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arm64: add i8mm route with SVE ggml_vec_dot_q4_K_q8_K and ggml_vec_dot_q6_K_… #15277

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arm64: add i8mm route with SVE ggml_vec_dot_q4_K_q8_K and ggml_vec_dot_q6_K_… #15277

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@fj-y-saito fj-y-saito commented Aug 13, 2025 •
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This PR improves q4_k_q8_k and q6_K_q8_K gemm kernel with arm64 i8mm instruction with SVE.
similar proposal for NEON support is made in PR #13886
Since it uses SVE instructions, it is characterized by improved performance even on machines with a SIMD width of 128 bits or more.

Verifying Features

This PR contains the SVE implementation of the vector dot used to compute the Q4_K quantization.
By running a Q4_K_M quantized model of Llama-3.1-8B, I confirmed that the values match.
I also verified that the perplexity matches between the NEON and SVE implementations.

NEON SVE(this PR)
6.5772 +/- 0.04061 6.5774 +/- 0.04062

performance check

Performance was measured with AWS Graviton3.
Performance is improved as follows (measured with llama-bench).

original

| model                          |       size |     params | backend    | threads |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |       8 |             pp1 |         17.60 ± 0.01 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |       8 |             pp2 |         22.74 ± 0.01 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |       8 |             pp4 |         24.83 ± 0.01 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |       8 |             pp8 |         26.57 ± 0.00 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |       8 |           pp512 |         27.50 ± 0.00 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |       8 |           tg128 |         17.30 ± 0.00 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      16 |             pp1 |         31.50 ± 0.07 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      16 |             pp2 |         42.44 ± 0.03 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      16 |             pp4 |         47.74 ± 0.01 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      16 |             pp8 |         51.98 ± 0.01 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      16 |           pp512 |         54.69 ± 0.00 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      16 |           tg128 |         31.29 ± 0.01 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      32 |             pp1 |         40.51 ± 0.05 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      32 |             pp2 |         66.38 ± 0.08 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      32 |             pp4 |         78.73 ± 0.04 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      32 |             pp8 |         87.98 ± 0.02 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      32 |           pp512 |         96.20 ± 0.00 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      32 |           tg128 |         40.36 ± 0.02 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      48 |             pp1 |         45.10 ± 0.05 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      48 |             pp2 |         74.95 ± 0.10 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      48 |             pp4 |         99.42 ± 0.06 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      48 |             pp8 |        114.52 ± 0.02 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      48 |           pp512 |        136.11 ± 0.01 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      48 |           tg128 |         44.74 ± 0.01 |

This PR

| model                          |       size |     params | backend    | threads |            test |                  t/s1|
| ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------:1|
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |       8 |             pp1 |         17.36 ± 0.02 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |       8 |             pp2 |         27.59 ± 0.01 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |       8 |             pp4 |         31.10 ± 0.01 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |       8 |             pp8 |         33.53 ± 0.01 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |       8 |           pp512 |         35.36 ± 0.00 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |       8 |           tg128 |         17.20 ± 0.00 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      16 |             pp1 |         31.42 ± 0.03 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      16 |             pp2 |         50.81 ± 0.02 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      16 |             pp4 |         58.81 ± 0.01 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      16 |             pp8 |         65.04 ± 0.02 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      16 |           pp512 |         70.26 ± 0.00 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      16 |           tg128 |         31.08 ± 0.01 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      32 |             pp1 |         40.88 ± 0.10 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      32 |             pp2 |         73.11 ± 0.08 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      32 |             pp4 |         92.12 ± 0.02 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      32 |             pp8 |        105.67 ± 0.03 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      32 |           pp512 |        119.13 ± 0.00 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      32 |           tg128 |         40.56 ± 0.02 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      48 |             pp1 |         45.56 ± 0.11 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      48 |             pp2 |         76.08 ± 0.12 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      48 |             pp4 |        113.12 ± 0.23 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      48 |             pp8 |        134.91 ± 0.21 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      48 |           pp512 |        165.69 ± 0.01 |
| llama 8B Q4_K - Medium         |   4.58 GiB |     8.03 B | CPU        |      48 |           tg128 |         44.94 ± 0.01 |

@github-actions github-actions bot added the ggml changes relating to the ggml tensor library for machine learning label Aug 13, 2025
@fj-y-saito fj-y-saito changed the title add i8mm route with SVE ggml_vec_dot_q4_K_q8_K and ggml_vec_dot_q6_K_… arm64: add i8mm route with SVE ggml_vec_dot_q4_K_q8_K and ggml_vec_dot_q6_K_… Aug 13, 2025
ggerganov
ggerganov reviewed Aug 13, 2025
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Btw, it's probably a better idea to implement GEMM improvements through the repack mechanism in ggml. It would give you more flexibility for rearranging the data to better fit the instructions.

ggml/src/ggml-cpu/arch/arm/quants.c
r1 = svreinterpret_s8_s64(svzip2_s64(svreinterpret_s64_s8(q8bytes_0_h), svreinterpret_s64_s8(q8bytes_1_h)));
r2 = svreinterpret_s8_s64(svzip1_s64(svreinterpret_s64_s8(q8bytes_0_l), svreinterpret_s64_s8(q8bytes_1_l)));
r3 = svreinterpret_s8_s64(svzip2_s64(svreinterpret_s64_s8(q8bytes_0_l), svreinterpret_s64_s8(q8bytes_1_l)));
sumi2 = svmmla_s32(svmmla_s32(svmmla_s32(svmmla_s32(svdup_n_s32(0), r0, l0), r1, l1), r2, l2), r3, l3);
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Does svmmla_s32 require to check for __ARM_FEATURE_SVE_MATMUL_INT8?

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Yes. svmmla_s32() is intrinsic function for sve instruction and it need i8mm feature to CPU.

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