vulkan: Use mul_mat_vec_id for small values of n#18918
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0cc4m merged 1 commit intoggml-org:masterfrom Jan 21, 2026
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Change ggml_vk_mul_mat_vec_id_q_f16 to loop over the batch dimension and update the indexing calculations in get_offsets. Mat-vec is faster than mat-mat for small values of n. We don't get the same reuse of the weights as in the non-ID path, but with this the cost is linear in n rather than n>1 being far slower than n==1.
0cc4m
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Nice, good idea. Improvements all around, in my tests.
shaofeiqi
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Change ggml_vk_mul_mat_vec_id_q_f16 to loop over the batch dimension and update the indexing calculations in get_offsets. Mat-vec is faster than mat-mat for small values of n. We don't get the same reuse of the weights as in the non-ID path, but with this the cost is linear in n rather than n>1 being far slower than n==1.
Seunghhon
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Apr 26, 2026
Change ggml_vk_mul_mat_vec_id_q_f16 to loop over the batch dimension and update the indexing calculations in get_offsets. Mat-vec is faster than mat-mat for small values of n. We don't get the same reuse of the weights as in the non-ID path, but with this the cost is linear in n rather than n>1 being far slower than n==1.
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Change ggml_vk_mul_mat_vec_id_q_f16 to loop over the batch dimension and update the indexing calculations in get_offsets.
Mat-vec is faster than mat-mat for small values of n. We don't get the same reuse of the weights as in the non-ID path, but with this the cost is linear in n rather than n>1 being far slower than n==1.
Perf on 5090:
This came up when testing #18892. There are small batches that dominate the runtime:
That test also shows a lot of time in small batches of FA, but I'll fix that separately.