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#include "mean.cuh"
#include "reduce_rows.cuh"
#ifdef GGML_CUDA_USE_CUB
#include <cub/cub.cuh>
using namespace cub;
#endif // GGML_CUDA_USE_CUB
template <typename T> __global__ void divide_by_count(T * result, size_t count) {
*result /= static_cast<T>(count);
}
void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const float * src0_d = (const float *) src0->data;
float * dst_d = (float *) dst->data;
cudaStream_t stream = ctx.stream();
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
GGML_ASSERT(ggml_is_contiguous(src0));
const int64_t ncols = src0->ne[0];
const int64_t nrows = ggml_nrows(src0);
// Special case for reducing vectors
#ifdef GGML_CUDA_USE_CUB
#ifdef USE_CUDA_GRAPH
cudaStreamCaptureStatus iscapturing;
CUDA_CHECK(cudaStreamIsCapturing(stream, &iscapturing));
#endif // USE_CUDA_GRAPH
if ((nrows == 1) &&
#ifdef USE_CUDA_GRAPH
// Determine if CUDA graphs are effectively disabled for this context
// (no graph instance exists and we're not capturing, OR graphs are explicitly enabled)
(((ncols > 65536) &&
(((!ctx.any_cuda_graph_has_instance()) && (iscapturing == cudaStreamCaptureStatusNone)) ||
ctx.any_cuda_graph_enabled())) ||
// CUDA graphs are enabled - use lower threshold
((ncols > 32768) &&
!(((!ctx.any_cuda_graph_has_instance()) && (iscapturing == cudaStreamCaptureStatusNone)) ||
ctx.any_cuda_graph_enabled())))) {
#else
(ncols > 65536)) {
#endif // USE_CUDA_GRAPH
// Single row - use device-wide reduction
size_t tmp_size = 0;
ggml_cuda_pool & pool = ctx.pool();
DeviceReduce::Sum(nullptr, tmp_size, src0_d, dst_d, ncols, stream);
ggml_cuda_pool_alloc<uint8_t> tmp_alloc(pool, tmp_size);
DeviceReduce::Sum(tmp_alloc.ptr, tmp_size, src0_d, dst_d, ncols, stream);
// Divide by ncols
divide_by_count<float><<<1, 1, 0, stream>>>(dst_d, ncols);
return;
}
#endif // GGML_CUDA_USE_CUB
const dim3 block_nums(nrows, 1, 1);
const int id = ggml_cuda_get_device();
const int nsm = ggml_cuda_info().devices[id].nsm;
// Heuristic for block size selection to optimize occupancy.
// See discussion in: https://github.com/ggml-org/llama.cpp/pull/15132
if ((nrows / nsm) < 2) {
const dim3 block_dims(512, 1, 1);
reduce_rows_f32</*norm=*/true><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);
} else {
const dim3 block_dims(ncols < 1024 ? 32 : 128, 1, 1);
reduce_rows_f32</*norm=*/true><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);
}
}
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