背景
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Defog
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llama-3
意义
翻译自然语言到sql,类似脑机接口,大模型重要应用领域
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sql是数据库查询标准;关系数据库,工具(datax,sqoop,logstash,hive),非关系数据库(MongoDB,图数据库)等都支持sql查询
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BI,数字化运营,商业分析,大数据分析
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智能问数
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智能问答
没有大模型前智能问答方案 :
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开源项目 QABasedOnMedicaKnowledgeGraph
待完善
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可靠性
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复杂,不规范的数据库表
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信息安全
llama-3-sqlcoder-8b
要求
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能翻墙
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Nvidia 显卡
模型下载
环境配置
cuda
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检查电脑适配cuda版本
D:\working\code> nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 528.49 Driver Version: 528.49 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... WDDM | 00000000:03:00.0 On | N/A |
| N/A 32C P8 9W / 80W | 616MiB / 12288MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------++-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 1476 C+G C:\Windows\System32\dwm.exe N/A |
| 0 N/A N/A 2572 C+G ...wekyb3d8bbwe\Video.UI.exe N/A |
| 0 N/A N/A 2964 C+G ...d\runtime\WeChatAppEx.exe N/A |
| 0 N/A N/A 4280 C+G ...2txyewy\TextInputHost.exe N/A |
| 0 N/A N/A 4656 C+G ...artMenuExperienceHost.exe N/A |
| 0 N/A N/A 7636 C+G C:\Windows\explorer.exe N/A |
| 0 N/A N/A 7924 C+G ...icrosoft VS Code\Code.exe N/A |
| 0 N/A N/A 8796 C+G ...5n1h2txyewy\SearchApp.exe N/A |
| 0 N/A N/A 9376 C+G ...me\Application\chrome.exe N/A |
| 0 N/A N/A 10540 C ...rograms\Ollama\ollama.exe N/A |
| 0 N/A N/A 11720 C+G ...y\ShellExperienceHost.exe N/A |
| 0 N/A N/A 13676 C+G ...ontend\Docker Desktop.exe N/A |
+-----------------------------------------------------------------------------+
得到CUDA版本为12.0
- 下载
https://developer.nvidia.com/cuda-toolkit-archive
安装后的信息
Installed:
- Nsight for Visual Studio 2022
- Nsight Monitor
Not Installed:
- Nsight for Visual Studio 2019
Reason: VS2019 was not found
- Nsight for Visual Studio 2017
Reason: VS2017 was not found
- Integrated Graphics Frame Debugger and Profiler
Reason: see https://developer.nvidia.com/nsight-vstools
- Integrated CUDA Profilers
Reason: see https://developer.nvidia.com/nsight-vstools
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查看版本
C:\Users\Administrator>nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Mon_Oct_24_19:40:05_Pacific_Daylight_Time_2022
Cuda compilation tools, release 12.0, V12.0.76
Build cuda_12.0.r12.0/compiler.31968024_0
torch
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torch是一个Python库,用于构建和训练深度学习和张量计算模型
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去torch官网中查看老版本CUDA适配的torch版本:
https://pytorch.org/get-started/locally/
C:\Users\Administrator>pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
Looking in indexes: https://download.pytorch.org/whl/cu121
Requirement already satisfied: torch in c:\python312\lib\site-packages (2.3.0)
Collecting torchvision
Downloading https://download.pytorch.org/whl/cu121/torchvision-0.18.1%2Bcu121-cp312-cp312-win_amd64.whl (5.7 MB)
---------------------------------------- 5.7/5.7 MB 5.9 MB/s eta 0:00:00
Collecting torchaudio
Downloading https://download.pytorch.org/whl/cu121/torchaudio-2.3.1%2Bcu121-cp312-cp312-win_amd64.whl (4.1 MB)
---------------------------------------- 4.1/4.1 MB 7.2 MB/s eta 0:00:00
Requirement already satisfied: filelock in c:\python312\lib\site-packages (from torch) (3.14.0)
Requirement already satisfied: typing-extensions>=4.8.0 in c:\python312\lib\site-packages (from torch) (4.12.1)
Requirement already satisfied: sympy in c:\python312\lib\site-packages (from torch) (1.12.1)
Requirement already satisfied: networkx in c:\python312\lib\site-packages (from torch) (3.3)
Requirement already satisfied: jinja2 in c:\python312\lib\site-packages (from torch) (3.1.4)
Requirement already satisfied: fsspec in c:\python312\lib\site-packages (from torch) (2024.5.0)
Requirement already satisfied: mkl<=2021.4.0,>=2021.1.1 in c:\python312\lib\site-packages (from torch) (2021.4.0)
Requirement already satisfied: numpy in c:\python312\lib\site-packages (from torchvision) (1.26.4)
Collecting torch
Downloading https://download.pytorch.org/whl/cu121/torch-2.3.1%2Bcu121-cp312-cp312-win_amd64.whl (2423.5 MB)
---------------------------------------- 2.4/2.4 GB 501.6 kB/s eta 0:00:00
Collecting pillow!=8.3.*,>=5.3.0 (from torchvision)
Downloading https://download.pytorch.org/whl/pillow-10.2.0-cp312-cp312-win_amd64.whl (2.6 MB)
---------------------------------------- 2.6/2.6 MB 2.5 MB/s eta 0:00:00
Requirement already satisfied: intel-openmp==2021.* in c:\python312\lib\site-packages (from mkl<=2021.4.0,>=2021.1.1->torch) (2021.4.0)
Requirement already satisfied: tbb==2021.* in c:\python312\lib\site-packages (from mkl<=2021.4.0,>=2021.1.1->torch) (2021.12.0)
Requirement already satisfied: MarkupSafe>=2.0 in c:\python312\lib\site-packages (from jinja2->torch) (2.1.5)
Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in c:\python312\lib\site-packages (from sympy->torch) (1.3.0)
Installing collected packages: pillow, torch, torchvision, torchaudio
Attempting uninstall: torch
Found existing installation: torch 2.3.0
Uninstalling torch-2.3.0:
Successfully uninstalled torch-2.3.0
Successfully installed pillow-10.2.0 torch-2.3.1+cu121 torchaudio-2.3.1+cu121 torchvision-0.18.1+cu121
transformers
pip install transformers
编写脚本
python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import sqlparse
print("是否可用:", torch.cuda.is_available()) # 查看GPU是否可用
print("GPU数量:", torch.cuda.device_count()) # 查看GPU数量
print("torch方法查看CUDA版本:", torch.version.cuda) # torch方法查看CUDA版本
print("GPU索引号:", torch.cuda.current_device()) # 查看GPU索引号
print("GPU名称:", torch.cuda.get_device_name(0)) # 根据索引号得到GPU名称
available_memory = torch.cuda.get_device_properties(0).total_memory
print("GPU内存大小 :",available_memory)
model_name = "llama-3-sqlcoder-8b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
if available_memory > 20e9:
# if you have atleast 20GB of GPU memory, run load the model in float16
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
torch_dtype=torch.float16,
device_map="auto",
use_cache=True,
)
else:
# else, load in 4 bits -- this is slower and less accurate
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
# torch_dtype=torch.float16,
load_in_4bit=True,
device_map="auto",
use_cache=True,
)
prompt = """<|begin_of_text|><|start_header_id|>user<|end_header_id|>
Generate a SQL query to answer this question: `{question}`
DDL statements:
CREATE TABLE products (
product_id INTEGER PRIMARY KEY, -- Unique ID for each product
name VARCHAR(50), -- Name of the product
price DECIMAL(10,2), -- Price of each unit of the product
quantity INTEGER -- Current quantity in stock
);
CREATE TABLE customers (
customer_id INTEGER PRIMARY KEY, -- Unique ID for each customer
name VARCHAR(50), -- Name of the customer
address VARCHAR(100) -- Mailing address of the customer
);
CREATE TABLE salespeople (
salesperson_id INTEGER PRIMARY KEY, -- Unique ID for each salesperson
name VARCHAR(50), -- Name of the salesperson
region VARCHAR(50) -- Geographic sales region
);
CREATE TABLE sales (
sale_id INTEGER PRIMARY KEY, -- Unique ID for each sale
product_id INTEGER, -- ID of product sold
customer_id INTEGER, -- ID of customer who made purchase
salesperson_id INTEGER, -- ID of salesperson who made the sale
sale_date DATE, -- Date the sale occurred
quantity INTEGER -- Quantity of product sold
);
CREATE TABLE product_suppliers (
supplier_id INTEGER PRIMARY KEY, -- Unique ID for each supplier
product_id INTEGER, -- Product ID supplied
supply_price DECIMAL(10,2) -- Unit price charged by supplier
);
-- sales.product_id can be joined with products.product_id
-- sales.customer_id can be joined with customers.customer_id
-- sales.salesperson_id can be joined with salespeople.salesperson_id
-- product_suppliers.product_id can be joined with products.product_id<|eot_id|><|start_header_id|>assistant<|end_header_id|>
The following SQL query best answers the question `{question}`:
```sql
"""
def generate_query(question):
updated_prompt = prompt.format(question=question)
inputs = tokenizer(updated_prompt, return_tensors="pt").to("cuda")
generated_ids = model.generate(
**inputs,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
max_new_tokens=400,
do_sample=False,
num_beams=1,
temperature=0.0,
top_p=1,
)
outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
torch.cuda.empty_cache()
torch.cuda.synchronize()
# empty cache so that you do generate more results w/o memory crashing
# particularly important on Colab -- memory management is much more straightforward
# when running on an inference service
# return sqlparse.format(outputs[0].split("[SQL]")[-1], reindent=True)
return outputs[0].split("```sql")[1].split(";")[0]
question = "What was our revenue by product in the new york region last month?"
generated_sql = generate_query(question)
print(sqlparse.format(generated_sql, reindent=True))
运行过程
python
D:\working\code> & C:/Python312/python.exe d:/working/code/sqlcode_v3.py
是否可用: True
GPU数量: 1
torch方法查看CUDA版本: 12.1
GPU索引号: 0
GPU名称: NVIDIA GeForce RTX 3060 Laptop GPU
GPU内存大小 : 12884377600
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.
Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:26<00:00, 6.59s/it]
C:\Python312\Lib\site-packages\transformers\generation\configuration_utils.py:515: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
warnings.warn(
C:\Python312\Lib\site-packages\bitsandbytes\nn\modules.py:426: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This
will lead to slow inference or training speed.
warnings.warn(
C:\Python312\Lib\site-packages\transformers\models\llama\modeling_llama.py:649: UserWarning: 1Torch was not compiled with flash attention. (Triggered internally at ..\aten\src\ATen\native\transformers\cuda\sdp_utils.cpp:455.)
attn_output = torch.nn.functional.scaled_dot_product_attention(
SELECT p.name,
SUM(s.quantity * p.price) AS total_revenue
FROM products p
JOIN sales s ON p.product_id = s.product_id
JOIN salespeople sp ON s.salesperson_id = sp.salesperson_id
WHERE sp.region = 'New York'
AND s.sale_date >= CURRENT_DATE - INTERVAL '1 month'
GROUP BY p.name
延伸场景
连接真实数据库
参照文档 Getting Started | Defog Docs
界面交互
- 百度智能云,千帆大模型
SQLCoder-7B是由Defog研发、基于Mistral-7B微调的语言模型 https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Hlo472sa2