{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GiU9qqZmdAkS", "outputId": "5b4f2d5a-b0c4-44fa-e70f-770c2db164d5" }, "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": "-vGCbZ3vdIKa", "outputId": "51a5a88b-6a3a-47f6-de37-c9a61b6c5c39" }, "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 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": "Fr4We25udIoz" }, "outputs": [], "source": [ "# 一些超参数\n", "learning_rate = 1e-3\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": [ "de50aa861bf64e8d8c6cb240953f5e47", "24eeddce1eaa46cdbf0901e9a1a68e18", "1ca0f5ce4a8a4db8803943a7942f394b", "532d4a3aae9146b4b08a459a08f8979f", "e813b375874c4eedbdf8a594e94a5952", "1afe92c64c6d4794a702e963206b85c0", "f00070ca187c471c8b2a795cb3d7addf", "1e01f84ac03543b39421e5866d306435", "49b16837b14c437ab01c675585ee1dba", "a70497ae538f4489bc255b08bc46cd76", "a9297ae61f744537ae13498629dd05e6", "95d829c925e44b7ab08157e3f824b64e", "dea24755106c423283709e7732ed22a8", "d3a3d51c8f65498ba70ebdd66be0e327", "0caf1508c59441a894fde393fa096628", "a4fe9299086a4dd98d9f6ce0dd65cb87", "0850ac0fee554affbea8871f56b01ff3", "3a14fae0130e460c957246789aad5c86", "22a970603d0f4f1abd8b7eda547aa219", "efb80e4518bb4a4783b116b7404a80e3", "64f934f5e3214a6ba67fd6ea57dfeb08", "4ee8fcb4dd6449468695d0f24524ed1c", "f7b2b0814c004a3691ee3fd79b78d2db", "ba4ddd87a38343cd99e8544492c59d10", "b0b18f262be74bc68e69425060479c54", "6b177ccda1794ccebde347b285f609ff", "290268493b8e46af8ebbddcbf33d92a1", "29f0c7766ed240c588dd182a6150cf23", "d7090dadd97d4f5d8d76547cbd2a5e47", "ba06321c5c514c4eb770a463c73f7224", "098b82d3b5154757944134e6d1fc9046", "d3a18d9d19454c3ebc22b2a9ab182610", "b2cc07a13dc84e3f86723a02d07a3b38", "2e000e35aa0e4a519f67216db5e6266d", "b43e016a14a64142a947e624794b03cc", "8b972418975a4bda9e2b4166fcf7ba31", "01b1f71aa9d644ef9d4a703542b587e4", "b78cb671216242ba881495744116c16d", "40e8c474f1064c979141c29c06b09714", "e834db048b6b42a4b33ee41e674caa0f", "860ef2206449424592dbd88c64dd60a8", "f40b4063e15a4e0ea9b5adfe344647f4", "1dae1dd3c659426ebb40d2858ec91180", "4b1994b0ff584c77a19c887cb46e6b89", "72947acabff045bd8016da8a02b2727a", "84540c691d684992918766cbf04e2099", "46504738731245d7b9672282b4ebd1a2", "f405bb5cbb9e4eeb875c8dc143635a04", "069c0b77d9b24f609b447b96583dfb20", "3bb1b4feb3e64d79b87d1219195233aa", "c883601e5014444ea8a0d505cfa8be77", "22d0a65ed4e647f3ace03fd93d444a2f", "7d94ff2b7cc4497388f0dfdceedfa7a4", "523bfe9f38e04dfc84ed1800c902fcec", "f4947d0be5e248a0ba055809eb287722", "48d6ec9ef5f7421598a53cfc154a9455", "331911d923ff47468a2e0fc340b07e05", "37927f7433114528963e9153d6f5ccdb", "0dc808eba6c641ee93837336214dba4b", "3ed02ca37f6040f2bffc74ce9625b32b", "8f2944f9c4b843fdb37413cffda9bd34", "70a7d11010a4417ba28bcc8eb5b0509e", "82e5ebb7ab434bbeac893548b52b7467", "9061058b1bbc447a93851b2b4dd67b55", "2a274f128b67492da26905bbcafa4100", "94c332df20644de9a08a7ceefcb0d0bb", "32877c0f7d804099a7ecb5667fc5baba", "054ad6dde0924de9ae7f193781eafeaf", "b97b7055f0854885a710f92fd8b877f7", "33f9102216ea4610aa0707bbd1751b0d", "e1507ff6e3414cf882bdbb2813a96d66", "37a5abeb6cca4071a3ea5f6458d28ef1", "780085184f2142c3b9f8cb557d04f2e7", "352f0b3efa22448bbcf48709ce9b6c30", "2aafb1fe8d9f4215946d3af4ef1b3420", "1dab88c8d0034260aae94d89df2d420e", "bd687c1eab92498a8e2b5c7135e5f5d5" ] }, "id": "JFDfX7hZdK3t", "outputId": "c20d15d3-bb4c-498f-e3a5-dc4f55331069" }, "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": "de50aa861bf64e8d8c6cb240953f5e47", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading builder script: 0%| | 0.00/8.44k [00:00)\n", "tensor([[0.0000, 0.0000, 0.4002]], grad_fn=)\n", "tensor([[0.0000, 0.3787, 0.0000]], grad_fn=)\n", "tensor([[0.0218, 0.1896, 0.0000]], grad_fn=)\n" ] } ], "source": [ "r_model = RNNCell(2, 3)\n", "data = torch.randn(4, 1, 2)\n", "hidden = None\n", "\n", "for i in range(data.shape[0]):\n", " hidden = r_model(data[i], hidden)\n", " print(hidden)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "kIDE8AYWqCTE" }, "outputs": [], "source": [ "class CharRNN(nn.Module):\n", "\n", " def __init__(self, vs):\n", " super().__init__()\n", " self.emb = nn.Embedding(vs, 30)\n", " self.rnn = RNNCell(30, 50)\n", " self.lm = nn.Linear(50, vs)\n", "\n", " def forward(self, x, hidden=None):\n", " # x: (1)\n", " # hidden: (1, 50)\n", " embeddings = self.emb(x) # (1, 30)\n", " hidden = self.rnn(embeddings, hidden) # (1, 50)\n", " out = self.lm(hidden) # (1, vs)\n", " return out, hidden" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "id": "VATK6ihnxPHd" }, "outputs": [], "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 + 2 for i, s in enumerate(chars)}\n", " self.char2ind = {s: i + 1 for i, s in enumerate(chars)}\n", " #self.char2ind['<|b|>'] = 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": 30, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 71 }, "id": "FvCEBwPTyAYI", "outputId": "c27f2980-e31f-48fb-99cd-c6cc13d08f39" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[70, 71, 72, 2, 72, 10, 90, 11, 28]\n" ] }, { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'<|e|>\\n !\"#$%&\\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\\\]^_`abcdefghijklmnopqrstuvwxyz{|}~ö'" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "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": 31, "metadata": { "id": "-XXjAeZCyVZW" }, "outputs": [], "source": [ "c_model = CharRNN(len(tokenizer.char2ind)).to(device)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "50HIYjKWyeZc", "outputId": "d63e0e57-c1e4-480f-bc31-9aee5f7c9209" }, "outputs": [ { "data": { "text/plain": [ "CharRNN(\n", " (emb): Embedding(98, 30)\n", " (rnn): RNNCell(\n", " (i2h): Linear(in_features=80, out_features=50, bias=True)\n", " )\n", " (lm): Linear(in_features=50, out_features=98, bias=True)\n", ")" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c_model" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "K9HfwXv0yft2", "outputId": "1185e0f6-e684-4129-e1dd-30d003d0e5ae" }, "outputs": [ { "data": { "text/plain": [ "(torch.Size([1, 98]), torch.Size([1, 50]))" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inputs = torch.tensor(tokenizer.encode('d'), device=device)\n", "out, hidden = c_model(inputs)\n", "out.shape, hidden.shape" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "id": "qpdDtltly2di" }, "outputs": [], "source": [ "@torch.no_grad()\n", "def generate(model, idx, tokenizer, max_new_tokens=300):\n", " # idx: (1)\n", " out = idx.tolist()\n", " hidden = None\n", " model.eval()\n", " for _ in range(max_new_tokens):\n", " logits, hidden = model(idx, hidden)\n", " probs = F.softmax(logits, 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[:, 1:], ix), dim=-1)\n", " out.append(ix.item())\n", " idx = ix.squeeze(0)\n", " if out[-1] == tokenizer.end_ind:\n", " break\n", " model.train()\n", " return out" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "S5EbxIcPzfl9", "outputId": "6044e7b4-e2aa-4c85-cb8f-95ff8a0ec02b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dpZ\\[DTWFpe\n", "\"omF^3drBZ708qbyUl+bC71q^&GG%04$bm{og$OT+:A_ST49/EpBQro!&oJnJn9iU#53^\n" ] } ], "source": [ "inputs = torch.tensor(tokenizer.encode('d'), device=device)\n", "print(''.join(tokenizer.decode(generate(c_model, inputs, tokenizer))))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "id": "r7d2DgXazqhZ" }, "outputs": [], "source": [ "def process(text, tokenizer):\n", " # text: str\n", " enc = tokenizer.encode(text)\n", " inputs = enc\n", " labels = enc[1:] + [tokenizer.end_ind]\n", " return torch.tensor(inputs, device=device), torch.tensor(labels, device=device)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dZgZBZ970i1z", "outputId": "e36427d3-88e2-4ead-abfe-c28d8aa81269" }, "outputs": [ { "data": { "text/plain": [ "(tensor([70, 71, 72, 2, 72, 10, 90, 11, 28], device='cuda:0'),\n", " tensor([71, 72, 2, 72, 10, 90, 11, 28, 0], device='cuda:0'))" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "process(test_str, tokenizer)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "id": "2jp6RCrA2kuP" }, "outputs": [], "source": [ "lossi = []\n", "epochs = 1\n", "optimizer = optim.Adam(c_model.parameters(), lr=learning_rate)\n", "\n", "for e in range(epochs):\n", " for data in datasets:\n", " inputs, labels = process(data['whole_func_string'], tokenizer)\n", " hidden = None\n", " _loss = 0.0\n", " lens = len(inputs)\n", " for i in range(lens):\n", " logits, hidden = c_model(inputs[i].unsqueeze(0), hidden)\n", " _loss += F.cross_entropy(logits, labels[i].unsqueeze(0)) / lens\n", " lossi.append(_loss.item())\n", " optimizer.zero_grad()\n", " _loss.backward()\n", " optimizer.step()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "INFoy7iy2-li", "outputId": "7a8f34a1-5aab-448b-dc55-84cc5bb3cb18" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dofUf_an(th \"\"\"\n", " sedCon)<|e|>\n" ] } ], "source": [ "inputs = torch.tensor(tokenizer.encode('d'), device=device)\n", "print(''.join(tokenizer.decode(generate(c_model, inputs, tokenizer))))" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 448 }, "id": "86Zsoo1p5JiJ", "outputId": "5c27675a-0d9a-465d-dc79-faac7ff2d886" }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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