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CUDA: use radix-select TOP_K in the CUB fallback for wide rows #28671

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CUDA: use radix-select TOP_K in the CUB fallback for wide rows #28671

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16 changes: 14 additions & 2 deletions ggml/src/ggml-cuda/top-k.cu
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Original file line number Diff line number Diff line change
@@ -1,6 +1,8 @@
#include "argsort.cuh"
#include "top-k.cuh"

#include <cstdlib>

#ifdef GGML_CUDA_USE_CUB
# include <cub/cub.cuh>
# if (CCCL_MAJOR_VERSION >= 3 && CCCL_MINOR_VERSION >= 2)
Expand Down Expand Up @@ -48,7 +50,8 @@ static int next_power_of_2(int x) {

#endif // CUB_TOP_K_AVAILABLE

#if !defined(GGML_CUDA_USE_CUB) && defined(GGML_USE_HIP)
// radix-select top-k: used by HIP for wide rows and by CUB builds without DeviceTopK (CCCL < 3.2)
#if !defined(CUB_TOP_K_AVAILABLE) && (defined(GGML_CUDA_USE_CUB) || defined(GGML_USE_HIP))

static __device__ __forceinline__ uint32_t top_k_float_to_ordered(float value) {
const uint32_t bits = __float_as_uint(value);
Expand Down Expand Up @@ -208,7 +211,7 @@ static void top_k_radix_cuda(
src, dst, states, ncols, k, blocks_per_row);
}

#endif // !defined(GGML_CUDA_USE_CUB) && defined(GGML_USE_HIP)
#endif // !defined(CUB_TOP_K_AVAILABLE) && (defined(GGML_CUDA_USE_CUB) || defined(GGML_USE_HIP))

void ggml_cuda_op_top_k(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
Expand All @@ -233,6 +236,15 @@ void ggml_cuda_op_top_k(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
top_k_cub(pool, src0_d + i * ncols, dst_d + i * k, ncols, k, stream);
}
#elif defined(GGML_CUDA_USE_CUB) // CUB_TOP_K_AVAILABLE
// No DeviceTopK in this CCCL. Sorting every key of every row and copying the first k is expensive for wide rows,
// so use the radix-select top-k for rows of at least 8192 columns. The threshold is conservative: on an RTX 3090
// the segmented sort was still faster at 4096 columns and the radix-select faster from 8192 columns on.
// GGML_CUDA_TOPK_ARGSORT=1 forces the argsort path (for A/B comparisons within one binary).
static const bool force_argsort = getenv("GGML_CUDA_TOPK_ARGSORT") != nullptr && atoi(getenv("GGML_CUDA_TOPK_ARGSORT")) != 0;
if (!force_argsort && ncols >= 8192) {
top_k_radix_cuda(pool, src0_d, dst_d, ncols, nrows, k, stream);
return;
}
// Fall back to argsort + copy
const int ncols_pad = next_power_of_2(ncols);
const size_t shared_mem = ncols_pad * sizeof(int);
Expand Down