#include "conv2d.cuh"
#include "convert.cuh"

struct conv_params {
    const int64_t IW, IH;
    const int64_t OW, OH;
    const int64_t KW, KH;
    const int64_t ST_X, ST_Y;
    const int64_t PD_X, PD_Y;
    const int64_t DL_X, DL_Y;
    const int64_t IC, OC;
    const int64_t B;
    const int64_t TOTAL;
};

struct kernel_bounds {
    int64_t y_min, y_max;
    int64_t x_min, x_max;
};

__device__ __forceinline__ int64_t max64(int64_t a, int64_t b) {
    return (a > b) ? a : b;
}

__device__ __forceinline__ int64_t min64(int64_t a, int64_t b) {
    return (a < b) ? a : b;
}

__device__ __forceinline__ kernel_bounds calculate_kernel_bounds(int64_t out_x, int64_t out_y, const conv_params & P) {
    kernel_bounds bounds;
    bounds.y_min = max64(0, (P.PD_Y - out_y * P.ST_Y + P.DL_Y - 1) / P.DL_Y);
    bounds.y_max = min64(P.KH, (P.IH + P.PD_Y - out_y * P.ST_Y + P.DL_Y - 1) / P.DL_Y);
    bounds.x_min = max64(0, (P.PD_X - out_x * P.ST_X + P.DL_X - 1) / P.DL_X);
    bounds.x_max = min64(P.KW, (P.IW + P.PD_X - out_x * P.ST_X + P.DL_X - 1) / P.DL_X);
    return bounds;
}

__device__ __forceinline__ int calculate_input_coord(int64_t out_coord,
                                                     int64_t kern_coord,
                                                     int64_t stride,
                                                     int64_t dilation,
                                                     int64_t padding) {
    return out_coord * stride + kern_coord * dilation - padding;
}

struct whcn_layout {
    __device__ static int64_t input_index(int64_t n, int64_t c, int64_t y, int64_t x, const conv_params & P) {
        return n * (P.IC * P.IW * P.IH) + c * P.IW * P.IH + y * P.IW + x;
    }

    __device__ static int64_t kernel_index(int64_t c_out, int64_t c_in, int64_t ky, int64_t kx, const conv_params & P) {
        return c_out * (P.IC * P.KH * P.KW) + c_in * (P.KH * P.KW) + ky * P.KW + kx;
    }

    __device__ static int64_t output_index(int64_t n, int64_t c, int64_t y, int64_t x, const conv_params & P) {
        return n * (P.OC * P.OW * P.OH) + c * P.OW * P.OH + y * P.OW + x;
    }

    __device__ static void unpack_indices(int64_t             global_idx,
                                          const conv_params & P,
                                          int64_t &           n,
                                          int64_t &           c,
                                          int64_t &           out_y,
                                          int64_t &           out_x) {
        out_x = global_idx % P.OW;
        out_y = (global_idx / P.OW) % P.OH;
        c     = (global_idx / (P.OW * P.OH)) % P.OC;
        n     = global_idx / (P.OW * P.OH * P.OC);
    }
};

template <typename T, typename Layout>
static __global__ void conv2d_kernel(const float * __restrict__ input,
                                     const T * __restrict__ kernel,
                                     float * __restrict__ output,
                                     const conv_params P) {
    const int64_t global_idx = blockIdx.x * blockDim.x + threadIdx.x;

    if (global_idx >= P.TOTAL) {
        return;
    }

    int64_t n, c_out, out_y, out_x;
    Layout::unpack_indices(global_idx, P, n, c_out, out_y, out_x);

    float acc = 0.0f;

    for (int64_t c_in = 0; c_in < P.IC; ++c_in) {
        kernel_bounds bounds = calculate_kernel_bounds(out_x, out_y, P);

        for (int64_t ky = bounds.y_min; ky < bounds.y_max; ++ky) {
            const int64_t in_y = calculate_input_coord(out_y, ky, P.ST_Y, P.DL_Y, P.PD_Y);

            for (int64_t kx = bounds.x_min; kx < bounds.x_max; ++kx) {
                const int64_t in_x = calculate_input_coord(out_x, kx, P.ST_X, P.DL_X, P.PD_X);

                const float input_val = input[Layout::input_index(n, c_in, in_y, in_x, P)];
                const T kernel_val = kernel[Layout::kernel_index(c_out, c_in, ky, kx, P)];
                acc += (input_val * ggml_cuda_cast<float>(kernel_val));
            }
        }
    }

    // [N, OC, OH, OW]
    output[Layout::output_index(n, c_out, out_y, out_x, P)] = acc;
}

template <typename T>
static void conv2d_cuda(const float * X_D, const T * K_D, float * Y_D, const conv_params P, cudaStream_t st) {
    const int blocks = (P.TOTAL + CUDA_CONV2D_BLOCK_SIZE - 1) / CUDA_CONV2D_BLOCK_SIZE;
    conv2d_kernel<T, whcn_layout><<<blocks, CUDA_CONV2D_BLOCK_SIZE, 0, st>>>(X_D, K_D, Y_D, P);
}

static void conv2d_cuda_f16(const float * X_D, const half * K_D, float * Y_D, const conv_params P, cudaStream_t st) {
    conv2d_cuda<half>(X_D, K_D, Y_D, P, st);
}

static void conv2d_cuda_f32(const float * X_D, const float * K_D, float * Y_D, const conv_params P, cudaStream_t st) {
    conv2d_cuda<float>(X_D, K_D, Y_D, P, st);
}

void ggml_cuda_op_conv2d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    const ggml_tensor * kernel = dst->src[0];
    const ggml_tensor * input  = dst->src[1];
    float *             K_D    = (float *) kernel->data;
    const float *       X_D    = (const float *) input->data;
    float *             Y_D    = (float *) dst->data;

    GGML_ASSERT(ggml_is_contiguous(kernel));
    GGML_ASSERT(kernel->type == GGML_TYPE_F16 || kernel->type == GGML_TYPE_F32);

    // same number of input channels
    GGML_ASSERT(input->ne[2] == kernel->ne[2]);

    cudaStream_t st = ctx.stream();

    const int32_t * p    = (const int32_t *) dst->op_params;
    const int       ST_X = p[0];  // stride_x
    const int       ST_Y = p[1];  // stride_y
    const int       PD_X = p[2];  // padding_x
    const int       PD_Y = p[3];  // padding_y
    const int       DL_X = p[4];  // dilation_x
    const int       DL_Y = p[5];  // dilation_y

    // No cwhn
    GGML_ASSERT(p[6] == false);

    const int IW = input->ne[0];   // input_w
    const int IH = input->ne[1];   // input_h
    const int OW = dst->ne[0];     // output_w
    const int OH = dst->ne[1];     // output_h
    const int KW = kernel->ne[0];  // kernel_w
    const int KH = kernel->ne[1];  // kernel_h
    const int IC = input->ne[2];   // input_channels
    const int OC = kernel->ne[3];  // ouptut_chanles
    const int B  = input->ne[3];   // n_batches

    const int64_t total  = B * OC * OH * OW;
    conv_params   params = { IW, IH, OW, OH, KW, KH, ST_X, ST_Y, PD_X, PD_Y, DL_X, DL_Y, IC, OC, B, total };

    if (kernel->type == GGML_TYPE_F16) {
        conv2d_cuda_f16(X_D, (half *) K_D, Y_D, params, st);
    } else {
        conv2d_cuda_f32(X_D, K_D, Y_D, params, st);
    }
}