llama-cpp-python/vendor/llama.cpp/ggml-cuda/concat.cu

#include "concat.cuh"

static __global__ void concat_f32(const float * x,const float * y, float * dst, const int ne0, const int ne02) {
    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
    if (nidx >= ne0) {
        return;
    }
    // operation
    int offset_dst =
        nidx +
        blockIdx.y * ne0 +
        blockIdx.z * ne0 * gridDim.y;
    if (blockIdx.z < ne02) { // src0
        int offset_src =
            nidx +
            blockIdx.y * ne0 +
            blockIdx.z * ne0 * gridDim.y;
        dst[offset_dst] = x[offset_src];
    } else {
        int offset_src =
            nidx +
            blockIdx.y * ne0 +
            (blockIdx.z - ne02) * ne0 *  gridDim.y;
        dst[offset_dst] = y[offset_src];
    }
}

static void concat_f32_cuda(const float * x, const float * y, float * dst, const int ne0, int ne1, int ne2, int ne02, cudaStream_t stream) {
    int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE;
    dim3 gridDim(num_blocks, ne1, ne2);
    concat_f32<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne02);
}

void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    const ggml_tensor * src0 = dst->src[0];
    const ggml_tensor * src1 = dst->src[1];
    const float * src0_d = (const float *)src0->data;
    const float * src1_d = (const float *)src1->data;
    float * dst_d = (float *)dst->data;
    cudaStream_t stream = ctx.stream();

    GGML_ASSERT(src0->type == GGML_TYPE_F32);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F32);

    for (int i3 = 0; i3 < dst->ne[3]; i3++) {
        concat_f32_cuda(src0_d + i3 * (src0->nb[3] / 4), src1_d + i3 * (src1->nb[3] / 4), dst_d + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], stream);
    }
}