{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cnPUjAfwXmnd", "outputId": "e0a0e84f-3807-4ebc-fa78-1bde638eec53" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting datasets\n", " Downloading datasets-2.20.0-py3-none-any.whl (547 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.15.3)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.25.2)\n", "Collecting pyarrow>=15.0.0 (from datasets)\n", " Downloading pyarrow-16.1.0-cp310-cp310-manylinux_2_28_x86_64.whl (40.8 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 MB\u001b[0m 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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", "Installing collected packages: xxhash, requests, pyarrow, dill, multiprocess, datasets\n", " Attempting uninstall: requests\n", " Found existing installation: requests 2.31.0\n", " Uninstalling requests-2.31.0:\n", " Successfully uninstalled requests-2.31.0\n", " Attempting uninstall: pyarrow\n", " Found existing installation: pyarrow 14.0.2\n", " Uninstalling pyarrow-14.0.2:\n", " Successfully uninstalled pyarrow-14.0.2\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n", "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n", "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed datasets-2.20.0 dill-0.3.8 multiprocess-0.70.16 pyarrow-16.1.0 requests-2.32.3 xxhash-3.4.1\n" ] } ], "source": [ "!pip install datasets" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "s1hBHkhqX-0g", "outputId": "484bf4cb-2d74-4b7b-b3b0-d035004e1e49" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "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", "%matplotlib inline\n", "\n", "\n", "torch.manual_seed(12046)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "oAqA1CyqYBPM" }, "outputs": [], "source": [ "# 一些超参数\n", "learning_rate = 1e-3\n", "eval_iters = 10\n", "batch_size=1000\n", "sequence_len=64\n", "# 如果有GPU，该脚本将使用GPU进行计算\n", "device = 'cuda' if torch.cuda.is_available() else 'cpu'" ] }, { "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": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 440, "referenced_widgets": [ "763c6c3c70434e3db3cb8eba7d778e4c", "5f1d94cc7a3248c5aa1f9e7806884592", "26156bc23edb48059ba5c47db7d81244", "fad4669b121d4c6a85943b2a55f1a63c", "a7bc499c89da4467a3698bc58a2eaa14", "51e943794a9049d4a313bfc144a5bac7", "64a93d7801544ebab6c74c2adb080120", "fff6d3f5d527440a89ea4f7e6b970d23", "81e076b032b445bdab859dccb39faa1e", "f875ba793f514610b768e36566ef6598", "fd063a800af1435f809e4cad819ba7c9", "3f999cacb3fd42629049389d543ae8eb", "1af88388926c489a91f07f1f43a4c157", "7d5c73d82a604399b6d340e6d18fc03a", "47c7ae943b0f4023beda1ebc6eba6efe", "462824e7c8d94341a8ac36d10999f0d2", "21287cdd429b4b07857c3aaf870826e4", "177a9ee6ed024865a252bd5307d55a1e", "95609875104941dbbfdc0c89ba8c11c8", "6bbbf400e1154613b1fbe4faf100bfb1", "5994f5cfca0b4ee3984af738842beafa", "7efbed08684e48f9822718b31dd75918", "a811a9c9cc344293a40291ce00702644", "7336565d2c274b6ca2ebf83c137c0868", "f1c110e6a2b349178867466b751315c1", "e1b5e740bd20451da1f87e78e81468e8", "fdd5c0832ab44467b2d1c895eceee96e", "c0e4a1c91202455d81de2bd2902c6705", "12cacc3b083b4729bf23774e71d71bb3", "a2ae0a5d150f49afab1434a1113b8e2f", "3dd84f123e1245368c1a62e6f7bb825e", "2f82532449b741f284094a14daab08e4", "9eec1987e959460aa38de2463a31c8f1", "e0a99d5832db4c1cb45aeab59c0ccf02", "f09282fb91ae45a0927893a49e408504", "80c96cdc027c43dcbf505972b798815b", "e774480f559142d98bd91493fe5e838e", "13e65d2aed8f48478a4927efb7add40f", "35b34305a5cf4dce9504290346b02d43", "60d6d2001c6c41939f56748ab4459fe4", "66ae3f959eb148db88b418f9df79a746", "09eb9cf74dab40f196f288730980642c", "68935d37deda4bcea256bc39bf1fc45b", "6dd5152e729545ea93c9058213f88600", "929e8ddf07f44ee28d9b8e74b2cc110a", "2a146cea98f04ab6b3c5321239e69ea3", "411d1462e62244a3a12f1d9ac29b93cf", "b054834c892047af983f2e65a9fb42b2", "992966583b7542bda4b8d87ab69e4a26", "5860af9bd85a401a9e98d46260dfe74b", "92977bc186e94bd8b4fdc562657d5ac9", "e697dd6ad39748578d25ae40ba9b80e0", "d492a99230af441092acec622f1c6b32", "67049b8d34434c62941264b65b1d088b", "c7365582aae94916825836c738b30113", "3e9ddad950184bad99f88182917b8acc", "d56c417f9da04bb98e3f7d7990db35fa", "6f0415226f35453fa394431e5514497f", "cdad66ea862b423a8ff674c993c4675a", "cfe743133b6b45f387ee6377e9689cd6", "d6ee0dc5d72e4682afa37bb33f836536", "3e256498407b429199fc1f926cf67fe8", "4dcb8cc050664877ab026e5e7a45efe2", "27d48009aef3447eaccca6429172e30a", "fe981ab1745b40bcadead6d45ef04f35", "4fc1bcc335e54c8f94df5f7b7319336e", "5367797f6e614e6aa116876a41053e30", "817c501ad7684cd08e2a11d288bfef7a", "cbd6c22782234ce8aa7db69cc7548dd1", "94f1445ebde04bffbb22c31ae99e0961", "3de3a26aaa244d6ab4512b1380ca7c1f", "f169f928d94f48c782da3a391c4afced", "ca3621fa6f60452087a59ab2fc11b54c", "3500d2c9d2f94535a2d1ab5861db6ef3", "0033b99f75ac4227968ff00369f0cc13", "8864caac2e4c441bb23b276e4bdd2fb6", "bd175ac8032f42b5b98b5d0f127e89a7" ] }, "id": "bRHAWAD0YDb8", "outputId": "0e6ff4b6-f568-4699-884f-68bbce598583" }, "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": "763c6c3c70434e3db3cb8eba7d778e4c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading builder script: 0%| | 0.00/8.44k [00:00\\n !\"#$%&\\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\\\]^_`abcdefghijklmnopqrstuvwxyz{|}~ö'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class CharTokenizer:\n", "\n", " def __init__(self, data, end_ind=0):\n", " # data: list[str]\n", " # 得到所有的字符\n", " chars = sorted(list(set(''.join(data))))\n", " self.char2ind = {s: i + 1 for i, s in enumerate(chars)}\n", " self.char2ind['<|e|>'] = end_ind\n", " self.ind2char = {v: k for k, v in self.char2ind.items()}\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]\n", "\n", "tokenizer = CharTokenizer(datasets['whole_func_string'])\n", "test_str = 'def f(x):'\n", "re = tokenizer.encode(test_str)\n", "print(re)\n", "''.join(tokenizer.decode(range(len(tokenizer.char2ind))))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "yfO043iHYJG_" }, "outputs": [], "source": [ "class RNN(nn.Module):\n", "\n", " def __init__(self, input_size, hidden_size):\n", " super().__init__()\n", " self.input_size = input_size\n", " self.hidden_size = hidden_size\n", " self.i2h = nn.Linear(input_size + hidden_size, hidden_size)\n", "\n", " def forward(self, input, hidden=None):\n", " # input: (B, T, C)\n", " # hidden: (B, H)\n", " # out: (B, T, H)\n", " B, T, C = input.shape\n", " re = []\n", " if hidden is None:\n", " hidden = self.init_hidden(B, input.device)\n", " for i in range(T):\n", " combined = torch.concat((input[:, i, :], hidden), dim=-1) # (B, C + H)\n", " hidden = F.relu(self.i2h(combined))\n", " re.append(hidden)\n", " return torch.stack(re, dim=1) # (B, T, H)\n", "\n", " def init_hidden(self, B, device):\n", " return torch.zeros((B, self.hidden_size), device=device)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6MmC_FPJeDx1", "outputId": "c1a1700e-3ee5-456e-8d69-eac041b74940" }, "outputs": [ { "data": { "text/plain": [ "(torch.Size([3, 2, 4]),\n", " tensor([[0., 0., 0., 0.],\n", " [0., 0., 0., 0.],\n", " [0., 0., 0., 0.]]),\n", " tensor([[1., 1., 1., 1.],\n", " [1., 1., 1., 1.],\n", " [1., 1., 1., 1.]]))" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# torch stack展示\n", "a = torch.zeros(3, 4)\n", "b = a + 1\n", "c = torch.stack([a, b], dim=1)\n", "c.shape, c[:, 0, :], c[:, 1, :]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UORIkrkweYWA", "outputId": "542f00a3-3418-41b2-ac37-bf76274a76ef" }, "outputs": [ { "data": { "text/plain": [ "torch.Size([5, 2, 4])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r = RNN(3, 4)\n", "x = torch.randn(5, 2, 3)\n", "r(x).shape" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "2mIRcI_CpJSu" }, "outputs": [], "source": [ "class CharRNNBatch(nn.Module):\n", "\n", " def __init__(self, vs):\n", " super().__init__()\n", " emb_size = 256\n", " hidden_size = 128\n", " self.emb = nn.Embedding(vs, emb_size)\n", " #RNN不需要截距项\n", " self.rnn1 = RNN(emb_size, hidden_size)\n", " self.ln1 = nn.LayerNorm(hidden_size)\n", " self.rnn2 = RNN(hidden_size, hidden_size)\n", " self.ln2 = nn.LayerNorm(hidden_size)\n", " self.lm = nn.Linear(hidden_size, vs)\n", " self.dp = nn.Dropout(0.4)\n", "\n", " def forward(self, x):\n", " # x: (B, T)\n", " # 暂不实现初始隐藏状态的输入\n", " B = x.shape[0]\n", " embeddings = self.emb(x) # (B, T, emb_size)\n", " h = F.relu(self.ln1(self.rnn1(embeddings))) # (B, T, hidden_size)\n", " h = self.dp(h)\n", " h = F.relu(self.ln2(self.rnn2(h))) # (B, T, hidden_size)\n", " h = self.dp(h)\n", " out = self.lm(h) # (B, T, vs)\n", " return out" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "tOp58j4OsBoi", "outputId": "a4c6b893-b32e-49f8-b46d-49433b805bbd" }, "outputs": [ { "data": { "text/plain": [ "CharRNNBatch(\n", " (emb): Embedding(98, 256)\n", " (rnn1): RNN(\n", " (i2h): Linear(in_features=384, out_features=128, bias=True)\n", " )\n", " (ln1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", " (rnn2): RNN(\n", " (i2h): Linear(in_features=256, out_features=128, bias=True)\n", " )\n", " (ln2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", " (lm): Linear(in_features=128, out_features=98, bias=True)\n", " (dp): Dropout(p=0.4, inplace=False)\n", ")" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c_model = CharRNNBatch(len(tokenizer.char2ind)).to(device)\n", "c_model" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "id": "twawErZ5sKCz" }, "outputs": [], "source": [ "@torch.no_grad()\n", "def generate(model, context, tokenizer, max_new_tokens=300):\n", " # context: (1, T)\n", " #out = []\n", " out = context.tolist()[0]\n", " model.eval()\n", " for _ in range(max_new_tokens):\n", " #可以考虑截断背景，使得文本生成更加贴近训练\n", " #logits = model(context[:, -sequence_len:])\n", " logits = model(context) # (1, T, 98)\n", " probs = F.softmax(logits[:, -1, :], dim=-1) # (1, 98)\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, 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": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "z8uWvfqZt63k", "outputId": "5f1da774-86a4-4d67-ac4c-e611434b7bea" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "def*Zc(F/o|(\"YP{BqE G|uw,3<1'Y$?s9NN[{gQ=CK|AM:iKcam|+Q3j^DA11zc<|e|>\n" ] } ], "source": [ "context = torch.tensor(tokenizer.encode('def'), device=device).unsqueeze(0)\n", "print(''.join(tokenizer.decode(generate(c_model, context, tokenizer))))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "_e3imLnHv7G7" }, "outputs": [], "source": [ "def process(data, tokenizer, sequence_len=sequence_len):\n", " text = data['whole_func_string']\n", " # text is list[str]\n", " inputs, labels = [], []\n", " for t in text:\n", " enc = tokenizer.encode(t)\n", " enc += [tokenizer.end_ind]\n", " # 有bug，无法处理长度过小的数据\n", " for i in range(len(enc) - sequence_len):\n", " inputs.append(enc[i: i + sequence_len])\n", " labels.append(enc[i + 1: i + 1 + sequence_len])\n", " return {'inputs': inputs, 'labels': labels}" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 99, "referenced_widgets": [ "0e54a25101234aae9f96750046582beb", "684db63e53b848f6adf2f54b41b53dae", "d7ce8da648234b749ab35cf59bf1a46e", "e0a07f14786c4a68a75804b4eed9b6b8", "d85034f5d9e243e9a191d6554fefe2d4", "e24e1c1c0aab4b758b094ef5a0b7ec2a", "53e7e2ff540f4ab48ac41050b5f8713d", "53e50fab5b944fd1b1038bb2122685ea", "ecc51ffe5a984b6cb144525d7a374439", "757dfe6c9e1040a79a280cabd5d4e944", "b31ac42f997f49ed8d02447182291f1c", "99ca7bf2efbe447cac8ac3ee29dbf267", "7ad77433fd17489484fa8c9a977eb4b7", "0037add104e442ffa9ef6065f4f3f0c9", "41fc02b8921843518ddcc2e3cd40c234", "692d40e8c527410db0fc80ef41bb301c", "b29046c0ecd04940a9a105667627e2ab", "eeb63ceae9d3419f9366b2c631d1c25f", "9d65ffd1aeb7449598ee86b70073db9c", "e66a25d36d6f406b906f1439b85e8b93", "080b3562c8fe467ea48c95a322e88119", "8cb8e33872e144438cc834a3965418e0" ] }, "id": "6Vj4ax31wDA_", "outputId": "29bb74f6-7146-48a6-85ba-108fb087e0cc" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0e54a25101234aae9f96750046582beb", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map: 0%| | 0/617 [00:002}: {train_loss}, {test_loss}')\n", " return lossi" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EjFyrukgyPGi", "outputId": "07cf430f-61d4-479a-9a59-2b54fd155b07" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "epoch 0: train loss 1.4141, test loss 1.5309\n", "epoch 1: train loss 1.3120, test loss 1.4374\n", "epoch 2: train loss 1.2716, test loss 1.4007\n", "epoch 3: train loss 1.2389, test loss 1.3773\n", "epoch 4: train loss 1.2296, test loss 1.3687\n", "epoch 5: train loss 1.2187, test loss 1.3587\n", "epoch 6: train loss 1.2018, test loss 1.3493\n", "epoch 7: train loss 1.2042, test loss 1.3432\n", "epoch 8: train loss 1.1999, test loss 1.3372\n", "epoch 9: train loss 1.1887, test loss 1.3328\n" ] } ], "source": [ "l = train_model(c_model, optim.Adam(c_model.parameters(), lr=learning_rate))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1SnPw6m8yXAy", "outputId": "88293afc-f9b2-4a32-c71f-ea0bb6960618" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "def type(path.Stream() impartition are west, can multiples the xage if posits.\n", " a les that is the pochex, wheres ubrald exelicates wren the windows.\n", " Make the name is sp in i.nulp(classition.dict(sock)\n", "\n", " [Row()\n", " we(omplace,\n", " \n" ] } ], "source": [ "context = torch.tensor(tokenizer.encode('def'), device=device).unsqueeze(0)\n", "print(''.join(tokenizer.decode(generate(c_model, context, tokenizer))))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 448 }, "id": "zAxneB6N4cl4", "outputId": "68f428a7-c9ae-478f-f2c0-db766137b094" }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { 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\n", 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