/*
 * 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_DEVICE_NARY_HPP
#define MIGRAPHX_GUARD_RTGLIB_DEVICE_NARY_HPP

#include <migraphx/gpu/device/launch.hpp>
#include <migraphx/gpu/device/multi_index.hpp>
#include <migraphx/gpu/device/visit.hpp>
#include <migraphx/functional.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/array.hpp>
#include <migraphx/env.hpp>
#include <migraphx/permutation.hpp>
#include <migraphx/config.hpp>
#include <iostream>

namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace device {

MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_TRACE_NARY);

// NOLINTNEXTLINE
#define MIGRAPHX_TRACE_NARY_FUNCTION   \
    if(enabled(MIGRAPHX_TRACE_NARY{})) \
        std::cout << "nary device function: " << __PRETTY_FUNCTION__ << std::endl;

template <class... Ts>
constexpr auto pack(Ts... xs)
{
    return [=](auto f) { return f(xs...); };
}

template <class F, class... Arguments>
auto nary_nonstandard_nonpacked_impl(hipStream_t stream, F f, argument result, Arguments... args)
{
    MIGRAPHX_TRACE_NARY_FUNCTION
    shape s{result.get_shape().type(), result.get_shape().lens()};
    hip_visit_all(s, result, args...)([&](auto standard_shape, auto output, auto... inputs) {
        mi_gs_launch(stream,
                     standard_shape)([=](auto idx) __device__ { output[idx] = f(inputs[idx]...); });
    });
}

inline auto create_broadcast_index(index_int len, index_int stride)
{
    auto next_stride   = stride * len;
    auto e_next_stride = encode_divisor(next_stride);
    auto e_stride      = encode_divisor(stride);
    return [=](auto i) __device__ {
        // ( i % next_stride) / stride
        return fast_div(i, e_stride) - len * fast_div(i, e_next_stride);
    };
}

template <class F, class... Arguments>
auto nary_nonstandard_packed_impl(hipStream_t stream,
                                  F f,
                                  const argument& result,
                                  Arguments... args)
{
    MIGRAPHX_TRACE_NARY_FUNCTION
    auto arg_shape = make_array(args...).front().get_shape();
    auto perm      = find_permutation(arg_shape);
    auto s         = reorder_shape(arg_shape, perm);
    hip_visit_all(s, result.reshape(reorder_shape(result.get_shape(), perm)), args.reshape(s)...)(
        [&](auto standard_shape, auto output, auto... inputs) {
            mi_gs_launch(stream, standard_shape)(
                [=](auto idx) __device__ { output[idx] = f(inputs[idx]...); });
        });
}

template <class F, class... Arguments>
void nary_broadcast_vec_impl(
    hipStream_t stream, F f, argument result, argument barg, Arguments... args)
{
    MIGRAPHX_TRACE_NARY_FUNCTION
    const auto& output_shape = result.get_shape();
    const auto& b_shape      = barg.get_shape();
    auto bdim =
        std::distance(b_shape.strides().begin(),
                      std::find_if(b_shape.strides().begin(), b_shape.strides().end(), [](auto x) {
                          return x != 0;
                      }));
    auto bdim_len      = output_shape.lens()[bdim];
    auto bdim_stride   = output_shape.strides()[bdim];
    auto broadcast_idx = create_broadcast_index(bdim_len, bdim_stride);

    const index_int vec_size     = 4;
    const index_int nlocal       = 1024;
    const index_int nglobal      = 256 * nlocal;
    const index_int bdim_vec_len = bdim_len / vec_size;
    hip_vec_visit_all<vec_size>(result, barg, args...)(
        [&](auto output, auto binput, auto... inputs) {
            using type                = typename decltype(output)::value_type;
            const index_int nelements = output.size() / vec_size;
            launch(stream, nglobal, nlocal)([=](auto idx) __device__ {
                MIGRAPHX_DEVICE_SHARED type buffer[2048 / vec_size];
                // Load bias into LDS
                for(size_t i = idx.local; i < bdim_vec_len; i += nlocal)
                {
                    buffer[i] = binput.data()[i];
                }
                __syncthreads();
                const auto* bp = as_pointer(buffer);
                // Process the data
                for(size_t i = idx.global; i < nelements; i += nglobal)
                {
                    auto bidx = broadcast_idx(i * vec_size);
                    auto b    = bp[bidx];
                    auto out  = output.data()[i];
                    for(index_int j = 0; j < vec_size; j++)
                    {
                        out[j] = f(inputs.data()[i][j]..., b);
                    }
                    output.data()[i] = out;
                }
            });
        });
}

template <class F, class... Arguments>
void nary_broadcast_impl(hipStream_t stream, F f, argument result, argument barg, Arguments... args)
{
    MIGRAPHX_TRACE_NARY_FUNCTION
    const auto& output_shape = result.get_shape();
    const auto& b_shape      = barg.get_shape();
    auto bdim =
        std::distance(b_shape.strides().begin(),
                      std::find_if(b_shape.strides().begin(), b_shape.strides().end(), [](auto x) {
                          return x != 0;
                      }));
    auto bdim_len      = output_shape.lens()[bdim];
    auto bdim_stride   = output_shape.strides()[bdim];
    auto broadcast_idx = create_broadcast_index(bdim_len, bdim_stride);

    const index_int nlocal  = 1024;
    const index_int nglobal = 256 * nlocal;
    index_int nelements     = result.get_shape().elements();
    hip_visit_all(result, barg, args...)([&](auto output, auto binput, auto... inputs) {
        using type = typename decltype(output)::value_type;
        launch(stream, nglobal, nlocal)([=](auto idx) __device__ {
            MIGRAPHX_DEVICE_SHARED type buffer[2048];
            // Load bias into LDS
            for(size_t i = idx.local; i < bdim_len; i += nlocal)
            {
                buffer[i] = binput.data()[i];
            }
            __syncthreads();
            // Process the data
            for(size_t i = idx.global; i < nelements; i += nglobal)
            {
                auto bidx        = broadcast_idx(i);
                auto b           = buffer[bidx];
                output.data()[i] = f(inputs.data()[i]..., b);
            }
        });
    });
}

template <class F, class... Arguments>
void nary_double_broadcast_vec_impl(
    hipStream_t stream, F f, argument result, argument barg1, argument barg2, Arguments... args)
{
    MIGRAPHX_TRACE_NARY_FUNCTION
    assert(barg1.get_shape().broadcasted());
    assert(barg2.get_shape().broadcasted());
    assert(barg1.get_shape() == barg2.get_shape());
    const auto& output_shape = result.get_shape();
    const auto& b_shape      = barg1.get_shape();
    auto bdim =
        std::distance(b_shape.strides().begin(),
                      std::find_if(b_shape.strides().begin(), b_shape.strides().end(), [](auto x) {
                          return x != 0;
                      }));
    auto bdim_len      = output_shape.lens()[bdim];
    auto bdim_stride   = output_shape.strides()[bdim];
    auto broadcast_idx = create_broadcast_index(bdim_len, bdim_stride);

    const index_int vec_size     = 4;
    const index_int nlocal       = 1024;
    const index_int nglobal      = 256 * nlocal;
    const index_int bdim_vec_len = bdim_len / vec_size;
    hip_vec_visit_all<vec_size>(result, barg1, barg2, args...)(
        [&](auto output, auto binput1, auto binput2, auto... inputs) {
            using type                = typename decltype(output)::value_type;
            const index_int nelements = output.size() / vec_size;
            launch(stream, nglobal, nlocal)([=](auto idx) __device__ {
                MIGRAPHX_DEVICE_SHARED type buffer[2048 / vec_size];
                // Load bias into LDS
                for(size_t i = idx.local; i < bdim_vec_len; i += nlocal)
                {
                    buffer[i] = binput1.data()[i];
                }
                for(size_t i = idx.local; i < bdim_vec_len; i += nlocal)
                {
                    buffer[i + bdim_vec_len] = binput2.data()[i];
                }
                __syncthreads();
                const auto* bp = as_pointer(buffer);
                // Process the data
                for(size_t i = idx.global; i < nelements; i += nglobal)
                {
                    auto bidx = broadcast_idx(i * vec_size);
                    auto b1   = bp[bidx];
                    auto b2   = bp[bidx + bdim_len];
                    auto out  = output.data()[i];
                    for(index_int j = 0; j < vec_size; j++)
                    {
                        out[j] = f(inputs.data()[i][j]..., b2, b1);
                    }
                    output.data()[i] = out;
                }
            });
        });
}

template <class F, class... Arguments>
void nary_double_broadcast_impl(
    hipStream_t stream, F f, argument result, argument barg1, argument barg2, Arguments... args)
{
    MIGRAPHX_TRACE_NARY_FUNCTION
    assert(barg1.get_shape().broadcasted());
    assert(barg2.get_shape().broadcasted());
    assert(barg1.get_shape() == barg2.get_shape());
    const auto& output_shape = result.get_shape();
    const auto& b_shape      = barg1.get_shape();
    auto bdim =
        std::distance(b_shape.strides().begin(),
                      std::find_if(b_shape.strides().begin(), b_shape.strides().end(), [](auto x) {
                          return x != 0;
                      }));
    auto bdim_len      = output_shape.lens()[bdim];
    auto bdim_stride   = output_shape.strides()[bdim];
    auto broadcast_idx = create_broadcast_index(bdim_len, bdim_stride);

    const index_int nlocal  = 1024;
    const index_int nglobal = 256 * nlocal;
    index_int nelements     = result.get_shape().elements();
    hip_visit_all(result, barg1, barg2, args...)(
        [&](auto output, auto binput1, auto binput2, auto... inputs) {
            using type = typename decltype(output)::value_type;
            launch(stream, nglobal, nlocal)([=](auto idx) __device__ {
                MIGRAPHX_DEVICE_SHARED type buffer[2048];
                // Load bias into LDS
                for(size_t i = idx.local; i < bdim_len; i += nlocal)
                {
                    buffer[i] = binput1.data()[i];
                }
                for(size_t i = idx.local; i < bdim_len; i += nlocal)
                {
                    buffer[i + bdim_len] = binput2.data()[i];
                }
                __syncthreads();
                // Process the data
                for(size_t i = idx.global; i < nelements; i += nglobal)
                {
                    auto bidx        = broadcast_idx(i);
                    auto b1          = buffer[bidx];
                    auto b2          = buffer[bidx + bdim_len];
                    output.data()[i] = f(inputs.data()[i]..., b2, b1);
                }
            });
        });
}

template <class F, class... Arguments>
void nary_standard_vec_impl(hipStream_t stream, F f, argument result, Arguments... args)
{
    MIGRAPHX_TRACE_NARY_FUNCTION
    const auto& output_shape = result.get_shape();
    visit_all(result, args...)([&](auto output, auto... inputs) {
        using type = device_type<std::remove_cv_t<typename decltype(output)::value_type>>;
        const index_int vec_size = 4;
        auto data                = pack_vec<4>(device_cast(inputs.data())...);
        auto* outp               = as_vec<4>(device_cast(output.data()));
        gs_launch(stream, output_shape.elements() / vec_size)([=](auto i) __device__ {
            vec<type, 4> out = outp[i];
            data(
                [&](auto... xs) {
                    for(index_int j = 0; j < vec_size; j++)
                    {
                        out[j] = f(xs[j]...);
                    }
                },
                i);
            outp[i] = out;
        });
    });
}

template <class F, class... Arguments>
void nary_standard_impl(hipStream_t stream, F f, argument result, Arguments... args)
{
    MIGRAPHX_TRACE_NARY_FUNCTION
    index_int nelements = result.get_shape().elements();
    hip_pointer_visit_all(result, args...)([&](auto output, auto... inputs) {
        gs_launch(stream, nelements)([=](auto i) __device__ { output[i] = f(inputs[i]...); });
    });
}

template <class F, class... Arguments>
void nary_impl(hipStream_t stream, F f, argument result, Arguments... args)
{
    MIGRAPHX_TRACE_NARY_FUNCTION
    const auto shapes   = make_array(args.get_shape()...);
    const bool standard = all_of(shapes, [](const shape& s) { return s.standard(); });
    const bool packed =
        all_of(shapes, [](const shape& s) { return s.packed() and not s.broadcasted(); });
    const bool same_shapes =
        all_of(shapes, [&](const shape& s) { return s == result.get_shape(); });
    const bool same_input_shapes = all_of(shapes, [&](const shape& s) { return s == shapes[0]; });
    if((result.get_shape().standard() and standard) or (packed and same_shapes))
        nary_standard_impl(stream, f, result, args...);
    else if(packed and same_input_shapes)
        nary_nonstandard_packed_impl(stream, f, result, args...);
    else
        nary_nonstandard_nonpacked_impl(stream, f, result, args...);
}

template <class... Arguments>
auto nary_nonstandard(hipStream_t stream, argument result, Arguments... args)
{
    return [=](auto f) { nary_nonstandard_nonpacked_impl(stream, f, result, args...); };
}

template <class... Arguments>
auto nary_standard(hipStream_t stream, argument result, Arguments... args)
{
    return [=](auto f) { nary_standard_impl(stream, f, result, args...); };
}

template <class... Arguments>
bool broadcastable(bool& divisible_by_4,
                   index_int max_size,
                   const argument& result,
                   const argument& barg,
                   const Arguments&... args)
{
    divisible_by_4 = false;
    auto bshape    = barg.get_shape();
    const bool standard =
        all_of({args.get_shape()...}, [](const shape& s) { return s.standard(); });
    const bool same_shapes =
        all_of({args.get_shape()...}, [&](const shape& s) { return s == result.get_shape(); });
    // TODO: Check result and args shape is the same
    if(standard and same_shapes and bshape.broadcasted() and not bshape.scalar())
    {
        auto not_zero       = [](auto x) { return x != 0; };
        const auto& strides = bshape.strides();
        auto b_it           = std::find_if(strides.begin(), strides.end(), not_zero);
        auto b_idx          = std::distance(strides.begin(), b_it);
        auto b_len          = result.get_shape().lens()[b_idx];
        auto b_stride       = result.get_shape().strides()[b_idx];
        assert(bshape.lens()[b_idx] == b_len);
        if(b_len <= max_size and std::none_of(std::next(b_it), strides.end(), not_zero) and
           is_divisor_encodable(b_stride * b_len))
        {

            divisible_by_4 = (b_len % 4 == 0) and (b_stride % 4 == 0) and
                             (front_args(args...).get_shape().elements() % 4 == 0);
            return true;
        }
    }
    return false;
}

inline bool broadcastable(bool& divisible_by_4, index_int, const argument&, const argument&)
{
    divisible_by_4 = false;
    return false;
}

// Nullary
inline auto nary(hipStream_t stream, argument result)
{
    return [=](auto f) { nary_standard_impl(stream, f, result); };
}

// Unary
inline auto nary(hipStream_t stream, argument result, argument arg)
{
    return [=](auto f) { nary_impl(stream, f, result, arg); };
}

// Binary
inline auto nary(hipStream_t stream, argument result, argument arg, argument barg)
{
    return [=](auto f) {
        bool divisible_by_4 = false;
        if(broadcastable(divisible_by_4, 2048, result, barg, arg))
        {
            if(divisible_by_4)
                nary_broadcast_vec_impl(stream, f, result, barg, arg);
            else
                nary_broadcast_impl(stream, f, result, barg, arg);
        }
        else
        {
            nary_impl(stream, f, result, arg, barg);
        }
    };
}

template <class... Arguments>
auto nary(hipStream_t stream, argument result, Arguments... args)
{
    static_assert(sizeof...(args) > 2, "Args needs to be greater than 2");
    return [=](auto f) {
        auto barg1     = back_args(args...);
        bool fallback1 = pop_back_args(args...)([&](auto&&... args2) {
            auto barg2 = back_args(args2...);
            bool fallback2 =
                barg2.get_shape() != barg1.get_shape() or not barg2.get_shape().broadcasted() or
                pop_back_args(args2...)([&](auto&&... args3) {
                    bool divisible_by_4 = false;
                    if(broadcastable(divisible_by_4, 1024, result, barg2, args3...))
                    {
                        if(divisible_by_4)
                            nary_double_broadcast_vec_impl(
                                stream, f, result, barg1, barg2, args3...);
                        else
                            nary_double_broadcast_impl(stream, f, result, barg1, barg2, args3...);
                        return false;
                    }
                    return true;
                });
            if(not fallback2)
                return false;
            bool divisible_by_4 = false;
            if(broadcastable(divisible_by_4, 2048, result, barg1, args2...))
            {
                if(divisible_by_4)
                    nary_broadcast_vec_impl(stream, f, result, barg1, args2...);
                else
                    nary_broadcast_impl(stream, f, result, barg1, args2...);
                return false;
            }
            return true;
        });
        if(fallback1)
            nary_impl(stream, f, result, args...);
    };
}

} // namespace device
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx

#endif
