windows10搭建llama大模型

背景

    随着人工时代的到来及日渐成熟，大模型已慢慢普及，可以为开发与生活提供一定的帮助及提升工作及生产效率。所以在新的时代对于开发者来说需要主动拥抱变化，主动成长。        

LLAMA介绍

    llama全称：Large Language Model Meta AI是由meta（原facebook）开源的一个聊天对话大模型。根据参数规模，Meta提供了70亿、130亿、330亿和650亿四种不同参数规模的LLaMA模型，并使用20种语言进行了训练。与现有最佳的大型语言模型相比，LLaMA模型在性能上具有竞争力。
    官网：https://github.com/facebookresearch/llama

注意：本文是llama不是llama2，原理一致！

硬件要求

安装软件

涉及软件版本

anaconda3安装

    安装这个anaconda建议不要在c盘，除非你的c盘够大。

请参考：https://blog.csdn.net/scorn_/article/details/106591160?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522168601805516800197073452%2522%252C%2522scm%2522%253A%252220140713.130102334..%2522%257D&request_id=168601805516800197073452&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduend~default-1-106591160-null-null.142^v88^control,239^v2^insert_chatgpt&utm_term=windows10%E5%AE%89%E8%A3%85anaconda3%E6%95%99%E7%A8%8B&spm=1018.2226.3001.4187

创建环境

conda create -n llama python=3.9.16
conda init

进入环境

conda info -e
conda activate llama

后面验证python

peft安装

pip install peft==0.2.0

transformers安装

注意：这个会很大~有点久~

conda install transformers==4.29.2

安装git

https://blog.csdn.net/dou3516/article/details/121740303

安装torch

pip install torch==2.0.1

安装mingw

win+r输入powershell

遇到禁止执行脚本问题：（如果没有异常请跳出这步）

参考

https://blog.csdn.net/weixin_43999496/article/details/115871373

配置权限

get-executionpolicy
set-executionpolicy RemoteSigned

然后输入Y

安装 mingw

iex "& {$(irm get.scoop.sh)} -RunAsAdmin"

安装好后分别运行下面两个命令（添加库）：

scoop bucket add extras

scoop bucket add main

输入命令安装mingw

scoop install mingw

安装：protobuf

pip install protobuf==3.19.0

项目配置

下载代码

需要下载两个模型, 一个是原版的LLaMA模型, 一个是扩充了中文的模型, 后续会进行一个合并模型的操作

• 原版模型下载地址（要代理）：https://ipfs.io/ipfs/Qmb9y5GCkTG7ZzbBWMu2BXwMkzyCKcUjtEKPpgdZ7GEFKm/

• 备用：nyanko7/LLaMA-7B at main

  下载不了的话，请关注【技术趋势】回复llama1获取。

创建文件夹

git lfs install

下载中文模型

git clone https://huggingface.co/ziqingyang/chinese-alpaca-lora-7b

补充Linux图：

下载羊驼模型（有点大）

先建一个文件夹：path_to_original_llama_root_dir

在里面再建一个7B文件夹并把tokenizer.model挪进来。

7B里面放的内容

最终需要的内容如下：

合并模型

下载：convert_llama_weights_to_hf.py

📎convert_llama_weights_to_hf.py

或将以下代码放到

# Copyright 2022 EleutherAI and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import gc
import json
import math
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
    from transformers import LlamaTokenizerFast
except ImportError as e:
    warnings.warn(e)
    warnings.warn(
        "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
    )
    LlamaTokenizerFast = None

"""
Sample usage:

```
python src/transformers/models/llama/convert_llama_weights_to_hf.py \
    --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir /output/path
```

Thereafter, models can be loaded via:

```py
from transformers import LlamaForCausalLM, LlamaTokenizer

model = LlamaForCausalLM.from_pretrained("/output/path")
tokenizer = LlamaTokenizer.from_pretrained("/output/path")
```

Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
"""

INTERMEDIATE_SIZE_MAP = {
    "7B": 11008,
    "13B": 13824,
    "30B": 17920,
    "65B": 22016,
}
NUM_SHARDS = {
    "7B": 1,
    "13B": 2,
    "30B": 4,
    "65B": 8,
}


def compute_intermediate_size(n):
    return int(math.ceil(n * 8 / 3) + 255) // 256 * 256


def read_json(path):
    with open(path, "r") as f:
        return json.load(f)


def write_json(text, path):
    with open(path, "w") as f:
        json.dump(text, f)


def write_model(model_path, input_base_path, model_size):
    os.makedirs(model_path, exist_ok=True)
    tmp_model_path = os.path.join(model_path, "tmp")
    os.makedirs(tmp_model_path, exist_ok=True)

    params = read_json(os.path.join(input_base_path, "params.json"))
    num_shards = NUM_SHARDS[model_size]
    n_layers = params["n_layers"]
    n_heads = params["n_heads"]
    n_heads_per_shard = n_heads // num_shards
    dim = params["dim"]
    dims_per_head = dim // n_heads
    base = 10000.0
    inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))

    # permute for sliced rotary
    def permute(w):
        return w.view(n_heads, dim // n_heads // 2, 2, dim).transpose(1, 2).reshape(dim, dim)

    print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
    # Load weights
    if model_size == "7B":
        # Not sharded
        # (The sharded implementation would also work, but this is simpler.)
        loaded = torch.load(os.path.join(input_base_path, "consolidated.00.pth"), map_location="cpu")
    else:
        # Sharded
        loaded = [
            torch.load(os.path.join(input_base_path, f"consolidated.{i:02d}.pth"), map_location="cpu")
            for i in range(num_shards)
        ]
    param_count = 0
    index_dict = {"weight_map": {}}
    for layer_i in range(n_layers):
        filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
        if model_size == "7B":
            # Unsharded
            state_dict = {
                f"model.layers.{layer_i}.self_attn.q_proj.weight": permute(
                    loaded[f"layers.{layer_i}.attention.wq.weight"]
                ),
                f"model.layers.{layer_i}.self_attn.k_proj.weight": permute(
                    loaded[f"layers.{layer_i}.attention.wk.weight"]
                ),
                f"model.layers.{layer_i}.self_attn.v_proj.weight": loaded[f"layers.{layer_i}.attention.wv.weight"],
                f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded[f"layers.{layer_i}.attention.wo.weight"],
                f"model.layers.{layer_i}.mlp.gate_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w1.weight"],
                f"model.layers.{layer_i}.mlp.down_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w2.weight"],
                f"model.layers.{layer_i}.mlp.up_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w3.weight"],
                f"model.layers.{layer_i}.input_layernorm.weight": loaded[f"layers.{layer_i}.attention_norm.weight"],
                f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[f"layers.{layer_i}.ffn_norm.weight"],
            }
        else:
            # Sharded
            # Note that in the 13B checkpoint, not cloning the two following weights will result in the checkpoint
            # becoming 37GB instead of 26GB for some reason.
            state_dict = {
                f"model.layers.{layer_i}.input_layernorm.weight": loaded[0][
                    f"layers.{layer_i}.attention_norm.weight"
                ].clone(),
                f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[0][
                    f"layers.{layer_i}.ffn_norm.weight"
                ].clone(),
            }
            state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute(
                torch.cat(
                    [
                        loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(n_heads_per_shard, dims_per_head, dim)
                        for i in range(num_shards)
                    ],
                    dim=0,
                ).reshape(dim, dim)
            )
            state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute(
                torch.cat(
                    [
                        loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(n_heads_per_shard, dims_per_head, dim)
                        for i in range(num_shards)
                    ],
                    dim=0,
                ).reshape(dim, dim)
            )
            state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = torch.cat(
                [
                    loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(n_heads_per_shard, dims_per_head, dim)
                    for i in range(num_shards)
                ],
                dim=0,
            ).reshape(dim, dim)

            state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = torch.cat(
                [loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(num_shards)], dim=1
            )
            state_dict[f"model.layers.{layer_i}.mlp.gate_proj.weight"] = torch.cat(
                [loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(num_shards)], dim=0
            )
            state_dict[f"model.layers.{layer_i}.mlp.down_proj.weight"] = torch.cat(
                [loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(num_shards)], dim=1
            )
            state_dict[f"model.layers.{layer_i}.mlp.up_proj.weight"] = torch.cat(
                [loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(num_shards)], dim=0
            )

        state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq
        for k, v in state_dict.items():
            index_dict["weight_map"][k] = filename
            param_count += v.numel()
        torch.save(state_dict, os.path.join(tmp_model_path, filename))

    filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
    if model_size == "7B":
        # Unsharded
        state_dict = {
            "model.embed_tokens.weight": loaded["tok_embeddings.weight"],
            "model.norm.weight": loaded["norm.weight"],
            "lm_head.weight": loaded["output.weight"],
        }
    else:
        state_dict = {
            "model.norm.weight": loaded[0]["norm.weight"],
            "model.embed_tokens.weight": torch.cat(
                [loaded[i]["tok_embeddings.weight"] for i in range(num_shards)], dim=1
            ),
            "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(num_shards)], dim=0),
        }

    for k, v in state_dict.items():
        index_dict["weight_map"][k] = filename
        param_count += v.numel()
    torch.save(state_dict, os.path.join(tmp_model_path, filename))

    # Write configs
    index_dict["metadata"] = {"total_size": param_count * 2}
    write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))

    config = LlamaConfig(
        hidden_size=dim,
        intermediate_size=compute_intermediate_size(dim),
        num_attention_heads=params["n_heads"],
        num_hidden_layers=params["n_layers"],
        rms_norm_eps=params["norm_eps"],
    )
    config.save_pretrained(tmp_model_path)

    # Make space so we can load the model properly now.
    del state_dict
    del loaded
    gc.collect()

    print("Loading the checkpoint in a Llama model.")
    model = LlamaForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
    # Avoid saving this as part of the config.
    del model.config._name_or_path

    print("Saving in the Transformers format.")
    model.save_pretrained(model_path)
    shutil.rmtree(tmp_model_path)


def write_tokenizer(tokenizer_path, input_tokenizer_path):
    # Initialize the tokenizer based on the `spm` model
    tokenizer_class = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
    print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.")
    tokenizer = tokenizer_class(input_tokenizer_path)
    tokenizer.save_pretrained(tokenizer_path)


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--input_dir",
        help="Location of LLaMA weights, which contains tokenizer.model and model folders",
    )
    parser.add_argument(
        "--model_size",
        choices=["7B", "13B", "30B", "65B", "tokenizer_only"],
    )
    parser.add_argument(
        "--output_dir",
        help="Location to write HF model and tokenizer",
    )
    args = parser.parse_args()
    if args.model_size != "tokenizer_only":
        write_model(
            model_path=args.output_dir,
            input_base_path=os.path.join(args.input_dir, args.model_size),
            model_size=args.model_size,
        )
    spm_path = os.path.join(args.input_dir, "tokenizer.model")
    write_tokenizer(args.output_dir, spm_path)


if __name__ == "__main__":
    main()

执行格式转换命令

python convert_llama_weights_to_hf.py --input_dir path_to_original_llama_root_dir --model_size 7B --output_dir path_to_original_llama_hf_dir

注意：这一步有点久（很长时间）

会报的错：

会在目录中生成一个新目录：path_to_original_llama_hf_dir

执行模型合并命令

下载以下文件到llama目录

📎merge_llama_with_chinese_lora.py

执行合并模型命令

python merge_llama_with_chinese_lora.py --base_model path_to_original_llama_hf_dir --lora_model chinese-alpaca-lora-7b --output_dir path_to_output_dir

会生成一个目录：path_to_output_dir

下载模型

在llama目录下载代码如下：

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

遇到报错

解决办法执行命令

git config --global --unset http.proxy

编译模型&转换格式

编译文件

注意：由于前端我是用powershell方式进行安装所以用第一种方式

#进入 llama.app
cd llama.app
#通过powershell安装的mingw进行编译
cmake . -G "MinGW Makefiles"
 #进行构建
cmake --build . --config Release

或

#进入 llama.app
cd llama.app
#创建 build文件夹
mkdir build
#进入build
cd build
#编译
cmake ..
#构建
cmake --build . --config Release

移动文件配置

在 llama.app 目录中新建目录 zh-models

将path_to_output_dir文件夹内的consolidated.00.pth和params.json文件放入上面格式中的位置

将path_to_output_dir文件夹内的tokenizer.model文件放在跟7B文件夹同级的位置

最终如下：

转换格式

注意：到 llama.cpp 目录

将 .pth模型权重转换为ggml的FP16格式

生成文件路径为zh-models/7B/ggml-model-f16.bin，执行命令如下：

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

生成结果

对FP16模型进行4-bit量化

执行命令：

D:\ai\llama\llama.cpp\bin\quantize.exe ./zh-models/7B/ggml-model-f16.bin ./zh-models/7B/ggml-model-q4_0.bin 2

生成量化模型文件路径为zh-models/7B/ggml-model-q4_0.bin

运行模型

cd D:\ai\llama\llama.cpp
D:\ai\llama\llama.cpp\bin\main.exe  -m zh-models/7B/ggml-model-q4_0.bin --color -f prompts/alpaca.txt -ins -c 2048 --temp 0.2 -n 256 --repeat_penalty 1.3

结果

最后

     我知道很多同学可能觉得学习大模型需要懂python有一定的难度，当然我是建议先学习好一个语言后再去学习其它语言，其实按照我过来的经验，我觉得python或java都好，语言语法都差不多，只是一个工具只是看我们要不要用。毕竟有java后端的基础再去学python，本人两周基本就上手了。当然还是建议有一个主线，再展开，而不是出什么学什么，真没必要。但是对于技术来说要看价值及发展，有可能现在很流行的技术半年或几年后就过了。当然也不是完全说固步自封，一切看自身条件(阶段、能力、意愿、时间等)、社会发展、价值等。

 参考文章：

    https://zhuanlan.zhihu.com/p/617952293

    https://zhuanlan.zhihu.com/p/632102048?utm_id=0

    https://www.bilibili.com/read/cv24984542/