{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "使用conda创建环境" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# sh Miniconda3-latest-Linux-x86_64.sh\n", "# conda create -n poem python==3.10.12\n", "# pip install peft==0.4.0\n", "# pip install transformers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "使用virtual创建环境" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# virtualenv -p miniconda3/envs/poem/bin/python3 poem_env\n", "# source poem_env/bin/activate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "恢复conda命令，记得**重启**后activate" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# vim ~/.bashrc\n", "# export PATH=\"/home/jovyan/miniconda3/bin:$PATH\"\n", "# source ~/.bashrc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "创建虚拟环境的kernel" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# python -m ipykernel install --user --name=poem" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import transformers\n", "import tensorboardX\n", "import peft" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 177, "referenced_widgets": [ "bc8b48f2d8014494b58219cab21c5445", "ef8c6be1feb44a2193080b300917027b", "64154875532b4107bc9c3eadce518e8a", "210d4e0be7d64b9aa93247de342dd990", "0cbf56a6d4e54872aa898bb1bbacc475", "80cf679ab4604cd889ebf3acb8ec4a78", "57f57f92fb6342b98d07c0afae64f6e4", "1340127ef52445f2a4356b6ad55c6a45", "d312964d3ea94b6d9f8487453e391776", "6ecdb81bdf3d4ef6bffee64220c68c1e", "1fc3d4b27aa54cda8b48db37ba2b240c", "d394b8338c7a4e529d4af78c0a7748cf", "7dbba4b2cbb740678c45d8290bc74506", "085ccd0def264fdc8f8e78c2ec4a4ad0", "3189695aaa524ef9849a330a3b6fe727", "0aab251e9e6e4707930bea523fa1cee5", "867c5de0517f489a9339f253398ad7b6", "e73a1968335f403f8decfe76fdd20fe7", "61d61869b2b4494e9f03ca19e46e3b50", "97cee7148df5400c946060539b85a0ae", "bf23807aa68d443ca3f4b3e9f412f905", "facc6fa0e89744849c5bf99dce2374a7", "7ce589aa38cf4df88472e8e38c4aabd3", "79ee28f5a6464744bd74bb9c3ce7ccb9", "f47755da03b1451aaf7f648f0005e13b", "0c3cfd026d1e4ba3b63efe8dd33aae31", "bc5a759785ab4610a5dc2bf13479cd96", "84bd60339a6a42ee9d615deb2e99a286", "66c24703ec64495cbde5c992c8bc423b", "ad0dbb5252e9453fab1459abab19571f", "46844d86b7f742f79bb7933cedfb2be0", "38dbfa3168694cc7b005ec67497dbb5c", "38a3389c5843443da15d8427bbb631f4", "fa8c4a0a0ed143a18ae0171bc19312a4", "599b874881c442b695e695ff2294efa6", "efd0c9db40454c3fadbab72d74318319", "9815b66a978044b28c2959010dfc7e62", "384521a223924c9c901429a06378c960", "2f2aaa8472754f14a6f0ce3b1de72a9c", "492313eff9104d7ba9d51142f1031450", "61533e85ab904af2a581beb4a716472e", "5c0a625830824721b920332d70c5f862", "dce096c718ea4f328ad469dc3c0f490a", "c0ee54660df248ef822e21d2c5db672d", "7883fecb6c154dfebff1bd587fc274cb", "6ede658a50b54b75aec4c626e866e912", "cae1d03de7ee444cadabc56cecfb90cf", "1799989a9ce649808373f4290f5aa9b7", "70687e0d20984292a9b51a0a278ae1ad", "2c3bca6f279344d298ab7bcbef42e43b", "dd2d0ca77e5c4ba2ac6053f127397c24", "6ae86202859a4077871039b102f12298", "9ecb40df23ec4267a38d99498dfb1f2f", "a16e5b91720a445283b67b1bd92c6a07", "90ae28c6c5ce43e29d4218f767c067c7" ] }, "id": "V8KOElonUG5V", "outputId": "e79acf43-f917-4689-e5a7-7e50fada42fc" }, "outputs": [], "source": [ "import time\n", "import math\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "from tqdm import tqdm\n", "import transformers\n", "from tensorboardX import SummaryWriter # 这个库提供Tensorboard的日志功能\n", "from transformers import AutoTokenizer, AutoModelForCausalLM\n", "from torch.utils.data import Dataset, DataLoader\n", "from peft import TaskType, LoraConfig, get_peft_model\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\"IDEA-CCNL/Wenzhong-GPT2-110M\")\n", "tokenizer.pad_token = tokenizer.eos_token\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "tE9fKB6yUQ7l" }, "outputs": [], "source": [ "class PoemDataset(Dataset):\n", " def __init__(self, path):\n", " super().__init__()\n", " self.poems = open(path, encoding='utf-8').readlines() # [:30000]\n", "\n", " def __getitem__(self, idx):\n", " text = self.poems[idx].strip()\n", " return text\n", "\n", " def __len__(self):\n", " return len(self.poems)\n", "\n", "\n", "def collate_fn(samples):\n", " return tokenizer(samples, padding=\"longest\", truncation=True, return_tensors='pt')\n", "\n", "\n", "def generate_prompt(bs, prompt=\"写一首唐诗：\"):\n", " return tokenizer([prompt] * bs, padding=\"longest\", truncation=True, return_tensors='pt')\n", "\n", "\n", "@torch.no_grad()\n", "def inference(model):\n", " inputs = tokenizer(\"写一首唐诗：折戟沉沙铁未销，自将磨洗认前朝。\", return_tensors='pt')\n", " inputs = inputs.to(device)\n", " outputs = model.generate(\n", " **inputs,\n", " return_dict_in_generate=True,\n", " max_length=150,\n", " do_sample=True,\n", " top_p=0.6,\n", " num_return_sequences=5\n", " )\n", " print(tokenizer.batch_decode(outputs.sequences, skip_special_tokens=True))\n", "\n", "\n", "@torch.no_grad()\n", "def evaluate(model, test_loader, epoch, **kwargs):\n", " loss_list = []\n", " ppl_list = []\n", " print(\"-\" * 20 + \" Evaluating \" + \"-\" * 20)\n", " model.eval()\n", " with torch.no_grad():\n", " for local_step, inputs in enumerate(tqdm(test_loader)):\n", " # prepare prompts\n", " prompt = generate_prompt(inputs.input_ids.shape[0]).to(device)\n", " inputs = inputs.to(device)\n", " input_ids = torch.cat([prompt.input_ids, inputs.input_ids], dim=1)\n", " no_loss_ids = torch.ones_like(prompt.input_ids) * -100\n", " label_ids = torch.cat([no_loss_ids, inputs.input_ids], dim=1)\n", " attention_mask = torch.cat([prompt.attention_mask, inputs.attention_mask], dim=1)\n", "\n", " outputs = model(\n", " input_ids=input_ids,\n", " attention_mask=attention_mask,\n", " labels=label_ids,\n", " return_dict=True,\n", " )\n", " loss_list.append(outputs.loss.cpu().item())\n", " # PPL\n", " probs = torch.softmax(outputs.logits, dim=-1).max(dim=-1)[0] # BLC->BL\n", " ppl = torch.exp(-probs.log().mean(-1))\n", " ppl_list.append(ppl.mean().cpu().item())\n", " # log\n", " avg_loss = sum(loss_list) / len(loss_list)\n", " avg_ppl = sum(ppl_list) / len(ppl_list)\n", " print(f\"Epoch {epoch}/{kwargs['max_epochs']} | loss:{avg_loss:.5f} | ppl: {avg_ppl: 5f}\")\n", " # writer.add_scalar('Test/Loss', sum(loss_list) / len(loss_list), epoch * kwargs['steps_per_epoch'])\n", "\n", "\n", "def train(model: nn.Module, dataloaders, optimizer, scheduler, **kwargs):\n", " \"\"\"训练的主函数\"\"\"\n", " train_loader, test_loader = dataloaders\n", " device = kwargs['device']\n", " writer = SummaryWriter(kwargs['logger_name'])\n", " model = model.to(device)\n", "\n", " for epoch in range(kwargs['max_epochs']):\n", " # ========== Train ==========\n", " loss_list = []\n", " model.train()\n", " last_time = time.time()\n", " for local_step, inputs in enumerate(train_loader):\n", " step = epoch * kwargs['steps_per_epoch'] + local_step\n", " # prepare prompts\n", " prompt = generate_prompt(inputs.input_ids.shape[0]).to(device)\n", " inputs = inputs.to(device)\n", " input_ids = torch.cat([prompt.input_ids, inputs.input_ids], dim=1)\n", " no_loss_ids = torch.ones_like(prompt.input_ids) * -100 # Don't cal loss of prompts\n", " label_ids = torch.cat([no_loss_ids, inputs.input_ids], dim=1)\n", " attention_mask = torch.cat([prompt.attention_mask, inputs.attention_mask], dim=1)\n", "\n", " optimizer.zero_grad()\n", "\n", " with torch.autocast(device_type='cuda'): # fp16\n", " outputs = model(\n", " input_ids=input_ids,\n", " attention_mask=attention_mask,\n", " labels=label_ids,\n", " return_dict=True,\n", " )\n", " loss = outputs.loss\n", "\n", " loss.backward()\n", " optimizer.step()\n", " scheduler.step()\n", "\n", " # log\n", " loss_list.append(loss.detach().cpu().item())\n", " if (local_step % 50 == 0 and local_step != 0) or local_step == kwargs['steps_per_epoch'] - 1:\n", " avg_loss = sum(loss_list) / len(loss_list)\n", " n_step_time = time.time() - last_time\n", " left_time = (kwargs['steps_per_epoch'] - local_step) // 50 * n_step_time\n", " print(\"Epoch {}/{} | Step {}/{} | loss:{:.5f} time:{:.1f}s left:{:.1f}m\".format(\n", " epoch, kwargs['max_epochs'], local_step, kwargs['steps_per_epoch'],\n", " avg_loss, n_step_time, left_time / 60\n", " ))\n", " last_time = time.time()\n", " writer.add_scalar('Train/Loss', loss, step)\n", " writer.add_scalar('Epoch', epoch, step)\n", "\n", " # if local_step % 6000 == 0 and local_step != 0:\n", " # evaluate(model, test_loader, epoch * kwargs['steps_per_epoch'] + local_step, **kwargs)\n", " # torch.save(model.named_parameters(), \"checkpoint.pth\")\n", " model.save_pretrained(\"checkpoint_lora_v2\")\n", " # ========== Eval ==========\n", " evaluate(model, test_loader, epoch, **kwargs)\n", " print(\"=\" * 53)\n", " # ========== Inference ==========\n", " inference(model)\n", "\n", "# tokenizer(\"写一首唐诗：\", padding=\"longest\", truncation=True, return_tensors='pt')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "dca88fb434d34dc2b46aa651f72fabbb", "1ad4907284244b3aa52915694298a60f", "014875e4813a494e9b1b450dbe97d69b", "7d599c7d05f34a9e92b4240c89f6873b", "20e60c841b9441a887470cc5be052626", "62d246dc092d4fd2bea5a062db74de48", "f55d45bf6c294064ba76a930f86b7ba3", "4832608a7f0c4708b7b45d174ebdc7e9", "4f26154b885d49188866ef861858be48", "e05e79f2e5764d43aaeff257f2a83f92", "a28bc3f520cf4d50bdbe43d8665134d6", "10df97518ed543ea9db9f31c764a415b", "0fed0c9b34784450b182cc0eef5a6168", "ae7fc0123ef945bca51844d06befc792", "486dc3389c9d44678e5f22acc062446c", "a4dcd5ace2f44aa78afb0c4b3b119e7c", "d411ea579aa64b97a4a41078bb03197d", "09d0fbef2ddf46b595d5686eb2cc568e", "ab0e65ad6fd3463f9f818ec79a3082f7", "0e53e25979ac4e33a57bba62fb43f670", "fdd1f6dff9b149f699ef84dc83372f6a", "1ff7bf7867f0443599e166146824d8cc" ] }, "id": "BzWXKl7iUhsE", "outputId": "f3cf0d0f-f5ac-4157-c2e8-788ce2cd9bae" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "trainable params: 691,968 || all params: 125,065,728 || trainable%: 0.5532834702725274\n" ] } ], "source": [ "# prepare data\n", "train_dataset = PoemDataset(\"data/train_poems_v2.txt\")\n", "test_dataset = PoemDataset(\"data/test_poems_v2.txt\")\n", "\n", "# Hyperparameter\n", "batch_size = 16\n", "lr = 1e-4\n", "# device = torch.device('cuda')\n", "device=torch.device(\"cuda\" if torch.cuda.is_available() else\"cpu\")\n", "max_epochs = 2\n", "num_warmup_steps = 200\n", "num_training_steps = max_epochs * math.ceil(len(train_dataset) / batch_size)\n", "seed = 2023\n", "logger_name = \"logs\"\n", "\n", "# prepare model\n", "gpt2_model = AutoModelForCausalLM.from_pretrained(\"IDEA-CCNL/Wenzhong-GPT2-110M\")\n", "peft_config = LoraConfig(\n", " task_type=TaskType.CAUSAL_LM,\n", " inference_mode=False,\n", " r=16, lora_alpha=16, lora_dropout=0.1, bias='all'\n", ")\n", "gpt2_model = get_peft_model(gpt2_model, peft_config)\n", "gpt2_model.print_trainable_parameters()\n", "\n", "# prepare optimizer\n", "optim = torch.optim.AdamW(filter(lambda p: p.requires_grad, gpt2_model.parameters()), lr=lr)\n", "sche = transformers.get_linear_schedule_with_warmup(optim, num_warmup_steps, num_training_steps)\n", "\n", "# train\n", "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn)\n", "val_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn)\n", "\n", "train(\n", " gpt2_model,\n", " (train_dataloader, val_dataloader),\n", " optimizer=optim,\n", " scheduler=sche,\n", " device=device,\n", " max_epochs=max_epochs,\n", " logger_name=logger_name,\n", " steps_per_epoch=len(train_dataloader)\n", ")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "写一首唐诗：一\n", "写一首唐诗：一江春水碧，见\n", "写一首唐诗：一江春水碧，见我岸边白。如\n", "写一首唐诗：一江春水碧，见我岸边白。如今长绿碧，故\n", "写一首唐诗：一江春水碧，见我岸边白。如今长绿碧，故园有香火。\n", "一江春水碧，见我岸边白。如今长绿碧，故园有香火。\n" ] } ], "source": [ "from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria\n", "from peft import PeftModel\n", "import torch\n", "import os\n", "import re\n", "\n", "class ChineseCharacterStop(StoppingCriteria):\n", " def __init__(self, chars: list[str]):\n", " self.chars = [\n", " tokenizer(i, add_special_tokens=False, return_tensors='pt').input_ids\n", " for i in chars\n", " ]\n", " # for chars, tokens in zip(chars, self.chars):\n", " # print(f\"'{chars}':{tokens}\")\n", "\n", " def __call__(self, input_ids: torch.LongTensor,\n", " scores: torch.FloatTensor, **kwargs) -> bool:\n", " for c in self.chars:\n", " c = c.to(input_ids.device)\n", " match = torch.eq(input_ids[..., -c.shape[1]:], c)\n", " if torch.any(torch.all(match, dim=1)):\n", " return True\n", " return False\n", "\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\"IDEA-CCNL/Wenzhong-GPT2-110M\")\n", "tokenizer.pad_token = tokenizer.eos_token\n", "gpt2_model = AutoModelForCausalLM.from_pretrained(\"IDEA-CCNL/Wenzhong-GPT2-110M\")\n", "model = PeftModel.from_pretrained(gpt2_model, 'checkpoint_lora_v4.1')\n", "\n", "\n", "def cang_tou(tou: str):\n", " poem_now = \"写一首唐诗：\"\n", " for c in tou:\n", " poem_now += c\n", " print(poem_now)\n", " inputs = tokenizer(poem_now, return_tensors='pt')\n", " outputs = model.generate(\n", " **inputs,\n", " return_dict_in_generate=True,\n", " max_length=150,\n", " do_sample=True,\n", " top_p=0.4,\n", " num_beams=1,\n", " num_return_sequences=1,\n", " stopping_criteria=[ChineseCharacterStop(['。', '，'])],\n", " pad_token_id=tokenizer.pad_token_id\n", " )\n", " poem_now = tokenizer.batch_decode(outputs.sequences, skip_special_tokens=True)[0]\n", " print(poem_now)\n", " return poem_now[6:]\n", "\n", "\n", "def prompt_gen(prompt):\n", " inputs = tokenizer(prompt, return_tensors='pt')\n", " outputs = model.generate(\n", " **inputs,\n", " return_dict_in_generate=True,\n", " max_length=200,\n", " do_sample=True,\n", " top_p=0.8,\n", " num_beams=5,\n", " num_return_sequences=3,\n", " # stopping_criteria=[ChineseCharacterStop(['。', '，', ''])],\n", " pad_token_id=tokenizer.pad_token_id\n", " )\n", " res = ''\n", " for line in tokenizer.batch_decode(outputs.sequences, skip_special_tokens=True):\n", " line = line[len(prompt):]\n", " res = res+line+'\\n'\n", " return res\n", "\n", "def handle(conf):\n", " prompt = conf[\"prompt\"]\n", " if conf['mode']==\"藏头诗\":\n", " res = cang_tou(prompt)\n", " if conf['mode']==\"根据提示生成古诗\":\n", " res = prompt_gen(prompt)\n", " return {\"result\": res}\n", "\n", "res = cang_tou(\"一见如故\")\n", "print(res)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded as API: https://wendyy-poem-generate.hf.space/ ✔\n" ] } ], "source": [ "from gradio_client import Client\n", "\n", "client = Client(\"https://wendyy-poem-generate.hf.space/\")\n", "\n", "def handle(conf):\n", " prompt = conf[\"prompt\"]\n", " if conf['mode']==\"藏头诗\":\n", " result = client.predict(\n", " prompt,\t# str in 'Prompt' Textbox component\n", " fn_index=0\n", " )\n", " if conf['mode']==\"根据提示生成古诗\":\n", " result = client.predict(\n", " prompt,\t# str in '藏头诗' Textbox component\n", " fn_index=1\n", " )\n", " return {\"result\": result}" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "result = client.predict(\n", " \"一见如故\",\t# str in 'Prompt' Textbox component\n", " fn_index=1)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'一曲江汉江，见解知音渺。如何细看江，故人何处寻。'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "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": "3.7.5" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "014875e4813a494e9b1b450dbe97d69b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": 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