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llama.cpp/examples/batched-bench
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Kerfuffle 6e08281e58
Extend llama_kv_cache_seq_rm to allow matching any sequence (#3843) 
* Extend llama_kv_cache_seq_rm to allow matichng any sequence

* Replace llama_kv_cache_tokens_rm with llama_kv_cache_clear

Use llama_kv_cache_clear for cache clearing

Change calls to llama_kv_cache_tokens_rm that want to delete by position to use llama_kv_cache_seq_rm functionality
2023-10-29 11:31:40 -06:00
..
batched-bench.cpp Extend llama_kv_cache_seq_rm to allow matching any sequence (#3843) 2023-10-29 11:31:40 -06:00
CMakeLists.txt batched : add bench tool (#3545) 2023-10-11 21:25:33 +03:00
README.md batched : add bench tool (#3545) 2023-10-11 21:25:33 +03:00

README.md

llama.cpp/example/batched-bench

Benchmark the batched decoding performance of llama.cpp

Usage

There are 2 modes of operation:

  • prompt not shared - each batch has a separate prompt of size PP (i.e. N_KV = B*(PP + TG))
  • prompt is shared - there is a common prompt of size PP used by all batches (i.e. N_KV = PP + B*TG)
./batched-bench MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>

# LLaMA 7B, F16, N_KV_MAX = 16384 (8GB), prompt not shared
./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 0 99

# LLaMA 7B, Q8_0, N_KV_MAX = 16384 (8GB), prompt is shared
./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 1 99

# custom set of batches
./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32

Sample results

  • PP - prompt tokens per batch
  • TG - generated tokens per batch
  • B - number of batches
  • N_KV - required KV cache size
  • T_PP - prompt processing time (i.e. time to first token)
  • S_PP - prompt processing speed ((B*PP)/T_PP or PP/T_PP)
  • T_TG - time to generate all batches
  • S_TG - text generation speed ((B*TG)/T_TG)
  • T - total time
  • S - total speed (i.e. all tokens / total time)
PP TG B N_KV T_PP s S_PP t/s T_TG s S_TG t/s T s S t/s
128 128 1 256 0.108 1186.64 3.079 41.57 3.187 80.32
128 128 2 512 0.198 1295.19 5.029 50.90 5.227 97.95
128 128 4 1024 0.373 1373.96 6.878 74.44 7.251 141.23
128 128 8 2048 0.751 1363.27 7.344 139.43 8.095 252.99
128 128 16 4096 1.570 1304.68 8.455 242.23 10.024 408.60
128 128 32 8192 3.408 1201.73 8.801 465.40 12.209 670.96
128 256 1 384 0.107 1196.70 6.329 40.45 6.436 59.67
128 256 2 768 0.194 1317.45 10.239 50.00 10.433 73.61
128 256 4 1536 0.366 1399.03 13.960 73.35 14.326 107.22
128 256 8 3072 0.751 1363.92 15.110 135.54 15.861 193.69
128 256 16 6144 1.569 1304.93 18.073 226.64 19.642 312.80
128 256 32 12288 3.409 1201.35 19.223 426.15 22.633 542.93