From 5147a198b6f606cda8ba5261c39ef804471b0eea Mon Sep 17 00:00:00 2001 From: Sanbei Date: Fri, 14 Jun 2024 10:58:52 +0000 Subject: [PATCH] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E4=BA=86=E6=A8=A1=E5=9E=8B:q?= =?UTF-8?q?wen2-0.5b?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- MemoAI/img/img3.png | Bin 0 -> 368001 bytes MemoAI/img/img4.png | Bin 0 -> 59064 bytes MemoAI/qwen2-0.5b/app.py | 186 +++++++++++++ MemoAI/qwen2-0.5b/di_config.json | 52 ++++ MemoAI/qwen2-0.5b/requirements.txt | 1 + MemoAI/qwen2-0.5b/train.ipynb | 419 +++++++++++++++++++++++++++++ doc/ai-qwen/readme.md | 22 ++ 7 files changed, 680 insertions(+) create mode 100644 MemoAI/img/img3.png create mode 100644 MemoAI/img/img4.png create mode 100644 MemoAI/qwen2-0.5b/app.py create mode 100644 MemoAI/qwen2-0.5b/di_config.json create mode 100644 MemoAI/qwen2-0.5b/requirements.txt create mode 100644 MemoAI/qwen2-0.5b/train.ipynb create mode 100644 doc/ai-qwen/readme.md diff --git 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zhvk&zW|#}-|J5YmKcI%`qyL`?-S<(w5{uS!k>ihR%#pM+4DX|!i0S-AUvzU9|1-s$ ujyy6{-*EUR-v4jQHT}{5h96&EK4HqSYI{2bC!ZDo7<9Fb9+ql4M*kZ}$cJPA literal 0 HcmV?d00001 diff --git a/MemoAI/qwen2-0.5b/app.py b/MemoAI/qwen2-0.5b/app.py new file mode 100644 index 0000000..e3727db --- /dev/null +++ b/MemoAI/qwen2-0.5b/app.py @@ -0,0 +1,186 @@ +import os +import copy +import random +import threading +import subprocess +import gradio as gr +from typing import List, Optional, Tuple, Dict + + +os.system("pip uninstall -y tensorflow tensorflow-estimator tensorflow-io-gcs-filesystem") +os.environ["LANG"] = "C" +os.environ["LC_ALL"] = "C" + +default_system = '你是一个微信聊天机器人' + +from dashinfer.helper import EngineHelper, ConfigManager + +log_lock = threading.Lock() + +config_file = "di_config.json" +config = ConfigManager.get_config_from_json(config_file) + +def download_model(model_id, revision, source="modelscope"): + print(f"Downloading model {model_id} (revision: {revision}) from {source}") + if source == "modelscope": + from modelscope import snapshot_download + model_dir = snapshot_download(model_id, revision=revision) + elif source == "huggingface": + from huggingface_hub import snapshot_download + model_dir = snapshot_download(repo_id=model_id) + else: + raise ValueError("Unknown source") + + print(f"Save model to path {model_dir}") + + return model_dir + +cmd = f"pip show dashinfer | grep 'Location' | cut -d ' ' -f 2" +package_location = subprocess.run(cmd, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + shell=True, + text=True) +package_location = package_location.stdout.strip() +os.environ["AS_DAEMON_PATH"] = package_location + "/dashinfer/allspark/bin" +os.environ["AS_NUMA_NUM"] = str(len(config["device_ids"])) +os.environ["AS_NUMA_OFFSET"] = str(config["device_ids"][0]) + +## download original model +## download model from modelscope +original_model = { + "source": "modelscope", + "model_id": config["model_space"] + config["model_name"], + "revision": "master", + "model_path": "" +} +original_model["model_path"] = download_model(original_model["model_id"], + original_model["revision"], + original_model["source"]) + +engine_helper = EngineHelper(config) +engine_helper.verbose = True +engine_helper.init_tokenizer(original_model["model_path"]) + +## convert huggingface model to dashinfer model +## only one conversion is required +engine_helper.convert_model(original_model["model_path"]) + +engine_helper.init_engine() +engine_max_batch = engine_helper.engine_config["engine_max_batch"] + +################################################### + +History = List[Tuple[str, str]] +Messages = List[Dict[str, str]] + + +class Role: + USER = 'user' + SYSTEM = 'system' + BOT = 'bot' + ASSISTANT = 'assistant' + ATTACHMENT = 'attachment' + + +def clear_session() -> History: + return '', [] + + +def modify_system_session(system: str) -> str: + if system is None or len(system) == 0: + system = default_system + return system, system, [] + + +def history_to_messages(history: History, system: str) -> Messages: + messages = [{'role': Role.SYSTEM, 'content': system}] + for h in history: + messages.append({'role': Role.USER, 'content': h[0]}) + messages.append({'role': Role.ASSISTANT, 'content': h[1]}) + return messages + + +def messages_to_history(messages: Messages) -> Tuple[str, History]: + assert messages[0]['role'] == Role.SYSTEM + system = messages[0]['content'] + history = [] + for q, r in zip(messages[1::2], messages[2::2]): + history.append([q['content'], r['content']]) + return system, history + + +def message_to_prompt(messages: Messages) -> str: + prompt = "" + for item in messages: + im_start, im_end = "<|im_start|>", "<|im_end|>" + prompt += f"\n{im_start}{item['role']}\n{item['content']}{im_end}" + prompt += f"\n{im_start}assistant\n" + return prompt + + +def model_chat(query: Optional[str], history: Optional[History], + system: str) -> Tuple[str, str, History]: + if query is None: + query = '' + if history is None: + history = [] + + messages = history_to_messages(history, system) + messages.append({'role': Role.USER, 'content': query}) + prompt = message_to_prompt(messages) + + gen_cfg = copy.deepcopy(engine_helper.default_gen_cfg) + gen_cfg["max_length"] = 1024 + gen_cfg["seed"] = random.randint(0, 10000) + + request_list = engine_helper.create_request([prompt], [gen_cfg]) + + request = request_list[0] + gen = engine_helper.process_one_request_stream(request) + for response in gen: + role = Role.ASSISTANT + system, history = messages_to_history(messages + [{'role': role, 'content': response}]) + yield '', history, system + + json_str = engine_helper.convert_request_to_jsonstr(request) + log_lock.acquire() + try: + print(f"{json_str}\n") + finally: + log_lock.release() + +################################################### + +with gr.Blocks() as demo: + demo_title = "

微信的你
" + gr.Markdown(demo_title) + with gr.Row(): + with gr.Column(scale=3): + system_input = gr.Textbox(value=default_system, + lines=1, + label='System') + with gr.Column(scale=1): + modify_system = gr.Button("🛠️ Set system prompt and clear history.", scale=2) + system_state = gr.Textbox(value=default_system, visible=False) + chatbot = gr.Chatbot(label=config["model_name"]) + textbox = gr.Textbox(lines=2, label='Input') + + with gr.Row(): + clear_history = gr.Button("🧹清除历史记录") + sumbit = gr.Button("🚀和我聊天!") + + sumbit.click(model_chat, + inputs=[textbox, chatbot, system_state], + outputs=[textbox, chatbot, system_input], + concurrency_limit=engine_max_batch) + clear_history.click(fn=clear_session, + inputs=[], + outputs=[textbox, chatbot], + concurrency_limit=engine_max_batch) + modify_system.click(fn=modify_system_session, + inputs=[system_input], + outputs=[system_state, system_input, chatbot], + concurrency_limit=engine_max_batch) + +demo.queue(api_open=False).launch(height=800, share=False, server_name="127.0.0.1", server_port=7860) diff --git a/MemoAI/qwen2-0.5b/di_config.json b/MemoAI/qwen2-0.5b/di_config.json new file mode 100644 index 0000000..b6d5b37 --- /dev/null +++ b/MemoAI/qwen2-0.5b/di_config.json @@ -0,0 +1,52 @@ +{ + "model_space": "YOUR-NAME-SPACE", + "model_name": "YOUR-MODEL-NAME", + "model_type": "Qwen_v20", + "model_path": "./dashinfer_models/", + "data_type": "float32", + "device_type": "CPU", + "device_ids": [ + 0 + ], + "multinode_mode": false, + "engine_config": { + "engine_max_length": 1024, + "engine_max_batch": 2, + "do_profiling": false, + "num_threads": 0, + "matmul_precision": "medium" + }, + "generation_config": { + "temperature": 0.7, + "early_stopping": true, + "top_k": 20, + "top_p": 0.8, + "repetition_penalty": 1.05, + "presence_penalty": 0.0, + "min_length": 0, + "max_length": 8192, + "no_repeat_ngram_size": 0, + "eos_token_id": 151643, + "seed": 1234, + "stop_words_ids": [ + [ + 151643 + ], + [ + 151644 + ], + [ + 151645 + ] + ] + }, + "convert_config": { + "do_dynamic_quantize_convert": false + }, + "quantization_config": { + "activation_type": "bfloat16", + "weight_type": "uint8", + "SubChannel": true, + "GroupSize": 512 + } +} \ No newline at end of file diff --git a/MemoAI/qwen2-0.5b/requirements.txt b/MemoAI/qwen2-0.5b/requirements.txt new file mode 100644 index 0000000..37d1d6e --- /dev/null +++ b/MemoAI/qwen2-0.5b/requirements.txt @@ -0,0 +1 @@ +dashinfer diff --git a/MemoAI/qwen2-0.5b/train.ipynb b/MemoAI/qwen2-0.5b/train.ipynb new file mode 100644 index 0000000..bc832d1 --- /dev/null +++ b/MemoAI/qwen2-0.5b/train.ipynb @@ -0,0 +1,419 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "de53995b-32ed-4722-8cac-ba104c8efacb", + "metadata": {}, + "source": [ + "# 导入环境" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52fac949-4150-4091-b0c3-2968ab5e385c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from datasets import Dataset\n", + "import pandas as pd\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForSeq2Seq, TrainingArguments, Trainer, GenerationConfig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e098d9eb", + "metadata": { + "ExecutionIndicator": { + "show": true + }, + "tags": [] + }, + "outputs": [], + "source": [ + "df = pd.read_json('train.json')\n", + "ds = Dataset.from_pandas(df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ac92d42-efae-49b1-a00e-ccaa75b98938", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ds[:3]" + ] + }, + { + "cell_type": "markdown", + "id": "380d9f69-9e98-4d2d-b044-1d608a057b0b", + "metadata": {}, + "source": [ + "# 下载模型" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "312d6439-1932-44a3-b592-9adbdb7ab702", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from modelscope import snapshot_download\n", + "model_dir = snapshot_download('qwen/Qwen2-0.5B-Instruct', cache_dir='qwen2-0.5b/')" + ] + }, + { + "cell_type": "markdown", + "id": "51d05e5d-d14e-4f03-92be-9a9677d41918", + "metadata": {}, + "source": [ + "# 处理数据集" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74ee5a67-2e55-4974-b90e-cbf492de500a", + "metadata": { + "ExecutionIndicator": { + "show": true + }, + "tags": [] + }, + "outputs": [], + "source": [ + "tokenizer = AutoTokenizer.from_pretrained('./qwen2-0.5b/qwen/Qwen2-0___5B-Instruct/', use_fast=False, trust_remote_code=True)\n", + "tokenizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2503a5fa-9621-4495-9035-8e7ef6525691", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def process_func(example):\n", + " MAX_LENGTH = 384 # Llama分词器会将一个中文字切分为多个token,因此需要放开一些最大长度,保证数据的完整性\n", + " input_ids, attention_mask, labels = [], [], []\n", + " instruction = tokenizer(f\"<|im_start|>system\\n现在你需要扮演我,和我的微信好友快乐聊天!<|im_end|>\\n<|im_start|>user\\n{example['instruction'] + example['input']}<|im_end|>\\n<|im_start|>assistant\\n\", add_special_tokens=False)\n", + " response = tokenizer(f\"{example['output']}\", add_special_tokens=False)\n", + " input_ids = instruction[\"input_ids\"] + response[\"input_ids\"] + [tokenizer.pad_token_id]\n", + " attention_mask = instruction[\"attention_mask\"] + response[\"attention_mask\"] + [1] # 因为eos token咱们也是要关注的所以 补充为1\n", + " labels = [-100] * len(instruction[\"input_ids\"]) + response[\"input_ids\"] + [tokenizer.pad_token_id] \n", + " if len(input_ids) > MAX_LENGTH: # 做一个截断\n", + " input_ids = input_ids[:MAX_LENGTH]\n", + " attention_mask = attention_mask[:MAX_LENGTH]\n", + " labels = labels[:MAX_LENGTH]\n", + " return {\n", + " \"input_ids\": input_ids,\n", + " \"attention_mask\": attention_mask,\n", + " \"labels\": labels\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84f870d6-73a9-4b0f-8abf-687b32224ad8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tokenized_id = ds.map(process_func, remove_columns=ds.column_names)\n", + "tokenized_id" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f7e15a0-4d9a-4935-9861-00cc472654b1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tokenizer.decode(tokenized_id[0]['input_ids'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97f16f66-324a-454f-8cc3-ef23b100ecff", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tokenizer.decode(list(filter(lambda x: x != -100, tokenized_id[1][\"labels\"])))" + ] + }, + { + "cell_type": "markdown", + "id": "424823a8-ed0d-4309-83c8-3f6b1cdf274c", + "metadata": {}, + "source": [ + "# 创建模型" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "170764e5-d899-4ef4-8c53-36f6dec0d198", + "metadata": { + "ExecutionIndicator": { + "show": true + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "model = AutoModelForCausalLM.from_pretrained('./qwen2-0.5b/qwen/Qwen2-0___5B-Instruct', device_map=\"auto\",torch_dtype=torch.bfloat16)\n", + "model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2323eac7-37d5-4288-8bc5-79fac7113402", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model.enable_input_require_grads()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f808b05c-f2cb-48cf-a80d-0c42be6051c7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model.dtype" + ] + }, + { + "cell_type": "markdown", + "id": "13d71257-3c1c-4303-8ff8-af161ebc2cf1", + "metadata": {}, + "source": [ + "# lora " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d304ae2-ab60-4080-a80d-19cac2e3ade3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from peft import LoraConfig, TaskType, get_peft_model\n", + "\n", + "config = LoraConfig(\n", + " task_type=TaskType.CAUSAL_LM, \n", + " target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n", + " inference_mode=False, # 训练模式\n", + " r=8, # Lora 秩\n", + " lora_alpha=32, # Lora alaph,具体作用参见 Lora 原理\n", + " lora_dropout=0.1# Dropout 比例\n", + ")\n", + "config" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c2489c5-eaab-4e1f-b06a-c3f914b4bf8e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model = get_peft_model(model, config)\n", + "config" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebf5482b-fab9-4eb3-ad88-c116def4be12", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model.print_trainable_parameters()" + ] + }, + { + "cell_type": "markdown", + "id": "ca055683-837f-4865-9c57-9164ba60c00f", + "metadata": {}, + "source": [ + "# 配置训练参数" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e76bbff-15fd-4995-a61d-8364dc5e9ea0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "args = TrainingArguments(\n", + " output_dir=\"./output/\",\n", + " per_device_train_batch_size=4,\n", + " gradient_accumulation_steps=4,\n", + " logging_steps=10,\n", + " num_train_epochs=3,\n", + " learning_rate=1e-4,\n", + " gradient_checkpointing=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f142cb9c-ad99-48e6-ba86-6df198f9ed96", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "trainer = Trainer(\n", + " model=model,\n", + " args=args,\n", + " train_dataset=tokenized_id,\n", + " data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aec9bc36-b297-45af-99e1-d4c4d82be081", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "id": "8abb2327-458e-4e96-ac98-2141b5b97c8e", + "metadata": {}, + "source": [ + "# 合并加载模型,这里的路径可能有点不太一样,lora_path填写为Output的最后的checkpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd2a415a-a9ad-49ea-877f-243558a83bfc", + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "import torch\n", + "from peft import PeftModel\n", + "\n", + "mode_path = './qwen2-0.5b/qwen/Qwen2-0___5B-Instruct'\n", + "lora_path = './output/checkpoint-10' #修改这里\n", + "# 加载tokenizer\n", + "tokenizer = AutoTokenizer.from_pretrained(mode_path, trust_remote_code=True)\n", + "\n", + "# 加载模型\n", + "model = AutoModelForCausalLM.from_pretrained(mode_path, device_map=\"auto\",torch_dtype=torch.bfloat16, trust_remote_code=True).eval()\n", + "\n", + "# 加载lora权重\n", + "model = PeftModel.from_pretrained(model, model_id=lora_path)\n", + "\n", + "prompt = \"在干啥呢?\"\n", + "inputs = tokenizer.apply_chat_template([{\"role\": \"user\", \"content\": \"现在你需要扮演我,和我的微信好友快乐聊天!\"},{\"role\": \"user\", \"content\": prompt}],\n", + " add_generation_prompt=True,\n", + " tokenize=True,\n", + " return_tensors=\"pt\",\n", + " return_dict=True\n", + " ).to('cuda')\n", + "\n", + "\n", + "gen_kwargs = {\"max_length\": 2500, \"do_sample\": True, \"top_k\": 1}\n", + "with torch.no_grad():\n", + " outputs = model.generate(**inputs, **gen_kwargs)\n", + " outputs = outputs[:, inputs['input_ids'].shape[1]:]\n", + " print(tokenizer.decode(outputs[0], skip_special_tokens=True))\n", + "\n", + "# 保存合并后的模型和tokenizer\n", + "save_directory = './model_merge'\n", + "\n", + "# 保存模型\n", + "\n", + "model.save_pretrained(save_directory)\n", + "\n", + "# 保存tokenizer\n", + "tokenizer.save_pretrained(save_directory)" + ] + }, + { + "cell_type": "markdown", + "id": "b67e5e0a-2566-4483-9bce-92b5be8b4b34", + "metadata": {}, + "source": [ + "# 然后把模型上传到modelscope开始下一步" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dafe4f24-af5c-407e-abbc-eefd9d44cb15", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/ai-qwen/readme.md b/doc/ai-qwen/readme.md new file mode 100644 index 0000000..e50e199 --- /dev/null +++ b/doc/ai-qwen/readme.md @@ -0,0 +1,22 @@ +**鉴于** 本仓库原来的训练模型 Chatllm3-6b 在低性能机器上部署比较困难,我在原基础上使用微型模型(但是较好满足微信聊天 AI 要求)的 Qwen2-0.5b-Instruct 模型完成模型训练到部署到免费 +Modelspace 创空间中,比较简单,**并且可做到全程免费** 下面是流程: +*** +# 第一步,[创建 Modelspace 免费 GPU](https://www.modelscope.cn/my/mynotebook/preset) +![GPU](/MemoAI/img/img3.png) + +# 开始训练 +**可以参照我的训练[模板](/MemoAI/qwen2-0.5b/train.ipynb)** +
+把 train.json 上传,一步步点击即可
+最后把模型上传到 Modelspace +
+![训练](/MemoAI/img/img4.png) +# 部署到创空间 +编辑 di_config.json 的一下两个字段 +> "model_space": "YOUR-NAME-SPACE" + +> "model_name": "YOUR-MODEL-NAME" + +**然后把一下MemoAI/qwen2-0.5b的三个文件上传到创空间,点击部署!** + +**最后看看成功吧:**[成品](https://www.modelscope.cn/studios/sanbei101/qwen-haoran/summary) \ No newline at end of file