{
"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": [
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"210d4e0be7d64b9aa93247de342dd990",
"0cbf56a6d4e54872aa898bb1bbacc475",
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"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",
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"a4dcd5ace2f44aa78afb0c4b3b119e7c",
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},
"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": []
}
],
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