b1971 + PR5093 + PR5118 #77
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Co-authored-by: Iwan Kawrakow <[email protected]>
* implemented dynamic temperature sampling from koboldcpp * removed trailing whitespace * removed unused temp parameter in llama_sample_entropy * exposed exponent_val in dynamic temp sampler * added debug check for printf statements * use nullptr in llama_sample_softmax call during llama_sample_entropy this avoids counting the time taken stats twice Co-authored-by: Georgi Gerganov <[email protected]> * return earlier if there is only 1 candiate (i.e. max_entropy == 0) * reformat 't' case in llama_sample_queue Co-authored-by: Jared Van Bortel <[email protected]> * check for one or zero candidates case in llama_sample_entropy --------- Co-authored-by: Georgi Gerganov <[email protected]> Co-authored-by: Jared Van Bortel <[email protected]>
Co-authored-by: Chenxiaotao03 <[email protected]>
* Initial bucket sort * Bucket sort: slightly better version * Bucket sort: another minor improvement --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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This reverts commit 1749e210d697d03f8a0d45e59257afa74b52f7f9.
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* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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This reverts commit 1749e210d697d03f8a0d45e59257afa74b52f7f9.
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* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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Oct 27, 2024
* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
Nexesenex
pushed a commit
that referenced
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Oct 27, 2024
* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
Nexesenex
pushed a commit
that referenced
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Oct 27, 2024
* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
Nexesenex
pushed a commit
that referenced
this pull request
Oct 27, 2024
* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
Nexesenex
pushed a commit
that referenced
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Dec 22, 2024
* Adding q6_0 - basics + AVX2/Zen4 working * Adding q6_0: CUDA dequantize works, but not mmvq * Adding q6_0: CUDA mmvq works * Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache * Add q6_0 to CPU flash attention Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache gives about the same PPL as q8_0 K-cache and q4_0 V-cache, while needing the exact same RAM. I.e., what was the point? * q6_0: slightly better kv-cache result Better than q8_0+q4_0, but not as good as q8_0+iq4_nl * q6_0: works on ARM_NEON * q6_0: dequantize works on Metal, but not vector dot product * q6_0: it now works on Metal Outperforms q5_0 by a significant margin. E.g. | model | size | params | backend | ngl | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 | | llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 | | llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 | * q6_0: can now be used for kv-cache on Metal --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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