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1 files changed, 41 insertions, 0 deletions

diff --git a/llama.cpp/ggml/src/ggml-cuda/sum.cu b/llama.cpp/ggml/src/ggml-cuda/sum.cu
new file mode 100644
index 0000000..c56257b
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-cuda/sum.cu
@@ -0,0 +1,41 @@
+#include "sum.cuh"
+#include "sumrows.cuh"
+
+#ifdef GGML_CUDA_USE_CUB
+#include <cub/cub.cuh>
+using namespace cub;
+#endif  // GGML_CUDA_USE_CUB
+
+#include <cstdint>
+
+void sum_f32_cuda(ggml_cuda_pool & pool, const float * x, float * dst, const int64_t ne, cudaStream_t stream) {
+#ifdef GGML_CUDA_USE_CUB
+    size_t tmp_size = 0;
+    DeviceReduce::Sum(nullptr,       tmp_size, x, dst, ne, stream);
+    ggml_cuda_pool_alloc<uint8_t> tmp_alloc(pool, tmp_size);
+    DeviceReduce::Sum(tmp_alloc.ptr, tmp_size, x, dst, ne, stream);
+#else
+    // Use (inefficient) sum_rows implementation as a fallback.
+    // For AMD there is rocPRIM which could be used as a drop-in replacement via hipcub but this would require C++11 -> C++14.
+    sum_rows_f32_cuda(x, dst, ne, 1, stream);
+    GGML_UNUSED(pool);
+#endif // GGML_CUDA_USE_CUB
+}
+
+void ggml_cuda_op_sum(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(ggml_is_contiguously_allocated(src0));
+
+    const float * src0_d = (const float *) src0->data;
+    float * dst_d = (float *) dst->data;
+
+    const int64_t ne = ggml_nelements(src0);
+
+    ggml_cuda_pool & pool = ctx.pool();
+    cudaStream_t stream = ctx.stream();
+
+    sum_f32_cuda(pool, src0_d, dst_d, ne, stream);
+}