ggml: aarch64: implement SVE kernels for q2_k_q8_k vector dot#12064
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ggerganov merged 5 commits intoggml-org:masterfrom Feb 28, 2025
Merged
ggml: aarch64: implement SVE kernels for q2_k_q8_k vector dot#12064ggerganov merged 5 commits intoggml-org:masterfrom
ggerganov merged 5 commits intoggml-org:masterfrom
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| svint32_t sumi1 = svdup_n_s32(0); | ||
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| for (int j = 0; j < QK_K/256; ++j) { |
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These loops seem redundant - are they needed? Can you simplify by knowing that this will always be a single iteration?
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Yes. This loop always run for one time. The code is simplified considering the for loop will run for 1 time only. I can remove the loop.
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…rg#12064) * Added SVE Support for Q2_K Quantized Models * Use 4-space indentation in the switch cases * removed comments lines * Remove the loop Retain the curly bracess for better understanding of code * Remove the comment like added for q3_k_q8_k kernel --------- Co-authored-by: vithulep <p.m.vithule1517@gmail.com>
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…rg#12064) * Added SVE Support for Q2_K Quantized Models * Use 4-space indentation in the switch cases * removed comments lines * Remove the loop Retain the curly bracess for better understanding of code * Remove the comment like added for q3_k_q8_k kernel --------- Co-authored-by: vithulep <p.m.vithule1517@gmail.com>
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This PR introduces support for SVE (Scalable Vector Extensions) kernels for the q2_K_q8_K vector dot on the Arm architecture. A similar proposal for SVE support is made in PR 7433 and 11227.
This PR contains the SVE implementation of the vector dot used to compute the Q2_K quantization.
By running a Q2_K quantized model of mistral-7b-v01, on Graviton 3E (Perf 21 XL), Accuracy and Performance are measured.
Performance
The performance enhancement with this PR (SVE) is ~ x1.03 to x1.09 faster than the NEON implementation.
The command used to measure the performance is
Perplexity
I have ran perplexity with the NEON(Original) and SVE (This PR) Implementation.
And below is the summary.
This correction does not appear to have any impact on accuracy.