Q8_KV: 8-bit quantization type targeting the KV cache (#208)

* 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>

2 files changed, 5 insertions, 0 deletions

diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index 95df06dc..0222c213 100644
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -339,6 +339,9 @@ static ggml_type ggml_type_from_name(const std::string & s) {
     if (s == "q6_0") {
         return GGML_TYPE_Q6_0;
     }
+    if (s == "q8_KV") {
+        return GGML_TYPE_Q8_KV;
+    }
 
     return GGML_TYPE_COUNT;
 }
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index 7ceee208..916f57ec 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -56,6 +56,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "Q5_0_R4",  LLAMA_FTYPE_MOSTLY_Q5_0_R4,  " 5.50 bpw quantization",            },
     { "Q6_0_R4",  LLAMA_FTYPE_MOSTLY_Q6_0_R4,  " 6.50 bpw quantization",            },
     { "Q8_0_R8",  LLAMA_FTYPE_MOSTLY_Q8_0_R8,  " 8.50 bpw quantization",            },
+    { "Q8_KV",    LLAMA_FTYPE_MOSTLY_Q8_KV,    " 8.00 bpw quantization",            },
     { "IQ4_XS",   LLAMA_FTYPE_MOSTLY_IQ4_XS,   " 4.25 bpw non-linear quantization", },
     { "IQ4_KS",   LLAMA_FTYPE_MOSTLY_IQ4_KS,   " 4.25 bpw non-linear quantization", },
     { "IQ4_KS_R4",LLAMA_FTYPE_MOSTLY_IQ4_KS_R4,"IQ4_KS repacked", },
@@ -82,6 +83,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "Q6_K",     LLAMA_FTYPE_MOSTLY_Q6_K,     " 5.15G, +0.0008 ppl @ LLaMA-v1-7B", },
     { "Q6_K_R4",  LLAMA_FTYPE_MOSTLY_Q6_K_R4,  "Q6_K repacked", },
     { "Q8_K_R8",  LLAMA_FTYPE_MOSTLY_Q8_K_R8,  "Q8_K repacked", },
+    { "Q8_KV_R8", LLAMA_FTYPE_MOSTLY_Q8_KV_R8, "Q8_KV repacked", },
     { "Q8_0",     LLAMA_FTYPE_MOSTLY_Q8_0,     " 6.70G, +0.0004 ppl @ LLaMA-v1-7B", },
     { "Q4_0_4_4", LLAMA_FTYPE_MOSTLY_Q4_0_4_4, " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
     { "Q4_0_4_8", LLAMA_FTYPE_MOSTLY_Q4_0_4_8, " 4.34G, +0.4685 ppl @ Llama-3-8B",  },