* FA: provide work buffer for K repacking
* Add header to avoid comp0iler warnings
* WIP
* WIP
* WIP
* WIP
* Slightly better
* WIP (Zen4)
* WIP
* Try to improve for unusual number of heads/number of threads
* Use mul_mat_qX_0_q8_2_Tx for q6_0 in FA
* Use mul_mat_qX_0_q8_2_Tx for q4_0 in FA
* Use Sum4q4 for q4_0
* WIP
* WIP
* Much better FA TG with q8_0 KV cache
Just repack it even for TG. But do the repacking for k_step rows,
not the whole K tensor.
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Slightly better CPU TG performance for GQA
* Better CPU FA implementation for TG when GQA
* Minor
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* iq3_k: slightly better quantization
Not much of a difference for most models, but this change
avoids what it looks like a catastrophic failure for DeepSeek-Lite
(PPL is now 7.041 vs 7.314 on main).
* Small improvement for type-1 quants
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* WIP - not working
* q8_0 without bells and wistles works
* It works for q8_0
* Use bf16 instead of f16,int16
* q4_0_r8
* q5_0_r4
* q6_0_r4
* Also q4_1 and q5_1
* q8_0_r8 on avx2
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Improve DeepSeek batched processing speed
* Revert the commented out section in iqk_mul_mat.cpp
It does have some benefit at long contexts.
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Repack a model with the quantize tool
* WIP
* Fixed various issues
As we don't have a way to tell if a repacked quant has been modified,
I had to remove the modification at the expense of a slight decrease
in performance. This affects q8_0_r8, q8_KV_r8, q8_k_r8 on Zen4, and
q4_0_r8 on ARM.
* Create wk_b and wv_b as Q8_0_R8 if the wkv_b type is interleaved
* Fix GCC 13.3 compilation error
* Another one
* Add missing include
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* FlashMLA-2: eliminate intermediate f32 tensors
This works on the CPU. PP performance is ~13% better for 16k tokens
and compute buffer is quite a bit smaller.
* FlashMLA-2: enable fast path only on the CPU for now
I did implement the necessary ops on CUDA, but something is
still wrong there, so for now we only use it when running
CPU-only.
* FlashMLA-2: slightly smaller computer buffer size
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* This is a better FA for TG
It should benefit MLA and GQA. Tested to work with
DeepSeek-Lite MLA, not yet for GQA.
For tg64@pp8192 it is ~13% faster than MLA without FA,
and 57% faster that the main branch FA.
* WIP
* Cleanup
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* FlashMLA - it finally works (on the CPU)
* FlashMLA: allow for f16 and bf16 cache in addition to q8_0
* It works with ggml FA, not with iqk FA
* WIP
* FlashMLA: it now works with iqk
I had forgotten to divide the Q stride by sizeof(float) and
that's why, very cobfusingly, it was working for TG but not for PP.
* WIP
* FlashMLA: that should be it for now
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Fusing MoE up * unary(gate)
* Fusing MoE up * unary(gate): CUDA
We get ~13% speedup for PP-512 and ~2% for TG-128
for DeepSeek-Lite
* On CUDA also fuse MoE down * (up * unary(gate))
in case the MUL_MAT_ID op for the down experts is the next
op in the graph.
* Command line option to enable fused MoE up*unary(gate)
* Add fmoe option to llama-bench
* Adding forgotten gelu, relu, silu on ARM
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* This seems to be a better way
to do the attention matrix multiplications in the TG case.
* Cleanup
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Adding q8_KV - Basics + AVX2 gemm/gemv
* q8_KV: Better AVX2 gemm
* q8_KV: Better Zen4 gemm
We get 225.7 t/s for L3-8B. In comparison q8_0 without
run-tinme-repacking is at 169 t/s.
* q8_KV: AVX2 gemm/gemv
We get 254 t/s for L3-8B vs 194 t/s for q8_0 without rtr.
* q8_KV: be able to use it for K cache
This required quite a few fixes in ggml and llama.cpp:
* ggml: do not calculate row size as n/block_size*type_size. I had
removed most of it when implementing the quants with per row scale,
bit it was stull lurking in ggml_copy. Not sure if these were the last
remnants of ggmil-style row sizes, or if there are still places left
* llama.cpp: get rid of the the 1d K cache assumption. Create and manage
the K-cache as a 2D tensor so we can have per row meta data as needed
by q8_KV.
Using q8_KV for K-cache results in non-negligible performance gains.
More details to follow, but for DeepSeek-Lite with MLA, we get
18% speedup for PP-8192 compared to q8_0 K-cache.
* q8_KV: be able to use it for K cache in FA
* q8_KV: repack it for K*Q in FA
* q8_KV: slightly faster gemv on Zen4
* q8_KV: slightly faster gemv on Zen4
* q8_KV: ARM_NEON
We get PP-512 = 167 t/s for L3-8B without interleaving!
We do the interleaving on the fly, so I wonder if this
could be done for other quants as well.
* q8_KV: use it in FA on NEON
* q8_KV_r8 - repacked q8_KV
On Zen4 it is slower than q8_k_r8 (292 vs 370 t/s)
This makes no sense whatsoever as the q8_KV_r8 GEMM is
basically the q8_k_r8 GEMM with the unnecessary block stuff
removed (so, one would think that it would be faster).
* q8_KV_r8: don't use nrc_y = 16 on Zen4
This is faster - 350 t/s. Why?
Much better than the 290 t/s we had before, but still slower
than the 370 t/s for q8_k_r8.
* q8_KV: nrc_y = 16 also doesn't pay off in FA
* Minor
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This allows us to optimize TG performance for GQA models.
E.g., for IQ4_XS L3-8B with 8k TG-64 goes from 8.6 to 10.26 t/s.
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Adding support for K head size != V head size
This is relevant for DeepSeek models.
At this point ggml CPU FA works.
Now I need to go and change iqk FA to make it work
with Dk != Dv.
* iqk support for K head size != V head size
To not have compilation time explode, just
Dk = 192, Dv = 128 for now (DeepSeek)
* FA: very slightly faster for nq = 1 (TG)
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Similar to the CUDA situation.
It is OFF by default.
If OFF, only F16, Q8_0, Q6_0, and, if the CPU provides native
BF16 support, BF16 FA kernels will be included.
To enable all, cmake -DGGML_IQK_FA_ALL_QUANTS=1 ...
This cuts compilation time for iqk_mul_mat.cpp by almost half
(45 seconds vs 81 seconds on my Ryzen-7950X).
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
