ikawrakow/ik_llama.cpp
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Kawrakow 1140b4568d 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>
2025-02-19 11:47:07 +02:00
..
CMakeLists.txt
build : link against build info instead of compiling against it (#3879)
2023-11-02 08:50:16 +02:00
llama-bench.cpp
Q8_KV: 8-bit quantization type targeting the KV cache (#208)
2025-02-19 11:47:07 +02:00
README.md
build: rename main → llama-cli, server → llama-server, llava-cli → llama-llava-cli, etc... (#7809)
2024-06-13 00:41:52 +01:00

README.md

llama.cpp/examples/llama-bench

Performance testing tool for llama.cpp.

Table of contents

  1. Syntax
  2. Examples
    1. Text generation with different models
    2. Prompt processing with different batch sizes
    3. Different numbers of threads
    4. Different numbers of layers offloaded to the GPU
  3. Output formats
    1. Markdown
    2. CSV
    3. JSON
    4. SQL

Syntax

usage: ./llama-bench [options]

options:
  -h, --help
  -m, --model <filename>              (default: models/7B/ggml-model-q4_0.gguf)
  -p, --n-prompt <n>                  (default: 512)
  -n, --n-gen <n>                     (default: 128)
  -pg <pp,tg>                         (default: 512,128)
  -b, --batch-size <n>                (default: 2048)
  -ub, --ubatch-size <n>              (default: 512)
  -ctk, --cache-type-k <t>            (default: f16)
  -ctv, --cache-type-v <t>            (default: f16)
  -t, --threads <n>                   (default: 16)
  -ngl, --n-gpu-layers <n>            (default: 99)
  -sm, --split-mode <none|layer|row>  (default: layer)
  -mg, --main-gpu <i>                 (default: 0)
  -nkvo, --no-kv-offload <0|1>        (default: 0)
  -fa, --flash-attn <0|1>             (default: 0)
  -mmp, --mmap <0|1>                  (default: 1)
  --numa <distribute|isolate|numactl> (default: disabled)
  -embd, --embeddings <0|1>           (default: 0)
  -ts, --tensor-split <ts0/ts1/..>    (default: 0)
  -r, --repetitions <n>               (default: 5)
  -o, --output <csv|json|md|sql>      (default: md)
  -v, --verbose                       (default: 0)

Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.

llama-bench can perform three types of tests:

  • Prompt processing (pp): processing a prompt in batches (-p)
  • Text generation (tg): generating a sequence of tokens (-n)
  • Prompt processing + text generation (pg): processing a prompt followed by generating a sequence of tokens (-pg)

With the exception of -r, -o and -v, all options can be specified multiple times to run multiple tests. Each pp and tg test is run with all combinations of the specified options. To specify multiple values for an option, the values can be separated by commas (e.g. -n 16,32), or the option can be specified multiple times (e.g. -n 16 -n 32).

Each test is repeated the number of times given by -r, and the results are averaged. The results are given in average tokens per second (t/s) and standard deviation. Some output formats (e.g. json) also include the individual results of each repetition.

For a description of the other options, see the main example.

Note:

  • When using SYCL backend, there would be hang issue in some cases. Please set --mmp 0.

Examples

Text generation with different models

$ ./llama-bench -m models/7B/ggml-model-q4_0.gguf -m models/13B/ggml-model-q4_0.gguf -p 0 -n 128,256,512
model size params backend ngl test t/s
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 99 tg 128 132.19 ± 0.55
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 99 tg 256 129.37 ± 0.54
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 99 tg 512 123.83 ± 0.25
llama 13B mostly Q4_0 6.86 GiB 13.02 B CUDA 99 tg 128 82.17 ± 0.31
llama 13B mostly Q4_0 6.86 GiB 13.02 B CUDA 99 tg 256 80.74 ± 0.23
llama 13B mostly Q4_0 6.86 GiB 13.02 B CUDA 99 tg 512 78.08 ± 0.07

Prompt processing with different batch sizes

$ ./llama-bench -n 0 -p 1024 -b 128,256,512,1024
model size params backend ngl n_batch test t/s
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 99 128 pp 1024 1436.51 ± 3.66
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 99 256 pp 1024 1932.43 ± 23.48
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 99 512 pp 1024 2254.45 ± 15.59
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 99 1024 pp 1024 2498.61 ± 13.58

Different numbers of threads

$ ./llama-bench -n 0 -n 16 -p 64 -t 1,2,4,8,16,32
model size params backend threads test t/s
llama 7B mostly Q4_0 3.56 GiB 6.74 B CPU 1 pp 64 6.17 ± 0.07
llama 7B mostly Q4_0 3.56 GiB 6.74 B CPU 1 tg 16 4.05 ± 0.02
llama 7B mostly Q4_0 3.56 GiB 6.74 B CPU 2 pp 64 12.31 ± 0.13
llama 7B mostly Q4_0 3.56 GiB 6.74 B CPU 2 tg 16 7.80 ± 0.07
llama 7B mostly Q4_0 3.56 GiB 6.74 B CPU 4 pp 64 23.18 ± 0.06
llama 7B mostly Q4_0 3.56 GiB 6.74 B CPU 4 tg 16 12.22 ± 0.07
llama 7B mostly Q4_0 3.56 GiB 6.74 B CPU 8 pp 64 32.29 ± 1.21
llama 7B mostly Q4_0 3.56 GiB 6.74 B CPU 8 tg 16 16.71 ± 0.66
llama 7B mostly Q4_0 3.56 GiB 6.74 B CPU 16 pp 64 33.52 ± 0.03
llama 7B mostly Q4_0 3.56 GiB 6.74 B CPU 16 tg 16 15.32 ± 0.05
llama 7B mostly Q4_0 3.56 GiB 6.74 B CPU 32 pp 64 59.00 ± 1.11
llama 7B mostly Q4_0 3.56 GiB 6.74 B CPU 32 tg 16 16.41 ± 0.79

Different numbers of layers offloaded to the GPU

$ ./llama-bench -ngl 10,20,30,31,32,33,34,35
model size params backend ngl test t/s
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 10 pp 512 373.36 ± 2.25
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 10 tg 128 13.45 ± 0.93
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 20 pp 512 472.65 ± 1.25
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 20 tg 128 21.36 ± 1.94
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 30 pp 512 631.87 ± 11.25
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 30 tg 128 40.04 ± 1.82
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 31 pp 512 657.89 ± 5.08
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 31 tg 128 48.19 ± 0.81
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 32 pp 512 688.26 ± 3.29
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 32 tg 128 54.78 ± 0.65
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 33 pp 512 704.27 ± 2.24
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 33 tg 128 60.62 ± 1.76
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 34 pp 512 881.34 ± 5.40
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 34 tg 128 71.76 ± 0.23
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 35 pp 512 2400.01 ± 7.72
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 35 tg 128 131.66 ± 0.49

Output formats

By default, llama-bench outputs the results in markdown format. The results can be output in other formats by using the -o option.

Markdown

$ ./llama-bench -o md
model size params backend ngl test t/s
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 99 pp 512 2368.80 ± 93.24
llama 7B mostly Q4_0 3.56 GiB 6.74 B CUDA 99 tg 128 131.42 ± 0.59

CSV

$ ./llama-bench -o csv

build_commit,build_number,cuda,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size,model_n_params,n_batch,n_threads,f16_kv,n_gpu_layers,main_gpu,mul_mat_q,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts
"3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","512","0","2023-09-23T12:09:01Z","212155977","732372","2413.341687","8.305961"
"3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","0","128","2023-09-23T12:09:02Z","969320879","2728399","132.052051","0.371342"

JSON

$ ./llama-bench -o json

[
  {
    "build_commit": "3469684",
    "build_number": 1275,
    "cuda": true,
    "metal": false,
    "gpu_blas": true,
    "blas": true,
    "cpu_info": "13th Gen Intel(R) Core(TM) i9-13900K",
    "gpu_info": "NVIDIA GeForce RTX 3090 Ti",
    "model_filename": "models/7B/ggml-model-q4_0.gguf",
    "model_type": "llama 7B mostly Q4_0",
    "model_size": 3825065984,
    "model_n_params": 6738415616,
    "n_batch": 512,
    "n_threads": 16,
    "f16_kv": true,
    "n_gpu_layers": 99,
    "main_gpu": 0,
    "mul_mat_q": true,
    "tensor_split": "0.00",
    "n_prompt": 512,
    "n_gen": 0,
    "test_time": "2023-09-23T12:09:57Z",
    "avg_ns": 212365953,
    "stddev_ns": 985423,
    "avg_ts": 2410.974041,
    "stddev_ts": 11.163766,
    "samples_ns": [ 213837238, 211635853, 212328053, 211329715, 212698907 ],
    "samples_ts": [ 2394.34, 2419.25, 2411.36, 2422.75, 2407.16 ]
  },
  {
    "build_commit": "3469684",
    "build_number": 1275,
    "cuda": true,
    "metal": false,
    "gpu_blas": true,
    "blas": true,
    "cpu_info": "13th Gen Intel(R) Core(TM) i9-13900K",
    "gpu_info": "NVIDIA GeForce RTX 3090 Ti",
    "model_filename": "models/7B/ggml-model-q4_0.gguf",
    "model_type": "llama 7B mostly Q4_0",
    "model_size": 3825065984,
    "model_n_params": 6738415616,
    "n_batch": 512,
    "n_threads": 16,
    "f16_kv": true,
    "n_gpu_layers": 99,
    "main_gpu": 0,
    "mul_mat_q": true,
    "tensor_split": "0.00",
    "n_prompt": 0,
    "n_gen": 128,
    "test_time": "2023-09-23T12:09:59Z",
    "avg_ns": 977425219,
    "stddev_ns": 9268593,
    "avg_ts": 130.965708,
    "stddev_ts": 1.238924,
    "samples_ns": [ 984472709, 974901233, 989474741, 970729355, 967548060 ],
    "samples_ts": [ 130.019, 131.295, 129.362, 131.86, 132.293 ]
  }
]

SQL

SQL output is suitable for importing into a SQLite database. The output can be piped into the sqlite3 command line tool to add the results to a database.

$ ./llama-bench -o sql

CREATE TABLE IF NOT EXISTS test (
  build_commit TEXT,
  build_number INTEGER,
  cuda INTEGER,
  metal INTEGER,
  gpu_blas INTEGER,
  blas INTEGER,
  cpu_info TEXT,
  gpu_info TEXT,
  model_filename TEXT,
  model_type TEXT,
  model_size INTEGER,
  model_n_params INTEGER,
  n_batch INTEGER,
  n_threads INTEGER,
  f16_kv INTEGER,
  n_gpu_layers INTEGER,
  main_gpu INTEGER,
  mul_mat_q INTEGER,
  tensor_split TEXT,
  n_prompt INTEGER,
  n_gen INTEGER,
  test_time TEXT,
  avg_ns INTEGER,
  stddev_ns INTEGER,
  avg_ts REAL,
  stddev_ts REAL
);

INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634');
INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692');