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pcl_pangu/README.md 0000664 0000000 0000000 00000016064 14300571201 0014305 0 ustar 00root root 0000000 0000000
# pcl_pangu: 鹏程·盘古系列模型SDK
## 简介
欢迎您使用鹏程·盘古SDK 发布版——pcl_pangu v1.1！，如果您使用过程中有任何问题请加入鹏程·盘古技术交流群进行交流[[加入](#微信交流群)]，或者在<任务>中提issue。
pcl_pangu项目的目标是将 `鹏程·盘古`[[项目主页](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha)]开源模型、以及基于该模型的一系列任务拓展与持续学习得到的模型从语料数据预处理到模型训练推理部署整个流水线封装成开发工具包SDK。其目标与特点在于：
- 向开发者提供简单易用的 鹏程·盘古应用拓展API，包括数据预处理、模型预训练、推理、微调、模型转换、模型压缩等功能。您可以快速体验鹏程·盘古大模型，并且可以轻松的开发自己的模型；
- 向开发者提供基于鹏程·盘古大模型的应用拓展案例，如：鹏程.盘古增强版（Evolution）；鹏程·盘古多语言模型（mPanGu）；
- 支持 mindspore 和 pytorch 框架，支持 NPU 和 GPU 设备；提供 mindspore 和 pytorch 模型转换工具，可实现鹏程·盘古模型异构框架的快速切换及兼容生态发展。
## SDK API
## 案例教程
- [SaaS化模型推理服务提供：支持用户快速调用SaaS服务，获得鹏程盘古系列模型推理结果](docs/README_PGSaas.md)
- [dataset 数据预处理接口：提供了从 txt 文件到训练文件格式的转换（mindrecord 格式/bin文件）](docs/README_DATASET.md)
- [alpha 鹏程·盘古的基础大模型：分别在 mindspore 和 pytorch 框架下的使用教程，包括预训练、fine-tune、推理等](docs/README_PG.md)
- [Evolution alpha的增强版模型： Evolution 具有更强的任务理解与处理能力](docs/README_PGE.md)
- [mPangu alpha的多语言版预训练语言模型及多语言机器翻译模型：具有53种语言预训练语言模型能力及53种语言间的多对多机器翻译能力，在53种语言的单语语料进行预训练，然后在平行语料中持续学习得到，主要是以中文为中心的多语言大模型及机器翻译大模型](docs/README_MPG.md)
- [model_converter 模型转换模块：提供了把 mindspore 模型转为 pytorch 模型的 api，和将 pytorch 分布式模型转为单机模型、单机模型转为分布式模型的api](docs/README_CONVERTER.md)
## 红利！！！ 免费算力申请
如果您需要华为昇腾NPU或英伟达GPU算力对pcl_pangu进行试用和测试，请登录[[启智开源社区](https://git.openi.org.cn/)]，注册后进入本项目即可直接申请免费算力。
- **模型推理**
参考[[alpha_2B6模型推理](https://git.openi.org.cn/PCL-Platform.Intelligence/pcl_pangu/modelarts/train-job/78547)]
参考[[mPangu_2B6模型推理](https://git.openi.org.cn/PCL-Platform.Intelligence/pcl_pangu/modelarts/train-job/78689)]
- **模型微调**
参考[[alpha_2B6模型训练](https://git.openi.org.cn/PCL-Platform.Intelligence/pcl_pangu/modelarts/train-job/78719)]
## 模型Saas服务
通过我们提供Python API获取我们的模型Saas服务，使用方法请参考：[**使用方法**](docs/README_PGSaas.md)。
**现阶段为免费使用，但因为服务资源有限会对总流量做限制，请勿高频调用。** 主要功能包括：
- 获取所有模型信息，相关接口为model_list()
- 获取指定模型信息，model_info(model)
- 在线推理， generate(model, prompt_input, max_token=None, top_k=None, top_p=None, api_key=None, **kwargs)
## 快速上手
- **模型推理**
提供了简单的推理流程，用户只需要输入需要生成的文本就可以在进行推理。首先需要在[模型下载页面](docs/README_MODELS.md)下载对应的模型，并且解压。下面展示如何快速使用 `alpha` 模型推理：
```python
from pcl_pangu.context import set_context
from pcl_pangu.model import alpha, evolution, mPangu
set_context(backend='mindspore')
config = alpha.model_config_npu(model='350M',load='350M/mode/path')
alpha.inference(config,input='四川的省会是?')
```
在 pytorch 框架上使用只需要把`set_context(backend='mindspore')` 改为 `backend='pytorch'`,并配置`model_config_gpu`
- **模型训练**
大模型的训练具有较高的入门门槛，不易上手，因此我们提供了一个简单的训练工具，可以快速的训练一个模型。用户只需要准备好训练数据，然后运行下面的代码：
- 建议训练【350M模型】使用至少1卡；
- 建议训练【2B6模型】使用8卡，至少2卡；默认模型并行数为2，可手工配置model_config.model_parallel_size <= 卡数
- 建议训练【13B模型】使用8卡；默认模型并行数为4，可手工配置model_config.model_parallel_size <= 卡数
```python
from pcl_pangu.context import set_context
from pcl_pangu.dataset import txt2mindrecord
from pcl_pangu.model import alpha, evolution, mPangu
set_context(backend='mindspore')
data_path = 'path/of/training/dataset'
txt2mindrecord(input_glob='your/txt/path/*.txt', output_prefix=data_path)
config = alpha.model_config_npu(model='350M',
load='path/to/save/ckpt',
data_path=data_path)
alpha.train(config)
```
- **模型 fine-tuning**
模型的 fine-tuning 流程基本和训练的流程一致，只需要更改为调用 `model.fine_tune()` ：
- 建议微调【350M模型】使用至少1卡；
- 建议微调【2B6模型】使用8卡，至少2卡；默认模型并行数为2，可手工配置model_config.model_parallel_size <= 卡数
- 建议微调【13B模型】使用8卡；默认模型并行数为4，可手工配置model_config.model_parallel_size <= 卡数
```python
from pcl_pangu.context import set_context
from pcl_pangu.dataset import txt2mindrecord
from pcl_pangu.model import alpha, evolution, mPangu
set_context(backend='mindspore')
data_path = 'path/of/training/dataset'
txt2mindrecord(input_glob='your/txt/path/*.txt', output_prefix=data_path)
config = alpha.model_config_npu(model='350M',
load='path/of/your/existing/ckpt',
data_path=data_path)
alpha.fine_tune(config)
```
## 环境搭建
- mindspore
1、推荐使用 openi 启智社区提供的 npu 环境，[使用教程](docs/README_OPENI_MS.md)：
2、裸机使用：需要安装 mindspore 环境，并且安装：
```bash
pip install pcl_pangu
```
- pytorch
如果你想在 pytorch 框架下使用，强烈建议使用我们预构建好的 docker 环境：
```bash
docker pull yands/pangu-alpha-megatron-lm-nvidia-pytorch:20.03.2
or
docker pull registry.cn-hangzhou.aliyuncs.com/pcl_hub/pangu_pytorch:pytorch.20.03.2
```
容器安装包`pip install pcl_pangu`，并使用 `/opt/conda/bin/python`。
### 模型下载
所有模型文件下载地址：[请参考文件](./docs/README_MODELS.md)
#### 微信交流群
添加微信:鹏程.盘古α交流群:
pcl_pangu/docs/ 0000775 0000000 0000000 00000000000 14300571201 0013747 5 ustar 00root root 0000000 0000000 pcl_pangu/docs/README_CONVERTER.md 0000664 0000000 0000000 00000003572 14300571201 0016664 0 ustar 00root root 0000000 0000000 # model_converter: 模型转换模块
该模块提供了以下功能：
1、将 mindspore 框架训练的模型转换为 pytorch 框架的模型
2、将分布式训练的 pytorch 模型转为单机模型，方便推理
3、将 pytorch 框架下的单机模型转为分布式训练模型；结合第二点功能，可以实现不同分布式训练策略的转换。
## 使用
- **将 mindspore 模型转换为 pytorch 模型**
* 第一步，将mindspore 模型提取成为 numpy 文件。这个步骤需要使用 mindspore 框架，所以需要事项安装好 mindspore 环境。代码：
```python
from pcl_pangu.model_converter import ms_2_numpy
ms_2_numpy(ms_ckpt_path='path/of/mindspore/ckpt',
npy_path='path/to/save/numpy/files')
```
`要求安装 mindspore`
* 第二步，将 numpy 文件加载到 pytorch 模型里面。需要先安装好 pytorch 环境。代码:
```python
from pcl_pangu.model_converter import numpy_2_pt
from pcl_pangu.model import alpha
alpha_config = alpha.model_config_gpu(model='2B6')
numpy_2_pt(config = alpha_config,
numpy_ckpt = 'path/to/numpy/files')
```
`要求安装 pytorch`
- **将分布式训练的 pytorch 模型转为单机模型**
```python
from pcl_pangu.model import alpha
from pcl_pangu.model_converter import merge_pt
alpha_config = alpha.model_config_gpu(model='2B6')
merge_pt(config = alpha_config,
sharding_path='path/of/your/sharding/models',
num_ranks='num/of/model/rank')
```
`要求安装 pytorch`
- **将单机模型转为分布式模型**
```python
from pcl_pangu.model import alpha
from pcl_pangu.model_converter import split_pt
alpha_config = alpha.model_config_gpu(model='2B6')
split_pt(config=alpha_config,
merged_path='path/of/merged/model',
num_ranks='num/of/rank/you/want/to/split')
```
`要求安装 pytorch`
pcl_pangu/docs/README_DATASET.md 0000664 0000000 0000000 00000002606 14300571201 0016377 0 ustar 00root root 0000000 0000000
### pcl_pangu：数据预处理
- 一、准备txt文本数据
- [`格式`]
- 1、每段完整的内容作为一个数据sample
- 2、sample之间用两个换行符\n\n隔开
- 二、数据集预处理成模型输入的要求格式
- [`mindspore-txt2mindrecord`]
- 1、确认当前环境使用mindspore框架
- 2、编写数据预处理代码
- 接口参数vocab_file属于['vocab_4w', 'vocab_13w', 'your_vocab_file'],
内置了预训练4w长度的词表'vocab_4w'和 mPangu 128320长度的词表'vocab_13w'，且支持用户自定义词表。
默认使用'vocab_4w'词表，如有需要可以切换或自定义词表文件。
```python
from pcl_pangu.context import set_context
from pcl_pangu.dataset import txt2mindrecord
set_context(backend="mindspore")
txt2mindrecord(input_glob='YOUR_DATASET_TXT_DIR/*', output_prefix='Your_Saving_DIR/mindrecord', vocab_file='vocab_4w')
```
- [`pytorch-txt2bin`]
- 1、确认当前环境使用pytorch框架
- 2、编写数据预处理代码
```python
from pcl_pangu.context import set_context
from pcl_pangu.dataset import txt2bin
set_context(backend="pytorch")
txt2bin(input_glob='YOUR_DATASET_TXT_DIR/*', output_prefix='Your_Saving_DIR/text_document')
``` pcl_pangu/docs/README_MODELS.md 0000664 0000000 0000000 00000011562 14300571201 0016276 0 ustar 00root root 0000000 0000000 **鹏程·盘古系列模型保存在 Openi 开源网站上，需要先注册成为用户才可以下载下面的模型。**
## 1、 alpha
| 模型 | 大小 | 来源 |md5|
| :-------------- | :---- | :------------------------------------------------------------ |:-------------------- |
| 鹏程·盘古α-2.6B | 2.6G |[2.6B_part0](https://git.openi.org.cn/attachments/59fac5b4-ec54-493f-95ba-d0b724f39ed6) |3904bb0e551f36206cfca96ab0f63cba |
| 鹏程·盘古α-2.6B | 2.6G |[2.6B_part1](https://git.openi.org.cn/attachments/6535b42b-67b5-469b-9506-067c5c5768e0) |19407cbc33ee4929d68f500e015869cc |
| 鹏程·盘古α-2.6B | 2.6G |[2.6B_part2](https://git.openi.org.cn/attachments/4dd9178d-43cb-47aa-b299-1c0bfe38c343) |9ce140312f962079e13f91f681a269ad |
| 鹏程·盘古α-2.6B | 2.6G |[2.6B_part3](https://git.openi.org.cn/attachments/c1acd001-03ec-4484-ab31-4b3636603d50) |290158ffc426a89b7e357018c701e71d |
| 鹏程·盘古α-2.6B | 391M |[2.6B_word_embedding](https://git.openi.org.cn/attachments/12319797-b0ce-4563-99e6-4f6a7d3ec0cb) |9ca91161bde9c089dd8304db0428455e |
| 鹏程·盘古α-2.6B | 10M |[2.6B_top_query_embedding](https://git.openi.org.cn/attachments/287dc9a4-8c81-4132-ad4d-d2e350701ba3) |320b401050319c1c63117c36b93651c1 |
| 鹏程·盘古α-2.6B | 10M |[2.6B_position_embedding](https://git.openi.org.cn/attachments/1f0f3dcb-9d82-4d59-84c0-3d6bade7de2f) |bf7eb84b7b9fd6004cb22260883ab080 |
| 鹏程·盘古α-13B | 12G |[13B_part0](https://git.openi.org.cn/attachments/341df568-12e7-4dad-afa4-612bd9181b41) |724bdaf87e0eb4aa9ca0f35e8af92464 |
| 鹏程·盘古α-13B | 12G |[13B_part1](https://git.openi.org.cn/attachments/bc6142f0-dee3-41bd-98b3-d8c9f75a6843) |493579a1ccc2a16a616cf04daedd031f |
| 鹏程·盘古α-13B | 12G |[13B_part2](https://git.openi.org.cn/attachments/15be215d-d488-4eee-9780-aea3c56efa47) |de4e8a023c56b7134bff4dfbcc230871 |
| 鹏程·盘古α-13B | 12G |[13B_part3](https://git.openi.org.cn/attachments/695f935a-5b4e-42e2-adaf-962fcb742382) |6a6bcfdadf5a0d23c567d6959bf4eaba |
| 鹏程·盘古α-13B | 781M |[13B_word_embedding](https://git.openi.org.cn/attachments/a4f86058-86a7-40de-8b39-6c35242b206f) |a4e0c5893d255500396dfa1bfe7ba3ba |
| 鹏程·盘古α-13B | 20M |[13B_top_query_embedding](https://git.openi.org.cn/attachments/22d49fa2-5aa9-4d3f-9d77-3f491350ffc0) |4d75c3b42f85db825cebdc99329160bd |
| 鹏程·盘古α-13B | 20M |[13B_position_embedding](https://git.openi.org.cn/attachments/76e796fa-0d97-48ea-8e2c-f49bb2f2b21b) |299ee4174cc4de7fdebec1c3e631e348 |
| 鹏程·盘古α-200B | ~T | 敬请期待 | |
| 模型 | 参数 | 平台 | 模型文件 | 大小 |
| ------ | ------ | ------- | ------ | --- |
| 鹏程·盘古α-350M | 350M | pytorch | [pangu-alpha_350m_fp16-pytorch.zip](https://git.openi.org.cn/attachments/6e516012-18be-4576-a54e-570218bb3dbd) | 600M |
| 鹏程·盘古α-2.6B |2.6B | pytorch | [Pangu-alpha_2.6B_fp16_mgt.zip](https://git.openi.org.cn/attachments/72aec03d-6bdb-4652-ac2a-8099db4b0bed) | 4.6G |
| 鹏程·盘古α-13B |13B | pytorch | [Pangu-alpha_13B_fp16_mgt.zip](https://git.openi.org.cn/attachments/937b3e2d-98fb-4871-9691-b32afb5a4d79?type=0) | 22G |
## 2、 evolution
| 参数 | 平台| 模型文件| 大小 |
|-------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------| --- |
| 2.6B | pytorch | [pangu-alpha-evolution_2.6B_fp16.zip](https://git.openi.org.cn/attachments/a8c58df2-92c5-4aa2-827f-69a24691ae2c?type=0") | 4.8G |
## 3、 mPanGu
### 多语言预训练模型
| 参数 | 平台 | 模型文件 | 大小 |
| ------ | ------- |------------------------------------------------------------------------------------------------------------------------| --- |
|2.6B | Mindspore | [mPanGu_Alpha-53-Pretrain_fp16.ckpt](https://git.openi.org.cn/attachments/42fbe1b7-8d61-4126-a74b-dd754fdc47e1?type=0) | 6.0G |
|2.6B | Mindspore | [mPanGu_Alpha-53-Pretrain_fp32.ckpt](https://git.openi.org.cn/attachments/584e1945-a01c-4f08-a9b6-9bd30f0e21be?type=0) | 12G |
### 多语言翻译模型
| 参数 | 平台 | 模型文件 | 大小 |
| ------ | ------- | ------ | --- |
|2.6B | Mindspore | [mPanGu_Alpha-53_fp16.ckpt](https://git.openi.org.cn/attachments/3fa0d3cb-bda6-4bd1-8c24-bab2600518c0?type=0) | 6.0G |
|2.6B | Mindspore | [mPanGu_Alpha-53_fp32.ckpt](https://git.openi.org.cn/attachments/584e1945-a01c-4f08-a9b6-9bd30f0e21be?type=0) | 12G |
pcl_pangu/docs/README_MPG.md 0000664 0000000 0000000 00000010367 14300571201 0015740 0 ustar 00root root 0000000 0000000 # 鹏程.mPanGu-α 53
## 简介
鹏程.mPanGu-α-53基于一带一路多语言翻译场景应用，支持多语言翻译任务的预训练、迁移学习、微调，支持在NPU/GPU上基于mindspore分布式训练（最少8卡）、 推理（全精度/FP16，1卡），单模型支持53种语言任意两种语言间的互译。
相关原理和方法，请参见[[项目主页](https://git.openi.org.cn/PCL-Platform.Intelligence/mPanGu-Alpha-53)]。
**要点：**
- 通过多语种模板匹配支持单语、单双语混合、双语训练模式，模型支持任两语种间的互译；
- 提供了基于盘古模型进行单语预训练和单双语混合增强训练的新范式，扩充低资源语种的语料多样性;
- 鹏程.mPanGu-α，参数量2.6B，学习多语种通用的跨语言表示，在低资源语种翻译效果上提升明显。
## 使用方法
### 环境搭建
原生支持基于mindspore的NPU/GPU环境，通过模型转换也可支持基于pytorch的GPU环境，环境信息：[请参考项目主页](../README.md)。
### 数据准备
如果基于鹏程.mPanGu-α做微调或者基于鹏程.mPanGu-α直接训练，请参考如下的所有数据准备过程，基于鹏程.mPanGu-α做推理预测只需要参考‘模型预测’流程即可。
- step1, 基于统任务格式将任务数据转换成文本生成形式，也可以直接下我们提供的已处理好的样本数据做调试实验。
```
示例：
转换后数据：
单语zh.txt：'Mary Ng （伍凤仪），加拿大华裔联邦国会议员，几天前刚刚被委任加拿大小型企业及出口促进部部长。'
双语zh-en_corpus.txt：'第三，无功绩。\t third, with no success.'
```
- step2，生成txt文本数据，便于step3程序的处理。
```
每段完整的内容作为一个数据样本。
样本间用两个换行符\n\n隔开
```
- step3，txt数据转化为mindRecord文件格式，将文本数据转换成能直接输入模型的数值数据。
```python
from pcl_pangu.context import set_context
from pcl_pangu.dataset import txt2mindrecord
set_context(backend="mindspore")
txt2mindrecord(input_glob='YOUR_DATASET_TXT_DIR/*', \
output_prefix='Your_Saving_DIR/text_document', \
vocab_file='../tokenizer/spm_13w/vocab')
```
### 模型训练
如果你想利用鹏程.mPanGu-α做训练，可以参考如下实现过程。
```python
from pcl_pangu.context import set_context
from pcl_pangu.dataset import txt2mindrecord
from pcl_pangu.model import mPangu
set_context(backend="mindspore")
data_path = 'path/of/training/dataset'
txt2mindrecord(input_glob='your/txt/path/*.txt', \
output_prefix=data_path, \
vocab_file='../tokenizer/spm_13w/vocab')
config = mPangu.model_config_npu(model='2B6', save='path/to/save/ckpt', data_path=data_path)
mPangu.train(config)
```
### 模型微调
如果你想基于鹏程.mPanGu-α用自己的数据做进一步的增量训练，可以参考如下实现过程。
首先需要在[模型下载页面](docs/README_MODELS.md)下载对应的`鹏程.mPanGu-α`多语言翻译模型。
模型微调流程基本和训练的流程一致，只需要更改为调用 `*.fine_tune()`。
```python
from pcl_pangu.context import set_context
from pcl_pangu.dataset import txt2mindrecord
from pcl_pangu.model import mPangu
set_context(backend="mindspore")
data_path = 'path/of/training/dataset'
txt2mindrecord(input_glob='your/txt/path/*.txt', \
output_prefix=data_path, \
vocab_file='../tokenizer/spm_13w/vocab')
config = mPangu.model_config_npu(model='2B6', save='path/of/your/existing/ckpt', data_path=data_path)
mPangu.fine_tune(config)
```
### 模型预测
提供了简单的推理流程，用户只需要输入需要生成的文本就可以在进行推理。首先需要在[模型下载页面](docs/README_MODELS.md)下载对应的模型。
```python
from pcl_pangu.context import set_context
from pcl_pangu.model import mPangu
set_context(backend='mindspore')
config = mPangu.model_config_npu(model='2B6',save='2B6/mode/path')
mPangu.inference(config,input='四川的省会是?', src_language='zh', tag_language='kk')
```
pcl_pangu/docs/README_OPENI_MS.md 0000664 0000000 0000000 00000001061 14300571201 0016555 0 ustar 00root root 0000000 0000000 如果你想在 mindspore 框架下使用，强烈建议使用openi启智社区提供的 npu 容器环境[1/2/4/8卡]，镜像选择[Mindspore-1.3.0-c78-python3.7-euleros2.8-aarch64]:
在项目根目录下，编写环境预安装脚本ma-pre-start.sh脚本，npu容器环境会在执行.py脚本前，预先执行该.sh脚本进行环境配置，sh脚本内容如下：
```bash
WORK_DIR=YOUR_CODE_DIR
LOCAL_DIR=$(cd "$(dirname "$0")";pwd)
cd ${LOCAL_DIR}/${WORK_DIR}/
pip install pcl_pangu
```
使用`/usr/bin/python`。 pcl_pangu/docs/README_PG.md 0000664 0000000 0000000 00000006344 14300571201 0015623 0 ustar 00root root 0000000 0000000 # 鹏程.盘古(PanGu-Alpha)
## 简介
「鹏程·盘古α」由以鹏城实验室为首的技术团队联合攻关，首次基于“鹏城云脑Ⅱ”和国产mindSpore框架的自动混合并行模式实现在2048卡算力集群上的大规模分布式训练，训练出业界首个2000亿参数以中文为核心的预训练生成语言模型。鹏程·盘古α预训练模型支持丰富的场景应用，在知识问答、知识检索、知识推理、阅读理解等文本生成领域表现突出，具备很强的小样本学习能力。
相关原理和方法，请参见[[项目主页](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha)]。
## 使用方法
### 环境搭建
目前仅支持pytorch 和 mindspore。环境信息：[请参考项目主页](../README.md)。
### 数据准备
如果基于鹏程.盘古做微调或者基于鹏程.盘古直接训练，请参考下面的数据准备过程。
- step1，组织训练预料的格式；转为txt文本数据，txt 文件格式如下：
```
每段完整的内容作为一个数据样本。
样本间用两个换行符\n\n隔开
```
- step2，数值化，将文本数据转换成能直接输入模型的数值数据。
pytorch
```python
from pcl_pangu.context import set_context
from pcl_pangu.dataset import txt2bin
set_context(backend="pytorch")
txt2bin(input_glob='YOUR_DATASET_TXT_DIR/*.txt', output_prefix='Your_Saving_DIR/text_document')
```
### 训练
如果你想利用鹏程.盘古的技术对预训练语言模型如鹏程.盘古做增强训练，可以参考如下实现过程。
首先需要在[模型下载页面](./README_MODELS.md)下载对应的`鹏程.盘古`模型，并且解压。
```python
from pcl_pangu.context import set_context
from pcl_pangu.dataset import txt2bin
from pcl_pangu.model import alpha
set_context(backend="pytorch") #or backend='mindspore'
data_path = 'path/of/training/dataset'
txt2bin(input_glob='your/txt/path/*.txt', output_prefix=data_path)
config = alpha.model_config_gpu(model='350M', save='path/to/save/ckpt', data_path=data_path)
alpha.train(config)
```
### 微调
如果你想基于鹏程.盘古对你任务数据做进一步的增量训练，可以参考如下实现过程。
首先需要在[模型下载页面](./README_MODELS.md)下载对应的`鹏程.盘古`模型，并且解压。
模型微调流程基本和训练的流程一致，只需要更改为调用 `model.fine_tune()`。
```python
from pcl_pangu.context import set_context
from pcl_pangu.dataset import txt2bin
from pcl_pangu.model import alpha
set_context(backend="pytorch")
data_path = 'path/of/training/dataset'
txt2bin(input_glob='your/txt/path/*.txt', output_prefix=data_path)
config = alpha.model_config_gpu(model='2B6', save='path/of/your/existing/ckpt', data_path=data_path)
alpha.fine_tune(config)
```
### 预测
如果你想利用鹏程.盘古直接用于下游的推理，可以参考如下实现过程。
首先需要在[模型下载页面](./README_MODELS.md)下载对应的`鹏程.盘古`模型，并且解压。
```python
from pcl_pangu.context import set_context
from pcl_pangu.model import alpha
set_context(backend='pytorch')
config = alpha.model_config_gpu(model='2B6', save='2B6/mode/path')
alpha.inference(config, input="四川的省会是?")
```
pcl_pangu/docs/README_PGE.md 0000664 0000000 0000000 00000011077 14300571201 0015727 0 ustar 00root root 0000000 0000000 # 鹏程.盘古增强版(PanGu-Alpha-Evolution)
## 简介
盘古增强版，更强的任务理解与处理能力，更加擅长遵循您的任务说明。
目前盘古增强版2.6B GPU版本的模型推理阶段显存占用控制在7G以内，8G显存的机器即可推理。
相关原理和方法，请参见[[项目主页](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha-Evolution)]。
**要点：**
- 创新应用了多任务学习、任务统一格式、提示微调和持续学习技术，对基本版模型进行了能力扩展和增强，性能得到大幅提升。
- 提供了基于盘古大模型进行提示微调和持续学习的性能优化新范式，更好的识别用户的任务说明，同时做到尽量保持模型的原始生成能力。
- 盘古增强版，参数量2.6B，在中英翻译、开放域知识问答、文本分类、摘要生成等能力提升显著，在一张V100 GPU卡上可以完成多路并行推理。
## 使用方法
### 环境搭建
目前仅支持pytorch，环境信息：[请参考项目主页](../README.md)。
### 数据准备
如果基于鹏程.盘古增强版做微调或者基于鹏程.盘古直接训练，请参考如下的所有数据准备过程。如果基于鹏程.盘古增强版做推理预测，则只需要参考step1即可。
- step1, 基于统一任务格式将任务数据转换成文本生成形式，也可以直接下载我们提供的已处理好的[样本数据](https://git.openi.org.cn/attachments/c11e6124-f94c-4124-b3c0-af1133952cb2?type=0)做调试实验。
```
示例：
NLI任务，转换后样例："推理关系判断：\n前提：新的权利已经足够好了\n假设：每个人都很喜欢最新的福利\n选项：矛盾，蕴含，中立\n答案：中立"
分类任务，转换后样例："文本分类：\n基本上可以说是诈骗\n选项：积极，消极\n答案：消极"
QA任务，转换后样例："问题：\n郑州是那个省的\n答案：河南"
翻译任务，转换后样例："翻译成英文：\n不过他承认，美国与欧洲关系密切。\nHe admitted that the United States was relatively friendly with Europe."
```
- step2，生成txt文本数据，便于step3程序的处理。
```
每段完整的内容作为一个数据样本。
样本间用两个换行符\n\n隔开
```
- step3，数值化，将文本数据转换成能直接输入模型的数值数据。
pytorch
```python
from pcl_pangu.context import set_context
from pcl_pangu.dataset import txt2bin
set_context(backend="pytorch")
txt2bin(input_glob='YOUR_DATASET_TXT_DIR/*', output_prefix='Your_Saving_DIR/text_document')
```
### 训练
如果你想利用鹏程.盘古增强版的技术对预训练语言模型如鹏程.盘古做增强训练，可以参考如下实现过程。
首先需要在[模型下载页面](./README_MODELS.md)下载对应的`鹏程.盘古基本版`模型，并且解压。
```python
from pcl_pangu.context import set_context
from pcl_pangu.dataset import txt2bin
from pcl_pangu.model import evolution
set_context(backend="pytorch")
data_path = 'path/of/training/dataset'
txt2bin(input_glob='your/txt/path/*.txt', output_prefix=data_path)
config = evolution.model_config_gpu(model='350M', save='path/to/save/ckpt', data_path=data_path)
evolution.train(config)
```
### 微调
如果你想基于鹏程.盘古增强版对你的任务数据做进一步的增量训练，可以参考如下实现过程。
首先需要在[模型下载页面](./README_MODELS.md)下载对应的`鹏程.盘古增强版`模型，并且解压。
模型微调流程基本和训练的流程一致，只需要更改为调用 `model.fine_tune()`。
```python
from pcl_pangu.context import set_context
from pcl_pangu.dataset import txt2bin
from pcl_pangu.model import evolution
set_context(backend="pytorch")
data_path = 'path/of/training/dataset'
txt2bin(input_glob='your/txt/path/*.txt', output_prefix=data_path)
config = evolution.model_config_gpu(model='2B6', save='path/of/your/existing/ckpt', data_path=data_path)
evolution.fine_tune(config)
```
### 预测
如果你想利用鹏程.盘古增强版直接用于下游任务的推理，可以参考如下实现过程。
首先需要在[模型下载页面](./README_MODELS.md)下载对应的`鹏程.盘古增强版`模型，并且解压。
```python
from pcl_pangu.context import set_context
from pcl_pangu.model import evolution
set_context(backend='pytorch')
config = evolution.model_config_gpu(model='2B6', load='2B6/mode/path')
evolution.inference(config, input="文本分类：\n基本上可以说是诈骗\n选项：积极，消极\n答案：")
```
pcl_pangu/docs/README_PGSaas.md 0000664 0000000 0000000 00000002451 14300571201 0016426 0 ustar 00root root 0000000 0000000 # 鹏程.盘古系列大模型Saas化
## 简介
您可以通过我们提供Python API获取我们的模型Saas服务。目前支持模型如下：
- 鹏程·盘古Alpha 13B，prompt输入构造请参见[[项目主页](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha)]。
- 鹏程·盘古增强版 2.6B，prompt输入构造请参见[[项目主页](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha-Evolution)]。
### 获取模型信息
如果不了解我们目前提供Saas服务，可以通过以下接口获取。
```python
from pcl_pangu.online import ModelInfo
# 获取所有在线模型的基本信息
models = ModelInfo.model_list()
# 获取指定在线模型的基本信息
model = ModelInfo.model_info(model="pangu-alpha-13B-md")
```
### 推理
如果你想体验鹏程·盘古【增强版】SaaS服务，可以参考如下实现过程：
```python
from pcl_pangu.online import Infer
model = "pangu-alpha-evolution-2B6-pt"
prompt_input = "文本分类：\n基本上可以说是诈骗\n选项：积极，消极\n答案："
result = Infer.generate(model, prompt_input)
```
同理，鹏程·盘古【alpha】SaaS服务使用如下：
```python
from pcl_pangu.online import Infer
model = "pangu-alpha-13B-md"
prompt_input = "你好"
result = Infer.generate(model, prompt_input)
``` pcl_pangu/docs/README_mPanGu.md 0000664 0000000 0000000 00000010324 14300571201 0016475 0 ustar 00root root 0000000 0000000 # 鹏程.盘古增强版(PanGu-Alpha-Evolution)
## 简介
鹏程.mPanGu-α-53基于一带一路多语言翻译场景应用，支持多语言翻译任务的预训练、迁移学习、微调，支持在NPU/GPU上基于mindspore分布式训练（最少8卡）、
推理（全精度/FP16，1卡），单模型支持53种语言任意两种语言间的互译。
相关原理和方法，请参见[[项目主页](https://git.openi.org.cn/PCL-Platform.Intelligence/mPanGu-Alpha-53)]。
**要点：**
- 通过多语种模板匹配支持单语、单双语混合、双语训练模式，模型支持任两语种间的互译；
- 提供了基于盘古模型进行单语预训练和单双语混合增强训练的新范式，扩充低资源语种的语料多样性;
- 鹏程.mPanGu-α，参数量2.6B，学习多语种通用的跨语言表示，在低资源语种翻译效果上提升明显。
## 使用方法
### 环境搭建
原生支持基于mindspore的NPU/GPU环境，通过模型转换也可支持基于pytorch的GPU环境，环境信息：[请参考项目主页](../README.md)。
### 数据准备
如果基于鹏程.mPanGu-α做微调或者基于鹏程.mPanGu-α直接训练，请参考如下的所有数据准备过程，基于鹏程.mPanGu-α做推理预测只需要参考‘模型预测’流程即可。
- step1, 基于统任务格式将任务数据转换成文本生成形式，也可以直接下我们提供的已处理好的[样本数据](https://git.openi.org.cn/attachments/c11e6124-f94c-4124-b3c0-af1133952cb2?type=0)做调试实验。
```
示例：
转换后数据：
单语zh.txt：'Mary Ng （伍凤仪），加拿大华裔联邦国会议员，几天前刚刚被委任加拿大小型企业及出口促进部部长。'
双语zh-en_corpus.txt：'第三，无功绩。\t third, with no success.'
```
- step2，生成txt文本数据，便于step3程序的处理。
```
每段完整的内容作为一个数据样本。
样本间用两个换行符\n\n隔开
```
- step3，配置vocab_file为'vocab_13w', txt数据转化为mindrecord文件格式，将文本数据转换成能直接输入模型的数值数据。
```python
from pcl_pangu.context import set_context
from pcl_pangu.dataset import txt2mindrecord
set_context(backend="mindspore")
txt2mindrecord(input_glob='YOUR_DATASET_TXT_DIR/*', \
output_prefix='Your_Saving_DIR/text_document', \
vocab_file='vocab_13w')
```
### 模型训练
如果你想利用鹏程.mPanGu-α做训练，可以参考如下实现过程。
```python
from pcl_pangu.context import set_context
from pcl_pangu.dataset import txt2mindrecord
from pcl_pangu.model import mPangu
set_context(backend="mindspore")
data_path = 'path/of/training/dataset'
txt2mindrecord(input_glob='your/txt/path/*.txt', \
output_prefix=data_path, \
vocab_file='vocab_13w')
config = mPangu.model_config_npu(model='2B6', save='path/to/save/ckpt', data_path=data_path)
mPangu.train(config)
```
### 模型微调
如果你想基于鹏程.mPanGu-α用自己的数据做进一步的增量训练，可以参考如下实现过程。
首先需要在[模型下载页面](docs/README_MODELS.md)下载对应的`鹏程.mPanGu-α`多语言翻译模型。
模型微调流程基本和训练的流程一致，只需要更改为调用 `model.fine_tune()`。
```python
from pcl_pangu.context import set_context
from pcl_pangu.dataset import txt2mindrecord
from pcl_pangu.model import mPangu
set_context(backend="mindspore")
data_path = 'path/of/training/dataset'
txt2mindrecord(input_glob='your/txt/path/*.txt', \
output_prefix=data_path, \
vocab_file='vocab_13w')
config = mPangu.model_config_npu(model='2B6', save='path/of/your/existing/ckpt', data_path=data_path)
mPangu.fine_tune(config)
```
### 模型预测
提供了简单的推理流程，用户只需要输入需要生成的文本就可以在进行推理。首先需要在[模型下载页面](docs/README_MODELS.md)下载对应的模型。
```python
from pcl_pangu.context import set_context
from pcl_pangu.model import mPangu
set_context(backend='mindspore')
config = mPangu.model_config_npu(model='2B6',save='2B6/mode/path')
mPangu.inference(config,input='四川的省会是?', src_language='zh', tag_language='kk')
```
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