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})>                  \
-    friend constexpr auto operator binary_op(const U& x, const array& y)           \
-    {                                                                              \
-        array<decltype(T {} binary_op U{}), N> z{};                                \
-        for(index_int i = 0; i < N; i++)                                           \
-            z[i] = x binary_op y[i];                                               \
-        return z;                                                                  \
+#define MIGRAPHX_DEVICE_ARRAY_OP(op, binary_op)                                              \
+    template <class U>                                                                       \
+    constexpr array& operator op(const array<U, N>& x)                                       \
+    {                                                                                        \
+        array_detail::array_for_each(*this, x)([](auto& sy, auto sx) { sy op sx; });         \
+        return *this;                                                                        \
+    }                                                                                        \
+    template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})>                            \
+    constexpr array& operator op(const U& x)                                                 \
+    {                                                                                        \
+        array_detail::array_for_each (*this)([&](auto& sy) { sy op x; });                    \
+        return *this;                                                                        \
+    }                                                                                        \
+    template <class U>                                                                       \
+    friend constexpr auto operator binary_op(const array& x, const array<U, N>& y)           \
+    {                                                                                        \
+        array<decltype(T {} binary_op U{}), N> z{};                                          \
+        array_detail::array_for_each(z, x, y)(                                               \
+            [&](auto& sz, auto sx, auto sy) { sz = sx binary_op sy; });                      \
+        return z;                                                                            \
+    }                                                                                        \
+    template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})>                            \
+    friend constexpr auto operator binary_op(const array& x, const U& y)                     \
+    {                                                                                        \
+        array<decltype(T {} binary_op U{}), N> z{};                                          \
+        array_detail::array_for_each(z, x)([&](auto& sz, auto sx) { sz = sx binary_op y; }); \
+        return z;                                                                            \
+    }                                                                                        \
+    template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})>                            \
+    friend constexpr auto operator binary_op(const U& x, const array& y)                     \
+    {                                                                                        \
+        array<decltype(T {} binary_op U{}), N> z{};                                          \
+        array_detail::array_for_each(z, y)([&](auto& sz, auto sy) { sz = x binary_op sy; }); \
+        return z;                                                                            \
     }
 
+namespace array_detail {
+template <class T>
+constexpr auto is_vectorizable()
+{
+    return not is_same<T, bool>{} and (is_fundamental<T>{} or is_same<T, half>{});
+}
+
+template <class T>
+__device__ auto& array2vec(T& x)
+{
+    using value_type    = typename T::value_type;
+    constexpr auto size = decltype(x.size()){};
+    using type          = vec<value_type, size>;
+    if constexpr(is_const<T>{})
+        return reinterpret_cast<const type&>(x);
+    else
+        return reinterpret_cast<type&>(x);
+}
+
+template <class T, class... Ts>
+constexpr auto array_for_each(T& x, Ts&... xs)
+{
+    MIGRAPHX_ASSERT(((x.size() == xs.size()) and ...));
+    return [&](auto f) {
+        constexpr auto size = decltype(x.size()){};
+        if constexpr((is_vectorizable<typename T::value_type>() or
+                      (is_vectorizable<typename Ts::value_type>() or ...)) and
+                     size <= 8 and size > 1 and (size % 2 == 0))
+        {
+            if(__builtin_is_constant_evaluated())
+            {
+                for(index_int i = 0; i < size; i++)
+                    f(x[i], xs[i]...);
+            }
+            else
+            {
+                using vec_type = std::remove_reference_t<decltype(array2vec(x))>;
+                f(array2vec(x), __builtin_convertvector(array2vec(xs), vec_type)...);
+            }
+        }
+        else
+        {
+            for(index_int i = 0; i < size; i++)
+                f(x[i], xs[i]...);
+        }
+    };
+}
+} // namespace array_detail
+
 template <class T, index_int N>
 struct array
 {
+    using value_type = T;
     T d[N];
     constexpr T& operator[](index_int i)
     {
@@ -108,18 +154,13 @@ struct array
 
     constexpr T dot(const array& x) const
     {
-        T result = 0;
-        for(index_int i = 0; i < N; i++)
-            result += x[i] * d[i];
-        return result;
+        auto r = x * (*this);
+        return r.reduce([](auto a, auto b) { return a + b; }, 0);
     }
 
     constexpr T product() const
     {
-        T result = 1;
-        for(index_int i = 0; i < N; i++)
-            result *= d[i];
-        return result;
+        return reduce([](auto x, auto y) { return x * y; }, 1);
     }
 
     constexpr T single(index_int width = 100) const
@@ -134,6 +175,24 @@ struct array
         return result;
     }
 
+    template <class F>
+    constexpr auto apply(F f) const
+    {
+        array<decltype(f(d[0])), N> result;
+        for(index_int i = 0; i < N; i++)
+            result[i] = f(d[i]);
+        return result;
+    }
+
+    template <class F>
+    constexpr auto reduce(F f, T init) const
+    {
+        T result = init;
+        for(index_int i = 0; i < N; i++)
+            result = f(result, d[i]);
+        return result;
+    }
+
     MIGRAPHX_DEVICE_ARRAY_OP(+=, +)
     MIGRAPHX_DEVICE_ARRAY_OP(-=, -)
     MIGRAPHX_DEVICE_ARRAY_OP(*=, *)
@@ -153,7 +212,7 @@ struct array
         return true;
     }
 
-    friend constexpr bool operator!=(const array& x, const array& y) { return !(x == y); }
+    friend constexpr bool operator!=(const array& x, const array& y) { return not(x == y); }
     // This uses the product order rather than lexical order
     friend constexpr bool operator<(const array& x, const array& y)
     {
@@ -201,6 +260,11 @@ struct array
     }
 };
 
+template <class T, class... Ts>
+constexpr array<T, sizeof...(Ts) + 1> make_array(T x, Ts... xs)
+{
+    return {x, static_cast<T>(xs)...};
+}
 template <class T, T... Xs>
 struct integral_const_array : array<T, sizeof...(Xs)>
 {

src/targets/gpu/kernels/include/migraphx/kernels/concat.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.
+ */
+
+#include <migraphx/kernels/index.hpp>
+#include <migraphx/kernels/functional.hpp>
+#include <migraphx/kernels/tensor_view.hpp>
+
+#ifndef MIGRAPHX_GUARD_KERNELS_CONCAT_HPP
+#define MIGRAPHX_GUARD_KERNELS_CONCAT_HPP
+
+namespace migraphx {
+
+template <index_int Axis, class Output, class Input, class Start>
+constexpr auto concat_slice(Output out, Input, Start)
+{
+    constexpr auto lens    = get_shape_c<Input>{}.lens;
+    constexpr auto strides = get_shape_c<Output>{}.strides;
+    constexpr auto offset  = return_c([] {
+        constexpr auto output_shape = get_shape_c<Output>{};
+        return Start{} * output_shape.strides[Axis];
+    });
+    constexpr auto s       = make_shape(lens, strides);
+    return make_tensor_view(&out[offset], s);
+}
+
+template <index_int Axis, class Input>
+constexpr auto concat_ends(Input)
+{
+    constexpr auto lens = get_shape_c<Input>{}.lens;
+    return _c<lens[Axis]>;
+}
+
+template <index_int Axis, class Output, class... Inputs>
+__device__ void concat(Output output, Inputs... inputs)
+{
+    auto idx = make_index();
+    fold([&](auto start, auto input) {
+        auto y = concat_slice<Axis>(output, input, start);
+        idx.global_stride(input.get_shape().elements(), [&](auto i) { y[i] = input[i]; });
+        return start + concat_ends<Axis>(input);
+    })(_c<0>, inputs...);
+}
+
+} // namespace migraphx
+#endif // MIGRAPHX_GUARD_KERNELS_CONCAT_HPP

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

@@ -28,9 +28,60 @@
 #include <migraphx/kernels/types.hpp>
 #include <migraphx/kernels/integral_constant.hpp>
 #include <migraphx/kernels/type_traits.hpp>
+#include <migraphx/kernels/debug.hpp>
 
 namespace migraphx {
 
+#if defined(MIGRAPHX_NGLOBAL) && defined(MIGRAPHX_NLOCAL)
+#define MIGRAPHX_NGROUP ((MIGRAPHX_NGLOBAL + MIGRAPHX_NLOCAL - 1) / MIGRAPHX_NLOCAL)
+#endif
+
+inline __device__ __attribute__((const)) index_int compute_global_size()
+{
+#ifdef MIGRAPHX_NGLOBAL
+    return MIGRAPHX_NGLOBAL;
+#else
+    // This actualy works even when global is not divisible by local size.
+    // This doesnt actually do a multiplicatiosn. Instead it calls a device
+    // function to get the global size, which is why it works.
+    return blockDim.x * gridDim.x;  // NOLINT
+#endif
+}
+
+// We cant just use blockDim.x to get the local size since its broken on hip
+// when global is not divisible by local size. In this case, we calulate the
+// size for the last group.
+inline __device__ __attribute__((const)) index_int compute_local_size()
+{
+#ifdef MIGRAPHX_NLOCAL
+    const auto nlocal = MIGRAPHX_NLOCAL;
+#else
+    const auto nlocal = blockDim.x; // NOLINT
+#endif
+#ifdef MIGRAPHX_NGROUP
+    const auto ngroup = MIGRAPHX_NGROUP;
+#else
+    const auto ngroup = gridDim.x;  // NOLINT
+#endif
+    const auto group_id = blockIdx.x; // NOLINT
+    const auto nglobal  = compute_global_size();
+    if(group_id == ngroup - 1)
+    {
+        return 1 + (nglobal - 1) % nlocal;
+    }
+    else
+    {
+        return nlocal; // NOLINT
+    }
+}
+
+#ifdef MIGRAPHX_NGROUP
+// If global is divisible by local then local can be a const
+#if(MIGRAPHX_NGLOBAL % MIGRAPHX_NLOCAL == 0) || (MIGRAPHX_NGROUP == 1)
+#define MIGRAPHX_HAS_CONST_LOCAL 1
+#endif
+#endif
+
 struct index
 {
     index_int global = 0;
@@ -38,20 +89,44 @@ struct index
     index_int group  = 0;
 
 #ifdef MIGRAPHX_NGLOBAL
-    constexpr index_constant<MIGRAPHX_NGLOBAL> nglobal() const { return {}; }
+    constexpr index_constant<MIGRAPHX_NGLOBAL> nglobal() const
+    {
+        static_assert(MIGRAPHX_NGLOBAL > 0, "Global size must be greater than 0");
+        return {};
+    }
 #else
     __device__ index_int nglobal() const
     {
-        return blockDim.x * gridDim.x; // NOLINT
+        MIGRAPHX_ASSERT(compute_global_size() > 0);
+        return compute_global_size(); // NOLINT
     }
 #endif
 
-#ifdef MIGRAPHX_NLOCAL
-    constexpr index_constant<MIGRAPHX_NLOCAL> nlocal() const { return {}; }
+#ifdef MIGRAPHX_HAS_CONST_LOCAL
+    constexpr index_constant<MIGRAPHX_NLOCAL> nlocal() const
+    {
+        static_assert(MIGRAPHX_NLOCAL > 0, "Local size must be greater than 0");
+        return {};
+    }
 #else
     __device__ index_int nlocal() const
     {
-        return blockDim.x; // NOLINT
+#ifdef MIGRAPHX_NGROUP
+        static_assert((MIGRAPHX_NGLOBAL % MIGRAPHX_NLOCAL != 0) and (MIGRAPHX_NGROUP > 1),
+                      "Local size should be const");
+#endif
+        MIGRAPHX_ASSERT(compute_local_size() > 0);
+        return compute_local_size(); // NOLINT
+    }
+#endif
+
+#ifdef MIGRAPHX_NLOCAL
+    constexpr index_constant<MIGRAPHX_NLOCAL> max_nlocal() const { return {}; }
+#else
+    __device__ index_int max_nlocal() const
+    {
+        MIGRAPHX_ASSERT(blockDim.x > 0);
+        return blockDim.x;
     }
 #endif
     template <class N, class Stride>
@@ -63,6 +138,7 @@ struct index
     template <class F, class N, class Stride>
     static constexpr void for_stride(index_int start, N n, Stride stride, F f)
     {
+        MIGRAPHX_ASSERT(start < stride);
         if constexpr(not is_integral<N>{} and not is_integral<Stride>{} and
                      max_stride_iterations(n, stride) == 1)
         {

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

@@ -73,10 +73,10 @@ MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(!=)
 MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(&)
 MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(^)
 MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(|)
-MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(&&)
-MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(||)
+MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(and)
+MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(or)
 
-MIGRAPHX_INTEGRAL_CONSTANT_UNARY_OP(!)
+MIGRAPHX_INTEGRAL_CONSTANT_UNARY_OP(not )
 MIGRAPHX_INTEGRAL_CONSTANT_UNARY_OP(~)
 MIGRAPHX_INTEGRAL_CONSTANT_UNARY_OP(+)
 MIGRAPHX_INTEGRAL_CONSTANT_UNARY_OP(-)

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

@@ -29,6 +29,12 @@
 
 namespace migraphx {
 
+template <class T, index_int N, class Op>
+constexpr auto vec_reduce(const array<T, N>& a, Op op)
+{
+    return a.apply([&](auto x) { return vec_reduce(x, op); });
+}
+
 template <index_int Axis,
           class F,
           class BinOp,
@@ -43,23 +49,21 @@ __device__ void generic_binary_layernorm(
     reduce::block::run<reduce_output>([&](auto, auto r) {
         using value_type         = typename Input1::type;
         constexpr auto relements = r.template elements<Input1>();
-        auto mean                = [&](auto f) {
-            return r.reduce(op::sum{}, 0, [&](auto x1, auto x2) {
-                return f(x1, x2) / value_type{relements};
+        auto means =
+            r.reduce(op::sum{}, make_array<vec_type<value_type>>(0, 0), [&](auto x1, auto x2) {
+                auto x = op(x1, x2);
+                return make_array(x, x * x) * vec_type<value_type>{1.0 / relements};
             })(input1, input2);
-        };
-        // mean(x)
-        auto mean_x = mean(op);
-        // mean(m ^ 2)
-        auto mean_m2 = mean([&](auto x1, auto x2) {
-            auto m = op(x1, x2) - mean_x;
-            return m * m;
-        });
+
+        auto mean_x   = means[0];
+        auto mean_x2  = means[1];
+        auto variance = mean_x2 - (mean_x * mean_x);
 
         r.inner([&](auto& y, auto x1, auto x2, auto... xs) {
-            auto m = op(x1, x2) - mean_x;
+            auto x = op(x1, x2);
+            auto m = x - mean_x;
             // m * rsqrt(mean(m ^ 2) + 1e-12)
-            y = compute(m * rsqrt(mean_m2 + value_type{1e-12}), xs...);
+            y = compute(m * rsqrt(variance + value_type{1e-12}), xs...);
         })(output, input1, input2, inputs...);
     });
 }

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

@@ -94,16 +94,17 @@ MIGRAPHX_DPP_REDUCE(op::max, v_max)
 MIGRAPHX_DPP_REDUCE(op::min, v_min)
 MIGRAPHX_DPP_REDUCE(op::product, v_mul)
 
-template <class Op, class T, class F>
-__device__ auto block_reduce(index idx, Op op, T init, index_int n, F f)
+template <class Op, class T, class Index, class F>
+__device__ auto block_reduce(index idx, Op op, T init, Index n, F f)
 {
+    MIGRAPHX_ASSERT(idx.max_nlocal() == idx.nlocal());
 #if __AMDGCN_WAVEFRONT_SIZE == 32
     constexpr index_int lanes_per_thread = 16;
 #else
     constexpr index_int lanes_per_thread = 64;
 #endif
     using type = decltype(f(0));
-    __shared__ type buffer[idx.nlocal() / lanes_per_thread];
+    __shared__ type buffer[idx.max_nlocal() / lanes_per_thread];
     type x = init;
     idx.local_stride(n, [&](auto i) { x = op(x, f(i)); });
     dpp_reduce(x, op);
@@ -123,12 +124,12 @@ __device__ auto block_reduce(index idx, Op op, T init, index_int n, F f)
     return y;
 }
 #else
-template <class Op, class T, class F>
-__device__ auto block_reduce(index idx, Op op, T init, index_int n, F f)
+template <class Op, class T, class Index, class F>
+__device__ auto block_reduce(index idx, Op op, T init, Index n, F f)
 {
-
+    MIGRAPHX_ASSERT(idx.max_nlocal() == idx.nlocal());
     using type = decltype(f(0));
-    __shared__ type buffer[idx.nlocal()];
+    __shared__ type buffer[idx.max_nlocal()];
     type x = init;
     idx.local_stride(n, [&](auto i) { x = op(x, f(i)); });
     buffer[idx.local] = x;
@@ -201,12 +202,9 @@ struct block
         __device__ auto reduce(Op op, T init, Read read) const
         {
             return sliced(slice, [=](auto x, auto... xs) {
-                return vec_reduce(block_reduce(idx,
-                                               op,
-                                               init,
-                                               x.get_shape().elements(),
-                                               [&](auto j) { return read(x[j], xs[j]...); }),
-                                  op);
+                return block_reduce(idx, op, init, x.get_shape().elements(), [&](auto j) {
+                    return vec_reduce(read(x[j], xs[j]...), op);
+                });
             });
         }