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author | Kawrakow <48489457+ikawrakow@users.noreply.github.com> | 2024-07-27 07:55:01 +0200 |
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committer | GitHub <noreply@github.com> | 2024-07-27 07:55:01 +0200 |
commit | 154e0d75fccf1784fe9ff6fd76a630b66563da3d (patch) | |
tree | 81ce6dbb5b1900c1aa78a879f0593c694cab9d27 /ggml/src/ggml-cuda/mmvq.cuh | |
parent | 0684c3e9c70d49323b4fc517128cbe222cab7f96 (diff) |
Merge mainline llama.cpp (#3)
* Merging mainline - WIP
* Merging mainline - WIP
AVX2 and CUDA appear to work.
CUDA performance seems slightly (~1-2%) lower as it is so often
the case with llama.cpp/ggml after some "improvements" have been made.
* Merging mainline - fix Metal
* Remove check
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Diffstat (limited to 'ggml/src/ggml-cuda/mmvq.cuh')
-rw-r--r-- | ggml/src/ggml-cuda/mmvq.cuh | 9 |
1 files changed, 9 insertions, 0 deletions
diff --git a/ggml/src/ggml-cuda/mmvq.cuh b/ggml/src/ggml-cuda/mmvq.cuh new file mode 100644 index 00000000..d9e42fdd --- /dev/null +++ b/ggml/src/ggml-cuda/mmvq.cuh @@ -0,0 +1,9 @@ +#include "common.cuh" + +#define MMVQ_MAX_BATCH_SIZE 8 // Max. batch size for which to use MMVQ kernels. + +void ggml_cuda_op_mul_mat_vec_q( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, + const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, + const int64_t src1_padded_row_size, cudaStream_t stream); |