diff --git a/.gitignore b/.gitignore index b6e47617d..ea44648b7 100644 --- a/.gitignore +++ b/.gitignore @@ -20,6 +20,7 @@ parts/ sdist/ var/ wheels/ +models/ pip-wheel-metadata/ share/python-wheels/ *.egg-info/ diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 000000000..09a35ce9c --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,25 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Current File", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": true, + "env": {"PYTHONPATH": "${workspaceFolder}"}, + "envFile": "${workspaceFolder}/.env" + }, + { + "name": "Python: Module", + "type": "python", + "request": "launch", + "module": "pilot", + "justMyCode": true, + } + ] +} \ No newline at end of file diff --git a/README.md b/README.md index 7bc71a23b..370b035c5 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,25 @@ # DB-GPT A Open Database-GPT Experiment + ![GitHub Repo stars](https://img.shields.io/github/stars/csunny/db-gpt?style=social) +DB-GPT is an experimental open-source application that builds upon the fastchat model and uses vicuna as its base model. Additionally, it looks like this application incorporates langchain and llama-index embedding knowledge to improve Database-QA capabilities. -DB-GPT is an experimental open-source application, which based on the vicuna base model. +Overall, it appears to be a sophisticated and innovative tool for working with databases. If you have any specific questions about how to use or implement DB-GPT in your work, please let me know and I'll do my best to assist you. +# Install +1. Run model server +``` +cd pilot/server +uvicorn vicuna_server:app --host 0.0.0.0 +``` -## Featurs -Coming soon, please wait... \ No newline at end of file +2. Run gradio webui +``` +python app.py +``` + +# Featurs +- SQL-Generate +- Database-QA Based Knowledge +- SQL-diagnosis \ No newline at end of file diff --git a/asserts/readme.md b/asserts/readme.md new file mode 100644 index 000000000..e69de29bb diff --git a/docs/introduct.md b/docs/introduct.md new file mode 100644 index 000000000..4287ca861 --- /dev/null +++ b/docs/introduct.md @@ -0,0 +1 @@ +# \ No newline at end of file diff --git a/environment.yml b/environment.yml new file mode 100644 index 000000000..3ec4dfd98 --- /dev/null +++ b/environment.yml @@ -0,0 +1,62 @@ +name: db_pgt +channels: + - pytorch + - defaults + - anaconda +dependencies: + - python=3.9 + - cudatoolkit + - pip + - pytorch=1.12.1 + - pytorch-mutex=1.0=cuda + - torchaudio=0.12.1 + - torchvision=0.13.1 + - pip: + - accelerate==0.16.0 + - aiohttp==3.8.4 + - aiosignal==1.3.1 + - async-timeout==4.0.2 + - attrs==22.2.0 + - bitsandbytes==0.37.0 + - cchardet==2.1.7 + - chardet==5.1.0 + - contourpy==1.0.7 + - cycler==0.11.0 + - filelock==3.9.0 + - fonttools==4.38.0 + - frozenlist==1.3.3 + - huggingface-hub==0.13.4 + - importlib-resources==5.12.0 + - kiwisolver==1.4.4 + - matplotlib==3.7.0 + - multidict==6.0.4 + - openai==0.27.0 + - packaging==23.0 + - psutil==5.9.4 + - pycocotools==2.0.6 + - pyparsing==3.0.9 + - python-dateutil==2.8.2 + - pyyaml==6.0 + - regex==2022.10.31 + - tokenizers==0.13.2 + - tqdm==4.64.1 + - transformers==4.28.0 + - timm==0.6.13 + - spacy==3.5.1 + - webdataset==0.2.48 + - scikit-learn==1.2.2 + - scipy==1.10.1 + - yarl==1.8.2 + - zipp==3.14.0 + - omegaconf==2.3.0 + - opencv-python==4.7.0.72 + - iopath==0.1.10 + - tenacity==8.2.2 + - peft + - pycocoevalcap + - sentence-transformers + - umap-learn + - notebook + - gradio==3.24.1 + - gradio-client==0.0.8 + - wandb diff --git a/examples/gpt_index.py b/examples/gpt_index.py new file mode 100644 index 000000000..29c0a3fe0 --- /dev/null +++ b/examples/gpt_index.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import os +import logging +import sys + +from llama_index import SimpleDirectoryReader, GPTSimpleVectorIndex +logging.basicConfig(stream=sys.stdout, level=logging.INFO) +logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) + +# read the document of data dir +documents = SimpleDirectoryReader("data").load_data() +# split the document to chunk, max token size=500, convert chunk to vector + +index = GPTSimpleVectorIndex(documents) + +# save index +index.save_to_disk("index.json") \ No newline at end of file diff --git a/examples/t5_example.py b/examples/t5_example.py new file mode 100644 index 000000000..a63c9f961 --- /dev/null +++ b/examples/t5_example.py @@ -0,0 +1,240 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +from llama_index import SimpleDirectoryReader, LangchainEmbedding, GPTListIndex, GPTSimpleVectorIndex, PromptHelper +from langchain.embeddings.huggingface import HuggingFaceEmbeddings +from llama_index import LLMPredictor +import torch +from langchain.llms.base import LLM +from transformers import pipeline + + +class FlanLLM(LLM): + model_name = "google/flan-t5-large" + pipeline = pipeline("text2text-generation", model=model_name, device=0, model_kwargs={ + "torch_dtype": torch.bfloat16 + }) + + def _call(self, prompt, stop=None): + return self.pipeline(prompt, max_length=9999)[0]["generated_text"] + + def _identifying_params(self): + return {"name_of_model": self.model_name} + + def _llm_type(self): + return "custome" + +llm_predictor = LLMPredictor(llm=FlanLLM()) +hfemb = HuggingFaceEmbeddings() +embed_model = LangchainEmbedding(hfemb) + +text1 = """ + 执行计划是对一条 SQL 查询语句在数据库中执行过程的描述。用户可以通过 EXPLAIN 命令查看优化器针对指定 SQL 生成的逻辑执行计划。 + +如果要分析某条 SQL 的性能问题，通常需要先查看 SQL 的执行计划，排查每一步 SQL 执行是否存在问题。所以读懂执行计划是 SQL 优化的先决条件，而了解执行计划的算子是理解 EXPLAIN 命令的关键。 + +OceanBase 数据库的执行计划命令有三种模式：EXPLAIN BASIC、EXPLAIN 和 EXPLAIN EXTENDED。这三种模式对执行计划展现不同粒度的细节信息: + +EXPLAIN BASIC 命令用于最基本的计划展示。 + +EXPLAIN EXTENDED 命令用于最详细的计划展示（通常在排查问题时使用这种展示模式）。 + +EXPLAIN 命令所展示的信息可以帮助普通用户了解整个计划的执行方式。 + +EXPLAIN 命令格式如下： +EXPLAIN [BASIC | EXTENDED | PARTITIONS | FORMAT = format_name] [PRETTY | PRETTY_COLOR] explainable_stmt +format_name: + { TRADITIONAL | JSON } +explainable_stmt: + { SELECT statement + | DELETE statement + | INSERT statement + | REPLACE statement + | UPDATE statement } + + +EXPLAIN 命令适用于 SELECT、DELETE、INSERT、REPLACE 和 UPDATE 语句，显示优化器所提供的有关语句执行计划的信息，包括如何处理该语句，如何联接表以及以何种顺序联接表等信息。 + +一般来说，可以使用 EXPLAIN EXTENDED 命令，将表扫描的范围段展示出来。使用 EXPLAIN OUTLINE 命令可以显示 Outline 信息。 + +FORMAT 选项可用于选择输出格式。TRADITIONAL 表示以表格格式显示输出，这也是默认设置。JSON 表示以 JSON 格式显示信息。 + +使用 EXPLAIN PARTITITIONS 也可用于检查涉及分区表的查询。如果检查针对非分区表的查询，则不会产生错误，但 PARTIONS 列的值始终为 NULL。 + +对于复杂的执行计划，可以使用 PRETTY 或者 PRETTY_COLOR 选项将计划树中的父节点和子节点使用树线或彩色树线连接起来，使得执行计划展示更方便阅读。示例如下： +obclient> CREATE TABLE p1table(c1 INT ,c2 INT) PARTITION BY HASH(c1) PARTITIONS 2; +Query OK, 0 rows affected + +obclient> CREATE TABLE p2table(c1 INT ,c2 INT) PARTITION BY HASH(c1) PARTITIONS 4; +Query OK, 0 rows affected + +obclient> EXPLAIN EXTENDED PRETTY_COLOR SELECT * FROM p1table p1 JOIN p2table p2 ON p1.c1=p2.c2\G +*************************** 1. row *************************** +Query Plan: ========================================================== +|ID|OPERATOR |NAME |EST. ROWS|COST| +---------------------------------------------------------- +|0 |PX COORDINATOR | |1 |278 | +|1 | EXCHANGE OUT DISTR |:EX10001|1 |277 | +|2 | HASH JOIN | |1 |276 | +|3 | ├PX PARTITION ITERATOR | |1 |92 | +|4 | │ TABLE SCAN |P1 |1 |92 | +|5 | └EXCHANGE IN DISTR | |1 |184 | +|6 | EXCHANGE OUT DISTR (PKEY)|:EX10000|1 |184 | +|7 | PX PARTITION ITERATOR | |1 |183 | +|8 | TABLE SCAN |P2 |1 |183 | +========================================================== + +Outputs & filters: +------------------------------------- + 0 - output([INTERNAL_FUNCTION(P1.C1, P1.C2, P2.C1, P2.C2)]), filter(nil) + 1 - output([INTERNAL_FUNCTION(P1.C1, P1.C2, P2.C1, P2.C2)]), filter(nil), dop=1 + 2 - output([P1.C1], [P2.C2], [P1.C2], [P2.C1]), filter(nil), + equal_conds([P1.C1 = P2.C2]), other_conds(nil) + 3 - output([P1.C1], [P1.C2]), filter(nil) + 4 - output([P1.C1], [P1.C2]), filter(nil), + access([P1.C1], [P1.C2]), partitions(p[0-1]) + 5 - output([P2.C2], [P2.C1]), filter(nil) + 6 - (#keys=1, [P2.C2]), output([P2.C2], [P2.C1]), filter(nil), dop=1 + 7 - output([P2.C1], [P2.C2]), filter(nil) + 8 - output([P2.C1], [P2.C2]), filter(nil), + access([P2.C1], [P2.C2]), partitions(p[0-3]) + +1 row in set + + + + +## 执行计划形状与算子信息 + +在数据库系统中，执行计划在内部通常是以树的形式来表示的，但是不同的数据库会选择不同的方式展示给用户。 + +如下示例分别为 PostgreSQL 数据库、Oracle 数据库和 OceanBase 数据库对于 TPCDS Q3 的计划展示。 + +```sql +obclient> SELECT /*TPC-DS Q3*/ * + FROM (SELECT dt.d_year, + item.i_brand_id brand_id, + item.i_brand brand, + Sum(ss_net_profit) sum_agg + FROM date_dim dt, + store_sales, + item + WHERE dt.d_date_sk = store_sales.ss_sold_date_sk + AND store_sales.ss_item_sk = item.i_item_sk + AND item.i_manufact_id = 914 + AND dt.d_moy = 11 + GROUP BY dt.d_year, + item.i_brand, + item.i_brand_id + ORDER BY dt.d_year, + sum_agg DESC, + brand_id) + WHERE ROWNUM <= 100; + +PostgreSQL 数据库执行计划展示如下： +Limit (cost=13986.86..13987.20 rows=27 width=91) + Sort (cost=13986.86..13986.93 rows=27 width=65) + Sort Key: dt.d_year, (sum(store_sales.ss_net_profit)), item.i_brand_id + HashAggregate (cost=13985.95..13986.22 rows=27 width=65) + Merge Join (cost=13884.21..13983.91 rows=204 width=65) + Merge Cond: (dt.d_date_sk = store_sales.ss_sold_date_sk) + Index Scan using date_dim_pkey on date_dim dt (cost=0.00..3494.62 rows=6080 width=8) + Filter: (d_moy = 11) + Sort (cost=12170.87..12177.27 rows=2560 width=65) + Sort Key: store_sales.ss_sold_date_sk + Nested Loop (cost=6.02..12025.94 rows=2560 width=65) + Seq Scan on item (cost=0.00..1455.00 rows=16 width=59) + Filter: (i_manufact_id = 914) + Bitmap Heap Scan on store_sales (cost=6.02..658.94 rows=174 width=14) + Recheck Cond: (ss_item_sk = item.i_item_sk) + Bitmap Index Scan on store_sales_pkey (cost=0.00..5.97 rows=174 width=0) + Index Cond: (ss_item_sk = item.i_item_sk) + + + +Oracle 数据库执行计划展示如下： +Plan hash value: 2331821367 +-------------------------------------------------------------------------------------------------- +| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | +-------------------------------------------------------------------------------------------------- +| 0 | SELECT STATEMENT | | 100 | 9100 | 3688 (1)| 00:00:01 | +|* 1 | COUNT STOPKEY | | | | | | +| 2 | VIEW | | 2736 | 243K| 3688 (1)| 00:00:01 | +|* 3 | SORT ORDER BY STOPKEY | | 2736 | 256K| 3688 (1)| 00:00:01 | +| 4 | HASH GROUP BY | | 2736 | 256K| 3688 (1)| 00:00:01 | +|* 5 | HASH JOIN | | 2736 | 256K| 3686 (1)| 00:00:01 | +|* 6 | TABLE ACCESS FULL | DATE_DIM | 6087 | 79131 | 376 (1)| 00:00:01 | +| 7 | NESTED LOOPS | | 2865 | 232K| 3310 (1)| 00:00:01 | +| 8 | NESTED LOOPS | | 2865 | 232K| 3310 (1)| 00:00:01 | +|* 9 | TABLE ACCESS FULL | ITEM | 18 | 1188 | 375 (0)| 00:00:01 | +|* 10 | INDEX RANGE SCAN | SYS_C0010069 | 159 | | 2 (0)| 00:00:01 | +| 11 | TABLE ACCESS BY INDEX ROWID| STORE_SALES | 159 | 2703 | 163 (0)| 00:00:01 | +-------------------------------------------------------------------------------------------------- + +OceanBase 数据库执行计划展示如下： +|ID|OPERATOR |NAME |EST. ROWS|COST | +------------------------------------------------------- +|0 |LIMIT | |100 |81141| +|1 | TOP-N SORT | |100 |81127| +|2 | HASH GROUP BY | |2924 |68551| +|3 | HASH JOIN | |2924 |65004| +|4 | SUBPLAN SCAN |VIEW1 |2953 |19070| +|5 | HASH GROUP BY | |2953 |18662| +|6 | NESTED-LOOP JOIN| |2953 |15080| +|7 | TABLE SCAN |ITEM |19 |11841| +|8 | TABLE SCAN |STORE_SALES|161 |73 | +|9 | TABLE SCAN |DT |6088 |29401| +======================================================= + +由示例可见，OceanBase 数据库的计划展示与 Oracle 数据库类似。 + +OceanBase 数据库执行计划中的各列的含义如下： +列名 含义 +ID 执行树按照前序遍历的方式得到的编号（从 0 开始）。 +OPERATOR 操作算子的名称。 +NAME 对应表操作的表名（索引名）。 +EST. ROWS 估算该操作算子的输出行数。 +COST 该操作算子的执行代价（微秒）。 + + +OceanBase 数据库 EXPLAIN 命令输出的第一部分是执行计划的树形结构展示。其中每一个操作在树中的层次通过其在 operator 中的缩进予以展示，层次最深的优先执行，层次相同的以特定算子的执行顺序为标准来执行。 + +问题: update a not exists (b…) +我一开始以为 B是驱动表，B的数据挺多的 后来看到NLAJ，是说左边的表关联右边的表 +所以这个的驱动表是不是实际是A，用A的匹配B的，这个理解有问题吗 + +回答: 没错 A 驱动 B的 + +问题: 光知道最下最右的是驱动表了 所以一开始搞得有点懵 :sweat_smile: + +回答: nlj应该原理应该都是左表(驱动表)的记录探测右表(被驱动表)， 选哪张成为左表或右表就基于一些其他考量了，比如数据量， 而anti join/semi join只是对 not exist/exist的一种优化，相关的原理和资料网上可以查阅一下 + +问题: 也就是nlj 就是按照之前理解的谁先执行 谁就是驱动表 也就是执行计划中的最右的表 +而anti join/semi join，谁在not exist左面，谁就是驱动表。这么理解对吧 + +回答: nlj也是左表的表是驱动表，这个要了解下计划执行方面的基本原理，取左表的一行数据，再遍历右表，一旦满足连接条件，就可以返回数据 +anti/semi只是因为not exists/exist的语义只是返回左表数据，改成anti join是一种计划优化，连接的方式比子查询更优 +""" + +from llama_index import Document +text_list = [text1] +documents = [Document(t) for t in text_list] + +num_output = 250 +max_input_size = 512 + +max_chunk_overlap = 20 +prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap) + +index = GPTListIndex(documents, embed_model=embed_model, llm_predictor=llm_predictor, prompt_helper=prompt_helper) +index.save_to_disk("index.json") + + +if __name__ == "__main__": + import logging + logging.getLogger().setLevel(logging.CRITICAL) + for d in documents: + print(d) + + response = index.query("数据库的执行计划命令有多少?") + print(response) diff --git a/pilot/__init__.py b/pilot/__init__.py new file mode 100644 index 000000000..f102a9cad --- /dev/null +++ b/pilot/__init__.py @@ -0,0 +1 @@ +__version__ = "0.0.1" diff --git a/pilot/app.py b/pilot/app.py new file mode 100644 index 000000000..6a7a76f3d --- /dev/null +++ b/pilot/app.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- + +import gradio as gr +from langchain.agents import ( + load_tools, + initialize_agent, + AgentType +) +from pilot.model.vicuna_llm import VicunaRequestLLM, VicunaEmbeddingLLM +from llama_index import LLMPredictor, LangchainEmbedding, ServiceContext +from langchain.embeddings.huggingface import HuggingFaceEmbeddings +from llama_index import Document, GPTSimpleVectorIndex + +def agent_demo(): + llm = VicunaRequestLLM() + + tools = load_tools(['python_repl'], llm=llm) + agent = initialize_agent(tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True) + agent.run( + "Write a SQL script that Query 'select count(1)!'" + ) + +def knowledged_qa_demo(text_list): + llm_predictor = LLMPredictor(llm=VicunaRequestLLM()) + hfemb = VicunaEmbeddingLLM() + embed_model = LangchainEmbedding(hfemb) + documents = [Document(t) for t in text_list] + + service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model) + index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context) + return index + + +def get_answer(q): + base_knowledge = """ 这是一段测试文字 """ + text_list = [base_knowledge] + index = knowledged_qa_demo(text_list) + response = index.query(q) + return response.response + +if __name__ == "__main__": + # agent_demo() + + with gr.Blocks() as demo: + gr.Markdown("数据库智能助手") + with gr.Tab("知识问答"): + text_input = gr.TextArea() + text_output = gr.TextArea() + text_button = gr.Button() + + text_button.click(get_answer, inputs=text_input, outputs=text_output) + + demo.queue(concurrency_count=3).launch(server_name="0.0.0.0") + diff --git a/pilot/connections/mysql_conn.py b/pilot/connections/mysql_conn.py new file mode 100644 index 000000000..1f776fc63 --- /dev/null +++ b/pilot/connections/mysql_conn.py @@ -0,0 +1,2 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- \ No newline at end of file diff --git a/pilot/connections/pg_conn.py b/pilot/connections/pg_conn.py new file mode 100644 index 000000000..1f776fc63 --- /dev/null +++ b/pilot/connections/pg_conn.py @@ -0,0 +1,2 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- \ No newline at end of file diff --git a/pilot/model/__init__.py b/pilot/model/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/model/inference.py b/pilot/model/inference.py new file mode 100644 index 000000000..426043aa5 --- /dev/null +++ b/pilot/model/inference.py @@ -0,0 +1,87 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import torch + +@torch.inference_mode() +def generate_output(model, tokenizer, params, device, context_len=2048): + prompt = params["prompt"] + temperature = float(params.get("temperature", 1.0)) + max_new_tokens = int(params.get("max_new_tokens", 256)) + stop_parameter = params.get("stop", None) + + print(tokenizer.__dir__()) + if stop_parameter == tokenizer.eos_token: + stop_parameter = None + + stop_strings = [] + if isinstance(stop_parameter, str): + stop_strings.append(stop_parameter) + elif isinstance(stop_parameter, list): + stop_strings = stop_parameter + elif stop_parameter is None: + pass + else: + raise TypeError("Stop parameter must be string or list of strings.") + + pos = -1 + input_ids = tokenizer(prompt).input_ids + output_ids = [] + + max_src_len = context_len - max_new_tokens - 8 + input_ids = input_ids[-max_src_len:] + + for i in range(max_new_tokens): + if i == 0: + out = model(torch.as_tensor([input_ids], device=device), use_cache=True) + logits = out.logits + past_key_values = out.past_key_values + else: + out = model( + input_ids=torch.as_tensor([[token]], device=device), + use_cache=True, + past_key_values=past_key_values, + ) + logits = out.logits + past_key_values = out.past_key_value + last_token_logits = logits[0][-1] + + if temperature < 1e-4: + token = int(torch.argmax(last_token_logits)) + else: + probs = torch.softmax(last_token_logits / temperature, dim=1) + token = int(torch.multinomial(probs, num_samples=1)) + + output_ids.append(token) + + if token == tokenizer.eos_token_id: + stopped = True + else: + stopped = False + + output = tokenizer.decode(output_ids, skip_special_tokens=True) + for stop_str in stop_strings: + pos = output.rfind(stop_str) + if pos != -1: + output = output[:pos] + stoppped = True + break + else: + pass + + if stoppped: + break + + del past_key_values + if pos != -1: + return output[:pos] + return output + + +@torch.inference_mode() +def get_embeddings(model, tokenizer, prompt): + input_ids = tokenizer(prompt).input_ids + input_embeddings = model.get_input_embeddings() + embeddings = input_embeddings(torch.LongTensor([input_ids])) + mean = torch.mean(embeddings[0], 0).cpu().detach() + return mean \ No newline at end of file diff --git a/pilot/model/loader.py b/pilot/model/loader.py new file mode 100644 index 000000000..5f18a023c --- /dev/null +++ b/pilot/model/loader.py @@ -0,0 +1,42 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import torch +from transformers import ( + AutoTokenizer, + AutoModelForCausalLM, +) + +from fastchat.serve.compression import compress_module + +class ModerLoader: + + kwargs = {} + + def __init__(self, + model_path) -> None: + + self.device = "cuda" if torch.cuda.is_available() else "cpu" + self.model_path = model_path + self.kwargs = { + "torch_dtype": torch.float16, + "device_map": "auto", + } + + def loader(self, load_8bit=False, debug=False): + + tokenizer = AutoTokenizer.from_pretrained(self.model_path, use_fast=False) + model = AutoModelForCausalLM.from_pretrained(self.model_path, low_cpu_mem_usage=True, **self.kwargs) + + if debug: + print(model) + + if load_8bit: + compress_module(model, self.device) + + # if self.device == "cuda": + # model.to(self.device) + + return model, tokenizer + + diff --git a/pilot/model/vicuna_llm.py b/pilot/model/vicuna_llm.py new file mode 100644 index 000000000..be433c7c3 --- /dev/null +++ b/pilot/model/vicuna_llm.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- + +import json +import requests +from urllib.parse import urljoin +from langchain.embeddings.base import Embeddings +from pydantic import BaseModel +from typing import Any, Mapping, Optional, List +from langchain.llms.base import LLM +from configs.model_config import * + +class VicunaRequestLLM(LLM): + + vicuna_generate_path = "generate" + def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str: + if isinstance(stop, list): + stop = stop + ["Observation:"] + + params = { + "prompt": prompt, + "temperature": 0, + "max_new_tokens": 256, + "stop": stop + } + response = requests.post( + url=urljoin(vicuna_model_server, self.vicuna_generate_path), + data=json.dumps(params) + ) + response.raise_for_status() + return response.json()["response"] + + @property + def _llm_type(self) -> str: + return "custome" + + def _identifying_params(self) -> Mapping[str, Any]: + return {} + + +class VicunaEmbeddingLLM(BaseModel, Embeddings): + + vicuna_embedding_path = "embedding" + + def _call(self, prompt: str) -> str: + p = prompt.strip() + print("Sending prompt ", p) + + response = requests.post( + url=urljoin(vicuna_model_server, self.vicuna_embedding_path), + json={ + "prompt": p + } + ) + response.raise_for_status() + return response.json()["response"] + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """ Call out to Vicuna's server embedding endpoint for embedding search docs. + + Args: + texts: The list of text to embed + + Returns: + List of embeddings. one for each text. + """ + results = [] + for text in texts: + response = self.embed_query(text) + results.append(response) + return results + + + def embed_query(self, text: str) -> List[float]: + """ Call out to Vicuna's server embedding endpoint for embedding query text. + + Args: + text: The text to embed. + Returns: + Embedding for the text + """ + embedding = self._call(text) + return embedding + diff --git a/pilot/server/__init__.py b/pilot/server/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/server/chatbot.py b/pilot/server/chatbot.py new file mode 100644 index 000000000..5e0ad9294 --- /dev/null +++ b/pilot/server/chatbot.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- + +import requests +import json +import time +from urllib.parse import urljoin +import gradio as gr +from pilot.configs.model_config import * +vicuna_base_uri = "http://192.168.31.114:21002/" +vicuna_stream_path = "worker_generate_stream" +vicuna_status_path = "worker_get_status" + +def generate(prompt): + params = { + "model": "vicuna-13b", + "prompt": prompt, + "temperature": 0.7, + "max_new_tokens": 512, + "stop": "###" + } + + sts_response = requests.post( + url=urljoin(vicuna_base_uri, vicuna_status_path) + ) + print(sts_response.text) + + response = requests.post( + url=urljoin(vicuna_base_uri, vicuna_stream_path), data=json.dumps(params) + ) + + skip_echo_len = len(params["prompt"]) + 1 - params["prompt"].count("") * 3 + for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"): + if chunk: + data = json.loads(chunk.decode()) + if data["error_code"] == 0: + output = data["text"] + yield(output) + + time.sleep(0.02) + +if __name__ == "__main__": + print(LLM_MODEL) + with gr.Blocks() as demo: + gr.Markdown("数据库SQL生成助手") + with gr.Tab("SQL生成"): + text_input = gr.TextArea() + text_output = gr.TextArea() + text_button = gr.Button("提交") + + + text_button.click(generate, inputs=text_input, outputs=text_output) + + demo.queue(concurrency_count=3).launch(server_name="0.0.0.0") + + \ No newline at end of file diff --git a/pilot/server/sqlgpt.py b/pilot/server/sqlgpt.py new file mode 100644 index 000000000..5fba9d819 --- /dev/null +++ b/pilot/server/sqlgpt.py @@ -0,0 +1,50 @@ +#!/usr/bin/env python3 +#-*- coding: utf-8 -*- + + +import json +import torch +import gradio as gr +from fastchat.serve.inference import generate_stream + +from transformers import AutoTokenizer, AutoModelForCausalLM +device = "cuda" if torch.cuda.is_available() else "cpu" +BASE_MODE = "/home/magic/workspace/github/DB-GPT/models/vicuna-13b" + +tokenizer = AutoTokenizer.from_pretrained(BASE_MODE, use_fast=False) +model = AutoModelForCausalLM.from_pretrained( + BASE_MODE, + low_cpu_mem_usage=True, + torch_dtype=torch.float16, + device_map="auto", +) + +def generate(prompt): + model.to(device) + print(model, tokenizer) + params = { + "model": "vicuna-13b", + "prompt": "这是一个用户与助手之间的对话, 助手精通数据库领域的知识, 并能够对数据库领域知识做出非常专业的回答。以下是用户的问题:" + prompt, + "temperature": 0.7, + "max_new_tokens": 512, + "stop": "###" + } + output = generate_stream( + model, tokenizer, params, device, context_len=2048, stream_interval=2) + + for chunk in output: + yield chunk + +if __name__ == "__main__": + with gr.Blocks() as demo: + gr.Markdown("数据库SQL生成助手") + with gr.Tab("SQL生成"): + text_input = gr.TextArea() + text_output = gr.TextArea() + text_button = gr.Button("提交") + + + text_button.click(generate, inputs=text_input, outputs=text_output) + + demo.queue(concurrency_count=3).launch(server_name="0.0.0.0") + diff --git a/pilot/server/vicuna_server.py b/pilot/server/vicuna_server.py new file mode 100644 index 000000000..20ed928d0 --- /dev/null +++ b/pilot/server/vicuna_server.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +from typing import Optional, List +from fastapi import FastAPI +from pydantic import BaseModel +from pilot.model.inference import generate_output, get_embeddings +from pilot.model.loader import ModerLoader +from pilot.configs.model_config import * + +model_path = llm_model_config[LLM_MODEL] +ml = ModerLoader(model_path=model_path) +model, tokenizer = ml.loader(load_8bit=isload_8bit, debug=isdebug) + +app = FastAPI() + +class PromptRequest(BaseModel): + prompt: str + temperature: float + max_new_tokens: int + stop: Optional[List[str]] = None + + +class EmbeddingRequest(BaseModel): + prompt: str + + +@app.post("/generate") +def generate(prompt_request: PromptRequest): + params = { + "prompt": prompt_request.prompt, + "temperature": prompt_request.temperature, + "max_new_tokens": prompt_request.max_new_tokens, + "stop": prompt_request.stop + } + + print("Receive prompt: ", params["prompt"]) + output = generate_output(model, tokenizer, params, DEVICE) + print("Output: ", output) + return {"response": output} + + +@app.post("/embedding") +def embeddings(prompt_request: EmbeddingRequest): + params = {"prompt": prompt_request.prompt} + print("Received prompt: ", params["prompt"]) + output = get_embeddings(model, tokenizer, params["prompt"]) + return {"response": [float(x) for x in output]} \ No newline at end of file diff --git a/pilot/utils.py b/pilot/utils.py new file mode 100644 index 000000000..093b14f99 --- /dev/null +++ b/pilot/utils.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- + +import torch + +def get_gpu_memory(max_gpus=None): + gpu_memory = [] + num_gpus = ( + torch.cuda.device_count() + if max_gpus is None + else min(max_gpus, torch.cuda.device_count()) + ) + + for gpu_id in range(num_gpus): + with torch.cuda.device(gpu_id): + device = torch.cuda.current_device() + gpu_properties = torch.cuda.get_device_properties(device) + total_memory = gpu_properties.total_memory / (1024 ** 3) + allocated_memory = torch.cuda.memory_allocated() / (1024 ** 3) + available_memory = total_memory - allocated_memory + gpu_memory.append(available_memory) + return gpu_memory diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 000000000..dd7bf5189 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,52 @@ +accelerate==0.16.0 +torch==2.0.0 +torchvision==0.13.1 +torchaudio==0.12.1 +accelerate==0.16.0 +aiohttp==3.8.4 +aiosignal==1.3.1 +async-timeout==4.0.2 +attrs==22.2.0 +bitsandbytes==0.37.0 +cchardet==2.1.7 +chardet==5.1.0 +contourpy==1.0.7 +cycler==0.11.0 +filelock==3.9.0 +fonttools==4.38.0 +frozenlist==1.3.3 +huggingface-hub==0.13.4 +importlib-resources==5.12.0 +kiwisolver==1.4.4 +matplotlib==3.7.0 +multidict==6.0.4 +openai==0.27.0 +packaging==23.0 +psutil==5.9.4 +pycocotools==2.0.6 +pyparsing==3.0.9 +python-dateutil==2.8.2 +pyyaml==6.0 +regex==2022.10.31 +tokenizers==0.13.2 +tqdm==4.64.1 +transformers==4.28.0 +timm==0.6.13 +spacy==3.5.1 +webdataset==0.2.48 +scikit-learn==1.2.2 +scipy==1.10.1 +yarl==1.8.2 +zipp==3.14.0 +omegaconf==2.3.0 +opencv-python==4.7.0.72 +iopath==0.1.10 +tenacity==8.2.2 +peft +pycocoevalcap +sentence-transformers +umap-learn +notebook +gradio==3.24.1 +gradio-client==0.0.8 +wandb \ No newline at end of file