// !!! This is a file automatically generated by hipify!!!
#include "hip/hip_runtime.h"
#include "custom_hip_layers.h"

#define MAX_CAP 4
#define MAX_SEQ 2048

inline __device__ float gelu(const float x)
{
    const float sqrt_param = 0.79788456080286535587989211986876f;
    const float mul_param = 0.044715;
    return x * 0.5f * (1.0f + tanhf(sqrt_param * (x + mul_param * x * x * x)));
}

__global__ void fused_bias_gelu(float* input,
                                const float* bias,
                                int total_count,
                                int intermediate_size)
{
    float4* input_cast = reinterpret_cast<float4*>(input);
    const float4* bias_cast = reinterpret_cast<const float4*>(bias);
    int offset = blockIdx.x * blockDim.x + threadIdx.x;

    if (offset < total_count) {
        float4 data = input_cast[offset];
        float4 bias_data = bias_cast[offset % intermediate_size];

        data.x += bias_data.x;
        data.y += bias_data.y;
        data.z += bias_data.z;
        data.w += bias_data.w;

        data.x = gelu(data.x);
        data.y = gelu(data.y);
        data.z = gelu(data.z);
        data.w = gelu(data.w);

        input_cast[offset] = data;
    }
}

__global__ void fused_bias_gelu(__half* input,
                                const __half* bias,
                                int total_count,
                                int intermediate_size)
{
#ifdef HALF_PRECISION_AVAILABLE

    float2* input_cast = reinterpret_cast<float2*>(input);
    const float2* bias_cast = reinterpret_cast<const float2*>(bias);

    int offset = blockIdx.x * blockDim.x + threadIdx.x;

    if (offset < total_count) {
        float2 vals_vec = input_cast[offset];
        float2 bias_vec = bias_cast[offset % intermediate_size];

        __half2* vals_half = reinterpret_cast<__half2*>(&vals_vec);
        __half2* bias_half = reinterpret_cast<__half2*>(&bias_vec);

        float2 low_data = __half22float2(vals_half[0]);
        float2 high_data = __half22float2(vals_half[1]);

        float2 low_bias = __half22float2(bias_half[0]);
        float2 high_bias = __half22float2(bias_half[1]);

        low_data.x += low_bias.x;
        low_data.y += low_bias.y;
        high_data.x += high_bias.x;
        high_data.y += high_bias.y;

        low_data.x = gelu(low_data.x);
        low_data.y = gelu(low_data.y);
        high_data.x = gelu(high_data.x);
        high_data.y = gelu(high_data.y);

        vals_half[0] = __float22half2_rn(low_data);
        vals_half[1] = __float22half2_rn(high_data);

        input_cast[offset] = vals_vec;
    }
#endif
}

template <typename T>
void launch_bias_gelu(T* input,
                      const T* bias,
                      int intermediate_size,
                      int batch_size,
                      hipStream_t stream)
{
    int total_count = batch_size * (intermediate_size / 4);
    int threads = 1024;  // intermediate_size / iterations / 4;
    dim3 block_dims(threads);
    dim3 grid_dims(((total_count - 1) / 1024 + 1));  // (batch_size);

   hipLaunchKernelGGL(( fused_bias_gelu), dim3(grid_dims), dim3(block_dims), 0, stream, 
        input, bias, total_count, intermediate_size / 4);
}

template void launch_bias_gelu<float>(float*, const float*, int, int, hipStream_t);
template void launch_bias_gelu<__half>(__half*, const __half*, int, int, hipStream_t);

__global__ void fused_bias_add(float* input, const float* bias, int total_count, int hidden_size)
{
    float4* input_cast = reinterpret_cast<float4*>(input);
    const float4* bias_cast = reinterpret_cast<const float4*>(bias);
    int offset = blockIdx.x * blockDim.x + threadIdx.x;

    if (offset < total_count) {
        float4 data = input_cast[offset];
        float4 bias_data = bias_cast[offset % hidden_size];

        data.x += bias_data.x;
        data.y += bias_data.y;
        data.z += bias_data.z;
        data.w += bias_data.w;

        input_cast[offset] = data;
    }
}

__global__ void fused_bias_add(__half* input, const __half* bias, int total_count, int hidden_size)
{
#ifdef HALF_PRECISION_AVAILABLE

    float2* input_cast = reinterpret_cast<float2*>(input);
    const float2* bias_cast = reinterpret_cast<const float2*>(bias);

    int offset = blockIdx.x * blockDim.x + threadIdx.x;

    if (offset < total_count) {
        float2 vals_vec = input_cast[offset];
        float2 bias_vec = bias_cast[offset % hidden_size];

        __half2* vals_half = reinterpret_cast<__half2*>(&vals_vec);
        __half2* bias_half = reinterpret_cast<__half2*>(&bias_vec);

        float2 low_data = __half22float2(vals_half[0]);
        float2 high_data = __half22float2(vals_half[1]);

        float2 low_bias = __half22float2(bias_half[0]);
        float2 high_bias = __half22float2(bias_half[1]);

        low_data.x += low_bias.x;
        low_data.y += low_bias.y;
        high_data.x += high_bias.x;
        high_data.y += high_bias.y;

        vals_half[0] = __float22half2_rn(low_data);
        vals_half[1] = __float22half2_rn(high_data);

        input_cast[offset] = vals_vec;
    }
#endif
}

template <typename T>
void launch_bias_add(T* input, const T* bias, int hidden_size, int batch_size, hipStream_t stream)
{
    int total_count = batch_size * (hidden_size / 4);
    int threads = 1024;  // hidden_size / iterations / 4;
    dim3 block_dims(threads);
    dim3 grid_dims(((total_count - 1) / threads + 1));  // (batch_size);

   hipLaunchKernelGGL(( fused_bias_add), dim3(grid_dims), dim3(block_dims), 0, stream, input, bias, total_count, hidden_size / 4);
}

template void launch_bias_add<float>(float*, const float*, int, int, hipStream_t);
template void launch_bias_add<__half>(__half*, const __half*, int, int, hipStream_t);

__global__ void fused_bias_residual(float* input,
                                    float* output,
                                    float* attn,
                                    float* bias,
                                    float* attnbias,
                                    int total_count,
                                    int intermediate_size,
                                    int mp_size)
{
    float4* input_cast = reinterpret_cast<float4*>(input);
    float4* output_cast = reinterpret_cast<float4*>(output);
    float4* attn_cast = reinterpret_cast<float4*>(attn);
    float4* bias_cast = reinterpret_cast<float4*>(bias);
    float4* attnbias_cast = reinterpret_cast<float4*>(attnbias);
    int offset = blockIdx.x * blockDim.x + threadIdx.x;

    if (offset < total_count) {
        float4 data = input_cast[offset];
        float4 out = output_cast[offset];
        float4 res_vec = attn_cast[offset];
        float4 bias_data = bias_cast[offset % intermediate_size];
        float4 attn_bias = attnbias_cast[offset % intermediate_size];

        data.x = (data.x + res_vec.x) * mp_size + (out.x + bias_data.x + attn_bias.x);
        data.y = (data.y + res_vec.y) * mp_size + (out.y + bias_data.y + attn_bias.y);
        data.z = (data.z + res_vec.z) * mp_size + (out.z + bias_data.z + attn_bias.z);
        data.w = (data.w + res_vec.w) * mp_size + (out.w + bias_data.w + attn_bias.w);

        output_cast[offset] = data;
    }
}

__global__ void fused_bias_residual(__half* input,
                                    __half* output,
                                    __half* attn,
                                    __half* bias,
                                    __half* attn_bias,
                                    int total_count,
                                    int intermediate_size,
                                    int mp_size)
{
#ifdef HALF_PRECISION_AVAILABLE

    float2* input_cast = reinterpret_cast<float2*>(input);
    float2* output_cast = reinterpret_cast<float2*>(output);
    float2* attn_cast = reinterpret_cast<float2*>(attn);

    float2* bias_cast = reinterpret_cast<float2*>(bias);
    float2* attnbias_cast = reinterpret_cast<float2*>(attn_bias);

    int offset = blockIdx.x * blockDim.x + threadIdx.x;

    if (offset < total_count) {
        float2 vals_vec = input_cast[offset];
        float2 out_vec = output_cast[offset];
        float2 res_vec = attn_cast[offset];

        float2 bias_vec = bias_cast[offset % intermediate_size];
        float2 attn_bias_vec = attnbias_cast[offset % intermediate_size];

        __half2* vals_half = reinterpret_cast<__half2*>(&vals_vec);
        __half2* out_half = reinterpret_cast<__half2*>(&out_vec);
        __half2* res_half = reinterpret_cast<__half2*>(&res_vec);
        __half2* bias_half = reinterpret_cast<__half2*>(&bias_vec);
        __half2* attnbias_half = reinterpret_cast<__half2*>(&attn_bias_vec);

        float2 low_data = __half22float2(vals_half[0]);
        float2 high_data = __half22float2(vals_half[1]);

        float2 low_out = __half22float2(out_half[0]);
        float2 high_out = __half22float2(out_half[1]);

        float2 low_res = __half22float2(res_half[0]);
        float2 high_res = __half22float2(res_half[1]);

        float2 low_bias = __half22float2(bias_half[0]);
        float2 high_bias = __half22float2(bias_half[1]);

        float2 attn_low_bias = __half22float2(attnbias_half[0]);
        float2 attn_high_bias = __half22float2(attnbias_half[1]);

        low_data.x =
            (low_data.x + low_res.x) * mp_size + (low_out.x + (low_bias.x + attn_low_bias.x));
        low_data.y =
            (low_data.y + low_res.y) * mp_size + (low_out.y + (low_bias.y + attn_low_bias.y));
        high_data.x =
            (high_data.x + high_res.x) * mp_size + (high_out.x + (high_bias.x + attn_high_bias.x));
        high_data.y =
            (high_data.y + high_res.y) * mp_size + (high_out.y + (high_bias.y + attn_high_bias.y));

        vals_half[0] = __float22half2_rn(low_data);
        vals_half[1] = __float22half2_rn(high_data);

        output_cast[offset] = vals_vec;
    }
#endif
}

template <typename T>
void launch_bias_residual(T* input,
                          T* output,
                          T* attn,
                          T* bias,
                          T* attn_bias,
                          int batch,
                          int hidden_dim,
                          int mp_size,
                          hipStream_t stream)
{
    int total_count = batch * hidden_dim / 4;
    dim3 block_dims(1024);
    dim3 grid_dims((total_count - 1) / 1024 + 1);  // (batch_size);

   hipLaunchKernelGGL(( fused_bias_residual), dim3(grid_dims), dim3(block_dims), 0, stream, 
        input, output, attn, bias, attn_bias, total_count, hidden_dim / 4, 1.0 / mp_size);
}

template void
launch_bias_residual<float>(float*, float*, float*, float*, float*, int, int, int, hipStream_t);
template void launch_bias_residual<__half>(__half*,
                                           __half*,
                                           __half*,
                                           __half*,
                                           __half*,
                                           int,
                                           int,
                                           int,
                                           hipStream_t);

__global__ void gptj_residual_add(float* input,
                                  float* output,
                                  float* attn,
                                  float* bias,
                                  float* attnbias,
                                  int total_count,
                                  int intermediate_size,
                                  float mp_size)
{
    float4* input_cast = reinterpret_cast<float4*>(input);
    float4* output_cast = reinterpret_cast<float4*>(output);
    float4* attn_cast = reinterpret_cast<float4*>(attn);
    float4* bias_cast = reinterpret_cast<float4*>(bias);
    float4* attnbias_cast = reinterpret_cast<float4*>(attnbias);
    int offset = blockIdx.x * blockDim.x + threadIdx.x;

    if (offset < total_count) {
        float4 data = input_cast[offset];
        float4 out = output_cast[offset];
        float4 res_vec = attn_cast[offset];
        float4 bias_data = bias_cast[offset % intermediate_size];
        float4 attn_bias = attnbias_cast[offset % intermediate_size];

        data.x = data.x * mp_size + (out.x + res_vec.x + bias_data.x + attn_bias.x);
        data.y = data.y * mp_size + (out.y + res_vec.y + bias_data.y + attn_bias.y);
        data.z = data.z * mp_size + (out.z + res_vec.z + bias_data.z + attn_bias.z);
        data.w = data.w * mp_size + (out.w + res_vec.w + bias_data.w + attn_bias.w);

        output_cast[offset] = data;
    }
}

__global__ void gptj_residual_add(__half* input,
                                  __half* output,
                                  __half* attn,
                                  __half* bias,
                                  __half* attn_bias,
                                  int total_count,
                                  int intermediate_size,
                                  float mp_size)
{
#if __CUDA_ARCH__ >= 700 || defined(__HIP_PLATFORM_HCC__)

    float2* input_cast = reinterpret_cast<float2*>(input);
    float2* output_cast = reinterpret_cast<float2*>(output);
    float2* attn_cast = reinterpret_cast<float2*>(attn);

    float2* bias_cast = reinterpret_cast<float2*>(bias);
    float2* attnbias_cast = reinterpret_cast<float2*>(attn_bias);

    int offset = blockIdx.x * blockDim.x + threadIdx.x;

    if (offset < total_count) {
        float2 vals_vec = input_cast[offset];
        float2 out_vec = output_cast[offset];
        float2 res_vec = attn_cast[offset];

        float2 bias_vec = bias_cast[offset % intermediate_size];
        float2 attn_bias_vec = attnbias_cast[offset % intermediate_size];

        __half2* vals_half = reinterpret_cast<__half2*>(&vals_vec);
        __half2* out_half = reinterpret_cast<__half2*>(&out_vec);
        __half2* res_half = reinterpret_cast<__half2*>(&res_vec);
        __half2* bias_half = reinterpret_cast<__half2*>(&bias_vec);
        __half2* attnbias_half = reinterpret_cast<__half2*>(&attn_bias_vec);

        float2 low_data = __half22float2(vals_half[0]);
        float2 high_data = __half22float2(vals_half[1]);

        float2 low_out = __half22float2(out_half[0]);
        float2 high_out = __half22float2(out_half[1]);

        float2 low_res = __half22float2(res_half[0]);
        float2 high_res = __half22float2(res_half[1]);

        float2 low_bias = __half22float2(bias_half[0]);
        float2 high_bias = __half22float2(bias_half[1]);

        float2 attn_low_bias = __half22float2(attnbias_half[0]);
        float2 attn_high_bias = __half22float2(attnbias_half[1]);

        low_data.x =
            low_data.x * mp_size + (low_out.x + low_res.x + (low_bias.x + attn_low_bias.x));
        low_data.y =
            low_data.y * mp_size + (low_out.y + low_res.y + (low_bias.y + attn_low_bias.y));
        high_data.x =
            high_data.x * mp_size + (high_out.x + high_res.x + (high_bias.x + attn_high_bias.x));
        high_data.y =
            high_data.y * mp_size + (high_out.y + high_res.y + (high_bias.y + attn_high_bias.y));

        vals_half[0] = __float22half2_rn(low_data);
        vals_half[1] = __float22half2_rn(high_data);

        output_cast[offset] = vals_vec;
    }
#endif
}

template <typename T>
void launch_gptj_residual_add(T* input,
                              T* output,
                              T* attn,
                              T* bias,
                              T* attn_bias,
                              int hidden_dim,
                              int batch,
                              int mp_size,
                              hipStream_t stream)
{
    int total_count = batch * hidden_dim / 4;
    dim3 block_dims(1024);
    dim3 grid_dims((total_count - 1) / 1024 + 1);  // (batch_size);

   hipLaunchKernelGGL(( gptj_residual_add), dim3(grid_dims), dim3(block_dims), 0, stream, 
        input, output, attn, bias, attn_bias, total_count, hidden_dim / 4, 1.0 / mp_size);
}

template void launch_gptj_residual_add<float>(float*,
                                              float*,
                                              float*,
                                              float*,
                                              float*,
                                              int,
                                              int,
                                              int,
                                              hipStream_t);
template void launch_gptj_residual_add<__half>(__half*,
                                               __half*,
                                               __half*,
                                               __half*,
                                               __half*,
                                               int,
                                               int,
                                               int,
                                               hipStream_t);

__global__ void moe_res_matmul(float* residual,
                               float* coef,
                               float* mlp_out,
                               int seq_len,
                               int hidden_dim)
{
    unsigned tid = threadIdx.x;
    float4* residual_cast = reinterpret_cast<float4*>(residual);
    float4* coef_cast = reinterpret_cast<float4*>(coef);
    float4* mlp_out_cast = reinterpret_cast<float4*>(mlp_out);

    residual_cast += blockIdx.x * hidden_dim;
    mlp_out_cast += blockIdx.x * hidden_dim;

    float4* coef_cast2 = coef_cast + hidden_dim;

    while (tid < hidden_dim) {
        float4 res = residual_cast[tid];
        float4 mlp = mlp_out_cast[tid];
        float4 coef1 = coef_cast[tid];
        float4 coef2 = coef_cast2[tid];
        mlp.x = mlp.x * coef2.x + res.x * coef1.x;
        mlp.y = mlp.y * coef2.y + res.y * coef1.y;
        mlp.z = mlp.z * coef2.z + res.z * coef1.z;
        mlp.w = mlp.w * coef2.w + res.w * coef1.w;
        mlp_out_cast[tid] = mlp;
        tid += blockDim.x;
    }
}

__global__ void moe_res_matmul(__half* residual,
                               __half* coef,
                               __half* mlp_out,
                               int seq_len,
                               int hidden_dim)
{
    unsigned tid = threadIdx.x;

    float2* residual_cast = reinterpret_cast<float2*>(residual);
    float2* mlp_out_cast = reinterpret_cast<float2*>(mlp_out);
    float2* coef_cast = reinterpret_cast<float2*>(coef);
    float2* coef_cast2 = coef_cast + hidden_dim;

    residual_cast += blockIdx.x * hidden_dim;
    mlp_out_cast += blockIdx.x * hidden_dim;

    while (tid < hidden_dim) {
        float2 res = residual_cast[tid];
        float2 coef1 = coef_cast[tid];
        float2 coef2 = coef_cast[tid];
        float2 data = mlp_out_cast[tid];
        __half* data_h = reinterpret_cast<__half*>(&data);
        __half* coef1_h = reinterpret_cast<__half*>(&coef1);
        __half* coef2_h = reinterpret_cast<__half*>(&coef2);
        __half* res_h = reinterpret_cast<__half*>(&res);
        data_h[0] = res_h[0] * coef1_h[0] + data_h[0] * coef2_h[0];
        data_h[1] = res_h[1] * coef1_h[1] + data_h[1] * coef2_h[1];
        data_h[2] = res_h[2] * coef1_h[2] + data_h[2] * coef2_h[2];
        data_h[3] = res_h[3] * coef1_h[3] + data_h[3] * coef2_h[3];

        mlp_out_cast[tid] = data;
        tid += blockDim.x;
    }
}

template <typename T>
void launch_moe_res_matmul(T* residual,
                           T* coef,
                           T* mlp_out,
                           int seq_len,
                           int hidden_dim,
                           hipStream_t stream)
{
    dim3 grid_dim(seq_len);
    dim3 block_dim(1024);
   hipLaunchKernelGGL(( moe_res_matmul), dim3(grid_dim), dim3(block_dim), 0, stream, 
        residual, coef, mlp_out, seq_len, hidden_dim / 4);
}

template void launch_moe_res_matmul(float* residual,
                                    float* coef,
                                    float* mlp_out,
                                    int seq_len,
                                    int hidden_dim,
                                    hipStream_t stream);
template void launch_moe_res_matmul(__half* residual,
                                    __half* coef,
                                    __half* mlp_out,
                                    int seq_len,
                                    int hidden_dim,
                                    hipStream_t stream);
