// !!! This is a file automatically generated by hipify!!!
#include "cpu_adam_hip.h"
#include <hip/hip_runtime_api.h>
#include <math.h>
#include <omp.h>
#include <torch/extension.h>
#include <iostream>
#include <memory>
#include <type_traits>
#include <unordered_map>
#include "rocblas.h"
#include "hip/hip_runtime.h"
#include "hiprand/hiprand.h"
#include "custom_hip_layers.h"

static std::unordered_map<int, std::shared_ptr<void>> s_optimizers;

// C++ interface

void Adam_Optimizer::Step_1(float* _params,
                            float* grads,
                            float* _exp_avg,
                            float* _exp_avg_sq,
                            size_t _param_size,
                            __half* dev_params,
                            bool half_precision)
{
    size_t rounded_size = 0;
#if defined(__AVX512__) or defined(__AVX256__)
    Step_AVX<1>(&rounded_size,
                _params,
                grads,
                _exp_avg,
                _exp_avg_sq,
                _param_size,
                dev_params,
                half_precision);
#endif
    if (_param_size > rounded_size) {
        float betta1_minus1 = 1 - _betta1;
        float betta2_minus1 = 1 - _betta2;

        float step_size = -1 * _alpha / _bias_correction1;
        float w_decay = -1 * _alpha * _weight_decay;
        __half* grads_cast_h;
        __half* params_cast_h;
        if (half_precision) {
            grads_cast_h = reinterpret_cast<__half*>(grads);
            params_cast_h = reinterpret_cast<__half*>(_params);
        }

        for (size_t t = rounded_size; t < _param_size; t += TILE) {
            size_t copy_size = TILE;
            if ((t + TILE) > _param_size) copy_size = _param_size - t;
            size_t offset = copy_size + t;
            if ((t / TILE) >= 2) { hipStreamSynchronize(_streams[_buf_index]); }

#pragma omp parallel for
            for (size_t k = t; k < offset; k++) {
                float grad = half_precision ? (float)grads_cast_h[k] : grads[k];
                float param = half_precision ? (float)params_cast_h[k] : _params[k];
                float momentum = _exp_avg[k];
                float variance = _exp_avg_sq[k];
                if (_weight_decay > 0 && !_adamw_mode) { grad = param * _weight_decay + grad; }
                momentum = momentum * _betta1;
                momentum = grad * betta1_minus1 + momentum;

                variance = variance * _betta2;
                grad = grad * grad;
                variance = grad * betta2_minus1 + variance;

                grad = sqrt(variance);
                grad = grad * _bias_correction2 + _eps;
                grad = momentum / grad;
                if (_weight_decay > 0 && _adamw_mode) { param += w_decay * param; }
                param = grad * step_size + param;
                if (dev_params) _doubled_buffer[_buf_index][k - t] = param;

                if (half_precision)
                    params_cast_h[k] = (__half)param;
                else
                    _params[k] = param;
                _exp_avg[k] = momentum;
                _exp_avg_sq[k] = variance;
            }
            if (dev_params) {
                launch_param_update(
                    _doubled_buffer[_buf_index], dev_params + t, (copy_size), _streams[_buf_index]);

                _buf_index = !_buf_index;
            }
        }
    }
}

void Adam_Optimizer::Step_4(float* _params,
                            float* grads,
                            float* _exp_avg,
                            float* _exp_avg_sq,
                            size_t _param_size,
                            __half* dev_params,
                            bool half_precision)
{
    size_t rounded_size = 0;
#if defined(__AVX512__) or defined(__AVX256__)
    Step_AVX<4>(&rounded_size,
                _params,
                grads,
                _exp_avg,
                _exp_avg_sq,
                _param_size,
                dev_params,
                half_precision);
#endif
    if (_param_size > rounded_size)
        Step_1((_params + rounded_size),
               (grads + rounded_size),
               (_exp_avg + rounded_size),
               (_exp_avg_sq + rounded_size),
               (_param_size - rounded_size),
               (dev_params != nullptr ? (dev_params + rounded_size) : dev_params),
               half_precision);
}

int create_adam_optimizer(int optimizer_id,
                          float alpha = 1e-3,
                          float betta1 = 0.9,
                          float betta2 = 0.999,
                          float eps = 1e-8,
                          float weight_decay = 0,
                          bool adamw_mode = true,
                          bool should_log = false)
{
    auto opt =
        std::make_shared<Adam_Optimizer>(alpha, betta1, betta2, eps, weight_decay, adamw_mode);

    s_optimizers[optimizer_id] = opt;

    if (should_log) {
        std::string avx_type = "";
#if defined(__AVX512__)
        avx_type = "AVX512";
#else
#if defined(__AVX256__)
        avx_type = "AVX2";
#else
        avx_type = "scalar";
#endif
#endif

        printf("Adam Optimizer #%d is created with %s arithmetic capability.\n",
               optimizer_id,
               avx_type.c_str());
        printf("Config: alpha=%f, betas=(%f, %f), weight_decay=%f, adam_w=%d\n",
               alpha,
               betta1,
               betta2,
               weight_decay,
               (int)adamw_mode);
    }

    return 0;
}

void Adam_Optimizer::Step_8(float* _params,
                            float* grads,
                            float* _exp_avg,
                            float* _exp_avg_sq,
                            size_t _param_size,
                            __half* dev_params,
                            bool half_precision)
{
    size_t rounded_size = 0;
#if defined(__AVX512__) or defined(__AVX256__)
    Step_AVX<8>(&rounded_size,
                _params,
                grads,
                _exp_avg,
                _exp_avg_sq,
                _param_size,
                dev_params,
                half_precision);
#endif
    if (_param_size > rounded_size)
        Step_4((_params + rounded_size),
               (grads + rounded_size),
               (_exp_avg + rounded_size),
               (_exp_avg_sq + rounded_size),
               (_param_size - rounded_size),
               (dev_params != nullptr ? (dev_params + rounded_size) : dev_params),
               half_precision);
}

int ds_adam_step(int optimizer_id,
                 size_t step,
                 float lr,
                 float beta1,
                 float beta2,
                 float epsilon,
                 float weight_decay,
                 bool bias_correction,
                 torch::Tensor& params,
                 torch::Tensor& grads,
                 torch::Tensor& exp_avg,
                 torch::Tensor& exp_avg_sq)
{
    auto params_c = params.contiguous();
    auto grads_c = grads.contiguous();
    auto exp_avg_c = exp_avg.contiguous();
    auto exp_avg_sq_c = exp_avg_sq.contiguous();

    // assert(params.options().dtype() == grads.options().dtype());

    float* params_ptr = (float*)params_c.data_ptr();
    float* grads_ptr = (float*)grads_c.data_ptr();
    float* exp_avg_ptr = (float*)exp_avg_c.data_ptr();
    float* exp_avg_sq_ptr = (float*)exp_avg_sq_c.data_ptr();

    std::shared_ptr<Adam_Optimizer> opt =
        std::static_pointer_cast<Adam_Optimizer>(s_optimizers[optimizer_id]);
    opt->IncrementStep(step, beta1, beta2);
    opt->update_state(lr, epsilon, weight_decay, bias_correction);

    opt->Step_8(params_ptr,
                grads_ptr,
                exp_avg_ptr,
                exp_avg_sq_ptr,
                params_c.size(0),
                nullptr,
                (params.options().dtype() == at::kHalf));

    opt->SynchronizeStreams();
    return 0;
}

int ds_adam_step_plus_copy(int optimizer_id,
                           size_t step,
                           float lr,
                           float beta1,
                           float beta2,
                           float epsilon,
                           float weight_decay,
                           bool bias_correction,
                           torch::Tensor& params,
                           torch::Tensor& grads,
                           torch::Tensor& exp_avg,
                           torch::Tensor& exp_avg_sq,
                           torch::Tensor& gpu_params)
{
    auto params_c = params.contiguous();
    auto gpu_params_c = gpu_params.contiguous();
    auto exp_avg_c = exp_avg.contiguous();
    auto exp_avg_sq_c = exp_avg_sq.contiguous();
    auto grads_c = grads.contiguous();

    float* params_ptr = (float*)params_c.data_ptr();
    float* grads_ptr = (float*)grads_c.data_ptr();
    __half* gpu_params_ptr = (__half*)gpu_params_c.data_ptr();
    float* exp_avg_ptr = (float*)exp_avg_c.data_ptr();
    float* exp_avg_sq_ptr = (float*)exp_avg_sq_c.data_ptr();

    std::shared_ptr<Adam_Optimizer> opt =
        std::static_pointer_cast<Adam_Optimizer>(s_optimizers[optimizer_id]);
    opt->IncrementStep(step, beta1, beta2);
    opt->update_state(lr, epsilon, weight_decay, bias_correction);
    opt->Step_8(params_ptr,
                grads_ptr,
                exp_avg_ptr,
                exp_avg_sq_ptr,
                params_c.size(0),
                gpu_params_ptr,
                (params.options().dtype() == at::kHalf));

    opt->SynchronizeStreams();
    return 0;
}

int destroy_adam_optimizer(int optimizer_id)
{
    s_optimizers.erase(optimizer_id);

    return 0;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
{
    m.def("adam_update", &ds_adam_step, "DeepSpeed CPU Adam update (C++)");
    m.def("adam_update_copy",
          &ds_adam_step_plus_copy,
          "DeepSpeed CPU Adam update and param copy (C++)");
    m.def("create_adam", &create_adam_optimizer, "DeepSpeed CPU Adam (C++)");
    m.def("destroy_adam", &destroy_adam_optimizer, "DeepSpeed CPU Adam destroy (C++)");
}
