{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8_v2Zzbc_WRt", "outputId": "50b46b10-01f9-4ed7-8278-3413f87bd7c6" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.20.0)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.15.1)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.25.2)\n", "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (16.1.0)\n", "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets) (0.6)\n", "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.8)\n", "Requirement already satisfied: pandas in 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/usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.2->datasets) (4.12.2)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.3.2)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.7)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2.0.7)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2024.6.2)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n", "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n", "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n" ] } ], "source": [ "!pip install datasets" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_q2UKXTofuvg", "outputId": "288815a8-fe8f-4ff4-e274-02d0a0542284" }, "outputs": [ { "data": { "text/plain": [ "{'a': 0,\n", " 'b': 1,\n", " 'c': 2,\n", " 'd': 3,\n", " 'e': 4,\n", " 'f': 5,\n", " 'g': 6,\n", " 'h': 7,\n", " 'i': 8,\n", " 'j': 9,\n", " 'k': 10,\n", " 'l': 11,\n", " 'm': 12,\n", " 'n': 13,\n", " 'o': 14,\n", " 'p': 15,\n", " 'q': 16,\n", " 'r': 17,\n", " 's': 18,\n", " 't': 19,\n", " 'u': 20,\n", " 'v': 21,\n", " 'w': 22,\n", " 'x': 23,\n", " 'y': 24,\n", " 'z': 25}" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import string\n", "\n", "# 定义字典\n", "char2indx = {s: i for i, s in enumerate(string.ascii_lowercase)}\n", "char2indx" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nfJTv6htym9a", "outputId": "f8aa88cb-e100-4737-a5ba-e02aa099fc5c" }, "outputs": [ { "data": { "text/plain": [ "tensor([11, 14, 21, 4])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "example = 'love'\n", "idx = []\n", "\n", "for i in example:\n", " idx.append(char2indx[i])\n", "\n", "idx = torch.tensor(idx)\n", "idx" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QQPzr1v3y8k4", "outputId": "056054db-d375-46f4-d838-9dfa34b21483" }, "outputs": [ { "data": { "text/plain": [ "(tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 1., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0.]]),\n", " torch.Size([4, 26]))" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 使用独热编码，将文本转换为二维张量\n", "num_claz = len(char2indx.keys())\n", "x = F.one_hot(idx, num_classes=num_claz).float()\n", "x, x.shape" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XLFzlIiPzlFP", "outputId": "001f5b4c-cd25-4e37-9503-b515b27f6271" }, "outputs": [ { "data": { "text/plain": [ "(tensor([[ 0.3965, -0.3958, -1.2064, -0.6724, -0.4768],\n", " [ 0.9620, 0.5876, 0.8376, -0.4350, -0.3379],\n", " [-2.5578, -0.0305, -0.2124, -0.5916, 0.1987],\n", " [-0.0834, 0.4715, 1.7552, -0.3915, 1.9801]]),\n", " torch.Size([4, 5]))" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dims = 5\n", "num_claz = len(char2indx.keys())\n", "x = F.one_hot(idx, num_classes=num_claz).float()\n", "w = torch.randn(num_claz, dims)\n", "(x @ w), (x @ w).shape" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fI9sMtsr0FxX", "outputId": "bf610d5d-a303-48ce-ad3b-b669aab8e166" }, "outputs": [ { "data": { "text/plain": [ "(tensor([[ 0.3965, -0.3958, -1.2064, -0.6724, -0.4768],\n", " [ 0.9620, 0.5876, 0.8376, -0.4350, -0.3379],\n", " [-2.5578, -0.0305, -0.2124, -0.5916, 0.1987],\n", " [-0.0834, 0.4715, 1.7552, -0.3915, 1.9801]]),\n", " torch.Size([4, 5]))" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w[idx], w[idx].shape" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "MXIyfigQ0aL0" }, "outputs": [], "source": [ "class Embedding:\n", "\n", " def __init__(self, num_embeddings, embedding_dims):\n", " self.weight = torch.randn((num_embeddings, embedding_dims), requires_grad=True)\n", "\n", " def __call__(self, x):\n", " self.out = self.weight[x]\n", " return self.out\n", "\n", " def parameters(self):\n", " return [self.weight]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "np2OATD71FOo", "outputId": "363f16ce-13cc-4e67-a6e3-675d40df60ec" }, "outputs": [ { "data": { "text/plain": [ "torch.Size([10, 5])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "em = Embedding(num_claz, 5)\n", "x = torch.randint(0, num_claz, (10, ))\n", "em(x).shape" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5IYT4WkO1YPG", "outputId": "e7c2ac1e-777a-442b-9037-00f2583e9f78" }, "outputs": [ { "data": { "text/plain": [ "torch.Size([20, 10, 5])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = torch.randint(0, num_claz, (20, 10))\n", "em(x).shape" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "s12aaPrT1ijj", "outputId": "3cccacd6-9e3f-4288-8df0-9a0df443ba70" }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "torch.cuda.is_available()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Qu238sYE_Gq_", "outputId": "4662e23b-3bc4-4140-d28b-e5f622559212" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_CudaDeviceProperties(name='Tesla T4', major=7, minor=5, total_memory=15102MB, multi_processor_count=40)\n" ] } ], "source": [ "for i in range(torch.cuda.device_count()):\n", " print(torch.cuda.get_device_properties(i))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "K0acVkVI_P14", "outputId": "31d1785f-9c74-4b61-f355-4591b957bdfc" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch.optim as optim\n", "from torch.utils.data import DataLoader\n", "from datasets import load_dataset\n", "import matplotlib.pyplot as plt\n", "\n", "torch.manual_seed(12046)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "id": "O7eABnOnJBXy" }, "outputs": [], "source": [ "device = 'cuda' if torch.cuda.is_available else 'cpu'\n", "batch_size = 1000\n", "learning_rate = 0.01\n", "eval_iters = 10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "感谢Wanghaha（xufengnian-bei）的贡献，如果在下载过程中遇到网络问题，请使用下面的步骤进行处理。\n", "\n", "* 访问 Hugging Face 数据集页面: https://huggingface.co/datasets/code_search_net\n", "* 在页面上找到 \"Files and versions\" 部分。\n", "* 点击data文件夹，下载对应的python.zip\n", "\n", "修改对应下载文件代码：\n", "\n", "datasets = load_dataset('json', data_files='data/python/python/final/jsonl/train/*.jsonl.gz') # 更换为自己的目录\n", "datasets = datasets['train'].filter(lambda x: 'apache/spark' in x['repo']) # 这里repository_name 更换为 repo\n", "\n", "print(datasets[8]['original_string']) # whole_func_string 更换为 original_string" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 440, "referenced_widgets": [ "2bed0a97406e46b9bf1c07e66be77dd5", "0dc5926e3ba0499a918a1c3cbfe862f3", "b704286c3f2c4fa7bcbbc6e6d4b57fb6", "49c33407cf8649bd95f63cf1ee22751b", "e62ac2ea2f8448ee8232dd70eb9850d8", "e2feea769fc14391bb944122c2e808e1", "5cb0d5f4438749eb9f24fe4aa50ab6b7", "a245080a91284641900af30ffbb70577", "1b5de35016af4f5686c1a09c3bcf88cd", "f5b190ba8db74c789b25bf8869d44f6b", "5a4d6fbda05e45f4b78368d4f02c6c38", "7dd2552a36ca4819a20c96cdbeed3e36", "26dfd6520e8049038e69114766b9a0ff", "bbbd5b6f8f44455faf0a031466b2702f", "6c6403ef2e0f482599d0e54d58481f39", "b35467fc4f9041fd9ce882eecd2a6021", "a60bb837834b44378e178e4d9b1a52d4", "b70d024a38864d6bb557bff17ae95d88", "50b76f3d1f3b4bebab396b6bca23e48b", "5d410efac6014533bc1ba839a05d0aaa", "c5453de040db461dae54c65cfadb8d39", "b0f0fc0e102a4acc995a8cc1641074d9", "7ab21750b1a0415d80f9f6f5aea4c13d", "8ef34aff5e9f4096add539f7f6ae29f2", "25e19856014d44c9b762cd7a1f8dc319", "5504bb59eb6e47f79d3b91d42db65911", "bab3bf9895a04dfdbdc76dc642311197", "a3cda732dbfb4a2f9768203548af20f5", "77efb896627947cfa3bdf21f9414cb8a", "4ab8ec295f644193b088d95a3a9b3ec5", "3298fe74f69146bbbbe364c2c4fe5332", "3c5b9b565db14586a40a4a8e54ebe7d3", "89502573192842bd8070a3d3120da01b", "4cbe13efb5dc40b0a7eebd9f7defffc6", "b9b6cf9874544468879f3302cbafdd0f", "09da2bb790a54c179f481d531d3405eb", "d5965607686242799233b3d462319eed", "2387b751d5d6472d8d8f8e7c030338af", "3f3463f177334349836d9cdc25fea796", "142b9dcdc4514ea684c2d0522746e0af", "179ba1b4b8b24b9eadc81a645ed702c8", "1d61f57335364ca0b02a0bd32b56fec6", "6b7f47c502144cafb10942d2f1987e78", "00bb3ff456444b0397816f21490a90f2", "afeb10d4301f44ae9728fc2ef77291a2", "df2c621d9afd438482ea96e8803d56c7", "d312591650f940e797746320007f3ef2", "57706c85ad7a44f4927b7f4e9c6fca75", "6f9585f9022c4360bcc6b5610b33600a", "f492252b9930497fb55837093a9d130f", "94bfa0c5b77e4bd2b17ff94739b457fe", "7d7c7114ff6041e7ae8b48624bba47fa", "08ac8d7ac893490f8cd625568cf4e5d1", "827d8054dfaa49edb71192929b9d6495", "80e437bb9dab47b6b0f4892a21847349", "f402ab9afaa24efda0494e78ba44b3cb", "d35c100aed8743efa1b0df5a7e89308c", "869716edc1fe4dcc99a8880d9033f773", "7c25ae55118e4ec7ae7bffc25a0ae43e", "4d2392c4d43142ca9b8d01b7b2ebb5fd", "81a506124060495d9dc6b59eba2e2490", "2c794c7d98024cbdb38a1ec3e5ccb31a", "3742bf06e2f540fb9d8786d7d2c8f317", "f441707f8fad401883c1220b2e360ff4", "23f6fe5d77c647c5b2c546b82b03667b", "82b0e10b205e48a8832bb8e8fbbb8160", "8fa918d042164c879da4bd580192fdad", "393fa623e36f4be992837c4d901fa23e", "b79a434bbbd44b41b0c2c94d794858ca", "39cc1628e42a4ecebe1e7541416a388d", "c896952f04184bd29b9e796fe0bbfa42", "7a9fb712ced44bf5bc48a4d53c60eeb5", "deb24f274ab3445eb1d02106a6d6e9cf", "4d562acc0dcb490992a549f0d3504627", "7d2ec54cf30c495791e3a338b2168dd1", "53b2163f134f4f9ca0fed9fbc431a4ec", "08d8c742014645279744eff2763845bd" ] }, "id": "KV9eGNEG_f0H", "outputId": "ec85b30d-0af9-46f9-fa73-cca305931016" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2bed0a97406e46b9bf1c07e66be77dd5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading builder script: 0%| | 0.00/8.44k [00:00'] = begin_ind\n", " self.char2ind['<|e|>'] = end_ind\n", " self.ind2char = {v: k for k, v in self.char2ind.items()}\n", " self.begin_ind = begin_ind\n", " self.end_ind = end_ind\n", "\n", " def encode(self, x):\n", " # x: str\n", " return [self.char2ind[i] for i in x]\n", "\n", " def decode(self, x):\n", " # x: int or list[x]\n", " if isinstance(x, int):\n", " return self.ind2char[x]\n", " return [self.ind2char[i] for i in x]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "id": "KkRi-pkyE5Ko" }, "outputs": [], "source": [ "tokenizer = CharTokenizer(datasets['whole_func_string'])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "D0Sd5bVpE_EN", "outputId": "decbfd77-e3f7-4f0a-e93e-7188b082b514" }, "outputs": [ { "data": { "text/plain": [ "[71, 72, 73, 3, 73, 11, 91, 12, 29]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_str = 'def f(x):'\n", "re = tokenizer.encode(test_str)\n", "re" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "JlZLjjEpFHTP", "outputId": "58a9bcb9-680e-47e6-af5e-de5546642f23" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'def f(x):'" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "''.join(tokenizer.decode(re))" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "id": "c5uc4QMPFXKx" }, "outputs": [], "source": [ "def autoregressive_trans(text, tokenizer, context_length=10):\n", " # text: str\n", " inputs, labels = [], []\n", " bind = tokenizer.begin_ind\n", " eind = tokenizer.end_ind\n", " enc = tokenizer.encode(text)\n", " # 增加特殊字符\n", " data = [bind] * context_length + enc + [eind]\n", " for i in range(len(data) - context_length):\n", " inputs.append(data[i: i + context_length])\n", " labels.append(data[i + context_length])\n", " return inputs, labels" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PwulyuENGUWH", "outputId": "29cb4625-9668-40f5-ee25-6dff7e57eea6" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<|b|><|b|><|b|> -----> d\n", "<|b|><|b|>d -----> e\n", "<|b|>de -----> f\n", "def -----> \n", "ef -----> f\n", "f f -----> (\n", " f( -----> x\n", "f(x -----> )\n", "(x) -----> :\n", "x): -----> <|e|>\n" ] } ], "source": [ "inputs, labels = autoregressive_trans(test_str, tokenizer, 3)\n", "for a, b in zip(inputs, labels):\n", " print(f'{\"\".join(tokenizer.decode(a))} -----> {tokenizer.decode(b)}')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "id": "75zAbCZVGfG2" }, "outputs": [], "source": [ "def process(data, tokenizer):\n", " text = data['whole_func_string']\n", " # text: str\n", " if isinstance(text, str):\n", " inputs, labels = autoregressive_trans(text, tokenizer)\n", " return {'inputs': inputs, 'labels': labels}\n", " # text: list[str]\n", " inputs, labels = [], []\n", " for t in text:\n", " i, l = autoregressive_trans(t, tokenizer)\n", " inputs += i\n", " labels += l\n", " return {'inputs': inputs, 'labels': labels}" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Yc9kF5c-ITm9", "outputId": "354ab6df-8681-448e-d33a-bf32fd5d8117" }, "outputs": [ { "data": { "text/plain": [ "{'inputs': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 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83, 68, 17, 86],\n", " [87, 88, 85, 81, 3, 83, 68, 17, 86, 70],\n", " [88, 85, 81, 3, 83, 68, 17, 86, 70, 75],\n", " [85, 81, 3, 83, 68, 17, 86, 70, 75, 72],\n", " [81, 3, 83, 68, 17, 86, 70, 75, 72, 80],\n", " [3, 83, 68, 17, 86, 70, 75, 72, 80, 68],\n", " [83, 68, 17, 86, 70, 75, 72, 80, 68, 11],\n", " [68, 17, 86, 70, 75, 72, 80, 68, 11, 73],\n", " [17, 86, 70, 75, 72, 80, 68, 11, 73, 76],\n", " [86, 70, 75, 72, 80, 68, 11, 73, 76, 72],\n", " [70, 75, 72, 80, 68, 11, 73, 76, 72, 79],\n", " [75, 72, 80, 68, 11, 73, 76, 72, 79, 71],\n", " [72, 80, 68, 11, 73, 76, 72, 79, 71, 86],\n", " [80, 68, 11, 73, 76, 72, 79, 71, 86, 12]],\n", " 'labels': [71,\n", " 72,\n", " 73,\n", " 3,\n", " 87,\n", " 82,\n", " 66,\n", " 68,\n", " 85,\n", " 85,\n", " 82,\n", " 90,\n", " 66,\n", " 86,\n", " 70,\n", " 75,\n", " 72,\n", " 80,\n", " 68,\n", " 11,\n", " 86,\n", " 70,\n", " 75,\n", " 72,\n", " 80,\n", " 68,\n", " 12,\n", " 29,\n", " 2,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 5,\n", " 5,\n", " 5,\n", " 3,\n", " 38,\n", " 82,\n", " 81,\n", " 89,\n", " 72,\n", " 85,\n", " 87,\n", " 3,\n", " 68,\n", " 3,\n", " 86,\n", " 70,\n", " 75,\n", " 72,\n", " 80,\n", " 68,\n", " 3,\n", " 73,\n", " 85,\n", " 82,\n", " 80,\n", " 3,\n", " 54,\n", " 83,\n", " 68,\n", " 85,\n", " 78,\n", " 3,\n", " 87,\n", " 82,\n", " 3,\n", " 36,\n", " 85,\n", " 85,\n", " 82,\n", " 90,\n", " 2,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 5,\n", " 5,\n", " 5,\n", " 2,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 76,\n", " 80,\n", " 83,\n", " 82,\n", " 85,\n", " 87,\n", " 3,\n", " 83,\n", " 92,\n", " 68,\n", " 85,\n", " 85,\n", " 82,\n", " 90,\n", " 3,\n", " 68,\n", " 86,\n", " 3,\n", " 83,\n", " 68,\n", " 2,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 73,\n", " 76,\n", " 72,\n", " 79,\n", " 71,\n", " 86,\n", " 3,\n", " 32,\n", " 3,\n", " 62,\n", " 83,\n", " 68,\n", " 17,\n", " 73,\n", " 76,\n", " 72,\n", " 79,\n", " 71,\n", " 11,\n", " 73,\n", " 76,\n", " 72,\n", " 79,\n", " 71,\n", " 17,\n", " 81,\n", " 68,\n", " 80,\n", " 72,\n", " 15,\n", " 3,\n", " 87,\n", " 82,\n", " 66,\n", " 68,\n", " 85,\n", " 85,\n", " 82,\n", " 90,\n", " 66,\n", " 87,\n", " 92,\n", " 83,\n", " 72,\n", " 11,\n", " 73,\n", " 76,\n", " 72,\n", " 79,\n", " 71,\n", " 17,\n", " 71,\n", " 68,\n", " 87,\n", " 68,\n", " 55,\n", " 92,\n", " 83,\n", " 72,\n", " 12,\n", " 15,\n", " 3,\n", " 81,\n", " 88,\n", " 79,\n", " 79,\n", " 68,\n", " 69,\n", " 79,\n", " 72,\n", " 32,\n", " 73,\n", " 76,\n", " 72,\n", " 79,\n", " 71,\n", " 17,\n", " 81,\n", " 88,\n", " 79,\n", " 79,\n", " 68,\n", " 69,\n", " 79,\n", " 72,\n", " 12,\n", " 2,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 73,\n", " 82,\n", " 85,\n", " 3,\n", " 73,\n", " 76,\n", " 72,\n", " 79,\n", " 71,\n", " 3,\n", " 76,\n", " 81,\n", " 3,\n", " 86,\n", " 70,\n", " 75,\n", " 72,\n", " 80,\n", " 68,\n", " 64,\n", " 2,\n", " 3,\n", " 3,\n", " 3,\n", " 3,\n", " 85,\n", " 72,\n", " 87,\n", " 88,\n", " 85,\n", " 81,\n", " 3,\n", " 83,\n", " 68,\n", " 17,\n", " 86,\n", " 70,\n", " 75,\n", " 72,\n", " 80,\n", " 68,\n", " 11,\n", " 73,\n", " 76,\n", " 72,\n", " 79,\n", " 71,\n", " 86,\n", " 12,\n", " 1]}" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "process(datasets[8: 9], tokenizer)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "id": "K2mgI6fZIdHD" }, "outputs": [], "source": [ "# 将数据分为训练集和测试集\n", "tokenized = datasets.train_test_split(test_size=0.1, seed=1024, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "id": "DthlmdhILLou" }, "outputs": [], "source": [ "f = lambda x: process(x, tokenizer)\n", "tokenized = tokenized.map(f, batched=True, remove_columns=datasets.column_names)\n", "tokenized.set_format(type='torch', device=device)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "w5tMPpYxLTDH", "outputId": "cbf5ffc0-1d89-4b6d-fc3c-513c64a598a5" }, "outputs": [ { "data": { "text/plain": [ "(torch.Size([645401, 10]), torch.Size([645401]))" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenized['train']['inputs'].shape, tokenized['train']['labels'].shape" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "id": "vDHUFgRZLXyQ" }, "outputs": [], "source": [ "train_loader = DataLoader(tokenized['train'], batch_size=batch_size, shuffle=True)\n", "test_loader = DataLoader(tokenized['test'], batch_size=batch_size, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ty-8deY9MKYc", "outputId": "ac08de19-5e46-438f-fbc6-489b10e7af90" }, "outputs": [ { "data": { "text/plain": [ "{'inputs': tensor([[ 2, 3, 3, ..., 3, 3, 85],\n", " [72, 87, 36, ..., 72, 81, 87],\n", " [68, 81, 3, ..., 3, 93, 76],\n", " ...,\n", " [ 3, 3, 3, ..., 33, 33, 3],\n", " [80, 51, 68, ..., 76, 82, 81],\n", " [ 5, 2, 3, ..., 82, 88, 81]], device='cuda:0'),\n", " 'labels': tensor([72, 86, 83, 88, 72, 3, 3, 3, 38, 82, 82, 3, 87, 73, 3, 81, 54, 3,\n", " 12, 92, 39, 72, 87, 79, 83, 16, 3, 3, 29, 76, 3, 70, 88, 80, 81, 12,\n", " 5, 72, 26, 86, 17, 85, 3, 2, 3, 70, 3, 85, 2, 72, 69, 17, 80, 68,\n", " 76, 11, 15, 3, 76, 87, 3, 68, 2, 3, 38, 70, 81, 51, 87, 16, 3, 71,\n", " 5, 87, 3, 3, 85, 78, 3, 72, 3, 72, 72, 87, 81, 66, 15, 3, 87, 76,\n", " 2, 3, 2, 80, 72, 89, 75, 87, 85, 17, 72, 49, 72, 85, 87, 3, 86, 76,\n", " 72, 3, 2, 85, 1, 3, 3, 3, 3, 3, 82, 3, 3, 72, 3, 68, 3, 3,\n", " 3, 11, 33, 3, 81, 38, 70, 3, 72, 66, 81, 67, 92, 3, 79, 86, 3, 64,\n", " 85, 3, 82, 16, 89, 73, 73, 82, 71, 29, 72, 87, 75, 68, 14, 71, 75, 72,\n", " 85, 72, 72, 72, 72, 72, 3, 86, 3, 85, 71, 3, 3, 3, 82, 15, 74, 79,\n", " 82, 17, 89, 62, 11, 76, 3, 3, 79, 66, 80, 2, 3, 32, 80, 3, 12, 11,\n", " 3, 3, 85, 73, 3, 76, 3, 38, 3, 3, 15, 91, 3, 72, 68, 3, 81, 11,\n", " 10, 32, 86, 3, 72, 87, 76, 3, 17, 3, 2, 3, 2, 72, 3, 87, 20, 84,\n", " 68, 39, 87, 2, 86, 72, 44, 12, 87, 85, 3, 17, 58, 3, 16, 92, 85, 3,\n", " 72, 88, 3, 3, 3, 36, 72, 72, 72, 3, 85, 3, 3, 86, 86, 3, 87, 3,\n", " 72, 83, 5, 68, 79, 82, 81, 3, 76, 82, 29, 3, 15, 12, 85, 20, 87, 87,\n", " 68, 72, 25, 80, 86, 73, 76, 70, 40, 85, 10, 3, 3, 73, 72, 2, 3, 12,\n", " 73, 3, 3, 2, 79, 83, 86, 94, 81, 72, 3, 86, 5, 3, 73, 3, 15, 59,\n", " 82, 12, 75, 73, 71, 3, 81, 3, 85, 1, 73, 3, 3, 71, 70, 3, 66, 76,\n", " 3, 68, 29, 86, 3, 3, 64, 74, 87, 76, 87, 72, 85, 76, 3, 3, 3, 68,\n", " 80, 19, 82, 3, 39, 72, 76, 19, 3, 33, 12, 3, 33, 73, 89, 3, 68, 3,\n", " 5, 11, 44, 83, 86, 87, 85, 17, 79, 71, 3, 3, 3, 11, 3, 3, 3, 76,\n", " 85, 80, 3, 3, 72, 3, 67, 3, 16, 3, 17, 2, 2, 87, 3, 3, 3, 87,\n", " 82, 87, 82, 3, 3, 3, 20, 2, 86, 88, 68, 86, 73, 82, 12, 85, 76, 3,\n", " 11, 76, 5, 72, 72, 3, 90, 66, 3, 3, 76, 83, 3, 2, 81, 75, 71, 83,\n", " 68, 6, 68, 3, 86, 53, 3, 72, 66, 33, 88, 79, 83, 72, 85, 75, 87, 55,\n", " 76, 3, 92, 3, 3, 29, 80, 3, 87, 72, 79, 3, 10, 5, 71, 85, 3, 85,\n", " 68, 3, 82, 3, 81, 76, 66, 3, 33, 16, 76, 3, 3, 64, 75, 2, 72, 71,\n", " 76, 3, 3, 3, 72, 71, 3, 86, 82, 3, 69, 72, 44, 3, 79, 82, 83, 3,\n", " 2, 71, 76, 3, 3, 69, 81, 3, 3, 76, 15, 3, 74, 2, 17, 87, 71, 67,\n", " 85, 68, 72, 3, 3, 93, 79, 79, 57, 56, 72, 82, 87, 83, 29, 5, 3, 3,\n", " 68, 3, 83, 51, 71, 72, 95, 82, 68, 3, 3, 17, 87, 3, 85, 68, 32, 82,\n", " 3, 88, 2, 82, 3, 87, 81, 68, 3, 38, 80, 76, 3, 33, 86, 3, 3, 92,\n", " 39, 3, 76, 76, 87, 88, 32, 88, 3, 72, 3, 3, 72, 3, 3, 3, 72, 82,\n", " 82, 3, 3, 83, 2, 62, 82, 82, 17, 74, 3, 87, 15, 3, 80, 2, 72, 87,\n", " 3, 72, 86, 3, 72, 17, 3, 92, 82, 3, 80, 91, 87, 88, 85, 3, 82, 33,\n", " 3, 2, 87, 76, 72, 2, 3, 85, 12, 76, 68, 81, 3, 3, 76, 3, 73, 82,\n", " 3, 3, 86, 3, 3, 85, 68, 73, 86, 3, 3, 3, 82, 3, 68, 87, 86, 72,\n", " 72, 53, 3, 72, 81, 23, 68, 86, 87, 86, 71, 20, 3, 3, 76, 3, 3, 39,\n", " 69, 72, 68, 3, 68, 68, 41, 85, 82, 85, 23, 23, 3, 29, 3, 51, 78, 3,\n", " 3, 29, 3, 70, 80, 3, 58, 3, 3, 87, 82, 88, 51, 72, 76, 3, 87, 86,\n", " 66, 3, 85, 32, 72, 72, 3, 3, 3, 82, 33, 49, 2, 82, 79, 53, 71, 80,\n", " 80, 86, 66, 72, 89, 10, 3, 3, 33, 70, 3, 3, 68, 85, 72, 29, 3, 68,\n", " 66, 5, 3, 3, 3, 72, 88, 75, 70, 82, 5, 15, 82, 16, 76, 3, 14, 86,\n", " 72, 87, 3, 3, 3, 17, 3, 56, 3, 3, 68, 3, 3, 12, 79, 81, 3, 73,\n", " 33, 3, 66, 76, 29, 5, 71, 3, 89, 2, 74, 3, 81, 3, 66, 68, 3, 11,\n", " 82, 3, 80, 82, 81, 3, 87, 83, 3, 92, 70, 3, 86, 87, 3, 92, 3, 12,\n", " 3, 3, 72, 3, 3, 3, 71, 17, 95, 85, 87, 3, 20, 69, 68, 82, 12, 83,\n", " 86, 72, 68, 41, 81, 79, 75, 3, 3, 24, 3, 72, 3, 80, 86, 85, 82, 70,\n", " 3, 85, 81, 87, 17, 3, 14, 87, 15, 3, 72, 20, 47, 86, 68, 3, 3, 3,\n", " 85, 41, 16, 87, 73, 20, 3, 68, 74, 3, 72, 83, 56, 11, 20, 80, 76, 64,\n", " 64, 3, 80, 77, 86, 3, 40, 3, 73, 3, 55, 10, 71, 11, 12, 72, 83, 85,\n", " 3, 81, 82, 3, 86, 66, 87, 3, 92, 3, 72, 79, 3, 72, 82, 38, 80, 3,\n", " 71, 72, 83, 3, 66, 87, 85, 79, 71, 3, 87, 87, 90, 75, 53, 5, 3, 76,\n", " 76, 29, 17, 75, 3, 33, 3, 75, 72, 80, 3, 3, 72, 72, 5, 48, 3, 3,\n", " 85, 3, 68, 89, 3, 8, 10, 85, 86, 71], device='cuda:0')}" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "next(iter(train_loader))" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "id": "aAaBn0NHNvzq" }, "outputs": [], "source": [ "class CharMLP(nn.Module):\n", "\n", " def __init__(self, vs):\n", " # vs 字典大小\n", " super().__init__()\n", " self.emb = nn.Embedding(vs, 30)\n", " self.hidden1 = nn.Linear(10 * 30, 200)\n", " self.hidden2 = nn.Linear(200, 100)\n", " self.lm = nn.Linear(100, vs)\n", "\n", " def forward(self, x):\n", " # x: (B, 10)\n", " B = x.shape[0]\n", " emb = self.emb(x) # (B, 10, 30)\n", " h = emb.view(B, -1) # (B, 300)\n", " h = F.relu(self.hidden1(h)) # (B, 200)\n", " h = F.relu(self.hidden2(h)) # (B, 100)\n", " out = self.lm(h) # (B, vs)\n", " return out" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "O8QEEixcN3-y", "outputId": "75d4b222-d519-41ed-ac6a-f0a50f11b867" }, "outputs": [ { "data": { "text/plain": [ "CharMLP(\n", " (emb): Embedding(99, 30)\n", " (hidden1): Linear(in_features=300, out_features=200, bias=True)\n", " (hidden2): Linear(in_features=200, out_features=100, bias=True)\n", " (lm): Linear(in_features=100, out_features=99, bias=True)\n", ")" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = CharMLP(len(tokenizer.char2ind)).to(device)\n", "model" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "eBwttuD0OFAp", "outputId": "744e8005-428b-438b-8a9e-8dd04e9c24a8" }, "outputs": [ { "data": { "text/plain": [ "{'train': 4.5956830978393555, 'test': 4.594418525695801}" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def estimate_loss(model):\n", " re = {}\n", " # 将模型切换至评估模式\n", " model.eval()\n", " re['train'] = _loss(model, train_loader)\n", " re['test'] = _loss(model, test_loader)\n", " # 将模型切换至训练模式\n", " model.train()\n", " return re\n", "\n", "@torch.no_grad()\n", "def _loss(model, data_loader):\n", " \"\"\"\n", " 计算模型在不同数据集下面的评估指标\n", " \"\"\"\n", " loss = []\n", " data_iter= iter(data_loader)\n", " # 随机使用多个批量数据来预估模型效果\n", " for k in range(eval_iters):\n", " data = next(data_iter, None)\n", " if data is None:\n", " data_iter = iter(data_loader)\n", " data = next(data_iter, None)\n", " inputs, labels = data['inputs'], data['labels']\n", " logits = model(inputs)\n", " loss.append(F.cross_entropy(logits, labels).item())\n", " return torch.tensor(loss).mean().item()\n", "\n", "estimate_loss(model)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "id": "VClmDyIBORwH" }, "outputs": [], "source": [ "def train_model(model, optimizer, epochs=10):\n", " # 记录模型在训练集上的模型损失\n", " lossi = []\n", " for epoch in range(epochs):\n", " for i, data in enumerate(train_loader, 0):\n", " inputs, labels = data['inputs'], data['labels']\n", " optimizer.zero_grad()\n", " logits = model(inputs)\n", " loss = F.cross_entropy(logits, labels)\n", " lossi.append(loss.item())\n", " loss.backward()\n", " optimizer.step()\n", " # 评估模型，并输出结果\n", " stats = estimate_loss(model)\n", " train_loss = f'train loss {stats[\"train\"]:.4f}'\n", " test_loss = f'test loss {stats[\"test\"]:.4f}'\n", " print(f'epoch {epoch:>2}: {train_loss}, {test_loss}')\n", " return lossi" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "id": "tjVqfYGYOZbI" }, "outputs": [], "source": [ "@torch.no_grad()\n", "def generate(model, context, tokenizer, max_new_tokens=300):\n", " # context: (1, 10)\n", " out = []\n", " model.eval()\n", " for _ in range(max_new_tokens):\n", " logits = model(context) # (1, 99)\n", " probs = F.softmax(logits, dim=-1) # (1, 99)\n", " # 随机生成文本\n", " ix = torch.multinomial(probs, num_samples=1) # (1, 1)\n", " # 更新背景\n", " context = torch.concat((context[:, 1:], ix), dim=-1)\n", " out.append(ix.item())\n", " if out[-1] == tokenizer.end_ind:\n", " break\n", " model.train()\n", " return out" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JqqwjlWKQFU6", "outputId": "f0b8c0be-1b72-41dc-c3a5-94cf48b4f16c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bcsLg]C@00yb<|b|>C?)M^)!iHST24GbhFOX%-Pi (^`^F:^7y\\ '`\"7o\n" ] } ], "source": [ "context = torch.zeros((1, 10), dtype=torch.long, device=device)\n", "print(''.join(tokenizer.decode(generate(model, context, tokenizer))))" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qMnzyCY8Qt0k", "outputId": "2ced7bd4-872f-4b02-ed7c-d854191ec368" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "epoch 0: train loss 1.3770, test loss 1.5173\n", "epoch 1: train loss 1.2562, test loss 1.4910\n", "epoch 2: train loss 1.2012, test loss 1.4271\n", "epoch 3: train loss 1.1598, test loss 1.4003\n", "epoch 4: train loss 1.1460, test loss 1.3597\n", "epoch 5: train loss 1.1409, test loss 1.3340\n", "epoch 6: train loss 1.0796, test loss 1.3965\n", "epoch 7: train loss 1.0876, test loss 1.3648\n", "epoch 8: train loss 1.0685, test loss 1.3592\n", "epoch 9: train loss 1.0921, test loss 1.3188\n" ] } ], "source": [ "l = train_model(model, optim.Adam(model.parameters(), lr=learning_rate))" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Pqy54pyIRD0D", "outputId": "d0376b01-92a2-4d26-c510-711d8d2e4604" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "def chars_s[numNote.Sprechosk(pirt franBy.\n", " name = minvalue; 6Labele(ible str.s))\n", " return checkpointWithRand.\n", " raise ValueError:\n", " `DataFrame().collect()\n", " elif type(frectis).) by wrapped whorhate\n", " TypeError(\"andhartini\n" ] } ], "source": [ "context = torch.zeros((1, 10), dtype=torch.long, device=device)\n", "print(''.join(tokenizer.decode(generate(model, context, tokenizer))))" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 448 }, "id": "fTEO7oANR1hd", "outputId": "ea08c382-8e8d-45f9-dc7d-7dc8f843df0e" }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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