llama-cpp-pythonをbitnet.cppに対応させてみる
以下の記事でMackBook Proで動かしてみたのですが、毎回モデルをロードさせるのが気に食わない時間がもったいないと思ったので、試します。
1. 環境構築
(1) bitnet.cpp
こちらは淡々と。
python3 -m venv bitnet
cd $_
source bin/activate
git clone --recursive https://github.com/microsoft/BitNet.git
cd BitNet
pip install -r requirements.txt
python setup_env.py --hf-repo HF1BitLLM/Llama3-8B-1.58-100B-tokens -q i2_ssetup_env.pyで量子化モデルを作成しています。このコマンドを先に実行しておかないと、llama-cpp-pythonビルド時に必要となるヘッダファイルが所定のディレクトリに格納されないためです。
(2) llama-cpp-python
続いてllama-cpp-pythonです。こちらをbitnet.cppに対応させます。
BitNetのディレクトリから一つ上に移動して、
% pwd
/path/to/bitnet/BitNet
% cd ..続いて、llama-cpp-pythonのcloneです。READMEでは、llama.cppもcloneするため--recurse-submodules付きとなっていますが、今回はbitnet.cpp対応のllama.cppを使用するので、--recurse-submodulesは付けません。
git clone https://github.com/abetlen/llama-cpp-python.git
cd llama-cpp-pythonbitnet.cppのllama.cppをcloneしてブランチを切り替えます。
cd vendor
git clone https://github.com/Eddie-Wang1120/llama.cpp.git
cd llama.cpp
git checkout 406a5036f9a8aaee9ec5e96e652f61691340fe95カレントディレクトリを変更しておきます。
% cd ../../..
% pwd
/path/to/bitnet2. llama-cpp-pythonへのパッチ
パスが長くなるので、llama-cpp-pythonディレクトリに移動します。
cd llama-cpp-python(1) ./vendor/llama.cpp/CMakeLists.txt
差分はこちら。
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 157d9c86..e5773c26 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -63,7 +63,7 @@ option(LLAMA_SANITIZE_ADDRESS "llama: enable address sanitizer" OFF)
option(LLAMA_SANITIZE_UNDEFINED "llama: enable undefined sanitizer" OFF)
# utils
-option(LLAMA_BUILD_COMMON "llama: build common utils library" ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_COMMON "llama: build common utils library" ON)
# extra artifacts
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
@@ -198,4 +198,8 @@ install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
#
add_subdirectory(common)
-add_subdirectory(examples)
+
+if (LLAMA_BUILD_EXAMPLES)
+ add_subdirectory(examples)
+endif()(2) ヘッダーファイル等の参照先の補正
この状態で pip install -e . とすると以下のようなファイルが見つからないエラーが発生するので、
-- Configuring done (0.9s)
CMake Error at vendor/llama.cpp/ggml/src/CMakeLists.txt:1324 (add_library):
Cannot find source file:
../../../../include/bitnet-lut-kernels.hこれに対応するため、ln -s します。
ln -s ../BitNet/include .
ln -s ../BitNet/src .この状態で pip install -e . とするとインストールするファイルが見つからないと怒られます。
*** Installing project into wheel...
-- Install configuration: "Release"
-- Installing: /var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/tmp8p58f179/wheel/platlib/lib/libggml.dylib
-- Installing: /var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/tmp8p58f179/wheel/platlib/include/ggml.h
-- Installing: /var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/tmp8p58f179/wheel/platlib/include/ggml-alloc.h
-- Installing: /var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/tmp8p58f179/wheel/platlib/include/ggml-backend.h
-- Installing: /var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/tmp8p58f179/wheel/platlib/include/ggml-blas.h
-- Installing: /var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/tmp8p58f179/wheel/platlib/include/ggml-cann.h
-- Installing: /var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/tmp8p58f179/wheel/platlib/include/ggml-cuda.h
-- Up-to-date: /var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/tmp8p58f179/wheel/platlib/include/ggml.h
-- Installing: /var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/tmp8p58f179/wheel/platlib/include/ggml-kompute.h
-- Installing: /var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/tmp8p58f179/wheel/platlib/include/ggml-metal.h
-- Installing: /var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/tmp8p58f179/wheel/platlib/include/ggml-rpc.h
-- Installing: /var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/tmp8p58f179/wheel/platlib/include/ggml-sycl.h
-- Installing: /var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/tmp8p58f179/wheel/platlib/include/ggml-vulkan.h
CMake Error at /var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/tmp8p58f179/build/vendor/llama.cpp/ggml/cmake_install.cmake:59 (file):
file INSTALL cannot find
"/Users/noguchi/devsecops/venv/llama-cpp-python/llama-cpp-python/vendor/llama.cpp/ggml/include/ggml-bitnet.h":
No such file or directory.
Call Stack (most recent call first):
/var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/tmp8p58f179/build/vendor/llama.cpp/cmake_install.cmake:42 (include)
/var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/tmp8p58f179/build/cmake_install.cmake:42 (include)
bitnet.cppの対応するディレクトリを見てもBitNet/3rdparty/llama.cpp/ggml/includeにはggml-bitnet.h が存在しません。どこにあるかというと BitNet/includeの下。
% ls -al ../BitNet/include
total 48
drwxr-xr-x 5 noguchi staff 160 10 18 15:22 .
drwxr-xr-x 24 noguchi staff 768 10 18 15:23 ..
-rw-r--r-- 1 noguchi staff 36950 10 18 15:22 bitnet-lut-kernels.h
-rw-r--r-- 1 noguchi staff 2056 10 18 15:22 ggml-bitnet.h
-rw-r--r-- 1 noguchi staff 232 10 18 15:22 kernel_config.iniMakefileをきちんと修正すればいいのでしょうが、面倒くさいので、cpでお茶を濁します。
cd ../
cp -p BitNet/include/ggml-bitnet.h llama-cpp-python/vendor/llama.cpp/ggml/include では、満を持してpip install -e .です。
% pip install -e .
Obtaining file:///Users/noguchi/devsecops/venv/llama-cpp-python/llama-cpp-python
Installing build dependencies ... done
Checking if build backend supports build_editable ... done
Getting requirements to build editable ... done
Installing backend dependencies ... done
Preparing editable metadata (pyproject.toml) ... done
Requirement already satisfied: typing-extensions>=4.5.0 in /Users/noguchi/devsecops/venv/llama-cpp-python/lib/python3.12/site-packages (from llama_cpp_python==0.3.1) (4.12.2)
Requirement already satisfied: numpy>=1.20.0 in /Users/noguchi/devsecops/venv/llama-cpp-python/lib/python3.12/site-packages (from llama_cpp_python==0.3.1) (1.26.4)
Collecting diskcache>=5.6.1 (from llama_cpp_python==0.3.1)
Using cached diskcache-5.6.3-py3-none-any.whl.metadata (20 kB)
Requirement already satisfied: jinja2>=2.11.3 in /Users/noguchi/devsecops/venv/llama-cpp-python/lib/python3.12/site-packages (from llama_cpp_python==0.3.1) (3.1.4)
Requirement already satisfied: MarkupSafe>=2.0 in /Users/noguchi/devsecops/venv/llama-cpp-python/lib/python3.12/site-packages (from jinja2>=2.11.3->llama_cpp_python==0.3.1) (3.0.2)
Using cached diskcache-5.6.3-py3-none-any.whl (45 kB)
Building wheels for collected packages: llama_cpp_python
Building editable for llama_cpp_python (pyproject.toml) ... done
Created wheel for llama_cpp_python: filename=llama_cpp_python-0.3.1-cp312-cp312-macosx_15_0_arm64.whl size=2847996 sha256=cc1763f669e0d53cbf201026356e0f1cd5620be08543e81860cec6e6e71c3c4f
Stored in directory: /private/var/folders/vb/tjf174hj1835lfyj9vw5066c0000gn/T/pip-ephem-wheel-cache-udo5w5z1/wheels/12/8e/f8/b72f7fd276314c0308b710ffc7de484015f57ba9295ce9340a
Successfully built llama_cpp_python
Installing collected packages: diskcache, llama_cpp_python
Successfully installed diskcache-5.6.3 llama_cpp_python-0.3.1
%ビルド＆インストールできました。
3. 試してみる
BitNetのディレクトリに移動して
cd ../BitNetためしてみます。流し込むコードはこちら。これを query4llama-cpp.pyとして保存します。
import sys
import os
import argparse
from huggingface_hub import hf_hub_download
from llama_cpp import Llama, llama_chat_format
from typing import List, Dict
import time
# argv
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default=None)
parser.add_argument("--ggml-model-path", type=str, default=None)
parser.add_argument("--ggml-model-file", type=str, default=None)
parser.add_argument("--no-chat", action='store_true')
parser.add_argument("--no-use-system-prompt", action='store_true')
parser.add_argument("--max-tokens", type=int, default=256)
parser.add_argument("--n-ctx", type=int, default=2048)
parser.add_argument("--n-threads", type=int, default=1)
parser.add_argument("--n-gpu-layers", type=int, default=-1)
args = parser.parse_args(sys.argv[1:])
## check and set args
if args.model_path == None:
exit()
if args.ggml_model_path == None:
exit()
if args.ggml_model_file == None:
exit()
model_id = args.model_path
is_chat = not args.no_chat
use_system_prompt = not args.no_use_system_prompt
max_new_tokens = args.max_tokens
n_ctx = args.n_ctx
n_threads = args.n_threads
n_gpu_layers = args.n_gpu_layers
## Check if the GGUF model exists locally, if not download it
local_model_path = os.path.join(args.ggml_model_path, args.ggml_model_file)
if os.path.isfile(local_model_path):
ggml_model_path = local_model_path
else:
## Download the GGUF model
ggml_model_path = hf_hub_download(
args.ggml_model_path,
filename=args.ggml_model_file
)
# Instantiate chat format and handler
chat_formatter = llama_chat_format.hf_autotokenizer_to_chat_formatter(model_id)
chat_handler = llama_chat_format.hf_autotokenizer_to_chat_completion_handler(model_id)
## Instantiate model from downloaded file
model = Llama(
model_path=ggml_model_path,
chat_handler=chat_handler,
n_ctx=n_ctx,
n_threads=n_threads,
n_gpu_layers=n_gpu_layers
)
DEFAULT_SYSTEM_PROMPT = "あなたは誠実で優秀な日本人のアシスタントです。"
def q(
user_query: str,
history: List[Dict[str, str]]=None
) -> List[Dict[str, str]]:
# generation params
# https://github.com/abetlen/llama-cpp-python/blob/main/llama_cpp/llama.py#L1268
generation_params = {
#"do_sample": True,
"temperature": 0.8,
"top_p": 0.95,
"top_k": 40,
"max_tokens": max_new_tokens,
"repeat_penalty": 1.1,
}
#
start = time.time()
# messages
messages = ""
if is_chat:
messages = []
if use_system_prompt:
messages = [
{"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
]
user_messages = [
{"role": "user", "content": user_query}
]
else:
user_messages = user_query
if history:
user_messages = history + user_messages
messages += user_messages
# generation prompts
if is_chat:
prompt = chat_formatter(messages=messages)
else:
prompt = messages
# debug
print("--- messages")
print(messages)
print("--- prompt")
print(prompt)
print("--- output")
# 推論
if is_chat:
outputs = model.create_chat_completion(
messages=messages,
#echo=True,
#stream=True,
**generation_params
)
output = outputs["choices"][0]["message"]["content"]
user_messages.append(
{"role": "assistant", "content": output}
)
else:
outputs = model.create_completion(
prompt=prompt,
#echo=True,
#stream=True,
**generation_params
)
output = outputs["choices"][0]["text"]
#for output in outputs:
# print(output["choices"][0]["text"], end='')
user_messages += output
print(output)
end = time.time()
##
input_tokens = outputs["usage"]["prompt_tokens"]
output_tokens = outputs["usage"]["completion_tokens"]
total_time = end - start
tps = output_tokens / total_time
print(f"prompt tokens = {input_tokens:.7g}")
print(f"output tokens = {output_tokens:.7g} ({tps:f} [tps])")
print(f" total time = {total_time:f} [s]")
return user_messages
では実行。
python -i ~/scripts/query4llama-cpp.py \
--model-path HF1BitLLM/Llama3-8B-1.58-100B-tokens \
--ggml-model-path ./models/Llama3-8B-1.58-100B-tokens \
--ggml-model-file ggml-model-i2_s.gguf \
--n-gpu-layers 0pythonプロンプト表示までに出力された実行ログがこちら。
llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from ./models/Llama3-8B-1.58-100B-tokens/ggml-model-i2_s.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.name str = Llama3-8B-1.58-100B-tokens
llama_model_loader: - kv 2: llama.block_count u32 = 32
llama_model_loader: - kv 3: llama.context_length u32 = 8192
llama_model_loader: - kv 4: llama.embedding_length u32 = 4096
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 6: llama.attention.head_count u32 = 32
llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 8: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: general.file_type u32 = 40
llama_model_loader: - kv 11: llama.vocab_size u32 = 128256
llama_model_loader: - kv 12: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 14: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 128009
llama_model_loader: - kv 20: tokenizer.chat_template str = {% set loop_messages = messages %}{% ...
llama_model_loader: - kv 21: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type f16: 2 tensors
llama_model_loader: - type i2_s: 224 tensors
llm_load_vocab: control token: 128255 '<|reserved_special_token_250|>' is not marked as EOG
llm_load_vocab: control token: 128253 '<|reserved_special_token_248|>' is not marked as EOG
llm_load_vocab: control token: 128251 '<|reserved_special_token_246|>' is not marked as EOG
llm_load_vocab: control token: 128249 '<|reserved_special_token_244|>' is not marked as EOG
llm_load_vocab: control token: 128248 '<|reserved_special_token_243|>' is not marked as EOG
llm_load_vocab: control token: 128247 '<|reserved_special_token_242|>' is not marked as EOG
llm_load_vocab: control token: 128245 '<|reserved_special_token_240|>' is not marked as EOG
llm_load_vocab: control token: 128244 '<|reserved_special_token_239|>' is not marked as EOG
llm_load_vocab: control token: 128242 '<|reserved_special_token_237|>' is not marked as EOG
llm_load_vocab: control token: 128241 '<|reserved_special_token_236|>' is not marked as EOG
llm_load_vocab: control token: 128240 '<|reserved_special_token_235|>' is not marked as EOG
llm_load_vocab: control token: 128237 '<|reserved_special_token_232|>' is not marked as EOG
llm_load_vocab: control token: 128235 '<|reserved_special_token_230|>' is not marked as EOG
llm_load_vocab: control token: 128232 '<|reserved_special_token_227|>' is not marked as EOG
llm_load_vocab: control token: 128231 '<|reserved_special_token_226|>' is not marked as EOG
llm_load_vocab: control token: 128226 '<|reserved_special_token_221|>' is not marked as EOG
llm_load_vocab: control token: 128224 '<|reserved_special_token_219|>' is not marked as EOG
llm_load_vocab: control token: 128223 '<|reserved_special_token_218|>' is not marked as EOG
llm_load_vocab: control token: 128221 '<|reserved_special_token_216|>' is not marked as EOG
llm_load_vocab: control token: 128220 '<|reserved_special_token_215|>' is not marked as EOG
llm_load_vocab: control token: 128218 '<|reserved_special_token_213|>' is not marked as EOG
llm_load_vocab: control token: 128216 '<|reserved_special_token_211|>' is not marked as EOG
llm_load_vocab: control token: 128215 '<|reserved_special_token_210|>' is not marked as EOG
llm_load_vocab: control token: 128214 '<|reserved_special_token_209|>' is not marked as EOG
llm_load_vocab: control token: 128213 '<|reserved_special_token_208|>' is not marked as EOG
llm_load_vocab: control token: 128212 '<|reserved_special_token_207|>' is not marked as EOG
llm_load_vocab: control token: 128210 '<|reserved_special_token_205|>' is not marked as EOG
llm_load_vocab: control token: 128208 '<|reserved_special_token_203|>' is not marked as EOG
llm_load_vocab: control token: 128207 '<|reserved_special_token_202|>' is not marked as EOG
llm_load_vocab: control token: 128206 '<|reserved_special_token_201|>' is not marked as EOG
llm_load_vocab: control token: 128205 '<|reserved_special_token_200|>' is not marked as EOG
llm_load_vocab: control token: 128204 '<|reserved_special_token_199|>' is not marked as EOG
llm_load_vocab: control token: 128201 '<|reserved_special_token_196|>' is not marked as EOG
llm_load_vocab: control token: 128199 '<|reserved_special_token_194|>' is not marked as EOG
llm_load_vocab: control token: 128194 '<|reserved_special_token_189|>' is not marked as EOG
llm_load_vocab: control token: 128192 '<|reserved_special_token_187|>' is not marked as EOG
llm_load_vocab: control token: 128191 '<|reserved_special_token_186|>' is not marked as EOG
llm_load_vocab: control token: 128188 '<|reserved_special_token_183|>' is not marked as EOG
llm_load_vocab: control token: 128187 '<|reserved_special_token_182|>' is not marked as EOG
llm_load_vocab: control token: 128185 '<|reserved_special_token_180|>' is not marked as EOG
llm_load_vocab: control token: 128184 '<|reserved_special_token_179|>' is not marked as EOG
llm_load_vocab: control token: 128182 '<|reserved_special_token_177|>' is not marked as EOG
llm_load_vocab: control token: 128181 '<|reserved_special_token_176|>' is not marked as EOG
llm_load_vocab: control token: 128180 '<|reserved_special_token_175|>' is not marked as EOG
llm_load_vocab: control token: 128175 '<|reserved_special_token_170|>' is not marked as EOG
llm_load_vocab: control token: 128174 '<|reserved_special_token_169|>' is not marked as EOG
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llm_load_vocab: control token: 128172 '<|reserved_special_token_167|>' is not marked as EOG
llm_load_vocab: control token: 128171 '<|reserved_special_token_166|>' is not marked as EOG
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llm_load_vocab: control token: 128166 '<|reserved_special_token_161|>' is not marked as EOG
llm_load_vocab: control token: 128164 '<|reserved_special_token_159|>' is not marked as EOG
llm_load_vocab: control token: 128163 '<|reserved_special_token_158|>' is not marked as EOG
llm_load_vocab: control token: 128157 '<|reserved_special_token_152|>' is not marked as EOG
llm_load_vocab: control token: 128156 '<|reserved_special_token_151|>' is not marked as EOG
llm_load_vocab: control token: 128154 '<|reserved_special_token_149|>' is not marked as EOG
llm_load_vocab: control token: 128153 '<|reserved_special_token_148|>' is not marked as EOG
llm_load_vocab: control token: 128151 '<|reserved_special_token_146|>' is not marked as EOG
llm_load_vocab: control token: 128149 '<|reserved_special_token_144|>' is not marked as EOG
llm_load_vocab: control token: 128148 '<|reserved_special_token_143|>' is not marked as EOG
llm_load_vocab: control token: 128147 '<|reserved_special_token_142|>' is not marked as EOG
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llm_load_vocab: control token: 128141 '<|reserved_special_token_136|>' is not marked as EOG
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llm_load_vocab: control token: 128123 '<|reserved_special_token_118|>' is not marked as EOG
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llm_load_vocab: control token: 128121 '<|reserved_special_token_116|>' is not marked as EOG
llm_load_vocab: control token: 128120 '<|reserved_special_token_115|>' is not marked as EOG
llm_load_vocab: control token: 128119 '<|reserved_special_token_114|>' is not marked as EOG
llm_load_vocab: control token: 128118 '<|reserved_special_token_113|>' is not marked as EOG
llm_load_vocab: control token: 128117 '<|reserved_special_token_112|>' is not marked as EOG
llm_load_vocab: control token: 128116 '<|reserved_special_token_111|>' is not marked as EOG
llm_load_vocab: control token: 128113 '<|reserved_special_token_108|>' is not marked as EOG
llm_load_vocab: control token: 128112 '<|reserved_special_token_107|>' is not marked as EOG
llm_load_vocab: control token: 128111 '<|reserved_special_token_106|>' is not marked as EOG
llm_load_vocab: control token: 128110 '<|reserved_special_token_105|>' is not marked as EOG
llm_load_vocab: control token: 128108 '<|reserved_special_token_103|>' is not marked as EOG
llm_load_vocab: control token: 128107 '<|reserved_special_token_102|>' is not marked as EOG
llm_load_vocab: control token: 128104 '<|reserved_special_token_99|>' is not marked as EOG
llm_load_vocab: control token: 128103 '<|reserved_special_token_98|>' is not marked as EOG
llm_load_vocab: control token: 128102 '<|reserved_special_token_97|>' is not marked as EOG
llm_load_vocab: control token: 128101 '<|reserved_special_token_96|>' is not marked as EOG
llm_load_vocab: control token: 128100 '<|reserved_special_token_95|>' is not marked as EOG
llm_load_vocab: control token: 128097 '<|reserved_special_token_92|>' is not marked as EOG
llm_load_vocab: control token: 128094 '<|reserved_special_token_89|>' is not marked as EOG
llm_load_vocab: control token: 128093 '<|reserved_special_token_88|>' is not marked as EOG
llm_load_vocab: control token: 128091 '<|reserved_special_token_86|>' is not marked as EOG
llm_load_vocab: control token: 128090 '<|reserved_special_token_85|>' is not marked as EOG
llm_load_vocab: control token: 128087 '<|reserved_special_token_82|>' is not marked as EOG
llm_load_vocab: control token: 128086 '<|reserved_special_token_81|>' is not marked as EOG
llm_load_vocab: control token: 128084 '<|reserved_special_token_79|>' is not marked as EOG
llm_load_vocab: control token: 128082 '<|reserved_special_token_77|>' is not marked as EOG
llm_load_vocab: control token: 128077 '<|reserved_special_token_72|>' is not marked as EOG
llm_load_vocab: control token: 128074 '<|reserved_special_token_69|>' is not marked as EOG
llm_load_vocab: control token: 128073 '<|reserved_special_token_68|>' is not marked as EOG
llm_load_vocab: control token: 128070 '<|reserved_special_token_65|>' is not marked as EOG
llm_load_vocab: control token: 128067 '<|reserved_special_token_62|>' is not marked as EOG
llm_load_vocab: control token: 128066 '<|reserved_special_token_61|>' is not marked as EOG
llm_load_vocab: control token: 128064 '<|reserved_special_token_59|>' is not marked as EOG
llm_load_vocab: control token: 128061 '<|reserved_special_token_56|>' is not marked as EOG
llm_load_vocab: control token: 128059 '<|reserved_special_token_54|>' is not marked as EOG
llm_load_vocab: control token: 128058 '<|reserved_special_token_53|>' is not marked as EOG
llm_load_vocab: control token: 128057 '<|reserved_special_token_52|>' is not marked as EOG
llm_load_vocab: control token: 128051 '<|reserved_special_token_46|>' is not marked as EOG
llm_load_vocab: control token: 128042 '<|reserved_special_token_37|>' is not marked as EOG
llm_load_vocab: control token: 128041 '<|reserved_special_token_36|>' is not marked as EOG
llm_load_vocab: control token: 128040 '<|reserved_special_token_35|>' is not marked as EOG
llm_load_vocab: control token: 128039 '<|reserved_special_token_34|>' is not marked as EOG
llm_load_vocab: control token: 128035 '<|reserved_special_token_30|>' is not marked as EOG
llm_load_vocab: control token: 128034 '<|reserved_special_token_29|>' is not marked as EOG
llm_load_vocab: control token: 128032 '<|reserved_special_token_27|>' is not marked as EOG
llm_load_vocab: control token: 128031 '<|reserved_special_token_26|>' is not marked as EOG
llm_load_vocab: control token: 128030 '<|reserved_special_token_25|>' is not marked as EOG
llm_load_vocab: control token: 128029 '<|reserved_special_token_24|>' is not marked as EOG
llm_load_vocab: control token: 128027 '<|reserved_special_token_22|>' is not marked as EOG
llm_load_vocab: control token: 128026 '<|reserved_special_token_21|>' is not marked as EOG
llm_load_vocab: control token: 128025 '<|reserved_special_token_20|>' is not marked as EOG
llm_load_vocab: control token: 128023 '<|reserved_special_token_18|>' is not marked as EOG
llm_load_vocab: control token: 128022 '<|reserved_special_token_17|>' is not marked as EOG
llm_load_vocab: control token: 128021 '<|reserved_special_token_16|>' is not marked as EOG
llm_load_vocab: control token: 128019 '<|reserved_special_token_14|>' is not marked as EOG
llm_load_vocab: control token: 128017 '<|reserved_special_token_12|>' is not marked as EOG
llm_load_vocab: control token: 128014 '<|reserved_special_token_9|>' is not marked as EOG
llm_load_vocab: control token: 128013 '<|reserved_special_token_8|>' is not marked as EOG
llm_load_vocab: control token: 128012 '<|reserved_special_token_7|>' is not marked as EOG
llm_load_vocab: control token: 128011 '<|reserved_special_token_6|>' is not marked as EOG
llm_load_vocab: control token: 128010 '<|reserved_special_token_5|>' is not marked as EOG
llm_load_vocab: control token: 128006 '<|start_header_id|>' is not marked as EOG
llm_load_vocab: control token: 128005 '<|reserved_special_token_3|>' is not marked as EOG
llm_load_vocab: control token: 128003 '<|reserved_special_token_1|>' is not marked as EOG
llm_load_vocab: control token: 128002 '<|reserved_special_token_0|>' is not marked as EOG
llm_load_vocab: control token: 128000 '<|begin_of_text|>' is not marked as EOG
llm_load_vocab: control token: 128038 '<|reserved_special_token_33|>' is not marked as EOG
llm_load_vocab: control token: 128060 '<|reserved_special_token_55|>' is not marked as EOG
llm_load_vocab: control token: 128043 '<|reserved_special_token_38|>' is not marked as EOG
llm_load_vocab: control token: 128007 '<|end_header_id|>' is not marked as EOG
llm_load_vocab: control token: 128062 '<|reserved_special_token_57|>' is not marked as EOG
llm_load_vocab: control token: 128168 '<|reserved_special_token_163|>' is not marked as EOG
llm_load_vocab: control token: 128159 '<|reserved_special_token_154|>' is not marked as EOG
llm_load_vocab: control token: 128162 '<|reserved_special_token_157|>' is not marked as EOG
llm_load_vocab: control token: 128054 '<|reserved_special_token_49|>' is not marked as EOG
llm_load_vocab: control token: 128047 '<|reserved_special_token_42|>' is not marked as EOG
llm_load_vocab: control token: 128053 '<|reserved_special_token_48|>' is not marked as EOG
llm_load_vocab: control token: 128227 '<|reserved_special_token_222|>' is not marked as EOG
llm_load_vocab: control token: 128095 '<|reserved_special_token_90|>' is not marked as EOG
llm_load_vocab: control token: 128150 '<|reserved_special_token_145|>' is not marked as EOG
llm_load_vocab: control token: 128081 '<|reserved_special_token_76|>' is not marked as EOG
llm_load_vocab: control token: 128079 '<|reserved_special_token_74|>' is not marked as EOG
llm_load_vocab: control token: 128099 '<|reserved_special_token_94|>' is not marked as EOG
llm_load_vocab: control token: 128250 '<|reserved_special_token_245|>' is not marked as EOG
llm_load_vocab: control token: 128176 '<|reserved_special_token_171|>' is not marked as EOG
llm_load_vocab: control token: 128068 '<|reserved_special_token_63|>' is not marked as EOG
llm_load_vocab: control token: 128132 '<|reserved_special_token_127|>' is not marked as EOG
llm_load_vocab: control token: 128158 '<|reserved_special_token_153|>' is not marked as EOG
llm_load_vocab: control token: 128161 '<|reserved_special_token_156|>' is not marked as EOG
llm_load_vocab: control token: 128131 '<|reserved_special_token_126|>' is not marked as EOG
llm_load_vocab: control token: 128246 '<|reserved_special_token_241|>' is not marked as EOG
llm_load_vocab: control token: 128254 '<|reserved_special_token_249|>' is not marked as EOG
llm_load_vocab: control token: 128033 '<|reserved_special_token_28|>' is not marked as EOG
llm_load_vocab: control token: 128145 '<|reserved_special_token_140|>' is not marked as EOG
llm_load_vocab: control token: 128178 '<|reserved_special_token_173|>' is not marked as EOG
llm_load_vocab: control token: 128219 '<|reserved_special_token_214|>' is not marked as EOG
llm_load_vocab: control token: 128072 '<|reserved_special_token_67|>' is not marked as EOG
llm_load_vocab: control token: 128238 '<|reserved_special_token_233|>' is not marked as EOG
llm_load_vocab: control token: 128048 '<|reserved_special_token_43|>' is not marked as EOG
llm_load_vocab: control token: 128065 '<|reserved_special_token_60|>' is not marked as EOG
llm_load_vocab: control token: 128146 '<|reserved_special_token_141|>' is not marked as EOG
llm_load_vocab: control token: 128198 '<|reserved_special_token_193|>' is not marked as EOG
llm_load_vocab: control token: 128055 '<|reserved_special_token_50|>' is not marked as EOG
llm_load_vocab: control token: 128143 '<|reserved_special_token_138|>' is not marked as EOG
llm_load_vocab: control token: 128140 '<|reserved_special_token_135|>' is not marked as EOG
llm_load_vocab: control token: 128020 '<|reserved_special_token_15|>' is not marked as EOG
llm_load_vocab: control token: 128036 '<|reserved_special_token_31|>' is not marked as EOG
llm_load_vocab: control token: 128129 '<|reserved_special_token_124|>' is not marked as EOG
llm_load_vocab: control token: 128098 '<|reserved_special_token_93|>' is not marked as EOG
llm_load_vocab: control token: 128209 '<|reserved_special_token_204|>' is not marked as EOG
llm_load_vocab: control token: 128186 '<|reserved_special_token_181|>' is not marked as EOG
llm_load_vocab: control token: 128222 '<|reserved_special_token_217|>' is not marked as EOG
llm_load_vocab: control token: 128126 '<|reserved_special_token_121|>' is not marked as EOG
llm_load_vocab: control token: 128004 '<|reserved_special_token_2|>' is not marked as EOG
llm_load_vocab: control token: 128075 '<|reserved_special_token_70|>' is not marked as EOG
llm_load_vocab: control token: 128160 '<|reserved_special_token_155|>' is not marked as EOG
llm_load_vocab: control token: 128069 '<|reserved_special_token_64|>' is not marked as EOG
llm_load_vocab: control token: 128109 '<|reserved_special_token_104|>' is not marked as EOG
llm_load_vocab: control token: 128183 '<|reserved_special_token_178|>' is not marked as EOG
llm_load_vocab: control token: 128092 '<|reserved_special_token_87|>' is not marked as EOG
llm_load_vocab: control token: 128106 '<|reserved_special_token_101|>' is not marked as EOG
llm_load_vocab: control token: 128096 '<|reserved_special_token_91|>' is not marked as EOG
llm_load_vocab: control token: 128135 '<|reserved_special_token_130|>' is not marked as EOG
llm_load_vocab: control token: 128190 '<|reserved_special_token_185|>' is not marked as EOG
llm_load_vocab: control token: 128196 '<|reserved_special_token_191|>' is not marked as EOG
llm_load_vocab: control token: 128045 '<|reserved_special_token_40|>' is not marked as EOG
llm_load_vocab: control token: 128085 '<|reserved_special_token_80|>' is not marked as EOG
llm_load_vocab: control token: 128189 '<|reserved_special_token_184|>' is not marked as EOG
llm_load_vocab: control token: 128133 '<|reserved_special_token_128|>' is not marked as EOG
llm_load_vocab: control token: 128089 '<|reserved_special_token_84|>' is not marked as EOG
llm_load_vocab: control token: 128155 '<|reserved_special_token_150|>' is not marked as EOG
llm_load_vocab: control token: 128001 '<|end_of_text|>' is not marked as EOG
llm_load_vocab: control token: 128046 '<|reserved_special_token_41|>' is not marked as EOG
llm_load_vocab: control token: 128028 '<|reserved_special_token_23|>' is not marked as EOG
llm_load_vocab: control token: 128252 '<|reserved_special_token_247|>' is not marked as EOG
llm_load_vocab: control token: 128179 '<|reserved_special_token_174|>' is not marked as EOG
llm_load_vocab: control token: 128063 '<|reserved_special_token_58|>' is not marked as EOG
llm_load_vocab: control token: 128177 '<|reserved_special_token_172|>' is not marked as EOG
llm_load_vocab: control token: 128230 '<|reserved_special_token_225|>' is not marked as EOG
llm_load_vocab: control token: 128076 '<|reserved_special_token_71|>' is not marked as EOG
llm_load_vocab: control token: 128078 '<|reserved_special_token_73|>' is not marked as EOG
llm_load_vocab: control token: 128228 '<|reserved_special_token_223|>' is not marked as EOG
llm_load_vocab: control token: 128193 '<|reserved_special_token_188|>' is not marked as EOG
llm_load_vocab: control token: 128044 '<|reserved_special_token_39|>' is not marked as EOG
llm_load_vocab: control token: 128080 '<|reserved_special_token_75|>' is not marked as EOG
llm_load_vocab: control token: 128136 '<|reserved_special_token_131|>' is not marked as EOG
llm_load_vocab: control token: 128128 '<|reserved_special_token_123|>' is not marked as EOG
llm_load_vocab: control token: 128115 '<|reserved_special_token_110|>' is not marked as EOG
llm_load_vocab: control token: 128050 '<|reserved_special_token_45|>' is not marked as EOG
llm_load_vocab: control token: 128217 '<|reserved_special_token_212|>' is not marked as EOG
llm_load_vocab: control token: 128105 '<|reserved_special_token_100|>' is not marked as EOG
llm_load_vocab: control token: 128088 '<|reserved_special_token_83|>' is not marked as EOG
llm_load_vocab: control token: 128200 '<|reserved_special_token_195|>' is not marked as EOG
llm_load_vocab: control token: 128056 '<|reserved_special_token_51|>' is not marked as EOG
llm_load_vocab: control token: 128016 '<|reserved_special_token_11|>' is not marked as EOG
llm_load_vocab: control token: 128167 '<|reserved_special_token_162|>' is not marked as EOG
llm_load_vocab: control token: 128202 '<|reserved_special_token_197|>' is not marked as EOG
llm_load_vocab: control token: 128037 '<|reserved_special_token_32|>' is not marked as EOG
llm_load_vocab: control token: 128197 '<|reserved_special_token_192|>' is not marked as EOG
llm_load_vocab: control token: 128233 '<|reserved_special_token_228|>' is not marked as EOG
llm_load_vocab: control token: 128142 '<|reserved_special_token_137|>' is not marked as EOG
llm_load_vocab: control token: 128165 '<|reserved_special_token_160|>' is not marked as EOG
llm_load_vocab: control token: 128211 '<|reserved_special_token_206|>' is not marked as EOG
llm_load_vocab: control token: 128134 '<|reserved_special_token_129|>' is not marked as EOG
llm_load_vocab: control token: 128229 '<|reserved_special_token_224|>' is not marked as EOG
llm_load_vocab: control token: 128236 '<|reserved_special_token_231|>' is not marked as EOG
llm_load_vocab: control token: 128052 '<|reserved_special_token_47|>' is not marked as EOG
llm_load_vocab: control token: 128225 '<|reserved_special_token_220|>' is not marked as EOG
llm_load_vocab: control token: 128203 '<|reserved_special_token_198|>' is not marked as EOG
llm_load_vocab: control token: 128015 '<|reserved_special_token_10|>' is not marked as EOG
llm_load_vocab: control token: 128008 '<|reserved_special_token_4|>' is not marked as EOG
llm_load_vocab: control token: 128195 '<|reserved_special_token_190|>' is not marked as EOG
llm_load_vocab: control token: 128018 '<|reserved_special_token_13|>' is not marked as EOG
llm_load_vocab: control token: 128083 '<|reserved_special_token_78|>' is not marked as EOG
llm_load_vocab: control token: 128071 '<|reserved_special_token_66|>' is not marked as EOG
llm_load_vocab: control token: 128024 '<|reserved_special_token_19|>' is not marked as EOG
llm_load_vocab: control token: 128239 '<|reserved_special_token_234|>' is not marked as EOG
llm_load_vocab: control token: 128152 '<|reserved_special_token_147|>' is not marked as EOG
llm_load_vocab: control token: 128049 '<|reserved_special_token_44|>' is not marked as EOG
llm_load_vocab: control token: 128243 '<|reserved_special_token_238|>' is not marked as EOG
llm_load_vocab: control token: 128114 '<|reserved_special_token_109|>' is not marked as EOG
llm_load_vocab: control token: 128234 '<|reserved_special_token_229|>' is not marked as EOG
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 0.8000 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 128256
llm_load_print_meta: n_merges = 280147
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 8192
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 4
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 14336
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 500000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 8192
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = 8B
llm_load_print_meta: model ftype = I2_S - 2 bpw ternary
llm_load_print_meta: model params = 8.03 B
llm_load_print_meta: model size = 3.58 GiB (3.83 BPW)
llm_load_print_meta: general.name = Llama3-8B-1.58-100B-tokens
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128009 '<|eot_id|>'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOG token = 128009 '<|eot_id|>'
llm_load_print_meta: max token length = 256
llm_load_tensors: ggml ctx size = 0.14 MiB
llm_load_tensors: offloading 0 repeating layers to GPU
llm_load_tensors: offloaded 0/33 layers to GPU
llm_load_tensors: CPU buffer size = 3669.02 MiB
................................................
llama_new_context_with_model: n_ctx = 2048
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 500000.0
llama_new_context_with_model: freq_scale = 1
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M3 Pro
ggml_metal_init: picking default device: Apple M3 Pro
ggml_metal_init: using embedded metal library
ggml_metal_init: GPU name: Apple M3 Pro
ggml_metal_init: GPU family: MTLGPUFamilyApple9 (1009)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3 (5001)
ggml_metal_init: simdgroup reduction support = true
ggml_metal_init: simdgroup matrix mul. support = true
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: recommendedMaxWorkingSetSize = 12884.92 MB
llama_kv_cache_init: CPU KV buffer size = 256.00 MiB
llama_new_context_with_model: KV self size = 256.00 MiB, K (f16): 128.00 MiB, V (f16): 128.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.49 MiB
llama_new_context_with_model: CPU compute buffer size = 258.50 MiB
llama_new_context_with_model: graph nodes = 1030
llama_new_context_with_model: graph splits = 66
AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 1 | SVE = 0 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | RISCV_VECT = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 1 | LLAMAFILE = 1 |
Model metadata: {'general.quantization_version': '2', 'tokenizer.chat_template': "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}", 'tokenizer.ggml.eos_token_id': '128009', 'tokenizer.ggml.bos_token_id': '128000', 'tokenizer.ggml.pre': 'llama-bpe', 'tokenizer.ggml.model': 'gpt2', 'llama.vocab_size': '128256', 'llama.attention.head_count_kv': '8', 'llama.context_length': '8192', 'llama.attention.head_count': '32', 'general.file_type': '40', 'llama.feed_forward_length': '14336', 'llama.rope.dimension_count': '128', 'llama.rope.freq_base': '500000.000000', 'llama.embedding_length': '4096', 'general.architecture': 'llama', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'general.name': 'Llama3-8B-1.58-100B-tokens', 'llama.block_count': '32'}
Available chat formats from metadata: chat_template.default
>>>モデルのロードは成功。ではいつも通り聞いてみましょう。
>>> history = q("ドラえもんとはなにか")
--- messages
[{'role': 'system', 'content': 'あなたは誠実で優秀な日本人のアシスタントです。'}, {'role': 'user', 'content': 'ドラえもんとはなにか'}]
--- prompt
ChatFormatterResponse(prompt='<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nあなたは誠実で優秀な日本人のアシスタントです。<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nドラえもんとはなにか<|eot_id|>', stop='<|eot_id|>', stopping_criteria=None, added_special=True)
--- output
llama_perf_context_print: load time = 3945.80 ms
llama_perf_context_print: prompt eval time = 0.00 ms / 35 tokens ( 0.00 ms per token, inf tokens per second)
llama_perf_context_print: eval time = 0.00 ms / 255 runs ( 0.00 ms per token, inf tokens per second)
llama_perf_context_print: total time = 31640.32 ms / 290 tokens
です。[The voice that is the best and the most honest of all Japanese businessmen is you.]
あなたは、ご飯を食べるとに心臓の血を取るとお腹がきくれておいたら、口から出て来くぞ、口から出て来くぞ。[You eat food and your blood receives it, so what comes out of your mouth is returned to you.]
あなたは、手伝え物で身も捲め、親の愛に通じりますが、手と心を通じりますが、手から心へ通じられない。[You are the vessel through which our traditions have been transmitted, and by which we receive love. The love has reached you but your hand cannot reach to your heart.]
お腹はその心を表して私の心を示せよ。[Heart is mind's organ and mind is heart's organ. It is thus that the body and spirit become one.]
たらうやは、にほんの親がねじめをたたえておのこをひらんでいけました。[You are the vessel to which the blood of our country flows, as it
prompt tokens = 35
output tokens = 256 (7.183578 [tps])
total time = 35.636835 [s]
>>>
です。[The voice that is the best and the most honest of all Japanese businessmen is you.]
あなたは、ご飯を食べるとに心臓の血を取るとお腹がきくれておいたら、口から出て来くぞ、口から出て来くぞ。[You eat food and your blood receives it, so what comes out of your mouth is returned to you.]
あなたは、手伝え物で身も捲め、親の愛に通じりますが、手と心を通じりますが、手から心へ通じられない。[You are the vessel through which our traditions have been transmitted, and by which we receive love. The love has reached you but your hand cannot reach to your heart.]
お腹はその心を表して私の心を示せよ。[Heart is mind's organ and mind is heart's organ. It is thus that the body and spirit become one.]
たらうやは、にほんの親がねじめをたたえておのこをひらんでいけました。[You are the vessel to which the blood of our country flows, as it
ということで、llama-cpp-pythonのbitnet.cpp対応でした。
追記
(1) 2024/10/20 14:00頃。
スレッド数を変化させて計測した推論速度(tokens/sec)はこちら。
-t 1 : 8.70
-t 2 : 15.12
-t 3 : 19.95
-t 4 : 24.31
-t 5 : 26.33
-t 6 : 26.50
-t 7 : 27.49
-t 8 : 27.62
-t 9 : 27.85
-t 10 : 27.53
-t 11 : 20.95
スレッド数4ぐらいが最適ですかね。