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With this we get TG-128 = 97 t/s.
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We get PP-512 = 702 t/s, TG-128 = 84 t/s.
This is almost on par with q4_0, which is rare on Metal
(to not say it does not exist).
For reference, q4_0 gives 726 t/s / 86 t/s for Bitnet.
TG is kind of funny because we hit 72 t/s on the CPU.
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We get PP-512 = 9600 t/s, TG-128 = 234 t/s
(but we need to use 8 CPU threads, else results are lower,
so clearly there is something being computed on the CPU).
PP-512 is very close to PP-512(fp16) = 9800 t/s
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We get PP-512 = 192 t/s, TG-128 = 72 t/s
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Just scaler and AVX2 for now.
PP-512 is even faster (325 t/s on the Ryzn-7950X, 404 t/s on
Ryzen-5975WX). We lose ~6-7% for TG due to being memory bound and
the model being 10% larger.
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We get PP-512 = 190 t/s and TG-128 = 75 t/s.
2 bpw TG on the CPU beats 1.75 bpw on the GPU!
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We get PP-512 = 322 t/s.
TG is already 51.6 t/s at 4 threads, then it saturates and
starts going down for more than 8 threads.
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The scalar dot product already chieves 37 t/s for TG!
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With the last change (which added the typo), I'm now getting
PP-512 = 300 t/s on the Ryzen-5975WX.
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We now get 214 t/s on the Ryzen-7950X
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PP is decent with 131 t/s (q4_0 has 150 t/s).
TG is better than last commit but still bad at 33.1 t/s
(in comparison q4_0 gets 52.3 t/s).
I had to go to the (0, 1, 2) table. Apple Silicon clearly
does not like operations with signs.
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Basically 2X slower tan q4_0.
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This should be good enough. One cannot ask
Apple Silicon to do too much work.
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PP performance is decent (668 t/s v 724 t/s for q4_0),
but TG is kind of low (60 t/s vs 81 t/s for q4_0).
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I now get PP-512 = 270 t/s on the Ryzen-5975WX
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We now get 207 t/s for PP-512 and 51 t/s for TG-128 using 16 threads.
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I had forgotten to adjust for the change to q8_K64.
On the M2 I'm getting 10.8 t/s with the scalar version!
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We get 174 t/s for PP-512 and 49 t/s for TG-128 using 16 threads.
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Verified that it works on AVX2.
Also turned on any combination of f16 and f32
(i.e., added f16 x 16 and f32 x f32).
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* Remove iqk_mul_mat from llamafile_sgemm
* Pass tensor types and strides to iqk_mul_mat
It is marked WIP because only tested on __aarch64__
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But only turning on f16 x f32 and f32 x f16 for now.
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It makes no difference on my Ryzen-7950X, but perhaps
it will be beneficial for CPU's with real AVX512.
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2x6 (Nx x Ny) tiles instead of 3x4. We get 142.7 t/s on the Ryzen-5975WX
up from 138 t/s. We use Nx registers to preload the fp16 weights,
so total registers required is Nx * (Ny + 1), so 15 in the case
of of 3 x 4 tiles and 14 for 2 x 6 tiles. I guess, the one spare
register helps. But maybe it is just a matter of how things get
loaded into the cache. On the 7950X I did try 3 x 8 and it did
not perform as well as 5 x 5.
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Basically use what I did for Arm.
Improves PP performance to 141.7 t/s up from 136 t/s
on the Ryzen-7950X (32 vector registers, so we use 5x5 tiling).
This is now 10% faster than tinyBLAS.
There is a minor improvement also on the Ryzen-5975WX
(16 vector registers, so we use 4x3 tiling): we get
138 t/s up from 136 t/s. tinyBLAS is at 132 t/s.
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~2% slower than tinyBLAS - not sure why.
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About 2% faster for q4_K.
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I was happily using _mm256_packs_epi32() to pack the
q8_0 x q8_0 dot products back to int16_t, and getting useful
results. But theoretically this can overflow, so it is
better to use _mm256_unpacklo_ and _mm256_unpackhi_ to combine
the 4 dot products using int32_t additions. This is (almost)
as fast, unlike _mm256_hadd_epi32(), which seems excessively
slow on the Ryzen-7950X.
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Here the performance gain is more significant. E.g., for q4_1,
PP-512 becomes 168 t/s up from 137 t/s.
Now the performance gap to q4_0 is so significant that I
wonder if I should change to using Q8_1 also for the
qX_0 legacy quants.
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It was actually ready but not turned on.
Having forgotten, I made a new implementation along the
lines of the fp16 implementation (i.e., using tiling).
That matched tiinyBLAS performance. But the existing
implementation that I now turned on is faster:
PP-512 = 134 t/s vs 128.3 t/s for tinyBLAS
TG-128 = 8.7 t/s vs 8.3 t/s for tinyBLAS (@ 4 threads)
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Use 4x3 tiling on a real AVX2 CPU (with only 16 vector registers).
This works best for the Ryzen-5975WX.
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It turns out on my Ryzen-7950X CPU using
AVX512 is slower.
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This simple implementation beats jart's tiniBLAS by a
small margin (143 t/s vs 137 t/s for PP-512, TG is
4.75 t/s, so exactly the same as ggml).
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