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 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
  
 #pragma once
  
 #include "ck_tile/core/config.hpp"
 #include "ck_tile/core/container/array.hpp"
 #include "ck_tile/core/container/sequence.hpp"
 #include "ck_tile/core/container/tuple.hpp"
 #include "ck_tile/core/container/container_helper.hpp"
 #include "ck_tile/core/tensor/tensor_adaptor.hpp"
 #include "ck_tile/core/tensor/null_tensor.hpp"
 #include "ck_tile/core/utility/functional.hpp"
 #include "ck_tile/core/utility/type_traits.hpp"
  
 namespace ck_tile {
  
 // TODO: support tensors with different distribution
 template <typename InOutElementFunc,
           typename... InOutDstrTensors,
           typename = std::enable_if_t<std::conjunction_v<
               std::negation<std::is_same<std::remove_const_t<InOutDstrTensors>, null_tensor>>...>>>
 CK_TILE_DEVICE void tile_elementwise_inout(const InOutElementFunc& inout_element_func,
                                            InOutDstrTensors&... inout_dstr_tensors)
 {
     // TODO: make sure all distributed tensors have same lengths and distribution
     // static_assert(xxx);
  
     constexpr index_t thread_buffer_size =
         __type_pack_element<0, InOutDstrTensors...>::get_thread_buffer_size();
  
     static_for<0, thread_buffer_size, 1>{}(
         [&](auto i) { inout_element_func(inout_dstr_tensors.get_thread_buffer().at(i)...); });
 }
  
 template <typename InElementFunc,
           typename... InTensor,
           typename = std::enable_if_t<
               std::conjunction_v<std::negation<std::is_same<InTensor, null_tensor>>...>>>
 CK_TILE_DEVICE auto tile_elementwise_in(const InElementFunc& in_element_func,
                                         const InTensor&... in_dstr_tensors)
 {
     using OutDataType = decltype(in_element_func(typename InTensor::DataType{}...));
  
     // TODO: make sure all distributed tensors have same lengths and distribution
     // static_assert(xxx);
     constexpr auto in_tile_dstr = __type_pack_element<0, InTensor...>::get_tile_distribution();
  
     constexpr index_t thread_buffer_size =
         __type_pack_element<0, InTensor...>::get_thread_buffer_size();
  
     auto out_dstr_tensor = make_static_distributed_tensor<OutDataType>(in_tile_dstr);
  
     static_for<0, thread_buffer_size, 1>{}([&](auto i) {
         out_dstr_tensor.get_thread_buffer()(i) =
             in_element_func(in_dstr_tensors.get_thread_buffer()[i]...);
     });
  
     return out_dstr_tensor;
 }
  
 template <typename InElementFunc, typename Tuple, size_t... I>
 CK_TILE_DEVICE auto tile_elementwise_inout_unpack(const InElementFunc& in_element_func,
                                                   const Tuple& t,
                                                   std::index_sequence<I...>)
 {
     return tile_elementwise_inout(in_element_func, t[number<I>{}]...);
 }
  
 template <typename InElementFunc, typename Tuple>
 CK_TILE_DEVICE auto tile_elementwise_inout_unpack(const InElementFunc& in_element_func,
                                                   const Tuple& t)
 {
     static constexpr auto size = Tuple::size();
     return tile_elementwise_inout_unpack(in_element_func, t, std::make_index_sequence<size>{});
 }
  
 template <typename DstrTensors, typename T>
 CK_TILE_DEVICE void set_tile(DstrTensors& dstr_tensor, const T& value)
 {
     tile_elementwise_inout(
         [&value](auto& x) {
             x = type_convert<typename DstrTensors::DataType, remove_cvref_t<T>>(value);
         },
         dstr_tensor);
 }
  
 template <typename T>
 CK_TILE_DEVICE void set_tile(null_tensor&, const T&)
 {
 }
  
 // TODO: prefer to use per-dword value to set a tensor, in case compiler not doing well with
 // sub-dword tensor...
 template <typename DstrTensors, index_t v, bool skip_subdword_opt = false>
 CK_TILE_DEVICE void
 set_tile(DstrTensors& dstr_tensor, number<v>, bool_constant<skip_subdword_opt> = {})
 {
     using elem_type             = typename DstrTensors::DataType;
     constexpr index_t elem_size = sizeof(elem_type);
  
     constexpr index_t tensor_bytes = DstrTensors::get_thread_buffer_size() * elem_size;
  
     // # bytes per write = 4
     if constexpr(v == 0 && tensor_bytes % 4 == 0 && !skip_subdword_opt)
     {
 #if CK_TILE_WORKAROUND_ROCM_6_1_SCRATCH_MEMORY_ISSUE
         auto& buffer = dstr_tensor.get_thread_buffer();
  
         static_for<0, tensor_bytes / 4, 1>{}([&](auto i_write) {
             if constexpr(elem_size == 1)
             {
                 // # elements per write = 4
                 constexpr auto values = ext_vector_t<elem_type, 4>{0, 0, 0, 0};
  
                 buffer[i_write * 4 + 0] = values.x;
                 buffer[i_write * 4 + 1] = values.y;
                 buffer[i_write * 4 + 2] = values.z;
                 buffer[i_write * 4 + 3] = values.w;
             }
             else if constexpr(elem_size == 2)
             {
                 // # elements per write = 2
                 constexpr auto values = ext_vector_t<elem_type, 2>{0, 0};
  
                 buffer[i_write * 2 + 0] = values.x;
                 buffer[i_write * 2 + 1] = values.y;
             }
             else if constexpr(elem_size == 4)
             {
                 // # elements per write = 1
                 constexpr elem_type value = 0;
  
                 buffer[i_write] = value;
             }
             else
             {
                 static_assert(false, "type not supported");
             }
         });
 #else
         using dvec_t = array<index_t, tensor_bytes / 4>;
         auto& tensor = reinterpret_cast<dvec_t&>(dstr_tensor.get_thread_buffer());
         for(auto i = 0; i < tensor.size(); i++)
             tensor.get(i) = v;
 #endif
     }
     else
     {
         tile_elementwise_inout([](auto& x) { x = type_convert<elem_type, index_t>(v); },
                                dstr_tensor);
     }
 }
  
 template <index_t v>
 CK_TILE_DEVICE void set_tile(null_tensor&, number<v>)
 {
 }
  
 template <typename DstrTensors>
 CK_TILE_DEVICE void clear_tile(DstrTensors& dstr_tensor)
 {
     set_tile(dstr_tensor, 0);
 }
  
 namespace impl {
 // TODO: this is ugly
 template <typename OutDataType, typename InTensor>
 CK_TILE_DEVICE auto cast_tile_pk_fp8_fp32(const InTensor& in_dstr_tensors)
 {
 #if defined(__gfx94__)
     // This API is designed to use the _pk_ serious of function
     constexpr auto in_tile_dstr = InTensor::get_tile_distribution();
  
     constexpr index_t thread_buffer_size = InTensor::get_thread_buffer_size();
     static_assert(thread_buffer_size % 4 == 0);
     constexpr index_t thread_buffer_size_pk = thread_buffer_size / 4;
  
     auto out_dstr_tensor = make_static_distributed_tensor<OutDataType>(in_tile_dstr);
 #pragma clang diagnostic push
 #pragma clang diagnostic ignored "-Wuninitialized"
     // __builtin_amdgcn_cvt_pk_fp8_f32() this builtin require the old value, and
     // will generate a v_mov_b32 vxxx [old] before cvt, which result in unwanted ISA
     // so we prepare an uninitialized variable purposely, and turn off the warning
     int dummy_old;
     static_for<0, thread_buffer_size_pk, 1>{}([&](auto i) {
         uint32_t x = __builtin_amdgcn_cvt_pk_fp8_f32(
             in_dstr_tensors.get_thread_buffer()[number<4 * i + 0>{}],
             in_dstr_tensors.get_thread_buffer()[number<4 * i + 1>{}],
             dummy_old,
             false); // false -> WORD0
  
         uint32_t y = __builtin_amdgcn_cvt_pk_fp8_f32(
             in_dstr_tensors.get_thread_buffer()[number<4 * i + 2>{}],
             in_dstr_tensors.get_thread_buffer()[number<4 * i + 3>{}],
             dummy_old,
             false); // false -> WORD0
  
         constexpr int32_t m0 = 0x05040100;
         using vec_t          = array<OutDataType, 4>;
  
         vec_t d = bit_cast<vec_t>(__builtin_amdgcn_perm(y, x, m0));
         out_dstr_tensor.get_thread_buffer().template set_as<vec_t>(number<i>{}, d);
     });
 #pragma clang diagnostic pop
  
     return out_dstr_tensor;
 #else
     // fallback
     return tile_elementwise_in(type_convert<OutDataType, typename InTensor::DataType>,
                                in_dstr_tensors);
 #endif
 }
  
 template <typename OutDataType, typename InTensor>
 CK_TILE_DEVICE auto cast_tile_pk_fp16_fp32(const InTensor& in_dstr_tensors)
 {
 #if defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx94__)
     // This API is designed to use the _pk_ serious of function
     constexpr auto in_tile_dstr = InTensor::get_tile_distribution();
  
     constexpr index_t thread_buffer_size = InTensor::get_thread_buffer_size();
     static_assert(thread_buffer_size % 2 == 0);
     constexpr index_t thread_buffer_size_pk = thread_buffer_size / 2;
  
     auto out_dstr_tensor = make_static_distributed_tensor<OutDataType>(in_tile_dstr);
  
     // TODO: this is rtz cvt, need be very careful
     for(index_t i = 0; i < thread_buffer_size_pk; i++)
     {
         auto o = __builtin_amdgcn_cvt_pkrtz(in_dstr_tensors.get_thread_buffer()[2 * i + 0],
                                             in_dstr_tensors.get_thread_buffer()[2 * i + 1]);
  
         out_dstr_tensor.get_thread_buffer().at(2 * i + 0) = o.x;
         out_dstr_tensor.get_thread_buffer().at(2 * i + 1) = o.y;
     }
  
     return out_dstr_tensor;
 #else
     // fallback
     return tile_elementwise_in(type_convert<OutDataType, typename InTensor::DataType>,
                                in_dstr_tensors);
 #endif
 }
  
 #if CK_TILE_USE_SUBDWORD_TILE_CAST
 // this function assume either src or dst (or both) date type is under 1 dword
 // we pack subdword value into 1 dword to avoid compiler's default subdword behavior(which is buggy)
 template <typename OutDataType, typename InTensor>
 CK_TILE_DEVICE auto cast_tile_opt_subdword(const InTensor& in_dstr_tensors)
 {
     constexpr auto in_tile_dstr = InTensor::get_tile_distribution();
  
     auto out_dstr_tensor = make_static_distributed_tensor<OutDataType>(in_tile_dstr);
  
     using i_type                   = remove_cvref_t<typename InTensor::DataType>;
     using o_type                   = remove_cvref_t<OutDataType>;
     constexpr index_t i_elem_bytes = sizeof(i_type);
     constexpr index_t o_elem_bytes = sizeof(o_type);
     static_assert(i_elem_bytes < 4 || o_elem_bytes < 4);
  
     constexpr index_t bulk_size =
         (i_elem_bytes >= o_elem_bytes) ? (4 / o_elem_bytes) : (4 / i_elem_bytes);
     static_assert(bulk_size != 0);
  
     using o_bulk_type =
         std::conditional_t<i_elem_bytes >= o_elem_bytes, float, array<o_type, bulk_size>>;
  
     constexpr index_t thread_buffer_size = InTensor::get_thread_buffer_size();
  
     constexpr index_t iters = thread_buffer_size / bulk_size;
     constexpr index_t rems  = thread_buffer_size % bulk_size;
  
     // cast the sequence per-bulk
     static_for<0, iters, 1>{}([&](auto i) {
         union bulk_wrapper
         {
             o_bulk_type bulk{};
             o_type data[bulk_size];
         } o_bulk;
  
         // TODO: should use below function, but somehow will result in spill (same as c-forloop)
         static_for<0, bulk_size, 1>{}([&o_bulk, &in_dstr_tensors, &i](auto ib) {
             o_bulk.data[ib.value] = static_cast<o_type>(
                 in_dstr_tensors.get_thread_buffer()
                     .template get_as<i_type>()[number<bulk_size * i.value + ib.value>{}]);
         });
  
         // TODO: fixme, should use above!
         // static_assert(sizeof(i_type) / sizeof(o_type) == 2);
         // o_bulk.data[0] = static_cast<o_type>(
         //     in_dstr_tensors.get_thread_buffer().template get_as<i_type>()[number<2 * i + 0>{}]);
         // o_bulk.data[1] = static_cast<o_type>(
         //     in_dstr_tensors.get_thread_buffer().template get_as<i_type>()[number<2 * i + 1>{}]);
  
         out_dstr_tensor.get_thread_buffer().template set_as<o_bulk_type>(i, o_bulk.bulk);
     });
  
     static_for<0, rems, 1>{}([&](auto r) {
         // TODO: introducing local scratch pad?
         auto idx = number<iters * bulk_size + r>{};
         out_dstr_tensor.get_thread_buffer().at(idx) =
             static_cast<o_type>(in_dstr_tensors.get_thread_buffer().at(idx));
     });
  
     return out_dstr_tensor;
 }
 #endif
 } // namespace impl
  
 template <typename DstType, typename SrcTensor>
 CK_TILE_DEVICE auto cast_tile(const SrcTensor& src_tensor)
 {
     if constexpr((std::is_same_v<DstType, fp8_t> || std::is_same_v<DstType, bf8_t>) &&
                  std::is_same_v<typename SrcTensor::DataType, float> &&
                  (SrcTensor::get_thread_buffer_size() % 4 == 0))
     {
         return impl::cast_tile_pk_fp8_fp32<DstType, SrcTensor>(src_tensor);
     }
 #if CK_TILE_USE_PK_FP16_TILE_CAST
     else if constexpr(std::is_same_v<DstType, fp16_t> &&
                       std::is_same_v<typename SrcTensor::DataType, float> &&
                       (SrcTensor::get_thread_buffer_size() % 2 == 0))
     {
         return impl::cast_tile_pk_fp16_fp32<DstType, SrcTensor>(src_tensor);
     }
 #endif
 #if CK_TILE_USE_SUBDWORD_TILE_CAST
     else if constexpr(sizeof(DstType) < 4 || sizeof(typename SrcTensor::DataType) < 4)
     {
         return impl::cast_tile_opt_subdword<DstType, SrcTensor>(src_tensor);
     }
 #endif
     else
         return tile_elementwise_in(type_convert<DstType, typename SrcTensor::DataType>, src_tensor);
 }
  
 // no-op function for null_tensor arguments
 template <typename InOutElementFunc,
           typename... MaybeNullTensor,
           typename = std::enable_if_t<
               std::disjunction_v<std::is_same<remove_cvref_t<MaybeNullTensor>, null_tensor>...>>>
 CK_TILE_DEVICE void tile_elementwise_inout(const InOutElementFunc&, MaybeNullTensor&&...)
 {
 }
  
 // no-op function for null_tensor arguments
 template <typename InElementFunc,
           typename... MaybeNullTensor,
           typename = std::enable_if_t<
               std::disjunction_v<std::is_same<remove_cvref_t<MaybeNullTensor>, null_tensor>...>>>
 CK_TILE_DEVICE auto tile_elementwise_in(const InElementFunc&, MaybeNullTensor&&...)
 {
     return null_tensor{};
 }
  
 } // namespace ck_tile