关于机器学习:本地推理单机运行MacM1芯片系统基于大语言模型C版本LLaMA部署本地版的ChatGPT

OpenAI 公司基于 GPT 模型的 ChatGPT 景色无两，眼看它起朱楼，眼看它宴宾客，FaceBook 终于坐不住了，公布了同样基于 LLM 的人工智能大语言模型 LLaMA，号称蕴含 70 亿、130 亿、330 亿和 650 亿这 4 种参数规模的模型，参数是指神经网络中的权重和偏置等可调整的变量，用于训练和优化神经网络的性能，70 亿意味着神经网络中有 70 亿个参数，由此类推。

在一些大型神经网络中，每个参数须要应用 32 位或 64 位浮点数进行存储，这意味着每个参数须要占用 4 字节或 8 字节的存储空间。因而，对于蕴含 70 亿个参数的神经网络，其存储空间将别离为 8 GB 或 12GB。

此外，神经网络的大小不仅取决于参数的数量，还取决于神经元的数目，层数和其余构造参数等。因而，70 亿的神经网络可能会占用更多的存储空间，具体取决于网络的构造和实现细节。

因而这种体量的模型单机跑相对够咱们喝一壶，所以本次应用最小的 LLaMA 7B 模型进行测试。

LLaMA 我的项目装置和模型配置

和 Stable-Diffusion 我的项目一模一样，FaceBook 开源的 LLaMA 我的项目默认写死应用 cuda 模式，这也就意味着必须有 NVIDIA 的 GPU 来训练和运行，不过好在大神 GeorgiGerganov 用 C++ 基于 LLaMA 我的项目重写了一个跑在 CPU 上的移植版本 llama.cpp 利用。

llama.cpp 首先适配的就是苹果的 M 系列芯片，这对于果粉来说无疑是一个重大利好，首先通过命令拉取 C ++ 版本的 LLaMA 我的项目：

git clone https://github.com/ggerganov/llama.cpp

随后进入我的项目目录：

llama.cpp

在我的项目中，须要独自建设一个模型文件夹 models:

mkdir models

随后去 huggingface 官网下载 LLaMA 的 7B 模型文件：https://huggingface.co/nyanko7/LLaMA-7B/tree/main

是的，主模型文件曾经达到了 13.5gb 之巨，如果本地硬盘空间告急，请审慎下载。

随后在 models 目录建设模型子目录 7B:

mkdir 7B

将 tokenizer.model 和 tokenizer\_checklist.chk 放入和 7B 平行的目录中：

➜  models git:(master) ✗ ls  
7B                      tokenizer.model         tokenizer_checklist.chk

随后将 checklist.chk consolidated.00.pth 和 params.json 放入 7B 目录中：

➜  7B git:(master) ✗ ls  
checklist.chk       consolidated.00.pth  params.json

至此，模型就配置好了。

LLaMA 模型转换

因为咱们没有应用 FaceBook 的原版我的项目，所以它的模型还须要进行转换，也就是转换为以后 C ++ 版本的 LLaMA 能够运行的模型。

这里通过 Python 脚本进行转换操作：

python3 convert-pth-to-ggml.py models/7B/ 1

第一个参数是模型所在目录，第二个参数为转换时应用的浮点类型，应用 float32，转换的后果文件会大一倍，当该参数值为 1 时，则应用 float16 这个默认值，这里咱们应用默认数据类型。

程序输入：

➜  llama.cpp git:(master) ✗ python convert-pth-to-ggml.py models/7B/ 1  
{'dim': 4096, 'multiple_of': 256, 'n_heads': 32, 'n_layers': 32, 'norm_eps': 1e-06, 'vocab_size': -1}  
n_parts = 1  
  
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Processing variable: layers.27.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16  
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Processing variable: layers.28.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16  
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Processing variable: layers.29.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16  
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Processing variable: layers.29.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16  
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Processing variable: layers.29.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16  
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Processing variable: layers.30.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16  
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Processing variable: layers.30.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16  
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Processing variable: layers.30.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16  
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Processing variable: layers.31.attention.wq.weight with shape: torch.Size([4096, 4096]) and type: torch.float16  
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Processing variable: layers.31.attention_norm.weight with shape: torch.Size([4096]) and type: torch.float16  
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Processing variable: layers.31.ffn_norm.weight with shape: torch.Size([4096]) and type: torch.float16  
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Done. Output file: models/7B//ggml-model-f16.bin, (part 0)

能够看到，如果转换胜利，会在 models/7B/ 目录生成一个 C ++ 能够调用的 ggml-model-f16.bin 模型文件。

LLaMA 模型调用

接下来就能够调用转换后的模型了，首先在编译 C ++ 我的项目：

make

程序返回：

➜  llama.cpp git:(master) ✗ make  
I llama.cpp build info:   
I UNAME_S:  Darwin  
I UNAME_P:  arm  
I UNAME_M:  arm64  
I CFLAGS:   -I.              -O3 -DNDEBUG -std=c11   -fPIC -pthread -DGGML_USE_ACCELERATE  
I CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -std=c++17 -fPIC -pthread  
I LDFLAGS:   -framework Accelerate  
I CC:       Apple clang version 14.0.0 (clang-1400.0.29.202)  
I CXX:      Apple clang version 14.0.0 (clang-1400.0.29.202)  
  
cc  -I.              -O3 -DNDEBUG -std=c11   -fPIC -pthread -DGGML_USE_ACCELERATE   -c ggml.c -o ggml.o  
c++ -I. -I./examples -O3 -DNDEBUG -std=c++17 -fPIC -pthread -c utils.cpp -o utils.o  
c++ -I. -I./examples -O3 -DNDEBUG -std=c++17 -fPIC -pthread main.cpp ggml.o utils.o -o main  -framework Accelerate  
./main -h  
usage: ./main [options]  
  
options:  
  -h, --help            show this help message and exit  
  -i, --interactive     run in interactive mode  
  -ins, --instruct      run in instruction mode (use with Alpaca models)  
  -r PROMPT, --reverse-prompt PROMPT  
                        in interactive mode, poll user input upon seeing PROMPT (can be  
                        specified more than once for multiple prompts).  
  --color               colorise output to distinguish prompt and user input from generations  
  -s SEED, --seed SEED  RNG seed (default: -1)  
  -t N, --threads N     number of threads to use during computation (default: 4)  
  -p PROMPT, --prompt PROMPT  
                        prompt to start generation with (default: empty)  
  --random-prompt       start with a randomized prompt.  
  -f FNAME, --file FNAME  
                        prompt file to start generation.  
  -n N, --n_predict N   number of tokens to predict (default: 128)  
  --top_k N             top-k sampling (default: 40)  
  --top_p N             top-p sampling (default: 0.9)  
  --repeat_last_n N     last n tokens to consider for penalize (default: 64)  
  --repeat_penalty N    penalize repeat sequence of tokens (default: 1.3)  
  -c N, --ctx_size N    size of the prompt context (default: 512)  
  --ignore-eos          ignore end of stream token and continue generating  
  --memory_f16          use f16 instead of f32 for memory key+value  
  --temp N              temperature (default: 0.8)  
  -b N, --batch_size N  batch size for prompt processing (default: 8)  
  -m FNAME, --model FNAME  
                        model path (default: models/llama-7B/ggml-model.bin)  
  
c++ -I. -I./examples -O3 -DNDEBUG -std=c++17 -fPIC -pthread quantize.cpp ggml.o utils.o -o quantize  -framework Accelerate

编译胜利后，本地会生成一个 main.cpp 文件。

随后依据编译后输入的阐明文档间接调用模型即可：

./main -m ./models/7B/ggml-model-f16.bin -p 'Hi i am'

程序输入：

➜  llama.cpp git:(master) ✗ ./main -m ./models/7B/ggml-model-f16.bin -p 'hi i am'  
main: seed = 1679400707  
llama_model_load: loading model from './models/7B/ggml-model-f16.bin' - please wait ...  
llama_model_load: n_vocab = 32000  
llama_model_load: n_ctx   = 512  
llama_model_load: n_embd  = 4096  
llama_model_load: n_mult  = 256  
llama_model_load: n_head  = 32  
llama_model_load: n_layer = 32  
llama_model_load: n_rot   = 128  
llama_model_load: f16     = 1  
llama_model_load: n_ff    = 11008  
llama_model_load: n_parts = 1  
llama_model_load: ggml ctx size = 13365.09 MB  
llama_model_load: memory_size =   512.00 MB, n_mem = 16384  
llama_model_load: loading model part 1/1 from './models/7B/ggml-model-f16.bin'  
llama_model_load: .................................... done  
llama_model_load: model size = 12853.02 MB / num tensors = 291  
  
system_info: n_threads = 4 / 10 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 |   
  
main: prompt: 'hi i am'  
main: number of tokens in prompt = 6  
     1 -> ''13450 ->' hi'423 ->'i'25523 ->' am'  
  
sampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000, repeat_last_n = 64, repeat_penalty = 1.300000  
  
  
 hi i am a pythoner, but sunk to become a ruby

说实话，推理速度切实不敢恭维，也可能是因为笔者的电脑配置太渣导致。

结语

LLaMA 7B 模型总体上须要纯英文的提醒词（prompt），对中文的理解能力还不够，劣势是的确能够单机跑起来，当然本地跑的话，缩小了网络传输数据的环节，推理效率天然也就更高，对于一般的 AI 爱好者来说，足矣。