4ebd0c125b
* fix USE_CUDA_GRAPH=OFF ggml-ci * check capture status * completely disable capturing check instead
74 lines
2.7 KiB
Plaintext
74 lines
2.7 KiB
Plaintext
#include "mean.cuh"
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#include "reduce_rows.cuh"
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#ifdef GGML_CUDA_USE_CUB
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#include <cub/cub.cuh>
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using namespace cub;
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#endif // GGML_CUDA_USE_CUB
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template <typename T> __global__ void divide_by_count(T * result, size_t count) {
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*result /= static_cast<T>(count);
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}
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void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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const ggml_tensor * src0 = dst->src[0];
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const float * src0_d = (const float *) src0->data;
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float * dst_d = (float *) dst->data;
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cudaStream_t stream = ctx.stream();
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GGML_ASSERT(src0->type == GGML_TYPE_F32);
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GGML_ASSERT(dst->type == GGML_TYPE_F32);
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GGML_ASSERT(ggml_is_contiguous(src0));
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const int64_t ncols = src0->ne[0];
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const int64_t nrows = ggml_nrows(src0);
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// Special case for reducing vectors
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#ifdef GGML_CUDA_USE_CUB
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#ifdef USE_CUDA_GRAPH
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cudaStreamCaptureStatus iscapturing;
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CUDA_CHECK(cudaStreamIsCapturing(stream, &iscapturing));
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#endif // USE_CUDA_GRAPH
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if ((nrows == 1) &&
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#ifdef USE_CUDA_GRAPH
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// CUDA_GRAPHS_DISABLED
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((ncols > 65536) &&
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((ctx.cuda_graph->instance == nullptr) && (iscapturing == cudaStreamCaptureStatusNone) ||
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ctx.cuda_graph->disable_due_to_gpu_arch || ctx.cuda_graph->disable_due_to_too_many_updates ||
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ctx.cuda_graph->disable_due_to_failed_graph_capture)) ||
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// CUDA_GRAPHS ENABLED
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((ncols > 32768) &&
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!((ctx.cuda_graph->instance == nullptr) && (iscapturing == cudaStreamCaptureStatusNone) ||
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ctx.cuda_graph->disable_due_to_gpu_arch || ctx.cuda_graph->disable_due_to_too_many_updates ||
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ctx.cuda_graph->disable_due_to_failed_graph_capture))) {
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#else
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(ncols > 65536)) {
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#endif // USE_CUDA_GRAPH
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// Single row - use device-wide reduction
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size_t tmp_size = 0;
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ggml_cuda_pool & pool = ctx.pool();
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DeviceReduce::Sum(nullptr, tmp_size, src0_d, dst_d, ncols, stream);
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ggml_cuda_pool_alloc<uint8_t> tmp_alloc(pool, tmp_size);
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DeviceReduce::Sum(tmp_alloc.ptr, tmp_size, src0_d, dst_d, ncols, stream);
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// Divide by ncols
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divide_by_count<float><<<1, 1, 0, stream>>>(dst_d, ncols);
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return;
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}
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#endif // GGML_CUDA_USE_CUB
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const dim3 block_nums(nrows, 1, 1);
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const int id = ggml_cuda_get_device();
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const int nsm = ggml_cuda_info().devices[id].nsm;
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if ((nrows / nsm) < 2) {
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const dim3 block_dims(512, 1, 1);
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reduce_rows_f32</*norm=*/true><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);
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} else {
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const dim3 block_dims(ncols < 1024 ? 32 : 128, 1, 1);
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reduce_rows_f32</*norm=*/true><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);
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}
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}
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