// !!! This is a file automatically generated by hipify!!!
#pragma once

#include <ATen/hip/HIPContext.h>
#include <hip/hip_runtime_api.h>
#include <cassert>
#include <iostream>
#include <vector>
#include "rocblas.h"
#include "hip/hip_runtime.h"
#include "hiprand/hiprand.h"

#define WARP_SIZE 32

#define CUDA_CHECK(callstr)                                                                    \
    {                                                                                          \
        hipError_t error_code = callstr;                                                      \
        if (error_code != hipSuccess) {                                                       \
            std::cerr << "CUDA error " << error_code << " at " << __FILE__ << ":" << __LINE__; \
            assert(0);                                                                         \
        }                                                                                      \
    }

#define CUDA_1D_KERNEL_LOOP(i, n) \
    for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); i += blockDim.x * gridDim.x)

#define CUDA_2D_KERNEL_LOOP(i, n, j, m)                                                          \
    for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); i += blockDim.x * gridDim.x) \
        for (size_t j = blockIdx.y * blockDim.y + threadIdx.y; j < (m); j += blockDim.y * gridDim.y)

#define DS_CUDA_NUM_THREADS 512
#define DS_MAXIMUM_NUM_BLOCKS 262144

inline int DS_GET_BLOCKS(const int N)
{
    return std::max(
        std::min((N + DS_CUDA_NUM_THREADS - 1) / DS_CUDA_NUM_THREADS, DS_MAXIMUM_NUM_BLOCKS),
        // Use at least 1 block, since CUDA does not allow empty block
        1);
}

class Context {
public:
    Context() : _workspace(nullptr), _seed(42), _curr_offset(0), _stream(0)
    {
        hiprandCreateGenerator(&_gen, HIPRAND_RNG_PSEUDO_DEFAULT);
        hiprandSetPseudoRandomGeneratorSeed(_gen, 123);
        if (rocblas_create_handle(&_cublasHandle) != rocblas_status_success) {
            auto message = std::string("Fail to create cublas handle.");
            std::cerr << message << std::endl;
            throw std::runtime_error(message);
        }
        rocblas_set_math_mode(_cublasHandle, CUBLAS_TENSOR_OP_MATH);
        hipEventCreate(&_comp1_event, (hipEventDisableTiming | hipEventBlockingSync));
        hipEventCreate(&_comp2_event, (hipEventDisableTiming | hipEventBlockingSync));
        hipEventCreate(&_comp_event, (hipEventDisableTiming | hipEventBlockingSync));
        hipEventCreate(&_comm_event, (hipEventDisableTiming | hipEventBlockingSync));
    }

    virtual ~Context()
    {
        rocblas_destroy_handle(_cublasHandle);
        hipFree(_workspace);
        hipEventDestroy(_comp1_event);
        hipEventDestroy(_comp2_event);
        hipEventDestroy(_comp_event);
        hipEventDestroy(_comm_event);
    }

    static Context& Instance()
    {
        static Context _ctx;
        return _ctx;
    }

    void GenWorkSpace(size_t size)
    {
        if (!_workspace) {
            assert(_workspace == nullptr);
            hipMalloc(&_workspace, size);
        } else if (_workSpaceSize < size) {
            hipFree(_workspace);
            hipMalloc(&_workspace, size);
        }

        _workSpaceSize = size;
    }

    hipEvent_t GetCompEvent(int id) { return id == 1 ? _comp1_event : _comp2_event; }

    size_t get_workspace_size() const { return _workSpaceSize; }
    void* GetWorkSpace() { return _workspace; }

    inline unsigned new_token(unsigned layer_id)
    {
        if (layer_id == 0) _token_length++;
        return _token_length;
    }

    inline void reset_tokens(unsigned initial_tokens = 0)
    {
        _num_tokens = initial_tokens;
    }  //_token_length = 0; }

    inline unsigned current_tokens() const { return _num_tokens; }

    inline void advance_tokens() { _num_tokens++; }

    hiprandGenerator_t& GetRandGenerator() { return _gen; }

    hipStream_t GetCommStream(bool async_op = false)
    {
        if (!_comm_stream)
            _comm_stream = async_op ? at::hip::getStreamFromPoolMasqueradingAsCUDA(true)
                                    : at::hip::getCurrentHIPStreamMasqueradingAsCUDA();
        return _comm_stream;
    }
    hipStream_t GetCurrentStream(bool other_stream = false)
    {
        // get current pytorch stream.
        if (other_stream) {
            if (!_stream) _stream = at::hip::getStreamFromPoolMasqueradingAsCUDA(true);
            return _stream;
        }
        hipStream_t stream = at::hip::getCurrentHIPStreamMasqueradingAsCUDA();
        return stream;
    }

    rocblas_handle GetCublasHandle() { return _cublasHandle; }

    std::pair<uint64_t, uint64_t> IncrementOffset(uint64_t offset_inc)
    {
        uint64_t offset = _curr_offset;
        _curr_offset += offset_inc;
        return std::pair<uint64_t, uint64_t>(_seed, offset);
    }

    void SetSeed(uint64_t new_seed) { _seed = new_seed; }

    const std::vector<std::array<int, 3>>& GetGemmAlgos() const { return _gemm_algos; }

    inline void SynchComp()
    {
        hipEventRecord(_comp_event, _comp_stream);
        hipStreamWaitEvent(_comm_stream, _comp_event, 0);
    }
    inline void SynchComm()
    {
        hipEventRecord(_comm_event, _comm_stream);
        hipStreamWaitEvent(_comp_stream, _comm_event, 0);
    }

private:
    hiprandGenerator_t _gen;
    rocblas_handle _cublasHandle;

    hipEvent_t _comp_event;
    hipEvent_t _comm_event;

    void* _workspace;
    uint64_t _seed;
    uint64_t _curr_offset;
    size_t _workSpaceSize;

    hipEvent_t _comp1_event;
    hipEvent_t _comp2_event;

    hipStream_t _stream;

    unsigned _token_length;
    unsigned _num_tokens;
    std::vector<std::array<int, 3>> _gemm_algos;

    hipStream_t _comp_stream;
    hipStream_t _comm_stream;

    std::unordered_map<int, int> _world_sizes;
};
