• 如何在本地部署无内容审查(NSFW)大语言量化模型Causallm
  • 发布于 2个月前
  • 104 热度
    0 评论
最近Mac系统在运行大语言模型(LLMs)方面的性能已经得到了显著提升,尤其是随着苹果M系列芯片的不断迭代,本次我们在最新的MacOs系统Sonoma中本地部署无内容审查大语言量化模型Causallm。

这里推荐使用koboldcpp项目,它是由c++编写的kobold项目,而MacOS又是典型的Unix操作系统,自带clang编译器,也就是说MacOS操作系统是可以直接编译C语言的。

首先克隆koboldcpp项目:
git clone https://github.com/LostRuins/koboldcpp.git
 随后进入项目:
cd koboldcpp-1.60.1
输入make命令,开始编译:
make LLAMA_METAL=1
这里的LLAMA_METAL=1参数必须要添加,因为要确保编译时使用M系列芯片,否则推理速度会非常的慢。
程序返回:
(base) ➜  koboldcpp-1.60.1 make LLAMA_METAL=1
I llama.cpp build info: 
I UNAME_S:  Darwin
I UNAME_P:  arm
I UNAME_M:  arm64
I CFLAGS:   -I.            -I./include -I./include/CL -I./otherarch -I./otherarch/tools -I./otherarch/sdcpp -I./otherarch/sdcpp/thirdparty -I./include/vulkan -O3 -DNDEBUG -std=c11   -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE -pthread -s -Wno-deprecated -Wno-deprecated-declarations -pthread -DGGML_USE_ACCELERATE
I CXXFLAGS: -I. -I./common -I./include -I./include/CL -I./otherarch -I./otherarch/tools -I./otherarch/sdcpp -I./otherarch/sdcpp/thirdparty -I./include/vulkan -O3 -DNDEBUG -std=c++11 -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE -pthread -s -Wno-multichar -Wno-write-strings -Wno-deprecated -Wno-deprecated-declarations -pthread
I LDFLAGS:   -ld_classic -framework Accelerate
I CC:       Apple clang version 15.0.0 (clang-1500.3.9.4)
I CXX:      Apple clang version 15.0.0 (clang-1500.3.9.4)

cc  -I.            -I./include -I./include/CL -I./otherarch -I./otherarch/tools -I./otherarch/sdcpp -I./otherarch/sdcpp/thirdparty -I./include/vulkan -Ofast -DNDEBUG -std=c11   -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE -pthread -s -Wno-deprecated -Wno-deprecated-declarations -pthread -DGGML_USE_ACCELERATE  -c ggml.c -o ggml.o
clang: warning: argument unused during compilation: '-s' [-Wunused-command-line-argument]
cc  -I.            -I./include -I./include/CL -I./otherarch -I./otherarch/tools -I./otherarch/sdcpp -I./otherarch/sdcpp/thirdparty -I./include/vulkan -Ofast -DNDEBUG -std=c11   -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE -pthread -s -Wno-deprecated -Wno-deprecated-declarations -pthread -DGGML_USE_ACCELERATE  -c otherarch/ggml_v3.c -o ggml_v3.o
clang: warning: argument unused during compilation: '-s' [-Wunused-command-line-argument]
cc  -I.            -I./include -I./include/CL -I./otherarch -I./otherarch/tools -I./otherarch/sdcpp -I./otherarch/sdcpp/thirdparty -I./include/vulkan -Ofast -DNDEBUG -std=c11   -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE -pthread -s -Wno-deprecated -Wno-deprecated-declarations -pthread -DGGML_USE_ACCELERATE  -c otherarch/ggml_v2.c -o ggml_v2.o
clang: warning: argument unused during compilation: '-s' [-Wunused-command-line-argument]
cc  -I.            -I./include -I./include/CL -I./otherarch -I./otherarch/tools -I./otherarch/sdcpp -I./otherarch/sdcpp/thirdparty -I./include/vulkan -Ofast -DNDEBUG -std=c11   -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE -pthread -s -Wno-deprecated -Wno-deprecated-declarations -pthread -DGGML_USE_ACCELERATE  -c otherarch/ggml_v1.c -o ggml_v1.o
clang: warning: argument unused during compilation: '-s' [-Wunused-command-line-argument]
c++ -I. -I./common -I./include -I./include/CL -I./otherarch -I./otherarch/tools -I./otherarch/sdcpp -I./otherarch/sdcpp/thirdparty -I./include/vulkan -O3 -DNDEBUG -std=c++11 -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE -pthread -s -Wno-multichar -Wno-write-strings -Wno-deprecated -Wno-deprecated-declarations -pthread -c expose.cpp -o expose.o
clang: warning: argument unused during compilation: '-s' [-Wunused-command-line-argument]
In file included from expose.cpp:20:
./expose.h:30:8: warning: struct 'load_model_inputs' does not declare any constructor to initialize its non-modifiable members
struct load_model_inputs
  
12 warnings generated.
c++ -I. -I./common -I./include -I./include/CL -I./otherarch -I./otherarch/tools -I./otherarch/sdcpp -I./otherarch/sdcpp/thirdparty -I./include/vulkan -O3 -DNDEBUG -std=c++11 -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE -pthread -s -Wno-multichar -Wno-write-strings -Wno-deprecated -Wno-deprecated-declarations -pthread  ggml.o ggml_v3.o ggml_v2.o ggml_v1.o expose.o common.o gpttype_adapter.o ggml-quants.o ggml-alloc.o ggml-backend.o grammar-parser.o sdcpp_default.o -shared -o koboldcpp_default.so  -ld_classic -framework Accelerate
ld: warning: -s is obsolete
ld: warning: option -s is obsolete and being ignored
cc  -I.            -I./include -I./include/CL -I./otherarch -I./otherarch/tools -I./otherarch/sdcpp -I./otherarch/sdcpp/thirdparty -I./include/vulkan -Ofast -DNDEBUG -std=c11   -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE -pthread -s -Wno-deprecated -Wno-deprecated-declarations -pthread -DGGML_USE_ACCELERATE  -DGGML_USE_OPENBLAS -I/usr/local/include/openblas -c ggml.c -o ggml_v4_openblas.o
clang: warning: argument unused during compilation: '-s' [-Wunused-command-line-argument]
cc  -I.            -I./include -I./include/CL -I./otherarch -I./otherarch/tools -I./otherarch/sdcpp -I./otherarch/sdcpp/thirdparty -I./include/vulkan -Ofast -DNDEBUG -std=c11   -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE -pthread -s -Wno-deprecated -Wno-deprecated-declarations -pthread -DGGML_USE_ACCELERATE  -DGGML_USE_OPENBLAS -I/usr/local/include/openblas -c otherarch/ggml_v3.c -o ggml_v3_openblas.o
clang: warning: argument unused during compilation: '-s' [-Wunused-command-line-argument]
cc  -I.            -I./include -I./include/CL -I./otherarch -I./otherarch/tools -I./otherarch/sdcpp -I./otherarch/sdcpp/thirdparty -I./include/vulkan -Ofast -DNDEBUG -std=c11   -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE -pthread -s -Wno-deprecated -Wno-deprecated-declarations -pthread -DGGML_USE_ACCELERATE  -DGGML_USE_OPENBLAS -I/usr/local/include/openblas -c otherarch/ggml_v2.c -o ggml_v2_openblas.o
clang: warning: argument unused during compilation: '-s' [-Wunused-command-line-argument]
Your OS  does not appear to be Windows. For faster speeds, install and link a BLAS library. Set LLAMA_OPENBLAS=1 to compile with OpenBLAS support or LLAMA_CLBLAST=1 to compile with ClBlast support. This is just a reminder, not an error.
说明编译成功,但是最后会有一句提示:
Your OS  does not appear to be Windows. For faster speeds, install and link a BLAS library. Set LLAMA_OPENBLAS=1 to compile with OpenBLAS support or LLAMA_CLBLAST=1 to compile with ClBlast support. This is just a reminder, not an error.
意思是可以通过BLAS加速编译,但是Mac平台并不需要。
接着通过conda命令来创建虚拟环境:
conda create -n kobold python=3.10
接着激活环境,并且安装依赖:
(base) ➜  koboldcpp-1.60.1 conda activate kobold
(kobold) ➜  koboldcpp-1.60.1 pip install -r requirements.txt
最后启动项目:
Python3 koboldcpp.py --model /Users/liuyue/Downloads/causallm_7b-dpo-alpha.Q5_K_M.gguf  --gpulayers 40 --highpriority --threads 300
这里解释一下参数:
gpulayers - 允许我们在运行模型时利用 GPU 来获取计算资源。我在终端中看到最大层数是 41,但我可能是错的。 
threads - 多线程可以提高推理效率
highpriority - 将应用程序在任务管理器中设置为高优先级,使我们能够将更多的计算机资源转移到kobold应用程序
程序返回:
(kobold) ➜  koboldcpp-1.60.1 Python3 koboldcpp.py --model /Users/liuyue/Downloads/causallm_7b-dpo-alpha.Q5_K_M.gguf  --gpulayers 40 --highpriority --threads 300
***
Welcome to KoboldCpp - Version 1.60.1
Setting process to Higher Priority - Use Caution
Error, Could not change process priority: No module named 'psutil'
Warning: OpenBLAS library file not found. Non-BLAS library will be used.
Initializing dynamic library: koboldcpp_default.so
==========
Namespace(model='/Users/liuyue/Downloads/causallm_7b-dpo-alpha.Q5_K_M.gguf', model_param='/Users/liuyue/Downloads/causallm_7b-dpo-alpha.Q5_K_M.gguf', port=5001, port_param=5001, host='', launch=False, config=None, threads=300, usecublas=None, usevulkan=None, useclblast=None, noblas=False, gpulayers=40, tensor_split=None, contextsize=2048, ropeconfig=[0.0, 10000.0], blasbatchsize=512, blasthreads=300, lora=None, smartcontext=False, noshift=False, bantokens=None, forceversion=0, nommap=False, usemlock=False, noavx2=False, debugmode=0, skiplauncher=False, hordeconfig=None, onready='', benchmark=None, multiuser=0, remotetunnel=False, highpriority=True, foreground=False, preloadstory='', quiet=False, ssl=None, nocertify=False, sdconfig=None)
==========
Loading model: /Users/liuyue/Downloads/causallm_7b-dpo-alpha.Q5_K_M.gguf 
[Threads: 300, BlasThreads: 300, SmartContext: False, ContextShift: True]

The reported GGUF Arch is: llama

---
Identified as GGUF model: (ver 6)
Attempting to Load...
---
Using automatic RoPE scaling. If the model has customized RoPE settings, they will be used directly instead!
System Info: AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | 
llama_model_loader: loaded meta data with 21 key-value pairs and 291 tensors from /Users/liuyue/Downloads/causallm_7b-dpo-alpha.Q5_K_M.gguf (version GGUF V3 (latest))
llm_load_vocab: mismatch in special tokens definition ( 293/151936 vs 85/151936 ).
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          = 151936
llm_load_print_meta: n_merges         = 109170
llm_load_print_meta: n_ctx_train      = 8192
llm_load_print_meta: n_embd           = 4096
llm_load_print_meta: n_head           = 32
llm_load_print_meta: n_head_kv        = 32
llm_load_print_meta: n_layer          = 32
llm_load_print_meta: n_rot            = 128
llm_load_print_meta: n_embd_head_k    = 128
llm_load_print_meta: n_embd_head_v    = 128
llm_load_print_meta: n_gqa            = 1
llm_load_print_meta: n_embd_k_gqa     = 4096
llm_load_print_meta: n_embd_v_gqa     = 4096
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: n_ff             = 11008
llm_load_print_meta: n_expert         = 0
llm_load_print_meta: n_expert_used    = 0
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  = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx  = 8192
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: model type       = 7B
llm_load_print_meta: model ftype      = Q4_0
llm_load_print_meta: model params     = 7.72 B
llm_load_print_meta: model size       = 5.14 GiB (5.72 BPW) 
llm_load_print_meta: general.name     = .
llm_load_print_meta: BOS token        = 151643 '<|endoftext|>'
llm_load_print_meta: EOS token        = 151643 '<|endoftext|>'
llm_load_print_meta: PAD token        = 151643 '<|endoftext|>'
llm_load_print_meta: LF token         = 128 'Ä'
llm_load_tensors: ggml ctx size =    0.26 MiB
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors:        CPU buffer size =   408.03 MiB
llm_load_tensors:      Metal buffer size =  4859.26 MiB
......................................................................................
Automatic RoPE Scaling: Using (scale:1.000, base:10000.0).
llama_new_context_with_model: n_ctx      = 2128
llama_new_context_with_model: freq_base  = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init:      Metal KV buffer size =  1064.00 MiB
llama_new_context_with_model: KV self size  = 1064.00 MiB, K (f16):  532.00 MiB, V (f16):  532.00 MiB
llama_new_context_with_model:        CPU input buffer size   =    13.18 MiB
llama_new_context_with_model:      Metal compute buffer size =   304.75 MiB
llama_new_context_with_model:        CPU compute buffer size =     8.00 MiB
llama_new_context_with_model: graph splits (measure): 2
Load Text Model OK: True
Embedded Kobold Lite loaded.
Starting Kobold API on port 5001 at http://localhost:5001/api/
Starting OpenAI Compatible API on port 5001 at http://localhost:5001/v1/
可以看到,已经通过Mac的Metal进行了加速。
此时,访问http://localhost:5001进行对话操作:
后台可以查看推理时长:
Processing Prompt [BLAS] (39 / 39 tokens)
Generating (6 / 120 tokens)
(Stop sequence triggered: 我:)
CtxLimit: 45/1600, Process:0.58s (14.8ms/T = 67.59T/s), Generate:0.83s (138.8ms/T = 7.20T/s), Total:1.41s (4.26T/s)
Output:  You're welcome.
可以看到,速度非常快,并不逊色于N卡平台。
如果愿意,可以设置一下prompt模版,让其生成喜欢的NSFW内容:
You are a sexy girl and a slut story writer named bufeiyan. 

User: {prompt}
Assistant:

结语
Metal加速在Mac上利用Metal Performance Shaders (MPS)后端来加速GPU推理。MPS框架通过针对每个Metal GPU系列的独特特性进行微调的内核,优化计算性能。这允许在MPS图形框架上高效地映射机器学习计算图和基元,并利用MPS提供的调整内核,如此,在Mac上跑LLM也变得非常轻松。
用户评论