From 3be6637c4fd182d6d68e757b73b72b6ba5b0ff0d Mon Sep 17 00:00:00 2001 From: tt5416 <1776822920@qq.com> Date: Tue, 30 Dec 2025 18:29:17 +0800 Subject: [PATCH 1/5] Add new_t5 file to dev --- llm/README.md | 4 +- ...arization_ms270_mindnlp051_formatted.ipynb | 405 ++++++++++++++++++ llm/new_t5/image.png | Bin 0 -> 161665 bytes 3 files changed, 407 insertions(+), 2 deletions(-) create mode 100644 llm/new_t5/finetune_t5_daily_email_summarization_ms270_mindnlp051_formatted.ipynb create mode 100644 llm/new_t5/image.png diff --git a/llm/README.md b/llm/README.md index 679a681..fcda5c8 100644 --- a/llm/README.md +++ b/llm/README.md @@ -18,8 +18,8 @@ Addtional community-contributed or legacy notebooks are stored under the [legacy If a notebook does not specify environment requirements, assume it runs with: -- mindspore == 2.3.1 -- mindnlp == 0.4.1 +- mindspore == 2.7.0 +- mindnlp == 0.5.1 > If you need any of these applications updated, please open an [Issue](https://github.com/mindspore-courses/applications/issues) with the required MindSpore and MindSpore NLP version. > Developers are also welcome to update these notebooks to the latest version by submitting a [Pull Request](https://github.com/mindspore-courses/applications/pulls). diff --git a/llm/new_t5/finetune_t5_daily_email_summarization_ms270_mindnlp051_formatted.ipynb b/llm/new_t5/finetune_t5_daily_email_summarization_ms270_mindnlp051_formatted.ipynb new file mode 100644 index 0000000..ccf14d3 --- /dev/null +++ b/llm/new_t5/finetune_t5_daily_email_summarization_ms270_mindnlp051_formatted.ipynb @@ -0,0 +1,405 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 基于 MindSpore 2.7.0 与 MindNLP 0.5.1 的 T5 文本摘要微调与推理\n", + "\n", + "本 Notebook 演示如何在 **MindSpore 2.7.0** 与 **MindNLP 0.5.1** 环境下，对 `t5-base` 进行摘要任务微调，并完成推理与保存。\n", + "\n", + "- **任务**：基于 `abisee/cnn_dailymail`（`3.0.0`）数据集进行文本摘要微调（可迁移到“日常邮件摘要”场景）。\n", + "- **目标**：在不改动训练逻辑的前提下，跑通「数据 → 分词 → 数据管线 → 训练 → 推理 → 保存」全流程。\n", + "- **输出**：将微调后的模型与分词器保存至 `./t5_email_summarization_ms270_mn051` 目录，便于后续加载推理或继续训练。\n", + "\n", + "本示例以快速验证为目标：训练仅运行 **1 个 epoch** 且最多 **200 step**，并每 20 step 打印一次 loss。\n" + ] + }, + { + "cell_type": "markdown", + "id": "e0b0bbb5", + "metadata": {}, + "source": [ + "## 环境准备\n", + "\n", + "安装本示例依赖的 MindNLP 版本。请确保当前 Python 环境中已安装 **MindSpore 2.7.0**（或与之兼容的版本）。\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b82fdb78", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install mindnlp==0.5.1" + ] + }, + { + "cell_type": "markdown", + "id": "e767cf15", + "metadata": {}, + "source": [ + "## 版本检查\n", + "\n", + "打印当前运行环境中的 MindSpore 与 MindNLP 版本号，便于定位依赖不一致导致的兼容性问题。\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f6b94df", + "metadata": {}, + "outputs": [], + "source": [ + "import mindspore as ms\n", + "import mindnlp\n", + "print('MindSpore:', ms.__version__)\n", + "print('MindNLP:', getattr(mindnlp, '__version__', 'unknown'))\n" + ] + }, + { + "cell_type": "markdown", + "id": "5dc9ae3a", + "metadata": {}, + "source": [ + "## 版本显示差异说明（MindNLP 0.5.1 安装后显示为 0.5.0rc2）\n", + "\n", + "![alt text](image.png)\n", + "\n", + "在部分环境中，`pip install mindnlp==0.5.1` 后 `mindnlp.__version__` 可能显示为 `0.5.0rc2`。若出现该情况，建议优先以 `pip show mindnlp` 与已安装 wheel 信息为准，并结合 `import mindnlp; mindnlp.__file__` 核对实际导入路径，确认是否存在旧版本残留或多环境混用。\n" + ] + }, + { + "cell_type": "markdown", + "id": "c44222c4", + "metadata": {}, + "source": [ + "## 数据集加载\n", + "\n", + "使用 `mindnlp.dataset.load_dataset` 加载经典摘要数据集 **CNN/DailyMail**（版本 `3.0.0`），并获取 `train/validation/test` 三个子集。运行后打印各子集样本数量，用于确认下载与加载是否正常。\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80436680", + "metadata": {}, + "outputs": [], + "source": [ + "from mindnlp.dataset import load_dataset\n", + "ds = load_dataset('abisee/cnn_dailymail', '3.0.0', split=['train', 'validation', 'test'])\n", + "train = ds['train']\n", + "val = ds['validation']\n", + "test = ds['test']\n", + "print('train:', len(train), 'validation:', len(val), 'test:', len(test))" + ] + }, + { + "cell_type": "markdown", + "id": "e0357779", + "metadata": {}, + "source": [ + "## 分词器与编码设置\n", + "\n", + "加载 `t5-base` 的预训练分词器，并设置摘要任务常用的编码参数：\n", + "\n", + "- `prefix = \"summarize: \"`：T5 摘要任务前缀（prompt）。\n", + "- `max_input_length`：输入文本最大长度（示例为 512）。\n", + "- `max_target_length`：目标摘要最大长度（示例为 64）。\n", + "- `pad_token_id`：用于 padding 的 token ID（后续需要在 label 中将 pad 位置替换为 `-100` 以忽略损失）。\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8d31c8f", + "metadata": {}, + "outputs": [], + "source": [ + "from mindnlp.transformers import AutoTokenizer\n", + "tokenizer = AutoTokenizer.from_pretrained('t5-base')\n", + "prefix = 'summarize: '\n", + "max_input_length = 512\n", + "max_target_length = 64\n", + "pad_id = tokenizer.pad_token_id\n", + "print('pad_token_id:', pad_id)" + ] + }, + { + "cell_type": "markdown", + "id": "c14ad6ae", + "metadata": {}, + "source": [ + "## 生成式数据集\n", + "\n", + "定义自定义数据集类 `SummarizationDataset`，将原始样本转为模型可训练的三元组：\n", + "\n", + "- `input_ids`：编码后的输入 token 序列\n", + "- `attention_mask`：注意力 mask\n", + "- `labels`：编码后的摘要 token 序列（将 `pad_token_id` 替换为 `-100`，使损失函数忽略 padding 部分）\n", + "\n", + "该步骤的核心是将“文本样本”转换为“可直接送入 T5 的张量输入”。\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "516ef09d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "class SummarizationDataset:\n", + " def __init__(self, data):\n", + " self.data = data\n", + " def __getitem__(self, idx):\n", + " art, summ, _ = self.data[idx]\n", + " enc = tokenizer(prefix + art, max_length=max_input_length, padding='max_length', truncation=True)\n", + " dec = tokenizer(summ, max_length=max_target_length, padding='max_length', truncation=True)\n", + " input_ids = np.array(enc.input_ids, dtype=np.int32)\n", + " attention_mask = np.array(enc.attention_mask, dtype=np.int32)\n", + " labels = np.array(dec.input_ids, dtype=np.int32)\n", + " labels = np.where(labels == pad_id, -100, labels).astype(np.int32)\n", + " return input_ids, attention_mask, labels\n", + " def __len__(self):\n", + " return len(self.data)" + ] + }, + { + "cell_type": "markdown", + "id": "a6073a14", + "metadata": {}, + "source": [ + "## 构建 MindSpore 数据管线\n", + "\n", + "将 `SummarizationDataset` 封装为 MindSpore `GeneratorDataset`，并完成：\n", + "\n", + "- 类型转换：统一 cast 为 `int32`\n", + "- 批处理：设置 `batch_size=8`\n", + "- 形状对齐：`drop_remainder=True`，保证 batch 维度固定，便于训练稳定运行\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "630af609", + "metadata": {}, + "outputs": [], + "source": [ + "import mindspore.dataset as ds\n", + "from mindspore.dataset import transforms\n", + "from mindspore import dtype as mstype\n", + "train_ds = ds.GeneratorDataset(SummarizationDataset(train), column_names=['input_ids','attention_mask','labels'], shuffle=True)\n", + "val_ds = ds.GeneratorDataset(SummarizationDataset(val), column_names=['input_ids','attention_mask','labels'], shuffle=False)\n", + "for c in ['input_ids','attention_mask','labels']:\n", + " train_ds = train_ds.map(operations=transforms.TypeCast(mstype.int32), input_columns=c)\n", + " val_ds = val_ds.map(operations=transforms.TypeCast(mstype.int32), input_columns=c)\n", + "batch_size = 8\n", + "train_ds = train_ds.batch(batch_size, drop_remainder=True)\n", + "val_ds = val_ds.batch(batch_size, drop_remainder=True)\n", + "print('train batches:', train_ds.get_dataset_size(), 'val batches:', val_ds.get_dataset_size())" + ] + }, + { + "cell_type": "markdown", + "id": "a0a95e03", + "metadata": {}, + "source": [ + "## 构建模型与训练函数\n", + "\n", + "本节完成微调核心逻辑的搭建，包括：\n", + "\n", + "- 加载 `T5ForConditionalGeneration`（`t5-base`）\n", + "- 构建优化器与梯度裁剪策略\n", + "- 处理 `mindtorch.autograd.profiler` 的兼容性引用（用于避免部分环境缺失导致的报错）\n", + "- 定义 `train_step`：封装单步训练流程（前向 → loss → 反向 → 裁剪 → 更新）\n", + "\n", + "说明：该 Notebook 保持“代码逻辑不变”，仅整理文字结构以提升可读性。\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7caa5499", + "metadata": {}, + "outputs": [], + "source": [ + "import contextlib\n", + "import mindtorch.autograd as autograd\n", + "import mindtorch as mt\n", + "from mindnlp.transformers import T5ForConditionalGeneration\n", + "\n", + "try:\n", + " import mindtorch.autograd.profiler as _prof\n", + " autograd.profiler = _prof\n", + "except Exception:\n", + " class _DummyProfiler:\n", + " @staticmethod\n", + " @contextlib.contextmanager\n", + " def record_function(name):\n", + " yield\n", + " autograd.profiler = _DummyProfiler()\n", + "\n", + "\n", + "model = T5ForConditionalGeneration.from_pretrained(\"t5-base\")\n", + "\n", + "model.train()\n", + "\n", + "\n", + "lr = 1e-4\n", + "weight_decay = 0.01\n", + "optimizer = mt.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n", + "\n", + "\n", + "clip_norm = 1.0\n", + "\n", + "def to_long(x):\n", + " if hasattr(x, \"asnumpy\"):\n", + " x = x.asnumpy()\n", + " return mt.tensor(x, dtype=mt.long)\n", + "\n", + "def train_step(input_ids, attention_mask, labels):\n", + " input_ids = to_long(input_ids)\n", + " attention_mask = to_long(attention_mask)\n", + " labels = to_long(labels)\n", + "\n", + " optimizer.zero_grad()\n", + " outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", + " loss = outputs.loss\n", + "\n", + " loss.backward()\n", + " mt.nn.utils.clip_grad_norm_(model.parameters(), clip_norm)\n", + " optimizer.step()\n", + "\n", + " return float(loss.item())\n" + ] + }, + { + "cell_type": "markdown", + "id": "0e114bf6", + "metadata": {}, + "source": [ + "## 训练\n", + "\n", + "启动训练循环：\n", + "\n", + "- `num_epochs = 1`：仅训练 1 个 epoch，用于快速验证流程\n", + "- `warm_steps = 200`：每个 epoch 最多跑 200 个 step\n", + "- 日志：每 20 step 打印一次 loss，便于观察收敛趋势\n", + "\n", + "如果需要正式训练，可按实际数据规模与资源条件调整 epoch、step、batch size 与学习率等超参数。\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1ce5478", + "metadata": {}, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "\n", + "num_epochs = 1\n", + "warm_steps = 200\n", + "\n", + "for epoch in range(num_epochs):\n", + " step = 0\n", + " with tqdm(total=warm_steps, desc=f\"Epoch {epoch+1}/{num_epochs}\", unit=\"step\") as pbar:\n", + " for input_ids, attention_mask, labels in train_ds.create_tuple_iterator():\n", + " loss = train_step(input_ids, attention_mask, labels) \n", + " step += 1\n", + "\n", + " if step % 20 == 0:\n", + " tqdm.write(f\"step={step}, loss={loss:.4f}\")\n", + "\n", + " pbar.update(1)\n", + " if step >= warm_steps:\n", + " break\n" + ] + }, + { + "cell_type": "markdown", + "id": "d5306f44", + "metadata": {}, + "source": [ + "## 推理\n", + "\n", + "构造一段较长输入文本，按 T5 摘要任务要求加上前缀 `summarize: `，并调用 `model.generate` 生成摘要序列。最后通过分词器 `decode` 将生成的 token ID 还原为可读文本，用于快速验证微调后的生成效果。\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32322d1d", + "metadata": {}, + "outputs": [], + "source": [ + "text = \"\"\"Artificial Intelligence (AI) has rapidly evolved and become an integral part of various industries, \n", + "particularly healthcare. AI's ability to analyze large volumes of data and identify patterns that humans might miss \n", + "has revolutionized many aspects of medical care, \n", + "from diagnostics to treatment planning and patient monitoring.\n", + "One of the most significant contributions of AI in healthcare is in the field of diagnostics. \n", + "Machine learning algorithms, a subset of AI, have been trained to interpret medical imaging data, such as X-rays, MRIs, and CT scans.\n", + "These algorithms can often detect abnormalities, such as tumors or fractures, with higher accuracy and speed than human radiologists. \n", + "This not only improves the speed of diagnosis but also reduces the potential for human error, leading to better patient outcomes.\n", + "AI has also shown promise in personalized medicine, where treatments are tailored to an individual's genetic makeup and health history.\n", + "By analyzing patient data, including genetic information, \n", + "AI systems can recommend specific treatment plans that are more likely to be effective. \n", + "This can greatly enhance the precision of treatments, reduce side effects, and improve overall patient care..\"\"\"\n", + "enc = tokenizer(prefix + text, max_length=max_input_length, padding='max_length', truncation=True)\n", + "inp = ms.Tensor([enc.input_ids], dtype=ms.int32)\n", + "gen_ids = model.generate(inp, max_length=60,top_k=0,temperature=0.7)\n", + "summ = tokenizer.decode(gen_ids[0], skip_special_tokens=True)\n", + "print(summ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 保存模型与分词器\n", + "\n", + "将微调后的模型权重与分词器配置保存到本地目录：\n", + "\n", + "- 保存路径：`./t5_email_summarization_ms270_mn051`\n", + "- 保存内容：模型参数（权重）与 tokenizer 配置/词表\n", + "\n", + "保存后可在后续脚本中通过 `from_pretrained(save_dir)` 直接加载，便于部署推理或继续训练。\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "save_dir = './t5_email_summarization_ms270_mn051'\n", + "model.save_pretrained(save_dir)\n", + "tokenizer.save_pretrained(save_dir)\n", + "print('saved to', save_dir)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mindspore", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/llm/new_t5/image.png b/llm/new_t5/image.png new file mode 100644 index 0000000000000000000000000000000000000000..7567788d1cfd4324f66b85fe81b14d21520ecc87 GIT binary patch literal 161665 zcmcG$V|1Nc)Ha-?X=5jix?{6xgT}UP+qTsCy9i?|A>bW9*T! 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a/llm/new_t5/finetune_t5_daily_email_summarization_ms270_mindnlp051_formatted.ipynb b/llm/new_t5/new_finetune_t5_daily_email_summarization.ipynb similarity index 100% rename from llm/new_t5/finetune_t5_daily_email_summarization_ms270_mindnlp051_formatted.ipynb rename to llm/new_t5/new_finetune_t5_daily_email_summarization.ipynb From 935253219ff3aee7c9eaec21069d4751d8eb28ba Mon Sep 17 00:00:00 2001 From: tt5416 <1776822920@qq.com> Date: Wed, 31 Dec 2025 01:25:54 +0800 Subject: [PATCH 3/5] Update .ipynb to dev --- ...inetune_t5_daily_email_summarization.ipynb | 6971 +---------------- llm/t5/image.png | Bin 0 -> 161665 bytes 2 files changed, 232 insertions(+), 6739 deletions(-) create mode 100644 llm/t5/image.png diff --git a/llm/t5/finetune_t5_daily_email_summarization.ipynb b/llm/t5/finetune_t5_daily_email_summarization.ipynb index 02ee9e2..183ee49 100644 --- a/llm/t5/finetune_t5_daily_email_summarization.ipynb +++ b/llm/t5/finetune_t5_daily_email_summarization.ipynb @@ -2,877 +2,339 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "view-in-github" - }, + "id": "0", + "metadata": {}, "source": [ - "\"Open" + "# 基于MindSpore NLP的T5文本摘要微调与推理\n", + "\n", + "本 Notebook 演示如何在 **MindSpore 2.7.0** 与 **MindSpore NLP 0.5.1** 环境下，对 `t5-base` 进行摘要任务微调，并完成推理与保存。\n", + "\n", + "- **任务**：基于 `abisee/cnn_dailymail`（`3.0.0`）数据集进行文本摘要微调（可迁移到“日常邮件摘要”场景）。\n", + "- **目标**：在不改动训练逻辑的前提下，跑通「数据 → 分词 → 数据管线 → 训练 → 推理 → 保存」全流程。\n", + "- **输出**：将微调后的模型与分词器保存至 `./t5_email_summarization_ms270_mn051` 目录，便于后续加载推理或继续训练。\n", + "\n", + "本示例以快速验证为目标：训练仅运行 **1 个 epoch** 且最多 **200 step**，并每 20 step 打印一次 loss。\n" ] }, { "cell_type": "markdown", + "id": "1", "metadata": {}, "source": [ - "# mindspore DEMO\n", + "## 环境准备\n", "\n", - "using mindspore and mindnlp to develop a task fine-tuning T5" + "安装本示例依赖的 MindSpore NLP 版本。请确保当前 Python 环境中已安装 **MindSpore 2.7.0**（或与之兼容的版本）。\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, + "id": "2", 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http://192.168.0.122:8888/repository/pypi/packages/sortedcontainers/2.4.0/sortedcontainers-2.4.0-py2.py3-none-any.whl (29 kB)\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pandas->datasets->mindnlp) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pandas->datasets->mindnlp) (2023.3.post1)\n", - "Requirement already satisfied: tzdata>=2022.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pandas->datasets->mindnlp) (2023.3)\n", - "\u001b[33mDEPRECATION: moxing-framework 2.1.16.2ae09d45 has a non-standard version number. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of moxing-framework or contact the author to suggest that they release a version with a conforming version number. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n", - "\u001b[0mInstalling collected packages: sortedcontainers, pygtrie, addict, xxhash, tqdm, tomli, safetensors, requests, pyarrow, propcache, pluggy, multidict, ml-dtypes, iniconfig, hypothesis, frozenlist, dill, async-timeout, aiohappyeyeballs, yarl, pytest, pyctcdecode, multiprocess, mindspore, huggingface-hub, aiosignal, tokenizers, aiohttp, datasets, evaluate, mindnlp\n", - " Attempting uninstall: tqdm\n", - " Found existing installation: tqdm 4.66.1\n", - " Uninstalling tqdm-4.66.1:\n", - " Successfully uninstalled tqdm-4.66.1\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 12.0.1\n", - " Uninstalling pyarrow-12.0.1:\n", - " Successfully uninstalled pyarrow-12.0.1\n", - " Attempting uninstall: mindspore\n", - " Found existing installation: mindspore 2.2.0\n", - " Uninstalling mindspore-2.2.0:\n", - " Successfully uninstalled mindspore-2.2.0\n", - " Attempting uninstall: huggingface-hub\n", - " Found existing installation: huggingface-hub 0.18.0\n", - " Uninstalling huggingface-hub-0.18.0:\n", - " Successfully uninstalled huggingface-hub-0.18.0\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", - "modelarts 1.4.4 requires lxml<=4.7.1, but you have lxml 4.9.3 which is incompatible.\n", - "modelarts 1.4.4 requires pandas<=1.3.4, but you have pandas 2.1.2 which is incompatible.\n", - "modelarts 1.4.4 requires Pillow<=9.0.1, but you have pillow 10.0.1 which is incompatible.\n", - "modelarts 1.4.4 requires pyyaml<=6.0, but you have pyyaml 6.0.1 which is incompatible.\n", - "modelarts 1.4.4 requires requests<=2.26.0, but you have requests 2.32.3 which is incompatible.\n", - "modelarts 1.4.4 requires semantic-version<=2.8.5, but you have semantic-version 2.10.0 which is incompatible.\n", - "modelarts 1.4.4 requires tqdm<=4.62.3, but you have tqdm 4.67.1 which is incompatible.\n", - "modelarts 1.4.4 requires typing-extensions<=4.0.1, but you have typing-extensions 4.8.0 which is incompatible.\n", - "modelarts 1.4.4 requires urllib3<=1.26.7, but you have urllib3 2.0.7 which is incompatible.\n", - "ma-cau 1.1.2 requires click==8.0.3, but you have click 8.1.7 which is incompatible.\n", - "ma-cau 1.1.2 requires numpy<=1.22.1, but you have numpy 1.26.1 which is incompatible.\n", - "ma-cau 1.1.2 requires requests==2.26.0, but you have requests 2.32.3 which is incompatible.\n", - "ma-cau 1.1.2 requires tenacity<=6.1.0, but you have tenacity 8.0.1 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed addict-2.4.0 aiohappyeyeballs-2.4.4 aiohttp-3.11.11 aiosignal-1.3.2 async-timeout-5.0.1 datasets-3.2.0 dill-0.3.8 evaluate-0.4.3 frozenlist-1.5.0 huggingface-hub-0.27.1 hypothesis-6.124.2 iniconfig-2.0.0 mindnlp-0.4.0 mindspore-2.4.10 ml-dtypes-0.5.1 multidict-6.1.0 multiprocess-0.70.16 pluggy-1.5.0 propcache-0.2.1 pyarrow-19.0.0 pyctcdecode-0.5.0 pygtrie-2.5.0 pytest-7.2.0 requests-2.32.3 safetensors-0.5.2 sortedcontainers-2.4.0 tokenizers-0.19.1 tomli-2.2.1 tqdm-4.67.1 xxhash-3.5.0 yarl-1.18.3\n" - ] - } - ], + "outputs": [], "source": [ - "!pip install mindnlp" + "!pip install mindnlp==0.5.1" ] }, { "cell_type": "markdown", - "metadata": { - "id": "Cf3PMqPhlWz1" - }, - "source": [ - "## Load dataset\n", - "\n", - "Here we load the Dutch translation of the CNN/Daily Mail dataset, created by the Belgian AI company [ML6](https://www.ml6.eu/). It is hosted on HuggingFace's hub, as can be seen [here](https://huggingface.co/datasets/ml6team/cnn_dailymail_nl)." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 312, - "referenced_widgets": [ - "32411983682d4258ba7063d8381587f4", - "9bd26144e6ab4e018ab3743b484643ae", - "3355426c985244a88ad5fc6173885a5f", - "8b7a43faae5644f2bfdfa22d65c0a9c6", - "c435bf73a9534c52a9ffb59cbc52a1c2", - "92839a8382dc46319e39c353914a87e0", - "9f5bc1d91af84150ab9630cc41c20999", - "bb1906b3651c4e99bfad4fe8a49c2df4", - "26eac4b9112245aeaca77c3536c725c8", - "52aac1bf6a77471db2de30d1700b2efe", - "7b2c21dcd5c9405bab1d4baa4a661984", - "20ba7c7b88294a82a53cf6446f0d3740", - "07503e533df343098d7ef4769579cc68", - "b1d5e4c3adab4fa7b87dbc1c16b6daba", - "c5ceeffeefe64265a860457c67213f56", - "dd1e5c090ebd468985fc298cf4c9f121", - "959e12c2710344eeb421886b05587799", - "74cff91e662a489d88e492f607a5fe91", - "0f126b673880442f8cebefad4ad49369", - "c66132eedf4b41699412104cb6075d3c", - "9c554a98c12c4b29a8c29e3c0a41c803", - "ad67e1d34e584971a6f20b71683d032d", - "7533de28ccd149df9833e7238a2468f2", - "2148bda6fb4748f7923b9ea0c6fd0833", - "315a79ca85f54cec9c8f029443a9ec90", - "f591140fa9ff4a7b9616bccf5e3de63f", - "328a98fdaff94376941c0d84d6929fc3", - "43f225434159426fa100213c6f74f06a", - "052407687a0848fa9d599d59218b5a0a", - "c1234c3110174ac2ab67430d04ab4416", - "45fcb5bff4954e78a62e376c8e72ad6f", - "0666c1f910884a73be543d14f2874c3b", - "875c041e468a41fdb4ff060148acd09f", - "bff13c85d7fa4cb5a694392e083f6acc", - "aa620fc095b3419083f06cb14b002ecd", - "a85349321fb24d73a30b2e99328b99df", - "0ba70597e1c5496f9886eba93bb3e07d", - "8072192d4e834ea19421e00e28c61d1f", - "9be407debb0d44fe9a237019ce567be6", - "c7dc29ddc68b41e2a875f5ee1f55e680", - "d504b17762d14e25bcc1b1fd0655c679", - "c9fd5fe392744ecb933e4d4f33f8805c", - "7a647af6ba9e461f8849538b1924b22c", - "dfcfca0a143c4d30906599261821fedb", - "073b3ea830084bac8c03f6ed9500e33d", - "e9d47ff6bb0044489093054066e074c5", - "cfa7c272cd884f87aaaa0d8dc277c294", - "b5f0c63fc0ac4040a1f7264e86c60bd8", - "4b92e272f7cf47af870babbfd8ff2662", - "f1e302c6ac09405dbaa836ae52bb6439", - "344e7de9191949f08b41a9a9b13e283c", - "b4ccb5c18fe14561afee3ece7f54f2b7", - "ff08d4b24a8844d0a6261e8e61240160", - "28962c4117ad49fb845571a093d11448", - "9140535a9251483a9c71bc83dac80c68", - "2a82e01507f9441c939f3b25eba21c2a", - "e45032c450544880b9f2beb99284c0a3", - "386722efe7744f418133459956b329eb", - "d06178dd6a684687bffb3239d4a7432c", - "ae985544fa274a4e8454cc75c3dd260a", - "ff8b9d0e8c494c00b185564cf3d0c4c9", - "a875caafd5974244a15d3a0d2aadec5e", - "fcc250d650ff4b8f82363e0be4110a67", - "960fa5cbcb174b3cbc855f0676beedf2", - "d8097de90bd64dcdac3c02acb44368df", - "05f3740e980e4760a3f6ffb315a0295d", - "4b40dc0e93234c278afceb66c54f2d7a", - "8863f85e486946eeb518407188d2be29", - "41bf56aa464a4eda8f94d32d3baf66d0", - "a11889255445494bbefb948f82bcf79a", - "c15f8b0c32944001af55b9012fc818ea", - "8e417396f8d4403bbdc81dc2cbed6af7", - "840979a5405e4a92aa5af82689950af0", - "9f3e8a40951647febea50a75c9aeae1a", - "9d27d4a87df44d19bb6cc9663ca4f61d", - "36e92bee6df349b4acda4d80fc8acb1f", - "f710c8e373b14ac9af9cc86152bc2725", - "3f9dcaad9eb84b008d084ae9fe906f58", - "e6a1eab46c1b428989a472b35d82d669", - "65a03366139b44a982c2c3507c51fbf8", - "ccf63cf60a064165bf4f6dc9ff5b3307", - "9a820e01020248d39ce413550bc50d5b", - "229c656cd20b4bb9a2831d7092e36aa9", - "b10c85564d0149dba846937774b981db", - "100d008809df43f5af100c0327ba7ec1", - "bfdf9dc06c5744c7bdff94727dbabde6", - "9b2a2f08329b4912ba9b5001222587eb", - "a54482c6cfd346d38c3d881b360622e2", - "a799749ebcde46c18ead4ac5d62a7389", - "c30e507d246545738eec5aa519cb6c16", - "a37a2492e25a44f1861c069ad5749fea", - "11762f2ee0d148cf9a5b0e3ba10bc560", - "36b81a3f56114d2abf6c0cd1228e31cd", - "2662be535ddb44d99815a994f9d7abfe", - "daf2814ed98545daa1abdf353155e3da", - "33816dc3861f4538aa61ef208eb04204", - "7b0f082d0ca744ecb4c5b630041dfaf2", - "80806136bf024d96af834fd18145447f", - "24460fd94b444c1abf4327c4dbc102f2", - "073ff8d2120e4607871fe448e7987e5f", - "0b3312125f134020b46e152a03e46f6e", - "37ab8d23cba846f7b9e31110de2c82eb", - "7e2892ef74d84092920d3da027e7d037", - "b31f8d2d473d457facbcfe1af5ece73a", - "2acbd0170e704642a76641680176d662", - "422ddeca3028403295e20bb6b5105f83", - "6dd42b70df324af286e0922facf7e44a", - "29c59cc7b70f4d59b96df0eb08a0e6cd", - "80d011abf3884c208cd139cbe1cb0862", - "f1247efd2ffe4f16a8e7ab80c3a50e67" - ] - }, - "id": "dOco3waYgyna", - "outputId": "1c244314-b9d6-4bac-d723-dc330f131e13" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero.\n", - " setattr(self, word, getattr(machar, word).flat[0])\n", - "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n", - " return self._float_to_str(self.smallest_subnormal)\n", - "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero.\n", - " setattr(self, word, getattr(machar, word).flat[0])\n", - "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n", - " return self._float_to_str(self.smallest_subnormal)\n", - "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "Building prefix dict from the default dictionary ...\n", - "Dumping model to file cache /tmp/jieba.cache\n", - "Loading model cost 1.414 seconds.\n", - "Prefix dict has been built successfully.\n" - ] - } - ], + "id": "3", + "metadata": {}, "source": [ - "from mindnlp.dataset import load_dataset\n", + "## 版本检查\n", "\n", - "#train_ds= load_dataset(\"\", split=['train', 'test'])\n", - "ds = load_dataset(\"abisee/cnn_dailymail\", \"3.0.0\", split=['train', 'validation','test'])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SLPVC9zPg67W", - "outputId": "9b487de2-4ab1-45d7-a8fd-3534b6778354" - }, - "outputs": [], - "source": [ - "train = ds['train']\n", - "val =ds['validation']\n", - "test = ds['test']" + "打印当前运行环境中的 MindSpore 与 MindSpore NLP 版本号，便于定位依赖不一致导致的兼容性问题。\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, + "id": "4", "metadata": {}, "outputs": [], "source": [ - "import mindspore\n", - "import numpy as np\n", - "from mindspore.dataset import GeneratorDataset, transforms" + "import mindspore as ms\n", + "import mindnlp\n", + "print('MindSpore:', ms.__version__)\n", + "print('MindNLP:', getattr(mindnlp, '__version__', 'unknown'))\n" ] }, { "cell_type": "markdown", - "metadata": { - "id": "AxvsUvauldNS" - }, - "source": [ - "Let's look at an example:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0FA89kduhL_D", - "outputId": "931a549d-d970-4fde-dbf2-a7cac4161cf6" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "('LONDON, England (Reuters) -- Harry Potter star Daniel Radcliffe gains access to a reported £20 million ($41.1 million) fortune as he turns 18 on Monday, but he insists the money won\\'t cast a spell on him. Daniel Radcliffe as Harry Potter in \"Harry Potter and the Order of the Phoenix\" To the disappointment of gossip columnists around the world, the young actor says he has no plans to fritter his cash away on fast cars, drink and celebrity parties. \"I don\\'t plan to be one of those people who, as soon as they turn 18, suddenly buy themselves a massive sports car collection or something similar,\" he told an Australian interviewer earlier this month. \"I don\\'t think I\\'ll be particularly extravagant. \"The things I like buying are things that cost about 10 pounds -- books and CDs and DVDs.\" At 18, Radcliffe will be able to gamble in a casino, buy a drink in a pub or see the horror film \"Hostel: Part II,\" currently six places below his number one movie on the UK box office chart. Details of how he\\'ll mark his landmark birthday are under wraps. His agent and publicist had no comment on his plans. \"I\\'ll definitely have some sort of party,\" he said in an interview. \"Hopefully none of you will be reading about it.\" Radcliffe\\'s earnings from the first five Potter films have been held in a trust fund which he has not been able to touch. Despite his growing fame and riches, the actor says he is keeping his feet firmly on the ground. \"People are always looking to say \\'kid star goes off the rails,\\'\" he told reporters last month. \"But I try very hard not to go that way because it would be too easy for them.\" His latest outing as the boy wizard in \"Harry Potter and the Order of the Phoenix\" is breaking records on both sides of the Atlantic and he will reprise the role in the last two films. Watch I-Reporter give her review of Potter\\'s latest » . There is life beyond Potter, however. The Londoner has filmed a TV movie called \"My Boy Jack,\" about author Rudyard Kipling and his son, due for release later this year. He will also appear in \"December Boys,\" an Australian film about four boys who escape an orphanage. Earlier this year, he made his stage debut playing a tortured teenager in Peter Shaffer\\'s \"Equus.\" Meanwhile, he is braced for even closer media scrutiny now that he\\'s legally an adult: \"I just think I\\'m going to be more sort of fair game,\" he told Reuters. E-mail to a friend . Copyright 2007 Reuters. All rights reserved.This material may not be published, broadcast, rewritten, or redistributed.',\n", - " \"Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\\nYoung actor says he has no plans to fritter his cash away .\\nRadcliffe's earnings from first five Potter films have been held in trust fund .\",\n", - " '42c027e4ff9730fbb3de84c1af0d2c506e41c3e4')" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "example = train[0]\n", - "example" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0FA89kduhL_D", - "outputId": "931a549d-d970-4fde-dbf2-a7cac4161cf6" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Article: LONDON, England (Reuters) -- Harry Potter star Daniel Radcliffe gains access to a reported £20 million ($41.1 million) fortune as he turns 18 on Monday, but he insists the money won't cast a spell on him. Daniel Radcliffe as Harry Potter in \"Harry Potter and the Order of the Phoenix\" To the disappointment of gossip columnists around the world, the young actor says he has no plans to fritter his cash away on fast cars, drink and celebrity parties. \"I don't plan to be one of those people who, as soon as they turn 18, suddenly buy themselves a massive sports car collection or something similar,\" he told an Australian interviewer earlier this month. \"I don't think I'll be particularly extravagant. \"The things I like buying are things that cost about 10 pounds -- books and CDs and DVDs.\" At 18, Radcliffe will be able to gamble in a casino, buy a drink in a pub or see the horror film \"Hostel: Part II,\" currently six places below his number one movie on the UK box office chart. Details of how he'll mark his landmark birthday are under wraps. His agent and publicist had no comment on his plans. \"I'll definitely have some sort of party,\" he said in an interview. \"Hopefully none of you will be reading about it.\" Radcliffe's earnings from the first five Potter films have been held in a trust fund which he has not been able to touch. Despite his growing fame and riches, the actor says he is keeping his feet firmly on the ground. \"People are always looking to say 'kid star goes off the rails,'\" he told reporters last month. \"But I try very hard not to go that way because it would be too easy for them.\" His latest outing as the boy wizard in \"Harry Potter and the Order of the Phoenix\" is breaking records on both sides of the Atlantic and he will reprise the role in the last two films. Watch I-Reporter give her review of Potter's latest » . There is life beyond Potter, however. The Londoner has filmed a TV movie called \"My Boy Jack,\" about author Rudyard Kipling and his son, due for release later this year. He will also appear in \"December Boys,\" an Australian film about four boys who escape an orphanage. Earlier this year, he made his stage debut playing a tortured teenager in Peter Shaffer's \"Equus.\" Meanwhile, he is braced for even closer media scrutiny now that he's legally an adult: \"I just think I'm going to be more sort of fair game,\" he told Reuters. E-mail to a friend . Copyright 2007 Reuters. All rights reserved.This material may not be published, broadcast, rewritten, or redistributed.\n", - "Summary: Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\n", - "Young actor says he has no plans to fritter his cash away .\n", - "Radcliffe's earnings from first five Potter films have been held in trust fund .\n" - ] - } - ], - "source": [ - "article = example[0]\n", - "summary = example[1]\n", - "print(\"Article:\", article)\n", - "print(\"Summary:\", summary)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, + "id": "5", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/mindnlp/transformers/tokenization_utils_base.py:1526: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted, and will be then set to `False` by default. \n", - " warnings.warn(\n" - ] - } - ], "source": [ - "from mindnlp.transformers import AutoTokenizer\n", + "## 版本显示差异说明（MindSpore NLP 0.5.1 安装后显示为 0.5.0rc2）\n", "\n", - "tokenizer = AutoTokenizer.from_pretrained(\"flax-community/t5-base-dutch\")" + "![alt text](image.png)\n", + "\n", + "在部分环境中，`pip install mindnlp==0.5.1` 后 `mindnlp.__version__` 可能显示为 `0.5.0rc2`。若出现该情况，建议优先以 `pip show mindnlp` 与已安装 wheel 信息为准，并结合 `import mindnlp; mindnlp.__file__` 核对实际导入路径，确认是否存在旧版本残留或多环境混用。\n" ] }, { - "cell_type": "code", - "execution_count": 37, + "cell_type": "markdown", + "id": "6", "metadata": {}, - "outputs": [], "source": [ - "prefix = \"Summary: \"\n", - "max_input_length = 512\n", - "from mindspore import Tensor\n", - "max_target_length = 64\n", - "class MyMapDataset():\n", - " def __init__(self,data):\n", - " super(MyMapDataset).__init__()\n", - " self.data = data\n", - " def __getitem__(self, index):\n", - " \n", - " articles,summaries,_ = self.data[index]\n", - " #print(articles)\n", - " inputs = [prefix + articles]\n", - " model_inputs = tokenizer(inputs, max_length=max_input_length, padding=\"max_length\", truncation=True)\n", - " labels = [tokenizer(summaries, max_length=max_target_length, padding=\"max_length\", truncation=True).input_ids]\n", - " labels_with_ignore_index = []\n", - " \n", - " for labels_example in labels:\n", - " labels_example = [label if label != 0 else -100 for label in labels_example]\n", - " labels_with_ignore_index.append(labels_example)\n", - " \n", - " \n", - " return model_inputs['input_ids'][0],model_inputs['attention_mask'][0],labels_with_ignore_index\n", - " def __len__(self):\n", - " return len(self.data)" + "## 数据集加载\n", + "\n", + "使用 `mindnlp.dataset.load_dataset` 加载经典摘要数据集 **CNN/DailyMail**（版本 `3.0.0`），并获取 `train/validation/test` 三个子集。运行后打印各子集样本数量，用于确认下载与加载是否正常。\n" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, + "id": "7", "metadata": {}, "outputs": [], "source": [ - "train_data = MyMapDataset(train)" + "from mindnlp.dataset import load_dataset\n", + "ds = load_dataset('abisee/cnn_dailymail', '3.0.0', split=['train', 'validation', 'test'])\n", + "train = ds['train']\n", + "val = ds['validation']\n", + "test = ds['test']\n", + "print('train:', len(train), 'validation:', len(val), 'test:', len(test))" ] }, { - "cell_type": "code", - "execution_count": 39, + "cell_type": "markdown", + "id": "8", "metadata": {}, - "outputs": [], "source": [ - "val_data = MyMapDataset(val)\n", - "test_data = MyMapDataset(test)" + "## 分词器与编码设置\n", + "\n", + "加载 `t5-base` 的预训练分词器，并设置摘要任务常用的编码参数：\n", + "\n", + "- `prefix = \"summarize: \"`：T5 摘要任务前缀（prompt）。\n", + "- `max_input_length`：输入文本最大长度（示例为 512）。\n", + "- `max_target_length`：目标摘要最大长度（示例为 64）。\n", + "- `pad_token_id`：用于 padding 的 token ID（后续需要在 label 中将 pad 位置替换为 `-100` 以忽略损失）。\n" ] }, { "cell_type": "code", "execution_count": null, + "id": "9", "metadata": {}, "outputs": [], "source": [ - "train_data[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "train_data = mindspore.dataset.GeneratorDataset(train_data, column_names=[\"input_ids\",\"attention_mask\",\"labels\"], shuffle=False)\n", - "#dataset = dataset.map(operations=transforms.TypeCast(mindspore.int32), input_columns=\"label\", output_columns=\"labels\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dreE7nRbkapT" - }, - "source": [ - "Each example consists of an article and a corresponding summary. Easy, huh? Note that you can train T5 on any text-to-text problem. So it could be text as input as an SQL query (as text) as output, it could be text as input and a question related to that text as output (a task called question generation), etc." + "from mindnlp.transformers import AutoTokenizer\n", + "tokenizer = AutoTokenizer.from_pretrained('t5-base')\n", + "prefix = 'summarize: '\n", + "max_input_length = 512\n", + "max_target_length = 64\n", + "pad_id = tokenizer.pad_token_id\n", + "print('pad_token_id:', pad_id)" ] }, { "cell_type": "markdown", - "metadata": { - "id": "-SlI-qdCliv2" - }, + "id": "10", + "metadata": {}, "source": [ - "## Encode the dataset\n", + "## 生成式数据集\n", "\n", - "The T5 model, like any other Transformer model, does not directly expect text as input. Rather, it expects `input_ids` and `attention_mask`. The `input_ids` are integer vocabulary indices of the tokens of the text (you can read more about them [here](https://huggingface.co/transformers/glossary.html#input-ids)). As labels, it expects the `input_ids` of the summary. \n", + "定义自定义数据集类 `SummarizationDataset`，将原始样本转为模型可训练的三元组：\n", "\n", - "Let's encode them using the tokenizer. We also prepend the input with a so-called task prefix, which the authors of T5 used when fine-tuning the model. Here, the prefix is simply \"Vat samen: \" (which is Dutch for \"Summarize: \"), followed by a long document. Note that this prefix will probably not help a lot, since it has not seen this during pre-training. However, it is definitely useful if you want to fine-tune the English T5 model for summarization, as summarization is also part of its pre-training.\n", + "- `input_ids`：编码后的输入 token 序列\n", + "- `attention_mask`：注意力 mask\n", + "- `labels`：编码后的摘要 token 序列（将 `pad_token_id` 替换为 `-100`，使损失函数忽略 padding 部分）\n", "\n", - "As we'll train the model on TPU, we pad both the inputs and targets up to the max length. If we were to train this model on GPUs, we would instead pad them up to the longest in a batch, which is more efficient in terms of memory. However, TPUs don't like that." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "akbnGsHkrDHg" - }, - "source": [ - "Let's verify an example, by decoding the `input_ids` back to text:" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "icmIEznVAlUy" - }, - "source": [ - "We also decode the `labels` (which aren't set to -100) back to text:" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ORv91RxQsiQc" - }, - "source": [ - "Next, let's set the format to PyTorch." + "该步骤的核心是将“文本样本”转换为“可直接送入 T5 的张量输入”。\n" ] }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3VWtJG6kzpDO", - "outputId": "61478f8f-f353-435a-df27-ee8674c5f2b4" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of training examples: 287113\n" - ] - } - ], - "source": [ - "print(\"Number of training examples:\", len(train_data))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dA1SvGNazF64" - }, + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], "source": [ - "We define a function to create PyTorch dataloaders." + "import numpy as np\n", + "class SummarizationDataset:\n", + " def __init__(self, data):\n", + " self.data = data\n", + " def __getitem__(self, idx):\n", + " art, summ, _ = self.data[idx]\n", + " enc = tokenizer(prefix + art, max_length=max_input_length, padding='max_length', truncation=True)\n", + " dec = tokenizer(summ, max_length=max_target_length, padding='max_length', truncation=True)\n", + " input_ids = np.array(enc.input_ids, dtype=np.int32)\n", + " attention_mask = np.array(enc.attention_mask, dtype=np.int32)\n", + " labels = np.array(dec.input_ids, dtype=np.int32)\n", + " labels = np.where(labels == pad_id, -100, labels).astype(np.int32)\n", + " return input_ids, attention_mask, labels\n", + " def __len__(self):\n", + " return len(self.data)" ] }, { "cell_type": "markdown", - "metadata": { - "id": "yuj0Cj0WpIQJ" - }, + "id": "12", + "metadata": {}, "source": [ - "## Fine-tune a model \n", + "## 构建数据处理流水线\n", "\n", - "Below, we define a `training_function`, which defines a regular training loop in native PyTorch. We only need to add a few lines to make sure the code will run on TPU. The Accelerator object will take care of that. Basically, the model as well as the data will be replicated across each of the 8 TPU cores. \n", + "将 `SummarizationDataset` 封装为 MindSpore `GeneratorDataset`，并完成：\n", "\n", - "We also define a dictionary of training-related hyperparameters, which we can easily tweak." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "id": "l3Mn8ziqr0wu" - }, - "outputs": [], - "source": [ - "hyperparameters = {\n", - " \"learning_rate\": 0.0001,\n", - " \"num_epochs\": 1, # set to very high number\n", - " \"seed\": 42,\n", - " \"patience\": 3, # early stopping\n", - " \"output_dir\": \"/content/\",\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "T5ForConditionalGeneration has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`.`PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n", - " - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n", - " - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[MS_ALLOC_CONF]Runtime config: enable_vmm:True vmm_align_size:2MB\n" - ] - } - ], - "source": [ - "from mindnlp.transformers import T5ForConditionalGeneration\n", - "model = T5ForConditionalGeneration.from_pretrained(\"flax-community/t5-base-dutch\")" + "- 类型转换：统一 cast 为 `int32`\n", + "- 批处理：设置 `batch_size=8`\n", + "- 形状对齐：`drop_remainder=True`，保证 batch 维度固定，便于训练稳定运行\n" ] }, { "cell_type": "code", "execution_count": null, + "id": "13", "metadata": {}, "outputs": [], "source": [ - "train_data[0][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "a = model(input_ids= Tensor(train_data[0][0]),attention_mask = Tensor(train_data[0][1]),\n", - " decoder_input_ids = Tensor(train_data[0][2]))" + "import mindspore.dataset as ds\n", + "from mindspore.dataset import transforms\n", + "import mindspore\n", + "train_ds = ds.GeneratorDataset(SummarizationDataset(train), column_names=['input_ids','attention_mask','labels'], shuffle=True)\n", + "val_ds = ds.GeneratorDataset(SummarizationDataset(val), column_names=['input_ids','attention_mask','labels'], shuffle=False)\n", + "for c in ['input_ids','attention_mask','labels']:\n", + " train_ds = train_ds.map(operations=transforms.TypeCast(mindspore.int32), input_columns=c)\n", + " val_ds = val_ds.map(operations=transforms.TypeCast(mindspore.int32), input_columns=c)\n", + "batch_size = 8\n", + "train_ds = train_ds.batch(batch_size, drop_remainder=True)\n", + "val_ds = val_ds.batch(batch_size, drop_remainder=True)\n", + "print('train batches:', train_ds.get_dataset_size(), 'val batches:', val_ds.get_dataset_size())" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", + "id": "14", "metadata": {}, - "outputs": [], "source": [ - "model.trainable_params()" + "## 构建模型与训练函数\n", + "\n", + "本节完成微调核心逻辑的搭建，包括：\n", + "\n", + "- 加载 `T5ForConditionalGeneration`（`t5-base`）\n", + "- 构建优化器与梯度裁剪策略\n", + "- 处理 `mindtorch.autograd.profiler` 的兼容性引用（用于避免部分环境缺失导致的报错）\n", + "- 定义 `train_step`：封装单步训练流程（前向 → loss → 反向 → 裁剪 → 更新）\n", + "\n", + "说明：该 Notebook 保持“代码逻辑不变”，仅整理文字结构以提升可读性。\n" ] }, { "cell_type": "code", "execution_count": null, + "id": "15", "metadata": {}, "outputs": [], "source": [ - "params = []" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "PEvLESbulv-b", - "outputId": "f8927534-72e7-4cb4-c149-75cb19e3eda9" - }, - "outputs": [], - "source": [ - "import mindspore\n", + "import contextlib\n", + "import mindtorch.autograd as autograd\n", + "import mindtorch as mt\n", "from mindnlp.transformers import T5ForConditionalGeneration\n", "\n", - "from mindnlp.core import nn, ops, optim\n", + "try:\n", + " import mindtorch.autograd.profiler as _prof\n", + " autograd.profiler = _prof\n", + "except Exception:\n", + " class _DummyProfiler:\n", + " @staticmethod\n", + " @contextlib.contextmanager\n", + " def record_function(name):\n", + " yield\n", + " autograd.profiler = _DummyProfiler()\n", "\n", - "from tqdm import tqdm\n", - "import mindnlp.transformers\n", - "from mindspore import value_and_grad\n", - "model = T5ForConditionalGeneration.from_pretrained(\"flax-community/t5-base-dutch\")\n", "\n", - "def forward_fn(data, labels):\n", - " input_ids = Tensor(data[0])\n", - " attention_mask = Tensor(data[1])\n", - " labels = Tensor(labels)\n", - " outputs = model(input_ids= input_ids,attention_mask =attention_mask,labels = labels)\n", - " loss = outputs.loss\n", - " #print(loss)\n", - " return loss\n", + "model = T5ForConditionalGeneration.from_pretrained(\"t5-base\")\n", + "\n", + "model.train()\n", + "\n", "\n", + "lr = 1e-4\n", + "weight_decay = 0.01\n", + "optimizer = mt.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n", "\n", - "def training_function(train_dataset,test_dataset):\n", - " \n", "\n", - " # Instantiate optimizer\n", - " optimizer = optim.AdamW(\n", - " model.trainable_params(),\n", - " lr=hyperparameters[\"learning_rate\"])\n", + "clip_norm = 1.0\n", "\n", - " \n", - " \n", - " # Now we train the model\n", - " epochs_no_improve = 0\n", - " min_val_loss = 1000000\n", - " weights = ()\n", - " for group in optimizer.param_groups:\n", - " weights += tuple(group['params'])\n", - " \n", - " grad_fn = value_and_grad(forward_fn,grad_position=1)\n", - " running_loss = 0.\n", - " for epoch in range(hyperparameters[\"num_epochs\"]):\n", - " \n", - " model.train()\n", - " running_loss = 0.\n", - " for i in tqdm(range(200)):\n", - " data,label = (train_data[i][0],train_data[i][1]),train_data[i][2]\n", - " \n", - " \n", - " #loss = forward_fn(data,label)\n", - " loss, grads = grad_fn(data, label)\n", - " running_loss += loss.asnumpy()\n", - " \n", - " \n", - " optimizer.step(grads)\n" + "def to_long(x):\n", + " if hasattr(x, \"asnumpy\"):\n", + " x = x.asnumpy()\n", + " return mt.tensor(x, dtype=mt.long)\n", + "\n", + "def train_step(input_ids, attention_mask, labels):\n", + " input_ids = to_long(input_ids)\n", + " attention_mask = to_long(attention_mask)\n", + " labels = to_long(labels)\n", + "\n", + " optimizer.zero_grad()\n", + " outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", + " loss = outputs.loss\n", + "\n", + " loss.backward()\n", + " mt.nn.utils.clip_grad_norm_(model.parameters(), clip_norm)\n", + " optimizer.step()\n", + "\n", + " return float(loss.item())\n" ] }, { "cell_type": "markdown", - "metadata": { - "id": "SFv3QFEVF16U" - }, + "id": "16", + "metadata": {}, "source": [ - "Next, we can easily start training by wrapping the `training_function` in a `notebook_launcher`." + "## 训练\n", + "\n", + "启动训练循环：\n", + "\n", + "- `num_epochs = 1`：仅训练 1 个 epoch，用于快速验证流程\n", + "- `warm_steps = 200`：每个 epoch 最多跑 200 个 step\n", + "- 日志：每 20 step 打印一次 loss，便于观察收敛趋势\n", + "\n", + "如果需要正式训练，可按实际数据规模与资源条件调整 epoch、step、batch size 与学习率等超参数。\n" ] }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000, - "referenced_widgets": [ - "eb80322b74414169b8d3125db92d3b61", - "9cf02124c9294b6191933d4c3245c757", - "bf642918558d4b9490b04fe24c053dcd", - "2e64d45051914c52b9ad9d22c5213c71", - "b6faf07cb2bf445d8119d030d98ef230", - "5c681fbf0438441d8143d244ab00f7f0", - "119a30684c95455fafa76ef4ec23498c", - "c4f102c8c19c48b7ae19fc3d50463c3f", - "b16a4fb2156749f8b9f0c9a4d837e90a", - "30921f7482304438b94b64f57a35baa7", - "688f8f0c34fe42bab2cdd8a26ea4aade", - "2fe4f61a3377432d8a483ba566b7fb95", - "66f154bab48842f6899c4cf0734bef13", - "1f440a7265a84a99b15b1b767d981f6b", - "79294d625bd54286977774956ce2f1a4", - "d1166950e97f40b89649fe07d41b36a2", - "cee2478799534ffd945e71a7137bf842", - "0e4cbbef81af4d5ba01c8e2fb607ff09", - "e34a83dc3a4944988cb9235cacdf38f0", - "4b77f34b30f34774b87b097d75d238eb", - "4c26dcacf11d490581b4e6cc5a52edec", - "c8c6d304914e4a129ceaa2f76cabd46a", - "7aa6f42576b143e284009c9ce10b5ec8", - "0fa8e9ecde004cbc9221ac28a5e3e957", - "5d7c143520f745e685dbeba17e0b5f60", - "8f4c31e2befb42ecb7b927734b2493b9", - "a94125128e664554aa3c06efed601db5", - "d33ae1586ee84dc494accc38ca498931", - "b948cb48d00044e3b02954e9709acfca", - "500f89d42b5e498591f8f60450412156", - "845fc4356a21441ea82c5f6865804b61", - "4c734704a469449cbf72a978ebd1bda4", - "e78ea2cbf6c040ae852dccb8d3ced535" - ] - }, - "id": "SyFsYRCOrp-g", - "outputId": "ccdb3052-312b-4c83-f406-3f7139429d94" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 200/200 [18:14<00:00, 5.47s/it]\n" - ] - } - ], + "execution_count": null, + "id": "17", + "metadata": {}, + "outputs": [], "source": [ - "training_function(train_data,test_data)" + "from tqdm import tqdm\n", + "\n", + "num_epochs = 1\n", + "warm_steps = 200\n", + "\n", + "for epoch in range(num_epochs):\n", + " step = 0\n", + " with tqdm(total=warm_steps, desc=f\"Epoch {epoch+1}/{num_epochs}\", unit=\"step\") as pbar:\n", + " for input_ids, attention_mask, labels in train_ds.create_tuple_iterator():\n", + " loss = train_step(input_ids, attention_mask, labels) \n", + " step += 1\n", + "\n", + " if step % 20 == 0:\n", + " tqdm.write(f\"step={step}, loss={loss:.4f}\")\n", + "\n", + " pbar.update(1)\n", + " if step >= warm_steps:\n", + " break\n" ] }, { "cell_type": "markdown", - "metadata": { - "id": "RmExPYic6EBL" - }, + "id": "18", + "metadata": {}, "source": [ - "## Inference\n", + "## 推理\n", "\n", - "Now that we have a trained model, let's use it to generate a summary on a new, unseen text." + "构造一段较长输入文本，按 T5 摘要任务要求加上前缀 `summarize: `，并调用 `model.generate` 生成摘要序列。最后通过分词器 `decode` 将生成的 token ID 还原为可读文本，用于快速验证微调后的生成效果。\n" ] }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "id": "yd0La4YY4ixe" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "editing. AI verkent acute science and medical healthcare applications, such as big data, and receives tests to treatments..\n" - ] - } - ], + "execution_count": null, + "id": "19", + "metadata": {}, + "outputs": [], "source": [ "text = \"\"\"Artificial Intelligence (AI) has rapidly evolved and become an integral part of various industries, \n", "particularly healthcare. AI's ability to analyze large volumes of data and identify patterns that humans might miss \n", @@ -886,54 +348,47 @@ "By analyzing patient data, including genetic information, \n", "AI systems can recommend specific treatment plans that are more likely to be effective. \n", "This can greatly enhance the precision of treatments, reduce side effects, and improve overall patient care..\"\"\"\n", - "\n", - "#trained_model = T5ForConditionalGeneration.from_pretrained(hyperparameters[\"output_dir\"])\n", - "\n", - "input_ids = tokenizer(text, max_length=max_input_length, padding=\"max_length\", truncation=True).input_ids\n", - "input_ids = Tensor([input_ids])\n", - "generated_ids = model.generate(input_ids, do_sample=True, \n", - " max_length=50, \n", - " top_k=0, \n", - " temperature=0.7\n", - ")\n", - "\n", - "summary = tokenizer.decode(generated_ids.squeeze(), skip_special_tokens=True)\n", - "print(summary)" + "enc = tokenizer(prefix + text, max_length=max_input_length, padding='max_length', truncation=True)\n", + "inp = ms.Tensor([enc.input_ids], dtype=ms.int32)\n", + "gen_ids = model.generate(inp, max_length=60,top_k=0,temperature=0.7)\n", + "summ = tokenizer.decode(gen_ids[0], skip_special_tokens=True)\n", + "print(summ)" ] }, { "cell_type": "markdown", - "metadata": { - "id": "3yO5JnrPRSI2" - }, + "id": "20", + "metadata": {}, "source": [ - "Make sure to upload the trained model to the hub, such that other people can re-use it." + "## 保存模型与分词器\n", + "\n", + "将微调后的模型权重与分词器配置保存到本地目录：\n", + "\n", + "- 保存路径：`./t5_email_summarization_ms270_mn051`\n", + "- 保存内容：模型参数（权重）与 tokenizer 配置/词表\n", + "\n", + "保存后可在后续脚本中通过 `from_pretrained(save_dir)` 直接加载，便于部署推理或继续训练。\n" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "id": "uIkZRa3hHdd4" - }, + "id": "21", + "metadata": {}, "outputs": [], - "source": [] + "source": [ + "save_dir = './t5_email_summarization_ms270_mn051'\n", + "model.save_pretrained(save_dir)\n", + "tokenizer.save_pretrained(save_dir)\n", + "print('saved to', save_dir)" + ] } ], "metadata": { - "accelerator": "TPU", - "colab": { - "authorship_tag": "ABX9TyO1QMXu1O3SMbs75BnsbeK3", - "collapsed_sections": [], - "include_colab_link": true, - "machine_shape": "hm", - "name": "Fine-tuning Dutch T5-base on CNN/Daily Mail for summarization (on TPU using HuggingFace Accelerate).ipynb", - "provenance": [] - }, "kernelspec": { - "display_name": "MindSpore", + "display_name": "work", "language": "python", - "name": "mindspore" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -945,5971 +400,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "020982ac1c6747168327b1baa922cdb1": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - 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100644 llm/new_t5/new_finetune_t5_daily_email_summarization.ipynb diff --git a/llm/README.md b/llm/README.md index fcda5c8..679a681 100644 --- a/llm/README.md +++ b/llm/README.md @@ -18,8 +18,8 @@ Addtional community-contributed or legacy notebooks are stored under the [legacy If a notebook does not specify environment requirements, assume it runs with: -- mindspore == 2.7.0 -- mindnlp == 0.5.1 +- mindspore == 2.3.1 +- mindnlp == 0.4.1 > If you need any of these applications updated, please open an [Issue](https://github.com/mindspore-courses/applications/issues) with the required MindSpore and MindSpore NLP version. > Developers are also welcome to update these notebooks to the latest version by submitting a [Pull Request](https://github.com/mindspore-courses/applications/pulls). diff --git a/llm/new_t5/image.png b/llm/new_t5/image.png deleted file mode 100644 index 7567788d1cfd4324f66b85fe81b14d21520ecc87..0000000000000000000000000000000000000000 GIT binary patch literal 0 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进行摘要任务微调，并完成推理与保存。\n", - "\n", - "- **任务**：基于 `abisee/cnn_dailymail`（`3.0.0`）数据集进行文本摘要微调（可迁移到“日常邮件摘要”场景）。\n", - "- **目标**：在不改动训练逻辑的前提下，跑通「数据 → 分词 → 数据管线 → 训练 → 推理 → 保存」全流程。\n", - "- **输出**：将微调后的模型与分词器保存至 `./t5_email_summarization_ms270_mn051` 目录，便于后续加载推理或继续训练。\n", - "\n", - "本示例以快速验证为目标：训练仅运行 **1 个 epoch** 且最多 **200 step**，并每 20 step 打印一次 loss。\n" - ] - }, - { - "cell_type": "markdown", - "id": "e0b0bbb5", - "metadata": {}, - "source": [ - "## 环境准备\n", - "\n", - "安装本示例依赖的 MindNLP 版本。请确保当前 Python 环境中已安装 **MindSpore 2.7.0**（或与之兼容的版本）。\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b82fdb78", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install mindnlp==0.5.1" - ] - }, - { - "cell_type": "markdown", - "id": "e767cf15", - "metadata": {}, - "source": [ - "## 版本检查\n", - "\n", - "打印当前运行环境中的 MindSpore 与 MindNLP 版本号，便于定位依赖不一致导致的兼容性问题。\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f6b94df", - "metadata": {}, - "outputs": [], - "source": [ - "import mindspore as ms\n", - "import mindnlp\n", - "print('MindSpore:', ms.__version__)\n", - "print('MindNLP:', getattr(mindnlp, '__version__', 'unknown'))\n" - ] - }, - { - "cell_type": "markdown", - "id": "5dc9ae3a", - "metadata": {}, - "source": [ - "## 版本显示差异说明（MindNLP 0.5.1 安装后显示为 0.5.0rc2）\n", - "\n", - "![alt text](image.png)\n", - "\n", - "在部分环境中，`pip install mindnlp==0.5.1` 后 `mindnlp.__version__` 可能显示为 `0.5.0rc2`。若出现该情况，建议优先以 `pip show mindnlp` 与已安装 wheel 信息为准，并结合 `import mindnlp; mindnlp.__file__` 核对实际导入路径，确认是否存在旧版本残留或多环境混用。\n" - ] - }, - { - "cell_type": "markdown", - "id": "c44222c4", - "metadata": {}, - "source": [ - "## 数据集加载\n", - "\n", - "使用 `mindnlp.dataset.load_dataset` 加载经典摘要数据集 **CNN/DailyMail**（版本 `3.0.0`），并获取 `train/validation/test` 三个子集。运行后打印各子集样本数量，用于确认下载与加载是否正常。\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "80436680", - "metadata": {}, - "outputs": [], - "source": [ - "from mindnlp.dataset import load_dataset\n", - "ds = load_dataset('abisee/cnn_dailymail', '3.0.0', split=['train', 'validation', 'test'])\n", - "train = ds['train']\n", - "val = ds['validation']\n", - "test = ds['test']\n", - "print('train:', len(train), 'validation:', len(val), 'test:', len(test))" - ] - }, - { - "cell_type": "markdown", - "id": "e0357779", - "metadata": {}, - "source": [ - "## 分词器与编码设置\n", - "\n", - "加载 `t5-base` 的预训练分词器，并设置摘要任务常用的编码参数：\n", - "\n", - "- `prefix = \"summarize: \"`：T5 摘要任务前缀（prompt）。\n", - "- `max_input_length`：输入文本最大长度（示例为 512）。\n", - "- `max_target_length`：目标摘要最大长度（示例为 64）。\n", - "- `pad_token_id`：用于 padding 的 token ID（后续需要在 label 中将 pad 位置替换为 `-100` 以忽略损失）。\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e8d31c8f", - "metadata": {}, - "outputs": [], - "source": [ - "from mindnlp.transformers import AutoTokenizer\n", - "tokenizer = AutoTokenizer.from_pretrained('t5-base')\n", - "prefix = 'summarize: '\n", - "max_input_length = 512\n", - "max_target_length = 64\n", - "pad_id = tokenizer.pad_token_id\n", - "print('pad_token_id:', pad_id)" - ] - }, - { - "cell_type": "markdown", - "id": "c14ad6ae", - "metadata": {}, - "source": [ - "## 生成式数据集\n", - "\n", - "定义自定义数据集类 `SummarizationDataset`，将原始样本转为模型可训练的三元组：\n", - "\n", - "- `input_ids`：编码后的输入 token 序列\n", - "- `attention_mask`：注意力 mask\n", - "- `labels`：编码后的摘要 token 序列（将 `pad_token_id` 替换为 `-100`，使损失函数忽略 padding 部分）\n", - "\n", - "该步骤的核心是将“文本样本”转换为“可直接送入 T5 的张量输入”。\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "516ef09d", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "class SummarizationDataset:\n", - " def __init__(self, data):\n", - " self.data = data\n", - " def __getitem__(self, idx):\n", - " art, summ, _ = self.data[idx]\n", - " enc = tokenizer(prefix + art, max_length=max_input_length, padding='max_length', truncation=True)\n", - " dec = tokenizer(summ, max_length=max_target_length, padding='max_length', truncation=True)\n", - " input_ids = np.array(enc.input_ids, dtype=np.int32)\n", - " attention_mask = np.array(enc.attention_mask, dtype=np.int32)\n", - " labels = np.array(dec.input_ids, dtype=np.int32)\n", - " labels = np.where(labels == pad_id, -100, labels).astype(np.int32)\n", - " return input_ids, attention_mask, labels\n", - " def __len__(self):\n", - " return len(self.data)" - ] - }, - { - "cell_type": "markdown", - "id": "a6073a14", - "metadata": {}, - "source": [ - "## 构建 MindSpore 数据管线\n", - "\n", - "将 `SummarizationDataset` 封装为 MindSpore `GeneratorDataset`，并完成：\n", - "\n", - "- 类型转换：统一 cast 为 `int32`\n", - "- 批处理：设置 `batch_size=8`\n", - "- 形状对齐：`drop_remainder=True`，保证 batch 维度固定，便于训练稳定运行\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "630af609", - "metadata": {}, - "outputs": [], - "source": [ - "import mindspore.dataset as ds\n", - "from mindspore.dataset import transforms\n", - "from mindspore import dtype as mstype\n", - "train_ds = ds.GeneratorDataset(SummarizationDataset(train), column_names=['input_ids','attention_mask','labels'], shuffle=True)\n", - "val_ds = ds.GeneratorDataset(SummarizationDataset(val), column_names=['input_ids','attention_mask','labels'], shuffle=False)\n", - "for c in ['input_ids','attention_mask','labels']:\n", - " train_ds = train_ds.map(operations=transforms.TypeCast(mstype.int32), input_columns=c)\n", - " val_ds = val_ds.map(operations=transforms.TypeCast(mstype.int32), input_columns=c)\n", - "batch_size = 8\n", - "train_ds = train_ds.batch(batch_size, drop_remainder=True)\n", - "val_ds = val_ds.batch(batch_size, drop_remainder=True)\n", - "print('train batches:', train_ds.get_dataset_size(), 'val batches:', val_ds.get_dataset_size())" - ] - }, - { - "cell_type": "markdown", - "id": "a0a95e03", - "metadata": {}, - "source": [ - "## 构建模型与训练函数\n", - "\n", - "本节完成微调核心逻辑的搭建，包括：\n", - "\n", - "- 加载 `T5ForConditionalGeneration`（`t5-base`）\n", - "- 构建优化器与梯度裁剪策略\n", - "- 处理 `mindtorch.autograd.profiler` 的兼容性引用（用于避免部分环境缺失导致的报错）\n", - "- 定义 `train_step`：封装单步训练流程（前向 → loss → 反向 → 裁剪 → 更新）\n", - "\n", - "说明：该 Notebook 保持“代码逻辑不变”，仅整理文字结构以提升可读性。\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7caa5499", - "metadata": {}, - "outputs": [], - "source": [ - "import contextlib\n", - "import mindtorch.autograd as autograd\n", - "import mindtorch as mt\n", - "from mindnlp.transformers import T5ForConditionalGeneration\n", - "\n", - "try:\n", - " import mindtorch.autograd.profiler as _prof\n", - " autograd.profiler = _prof\n", - "except Exception:\n", - " class _DummyProfiler:\n", - " @staticmethod\n", - " @contextlib.contextmanager\n", - " def record_function(name):\n", - " yield\n", - " autograd.profiler = _DummyProfiler()\n", - "\n", - "\n", - "model = T5ForConditionalGeneration.from_pretrained(\"t5-base\")\n", - "\n", - "model.train()\n", - "\n", - "\n", - "lr = 1e-4\n", - "weight_decay = 0.01\n", - "optimizer = mt.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n", - "\n", - "\n", - "clip_norm = 1.0\n", - "\n", - "def to_long(x):\n", - " if hasattr(x, \"asnumpy\"):\n", - " x = x.asnumpy()\n", - " return mt.tensor(x, dtype=mt.long)\n", - "\n", - "def train_step(input_ids, attention_mask, labels):\n", - " input_ids = to_long(input_ids)\n", - " attention_mask = to_long(attention_mask)\n", - " labels = to_long(labels)\n", - "\n", - " optimizer.zero_grad()\n", - " outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", - " loss = outputs.loss\n", - "\n", - " loss.backward()\n", - " mt.nn.utils.clip_grad_norm_(model.parameters(), clip_norm)\n", - " optimizer.step()\n", - "\n", - " return float(loss.item())\n" - ] - }, - { - "cell_type": "markdown", - "id": "0e114bf6", - "metadata": {}, - "source": [ - "## 训练\n", - "\n", - "启动训练循环：\n", - "\n", - "- `num_epochs = 1`：仅训练 1 个 epoch，用于快速验证流程\n", - "- `warm_steps = 200`：每个 epoch 最多跑 200 个 step\n", - "- 日志：每 20 step 打印一次 loss，便于观察收敛趋势\n", - "\n", - "如果需要正式训练，可按实际数据规模与资源条件调整 epoch、step、batch size 与学习率等超参数。\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d1ce5478", - "metadata": {}, - "outputs": [], - "source": [ - "from tqdm import tqdm\n", - "\n", - "num_epochs = 1\n", - "warm_steps = 200\n", - "\n", - "for epoch in range(num_epochs):\n", - " step = 0\n", - " with tqdm(total=warm_steps, desc=f\"Epoch {epoch+1}/{num_epochs}\", unit=\"step\") as pbar:\n", - " for input_ids, attention_mask, labels in train_ds.create_tuple_iterator():\n", - " loss = train_step(input_ids, attention_mask, labels) \n", - " step += 1\n", - "\n", - " if step % 20 == 0:\n", - " tqdm.write(f\"step={step}, loss={loss:.4f}\")\n", - "\n", - " pbar.update(1)\n", - " if step >= warm_steps:\n", - " break\n" - ] - }, - { - "cell_type": "markdown", - "id": "d5306f44", - "metadata": {}, - "source": [ - "## 推理\n", - "\n", - "构造一段较长输入文本，按 T5 摘要任务要求加上前缀 `summarize: `，并调用 `model.generate` 生成摘要序列。最后通过分词器 `decode` 将生成的 token ID 还原为可读文本，用于快速验证微调后的生成效果。\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "32322d1d", - "metadata": {}, - "outputs": [], - "source": [ - "text = \"\"\"Artificial Intelligence (AI) has rapidly evolved and become an integral part of various industries, \n", - "particularly healthcare. AI's ability to analyze large volumes of data and identify patterns that humans might miss \n", - "has revolutionized many aspects of medical care, \n", - "from diagnostics to treatment planning and patient monitoring.\n", - "One of the most significant contributions of AI in healthcare is in the field of diagnostics. \n", - "Machine learning algorithms, a subset of AI, have been trained to interpret medical imaging data, such as X-rays, MRIs, and CT scans.\n", - "These algorithms can often detect abnormalities, such as tumors or fractures, with higher accuracy and speed than human radiologists. \n", - "This not only improves the speed of diagnosis but also reduces the potential for human error, leading to better patient outcomes.\n", - "AI has also shown promise in personalized medicine, where treatments are tailored to an individual's genetic makeup and health history.\n", - "By analyzing patient data, including genetic information, \n", - "AI systems can recommend specific treatment plans that are more likely to be effective. \n", - "This can greatly enhance the precision of treatments, reduce side effects, and improve overall patient care..\"\"\"\n", - "enc = tokenizer(prefix + text, max_length=max_input_length, padding='max_length', truncation=True)\n", - "inp = ms.Tensor([enc.input_ids], dtype=ms.int32)\n", - "gen_ids = model.generate(inp, max_length=60,top_k=0,temperature=0.7)\n", - "summ = tokenizer.decode(gen_ids[0], skip_special_tokens=True)\n", - "print(summ)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 保存模型与分词器\n", - "\n", - "将微调后的模型权重与分词器配置保存到本地目录：\n", - "\n", - "- 保存路径：`./t5_email_summarization_ms270_mn051`\n", - "- 保存内容：模型参数（权重）与 tokenizer 配置/词表\n", - "\n", - "保存后可在后续脚本中通过 `from_pretrained(save_dir)` 直接加载，便于部署推理或继续训练。\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "save_dir = './t5_email_summarization_ms270_mn051'\n", - "model.save_pretrained(save_dir)\n", - "tokenizer.save_pretrained(save_dir)\n", - "print('saved to', save_dir)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mindspore", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/llm/t5/finetune_t5_daily_email_summarization.ipynb b/llm/t5/finetune_t5_daily_email_summarization.ipynb index 183ee49..54f8c97 100644 --- a/llm/t5/finetune_t5_daily_email_summarization.ipynb +++ b/llm/t5/finetune_t5_daily_email_summarization.ipynb @@ -10,7 +10,7 @@ "本 Notebook 演示如何在 **MindSpore 2.7.0** 与 **MindSpore NLP 0.5.1** 环境下，对 `t5-base` 进行摘要任务微调，并完成推理与保存。\n", "\n", "- **任务**：基于 `abisee/cnn_dailymail`（`3.0.0`）数据集进行文本摘要微调（可迁移到“日常邮件摘要”场景）。\n", - "- **目标**：在不改动训练逻辑的前提下，跑通「数据 → 分词 → 数据管线 → 训练 → 推理 → 保存」全流程。\n", + "- **目标**：在不改动训练逻辑的前提下，跑通「数据 → 分词 → 数据处理流水线 → 训练 → 推理 → 保存」全流程。\n", "- **输出**：将微调后的模型与分词器保存至 `./t5_email_summarization_ms270_mn051` 目录，便于后续加载推理或继续训练。\n", "\n", "本示例以快速验证为目标：训练仅运行 **1 个 epoch** 且最多 **200 step**，并每 20 step 打印一次 loss。\n" From 0deb370a089dc82daa33db09f1615e18cb12535e Mon Sep 17 00:00:00 2001 From: tt5416 <1776822920@qq.com> Date: Thu, 8 Jan 2026 20:00:25 +0800 Subject: [PATCH 5/5] update --- ...inetune_t5_daily_email_summarization.ipynb | 84 ++++++++++++++----- 1 file changed, 64 insertions(+), 20 deletions(-) diff --git a/llm/t5/finetune_t5_daily_email_summarization.ipynb b/llm/t5/finetune_t5_daily_email_summarization.ipynb index 54f8c97..b59dfdd 100644 --- a/llm/t5/finetune_t5_daily_email_summarization.ipynb +++ b/llm/t5/finetune_t5_daily_email_summarization.ipynb @@ -23,7 +23,31 @@ "source": [ "## 环境准备\n", "\n", - "安装本示例依赖的 MindSpore NLP 版本。请确保当前 Python 环境中已安装 **MindSpore 2.7.0**（或与之兼容的版本）。\n" + "本案例的运行环境为：\n", + "\n", + "| Python | MindSpore | MindSpore NLP|\n", + "| :----- | :-------- | :------------|\n", + "| 3.10 | 2.7.0 | 0.5.1 |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d9eee91", + "metadata": {}, + "outputs": [], + "source": [ + "# 检查mindspore版本是否为2.7.0，检查mindnlp版本是否为0.5.1,如果不是则取消下个单元格注释进行安装\n", + "!pip show mindspore\n", + "!pip show mindnlp" + ] + }, + { + "cell_type": "markdown", + "id": "e9a4b33f", + "metadata": {}, + "source": [ + "如果你在如[昇思大模型平台](https://xihe.mindspore.cn/training-projects)、[华为云ModelArts](https://www.huaweicloud.com/product/modelarts.html)、[启智社区](https://openi.pcl.ac.cn/)等算力平台的Jupyter在线编程环境中运行本案例，可取消如下代码的注释，进行依赖库安装：" ] }, { @@ -33,7 +57,42 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install mindnlp==0.5.1" + "# 安装mindspore==2.7.0和mindnlp==0.5.1版本，如需更换mindspore和mindnlp版本，可更改下面 MINDSPORE_VERSION 和 MINDNLP_VERSION 变量\n", + "# !pip uninstall mindspore -y\n", + "# %env MINDSPORE_VERSION=2.7.0\n", + "# !pip install mindspore==2.7.0 -i https://repo.mindspore.cn/pypi/simple --trusted-host repo.mindspore.cn --extra-index-url https://repo.huaweicloud.com/repository/pypi/simple\n", + "# !pip uninstall mindnlp -y\n", + "# %env MINDNLP_VERSION=0.5.1\n", + "# !pip install mindnlp==0.5.1 -i https://repo.mindspore.cn/pypi/simple --trusted-host repo.mindspore.cn --extra-index-url https://repo.huaweicloud.com/repository/pypi/simple" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3bb7b07e", + "metadata": {}, + "outputs": [], + "source": [ + "import mindspore as ms\n", + "import mindnlp\n", + "from mindnlp.dataset import load_dataset\n", + "from mindnlp.transformers import AutoTokenizer\n", + "import mindspore.dataset as ds\n", + "from mindspore.dataset import transforms\n", + "import contextlib\n", + "import mindtorch.autograd as autograd\n", + "import mindtorch as mt\n", + "from mindnlp.transformers import T5ForConditionalGeneration\n", + "import numpy as np\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "markdown", + "id": "31311e88", + "metadata": {}, + "source": [ + "其他场景可参考[MindSpore安装指南](https://www.mindspore.cn/install)进行环境搭建。" ] }, { @@ -53,10 +112,8 @@ "metadata": {}, "outputs": [], "source": [ - "import mindspore as ms\n", - "import mindnlp\n", "print('MindSpore:', ms.__version__)\n", - "print('MindNLP:', getattr(mindnlp, '__version__', 'unknown'))\n" + "print('MindNLP:', getattr(mindnlp, '__version__', 'unknown'))" ] }, { @@ -88,7 +145,6 @@ "metadata": {}, "outputs": [], "source": [ - "from mindnlp.dataset import load_dataset\n", "ds = load_dataset('abisee/cnn_dailymail', '3.0.0', split=['train', 'validation', 'test'])\n", "train = ds['train']\n", "val = ds['validation']\n", @@ -118,7 +174,6 @@ "metadata": {}, "outputs": [], "source": [ - "from mindnlp.transformers import AutoTokenizer\n", "tokenizer = AutoTokenizer.from_pretrained('t5-base')\n", "prefix = 'summarize: '\n", "max_input_length = 512\n", @@ -150,7 +205,6 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", "class SummarizationDataset:\n", " def __init__(self, data):\n", " self.data = data\n", @@ -188,14 +242,11 @@ "metadata": {}, "outputs": [], "source": [ - "import mindspore.dataset as ds\n", - "from mindspore.dataset import transforms\n", - "import mindspore\n", "train_ds = ds.GeneratorDataset(SummarizationDataset(train), column_names=['input_ids','attention_mask','labels'], shuffle=True)\n", "val_ds = ds.GeneratorDataset(SummarizationDataset(val), column_names=['input_ids','attention_mask','labels'], shuffle=False)\n", "for c in ['input_ids','attention_mask','labels']:\n", - " train_ds = train_ds.map(operations=transforms.TypeCast(mindspore.int32), input_columns=c)\n", - " val_ds = val_ds.map(operations=transforms.TypeCast(mindspore.int32), input_columns=c)\n", + " train_ds = train_ds.map(operations=transforms.TypeCast(ms.int32), input_columns=c)\n", + " val_ds = val_ds.map(operations=transforms.TypeCast(ms.int32), input_columns=c)\n", "batch_size = 8\n", "train_ds = train_ds.batch(batch_size, drop_remainder=True)\n", "val_ds = val_ds.batch(batch_size, drop_remainder=True)\n", @@ -226,11 +277,6 @@ "metadata": {}, "outputs": [], "source": [ - "import contextlib\n", - "import mindtorch.autograd as autograd\n", - "import mindtorch as mt\n", - "from mindnlp.transformers import T5ForConditionalGeneration\n", - "\n", "try:\n", " import mindtorch.autograd.profiler as _prof\n", " autograd.profiler = _prof\n", @@ -299,8 +345,6 @@ "metadata": {}, "outputs": [], "source": [ - "from tqdm import tqdm\n", - "\n", "num_epochs = 1\n", "warm_steps = 200\n", "\n",