infer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "35ab41e7-71da-4e38-a7e2-fb5d90719ec1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current working directory: /root/autodl-tmp\n",
      "Changed working directory to: /root/autodl-tmp\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "# 打印当前工作目录\n",
    "print(\"Current working directory:\", os.getcwd())\n",
    "\n",
    "# 更改当前工作目录（如果必要）\n",
    "new_dir = '/root/autodl-tmp/'\n",
    "os.chdir(new_dir)\n",
    "print(\"Changed working directory to:\", os.getcwd())\n",
    "\n",
    "# 确保当前目录在系统路径中\n",
    "if new_dir not in sys.path:\n",
    "    sys.path.append(new_dir)\n",
    "\n",
    "# 清除之前的导入缓存\n",
    "if 'myReader' in sys.modules:\n",
    "    del sys.modules['myReader']\n",
    "\n",
    "# 尝试导入 convert_example 函数\n",
    "from myReader import get_dataLoader\n",
    "\n",
    "# # 测试函数\n",
    "# convert_example()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d477d5c9-8a38-4ecb-a608-cde85f43a6cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T5ForConditionalGeneration(\n",
       "  (shared): Embedding(21228, 768)\n",
       "  (encoder): T5Stack(\n",
       "    (embed_tokens): Embedding(21228, 768)\n",
       "    (block): ModuleList(\n",
       "      (0): T5Block(\n",
       "        (layer): ModuleList(\n",
       "          (0): T5LayerSelfAttention(\n",
       "            (SelfAttention): T5Attention(\n",
       "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (relative_attention_bias): Embedding(32, 12)\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (1): T5LayerFF(\n",
       "            (DenseReluDense): T5DenseActDense(\n",
       "              (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
       "              (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "              (act): ReLU()\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (1-11): 11 x T5Block(\n",
       "        (layer): ModuleList(\n",
       "          (0): T5LayerSelfAttention(\n",
       "            (SelfAttention): T5Attention(\n",
       "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (1): T5LayerFF(\n",
       "            (DenseReluDense): T5DenseActDense(\n",
       "              (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
       "              (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "              (act): ReLU()\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (final_layer_norm): T5LayerNorm()\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (decoder): T5Stack(\n",
       "    (embed_tokens): Embedding(21228, 768)\n",
       "    (block): ModuleList(\n",
       "      (0): T5Block(\n",
       "        (layer): ModuleList(\n",
       "          (0): T5LayerSelfAttention(\n",
       "            (SelfAttention): T5Attention(\n",
       "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (relative_attention_bias): Embedding(32, 12)\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (1): T5LayerCrossAttention(\n",
       "            (EncDecAttention): T5Attention(\n",
       "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (2): T5LayerFF(\n",
       "            (DenseReluDense): T5DenseActDense(\n",
       "              (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
       "              (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "              (act): ReLU()\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (1-11): 11 x T5Block(\n",
       "        (layer): ModuleList(\n",
       "          (0): T5LayerSelfAttention(\n",
       "            (SelfAttention): T5Attention(\n",
       "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (1): T5LayerCrossAttention(\n",
       "            (EncDecAttention): T5Attention(\n",
       "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
       "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (2): T5LayerFF(\n",
       "            (DenseReluDense): T5DenseActDense(\n",
       "              (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
       "              (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "              (act): ReLU()\n",
       "            )\n",
       "            (layer_norm): T5LayerNorm()\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (final_layer_norm): T5LayerNorm()\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (lm_head): Linear(in_features=768, out_features=21228, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# !/usr/bin/env python3\n",
    "\"\"\"\n",
    "==== No Bugs in code, just some Random Unexpected FEATURES ====\n",
    "┌─────────────────────────────────────────────────────────────┐\n",
    "│┌───┬───┬───┬───┬───┬───┬───┬───┬───┬───┬───┬───┬───┬───┬───┐│\n",
    "││Esc│!1 │@2 │#3 │$4 │%5 │^6 │&7 │*8 │(9 │)0 │_- │+= │|\\ │`~ ││\n",
    "│├───┴─┬─┴─┬─┴─┬─┴─┬─┴─┬─┴─┬─┴─┬─┴─┬─┴─┬─┴─┬─┴─┬─┴─┬─┴─┬─┴───┤│\n",
    "││ Tab │ Q │ W │ E │ R │ T │ Y │ U │ I │ O │ P │{[ │}] │ BS  ││\n",
    "│├─────┴┬──┴┬──┴┬──┴┬──┴┬──┴┬──┴┬──┴┬──┴┬──┴┬──┴┬──┴┬──┴─────┤│\n",
    "││ Ctrl │ A │ S │ D │ F │ G │ H │ J │ K │ L │: ;│\" '│ Enter  ││\n",
    "│├──────┴─┬─┴─┬─┴─┬─┴─┬─┴─┬─┴─┬─┴─┬─┴─┬─┴─┬─┴─┬─┴─┬─┴────┬───┤│\n",
    "││ Shift  │ Z │ X │ C │ V │ B │ N │ M │< ,│> .│? /│Shift │Fn ││\n",
    "│└─────┬──┴┬──┴──┬┴───┴───┴───┴───┴───┴──┬┴───┴┬──┴┬─────┴───┘│\n",
    "│      │Fn │ Alt │         Space         │ Alt │Win│   HHKB   │\n",
    "│      └───┴─────┴───────────────────────┴─────┴───┘          │\n",
    "└─────────────────────────────────────────────────────────────┘\n",
    "\n",
    "测试训练好的模型效果。\n",
    "\n",
    "Author: pankeyu\n",
    "Date: 2023/01/05\n",
    "\"\"\"\n",
    "from rich import print\n",
    "from transformers import AutoTokenizer, T5ForConditionalGeneration, AutoModelForSeq2SeqLM\n",
    "import json\n",
    "\n",
    "device = 'cuda:0'\n",
    "max_source_seq_len = 256\n",
    "model_checkpoint = 'model_best2'\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint) \n",
    "\n",
    "model.to(device).eval()\n",
    "import torch\n",
    "\n",
    "def inference(question: str, context: str, answer: str):\n",
    "    \"\"\"\n",
    "    inference函数。\n",
    "\n",
    "    Args:\n",
    "        qustion (str): 问题\n",
    "        context (str): 原文\n",
    "    \"\"\"\n",
    "    input_seq = \"给定上下文：{} 。回答问题：{}\".format(context,question)\n",
    "    inputs = tokenizer(\n",
    "        text=input_seq,\n",
    "        truncation=True,\n",
    "        max_length=max_source_seq_len,\n",
    "        padding=True,\n",
    "        return_tensors='pt'\n",
    "    )\n",
    "    with torch.no_grad():\n",
    "        outputs = model.generate(\n",
    "            inputs[\"input_ids\"].to(device),\n",
    "            attention_mask=inputs[\"attention_mask\"].to(device),\n",
    "            max_length=64,\n",
    "            num_beams=8,\n",
    "            no_repeat_ngram_size=2,\n",
    "            eos_token_id=tokenizer.eos_token_id,\n",
    "        ).cpu().numpy()\n",
    "    output = tokenizer.decode(outputs[0], skip_special_tokens=True).replace(\" \", \"\")\n",
    "    print(f'Q: \"{question}\"')\n",
    "    print(f'C: \"{context}\"')\n",
    "    print(f'A: \"{output}\"')\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    # question = '治疗宫颈糜烂的最佳时间'\n",
    "    # context = '专家指出，宫颈糜烂治疗时间应选在月经干净后3-7日，因为治疗之后宫颈有一定的创面，如赶上月经期易发生感染。因此患者应在月经干净后3天尽快来医院治疗。同时应该注意，术前3天禁同房，有生殖道急性炎症者应治好后才可进行。'\n",
    "    # inference(qustion=question, context=context)\n",
    "    data_file = \"data/DuReaderQG/dev.json\"\n",
    "    with open(data_file, 'rt', encoding='utf-8') as f:\n",
    "        for idx, line in enumerate(f):\n",
    "            one_data = json.loads(line.strip())\n",
    "            inference(question=one_data['question'], context=one_data['context'], answer=one_data['answer'])\n",
    "\n",
    "                "
   ]
  }
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