/*
 * 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/ref/lowering.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/dfor.hpp>
#include <migraphx/op/identity.hpp>
#include <migraphx/op/convolution.hpp>
#include <migraphx/op/convolution_backwards.hpp>
#include <migraphx/op/quant_convolution.hpp>
#include <migraphx/op/dot.hpp>
#include <migraphx/op/quant_dot.hpp>
#include <migraphx/op/im2col.hpp>
#include <migraphx/op/logsoftmax.hpp>
#include <migraphx/op/loop.hpp>
#include <migraphx/op/lrn.hpp>
#include <migraphx/op/pad.hpp>
#include <migraphx/op/softmax.hpp>
#include <migraphx/op/argmax.hpp>
#include <migraphx/op/argmin.hpp>
#include <migraphx/op/rnn_var_sl_last_output.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/iterator_for.hpp>
#include <migraphx/par_dfor.hpp>
#include <migraphx/clamp.hpp>
#include <migraphx/ref/gemm.hpp>
#include <migraphx/register_op.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/tune_axis.hpp>
#include <migraphx/pad_calc.hpp>

#include <unordered_map>
#include <utility>
#include <iostream>

namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace ref {

template <typename T>
T zero(const T&)
{
    return T(0);
}

template <class T>
typename std::conditional_t<std::is_integral<T>{}, std::make_signed<T>, std::enable_if<true, T>>::
    type
    make_signed(T x)
{
    return x;
}

struct ref_lrn
{
    op::lrn op;

    template <class Self, class F>
    static auto reflect(Self& self, F f)
    {
        return migraphx::reflect(self.op, f);
    }

    std::string name() const { return "ref::lrn"; }
    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
    argument compute(context&, shape output_shape, std::vector<argument> args) const
    {
        argument result{output_shape};
        visit_all(result, args[0])([&](auto output, auto input) {
            int n_batch         = output_shape.lens()[0];
            int channels        = output_shape.lens()[1];
            int height          = output_shape.lens()[2];
            int width           = output_shape.lens()[3];
            float alphaoverarea = op.alpha / float(op.size);
            int radius_lower    = (op.size - 1) / 2;
            int radius_upper    = op.size / 2 + 1;

            par_dfor(n_batch, height, width)([&](int b, int h, int w) {
                float scale = 0;
                dfor(channels)([&](int c) {
                    auto start = (c - radius_lower) < 0 ? 0 : (c - radius_lower);
                    auto end   = (c + radius_upper) > channels ? channels : (c + radius_upper);
                    for(auto k = start; k < end; ++k)
                    {
                        scale += std::pow(input(b, k, h, w), 2);
                    }
                    scale *= alphaoverarea;
                    scale += op.bias;
                    scale              = std::pow(scale, -op.beta);
                    output(b, c, h, w) = input(b, c, h, w) * scale;
                });
            });
        });
        return result;
    }
};
MIGRAPHX_REGISTER_OP(ref_lrn)

template <class V, class T, class... Ts>
void visit_quantize_impl(V&& v, T&& x, Ts&&... xs)
{
    x.visit([&](auto y) { visit_all(xs...)([&](auto... ys) { v(y, ys...); }); });
}

template <class T, class... Ts>
auto visit_quantize(T&& x, Ts&&... xs)
{
    return [&](auto v) {
        // Workaround for https://gcc.gnu.org/bugzilla/show_bug.cgi?id=70100
        visit_quantize_impl(v, x, xs...);
    };
}

struct ref_im2col
{
    op::im2col op;

    template <class Self, class F>
    static auto reflect(Self& self, F f)
    {
        return migraphx::reflect(self.op, f);
    }

    static std::string name() { return "ref::im2col"; }
    shape compute_shape(const std::vector<shape>& inputs) const
    {
        return op.normalize_compute_shape(inputs);
    }

    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
    {
        argument result{output_shape};
        auto input_shape   = args[0].get_shape();
        auto weights_shape = args[1].get_shape();
        visit_all(result, args[0])([&](auto col, auto input) {
            const std::size_t& height   = input_shape.lens()[2];
            const std::size_t& width    = input_shape.lens()[3];
            const std::size_t& channels = weights_shape.lens()[1];
            const std::size_t& kernel_h = weights_shape.lens()[2];
            const std::size_t& kernel_w = weights_shape.lens()[3];
            const std::size_t& pad_h    = op.padding[0];
            const std::size_t& pad_w    = op.padding[1];
            const std::size_t& stride_h = op.stride[0];
            const std::size_t& stride_w = op.stride[1];

            long kdiv2_h = long(kernel_h) / 2;
            long kdiv2_w = long(kernel_w) / 2;
            // calculate output sizes
            const std::size_t col_height = (height - kernel_h + 2 * pad_h) / stride_h + 1;
            const std::size_t col_width  = (width - kernel_w + 2 * pad_w) / stride_w + 1;
            // account for padding for the starting position of the input pixels
            long iinput = kdiv2_h - long(pad_h);
            // loop over output pixels (ioutput, joutput)
            for(std::size_t ioutput = 0; ioutput < col_height; ioutput++, iinput += stride_h)
            {
                long jinput = kdiv2_w - long(pad_w);
                for(std::size_t joutput = 0; joutput < col_width; joutput++, jinput += stride_w)
                {
                    // compute linear index for output
                    std::size_t ldx = ioutput * col_width + joutput;
                    std::size_t p   = 0;
                    dfor(channels,
                         kernel_h,
                         kernel_w)([&](std::size_t c, std::size_t koffset, std::size_t loffset) {
                        auto idx    = iinput + long(koffset) - kdiv2_h;
                        auto jdx    = jinput + long(loffset) - kdiv2_w;
                        col(ldx, p) = ((idx >= 0) && (idx < height) && (jdx >= 0) && (jdx < width))
                                          ? input(0, c, idx, jdx)
                                          : 0;
                        p++;
                    });
                }
            }
        });
        return result;
    }
};
MIGRAPHX_REGISTER_OP(ref_im2col)

struct ref_op
{
    operation op = op::identity{};
    template <class Self, class F>
    static auto reflect(Self& self, F f)
    {
        return migraphx::reflect(self.op, f);
    }
    std::string name() const { return "ref::op"; }
    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
    argument compute(context&, const shape& output_shape, const std::vector<argument>& args) const
    {
        return op.compute(output_shape, args);
    }
    value to_value() const
    {
        value v;
        v["name"]     = op.name();
        v["operator"] = op.to_value();
        return v;
    }
    void from_value(const value& v)
    {
        op = make_op(v.at("name").to<std::string>(), v.at("operator"));
    }
    friend std::ostream& operator<<(std::ostream& os, const ref_op& x)
    {
        os << "ref::" << x.op;
        return os;
    }
};
MIGRAPHX_REGISTER_OP(ref_op)

struct ref_pad
{
    op::pad op;

    template <class Self, class F>
    static auto reflect(Self& self, F f)
    {
        return migraphx::reflect(self.op, f);
    }

    std::string name() const { return "ref::pad"; }
    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
    argument compute(context&, const dyn_output& dyn_out, std::vector<argument> args) const
    {
        assert(dyn_out.computed_shape.standard());
        argument result{dyn_out.computed_shape};
        result.visit([&](auto output) {
            using type = typename decltype(output)::value_type;
            std::fill(output.begin(), output.end(), pad_clamp<type>(op.value));
        });

        visit_all(result, args[0])([&](auto output, auto input) {
            shape_for_each(input.get_shape(), [&](const auto& idx) {
                std::vector<std::size_t> new_idx(idx.size());
                std::transform(
                    idx.begin(), idx.end(), op.pads.begin(), new_idx.begin(), [](auto i, auto j) {
                        return i + j;
                    });
                output(new_idx.begin(), new_idx.end()) = input(idx.begin(), idx.end());
            });
        });

        return result;
    }
};
MIGRAPHX_REGISTER_OP(ref_pad)

struct ref_gemm
{
    op::dot op;

    template <class Self, class F>
    static auto reflect(Self& self, F f)
    {
        return migraphx::reflect(self.op, f);
    }
    std::string name() const { return "ref::dot"; }
    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }

    argument compute(context&, const dyn_output& dyn_out, std::vector<argument> args) const
    {
        argument result{dyn_out.computed_shape};
        migemm(result, args[0], args[1], 1.0f, 0.0f);

        return result;
    }
};
MIGRAPHX_REGISTER_OP(ref_gemm)

struct ref_quant_gemm
{
    op::quant_dot op;

    template <class Self, class F>
    static auto reflect(Self& self, F f)
    {
        return migraphx::reflect(self.op, f);
    }

    std::string name() const { return "ref::quant_dot"; }
    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }

    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
    {
        argument result{output_shape};
        // first, convert the args[0] and args[1] from int8_t to int32_t
        argument arg_0{{shape::int32_type, {args.at(0).get_shape().lens()}}};
        argument arg_1{{shape::int32_type, {args.at(1).get_shape().lens()}}};
        arg_0.visit([&](auto output) {
            args.at(0).visit(
                [&](auto input) { std::copy(input.begin(), input.end(), output.begin()); });
        });

        arg_1.visit([&](auto output) {
            args.at(1).visit(
                [&](auto input) { std::copy(input.begin(), input.end(), output.begin()); });
        });

        migemm(result, arg_0, arg_1, int32_t{1}, int32_t{0});

        return result;
    }
};
MIGRAPHX_REGISTER_OP(ref_gemm)

template <class Op>
struct ref_softmax : auto_register_op<ref_softmax<Op>>
{
    ref_softmax() = default;

    ref_softmax(Op pop) : op(std::move(pop)) {}

    Op op;

    template <class Self, class F>
    static auto reflect(Self& self, F f)
    {
        return migraphx::reflect(self.op, f);
    }

    std::string name() const { return "ref::" + op.name(); }
    shape compute_shape(const std::vector<shape>& inputs) const
    {
        return op.normalize_compute_shape(inputs);
    }
    argument compute(context&, const dyn_output& dyn_out, std::vector<argument> args) const
    {
        argument result{dyn_out.computed_shape};
        auto batch_lens        = dyn_out.computed_shape.lens();
        int64_t tuned_axis     = tune_axis(args[0].get_shape().lens().size(), op.axis, op.name());
        std::size_t n_dims     = batch_lens[tuned_axis];
        batch_lens[tuned_axis] = 1;
        shape batch_shape{shape::int32_type, batch_lens};

        visit_all(result, args[0])([&](auto output, auto input) {
            using value_type = accumulator_type<typename decltype(input)::value_type>;
            std::vector<value_type> batch_max(batch_shape.elements(),
                                              std::numeric_limits<value_type>::lowest());
            std::vector<value_type> batch_sum(batch_shape.elements(), value_type(0));
            par_for(batch_shape.elements(), [&](auto i) {
                auto idx = batch_shape.multi(i);
                for(std::size_t j = 0; j < n_dims; ++j)
                {
                    idx[tuned_axis] = j;
                    batch_max[i] =
                        std::max<value_type>(batch_max[i], input(idx.begin(), idx.end()));
                }

                for(std::size_t j = 0; j < n_dims; ++j)
                {
                    idx[tuned_axis]   = j;
                    std::size_t index = dyn_out.computed_shape.index(idx);
                    output[index]     = std::exp(input[index] - batch_max[i]);
                }

                for(std::size_t j = 0; j < n_dims; ++j)
                {
                    idx[tuned_axis] = j;
                    batch_sum[i] += output(idx.begin(), idx.end());
                }

                for(std::size_t j = 0; j < n_dims; ++j)
                {
                    idx[tuned_axis] = j;
                    output(idx.begin(), idx.end()) =
                        op.output()(output(idx.begin(), idx.end()), batch_sum[i]);
                }
            });
        });

        return result;
    }
};

struct ref_rnn_var_sl_last_output
{
    op::rnn_var_sl_last_output op;

    template <class Self, class F>
    static auto reflect(Self& self, F f)
    {
        return migraphx::reflect(self.op, f);
    }

    std::string name() const { return "ref::rnn_var_sl_last_output"; }

    shape compute_shape(std::vector<shape> inputs) const
    {
        return op.compute_shape(std::move(inputs));
    }

    argument compute(const shape& output_shape, std::vector<argument> args) const
    {
        argument result{output_shape};
        auto out_comp_lens = args[0].get_shape().lens();
        out_comp_lens[0]   = 1;
        shape out_comp_s{output_shape.type(), out_comp_lens};

        visit_all(result, args[0])([&](auto output, auto input) {
            args[1].visit([&](auto seq_lens) {
                par_for(output_shape.elements(), [&](auto i) {
                    auto idx = out_comp_s.multi(i);
                    auto b   = idx[2];
                    if(op.direction == op::rnn_direction::reverse or idx[1] == 1)
                    {
                        idx[0] = 0;
                    }
                    else
                    {
                        idx[0] = seq_lens[b] - 1;
                    }
                    output[i] = input(idx.begin(), idx.end());
                });
            });
        });

        return result;
    }
};
MIGRAPHX_REGISTER_OP(ref_rnn_var_sl_last_output)

struct ref_apply
{
    module* mod;
    std::unordered_map<std::string, std::function<void(instruction_ref)>> apply_map{};

    template <class T>
    auto simple_op()
    {
        return [this](instruction_ref ins) { apply_simple_op<T>(ins); };
    }

    template <class T, class Op>
    auto extend_op()
    {
        return [this](instruction_ref ins) { apply_extend_op<T, Op>(ins); };
    }

    void init()
    {
        apply_map["dot"]        = extend_op<ref_gemm, op::dot>();
        apply_map["quant_dot"]  = extend_op<ref_quant_gemm, op::quant_dot>();
        apply_map["im2col"]     = extend_op<ref_im2col, op::im2col>();
        apply_map["logsoftmax"] = extend_op<ref_softmax<op::logsoftmax>, op::logsoftmax>();
        apply_map["lrn"]        = extend_op<ref_lrn, op::lrn>();
        apply_map["pad"]        = extend_op<ref_pad, op::pad>();
        apply_map["softmax"]    = extend_op<ref_softmax<op::softmax>, op::softmax>();
        apply_map["rnn_var_sl_last_output"] =
            extend_op<ref_rnn_var_sl_last_output, op::rnn_var_sl_last_output>();
    }

    void apply()
    {
        init();
        for(auto it : iterator_for(*mod))
        {
            if(apply_map.count(it->name()) > 0)
            {
                apply_map.at(it->name())(it);
            }
            else if(is_context_free(it->get_operator()))
            {
                apply_ref_op(it);
            }
        }
    }

    void apply_ref_op(instruction_ref ins) const
    {
        mod->replace_instruction(ins, ref_op{ins->get_operator()}, ins->inputs());
    }

    template <class T>
    void apply_simple_op(instruction_ref ins)
    {
        mod->replace_instruction(ins, T{}, ins->inputs());
    }

    template <class T, class Op>
    void apply_extend_op(instruction_ref ins)
    {
        auto&& op = any_cast<Op>(ins->get_operator());
        mod->replace_instruction(ins, T{op}, ins->inputs());
    }
};

void lowering::apply(module& m) const { ref_apply{&m}.apply(); }

} // namespace ref
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
