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add transformer chinese readme

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# 目录
<!-- TOC -->
- [目录](#目录)
- [Transfomer 概述](#transfomer-概述)
- [模型架构](#模型架构)
- [数据集](#数据集)
- [环境要求](#环境要求)
- [快速入门](#快速入门)
- [脚本说明](#脚本说明)
- [脚本和样例代码](#脚本和样例代码)
- [脚本参数](#脚本参数)
- [训练脚本参数](#训练脚本参数)
- [运行选项](#运行选项)
- [网络参数](#网络参数)
- [准备数据集](#准备数据集)
- [训练过程](#训练过程)
- [评估过程](#评估过程)
- [模型描述](#模型描述)
- [性能](#性能)
- [训练性能](#训练性能)
- [评估性能](#评估性能)
- [随机情况说明](#随机情况说明)
- [ModelZoo主页](#modelzoo主页)
<!-- /TOC -->
## Transfomer 概述
Transformer于2017年提出，用于处理序列数据。Transformer主要应用于自然语言处理（NLP）领域,如机器翻译或文本摘要等任务。不同于传统的循环神经网络按次序处理数据，Transformer采用注意力机制，提高并行，减少训练次数，从而实现在较大数据集上训练。自Transformer模型引入以来，许多NLP中出现的问题得以解决，衍生出众多网络模型，比如BERT(多层双向transformer编码器)和GPT(生成式预训练transformers) 。
[论文](https://arxiv.org/abs/1706.03762): Ashish Vaswani, Noam Shazeer, Niki Parmar, JakobUszkoreit, Llion Jones, Aidan N Gomez, Ł ukaszKaiser, and Illia Polosukhin. 2017. Attention is all you need. In NIPS 2017, pages 5998–6008.
## 模型架构
Transformer具体包括六个编码模块和六个解码模块。每个编码模块由一个自注意层和一个前馈层组成，每个解码模块由一个自注意层，一个编码-解码-注意层和一个前馈层组成。
## 数据集
- 训练数据集*WMT Englis-German*
- 评估数据集*WMT newstest2014*
## 环境要求
- 硬件（Ascend处理器）
- 使用Ascend处理器准备硬件环境。- 如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei，申请通过后，即可获得资源。
- 框架
- [MindSpore](https://gitee.com/mindspore/mindspore)
- 如需查看详情，请参见如下资源：
- [MindSpore教程](https://www.mindspore.cn/tutorial/training/zh-CN/master/index.html)
- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/zh-CN/master/index.html)
## 快速入门
数据集准备完成后，请按照如下步骤开始训练和评估：
```bash
# 运行训练示例
sh scripts/run_standalone_train_ascend.sh 0 52 /path/ende-l128-mindrecord
# 运行分布式训练示例
sh scripts/run_distribute_train_ascend.sh 8 52 /path/ende-l128-mindrecord rank_table.json
# 运行评估示例
python eval.py > eval.log 2>&1 &
```
## 脚本说明
### 脚本和样例代码
```shell
.
└─Transformer
├─README.md
├─scripts
├─process_output.sh
├─replace-quote.perl
├─run_distribute_train_ascend.sh
└─run_standalone_train_ascend.sh
├─src
├─__init__.py
├─beam_search.py
├─config.py
├─dataset.py
├─eval_config.py
├─lr_schedule.py
├─process_output.py
├─tokenization.py
├─transformer_for_train.py
├─transformer_model.py
└─weight_init.py
├─create_data.py
├─eval.py
└─train.py
```
### 脚本参数
#### 训练脚本参数
```text
usage: train.py [--distribute DISTRIBUTE] [--epoch_size N] [----device_num N] [--device_id N]
[--enable_save_ckpt ENABLE_SAVE_CKPT]
[--enable_lossscale ENABLE_LOSSSCALE] [--do_shuffle DO_SHUFFLE]
[--save_checkpoint_steps N] [--save_checkpoint_num N]
[--save_checkpoint_path SAVE_CHECKPOINT_PATH]
[--data_path DATA_PATH] [--bucket_boundaries BUCKET_LENGTH]
options:
--distribute pre_training by serveral devices: "true"(training by more than 1 device) | "false", default is "false"
--epoch_size epoch size: N, default is 52
--device_num number of used devices: N, default is 1
--device_id device id: N, default is 0
--enable_save_ckpt enable save checkpoint: "true" | "false", default is "true"
--enable_lossscale enable lossscale: "true" | "false", default is "true"
--do_shuffle enable shuffle: "true" | "false", default is "true"
--checkpoint_path path to load checkpoint files: PATH, default is ""
--save_checkpoint_steps steps for saving checkpoint files: N, default is 2500
--save_checkpoint_num number for saving checkpoint files: N, default is 30
--save_checkpoint_path path to save checkpoint files: PATH, default is "./checkpoint/"
--data_path path to dataset file: PATH, default is ""
--bucket_boundaries sequence lengths for different bucket: LIST, default is [16, 32, 48, 64, 128]
```
#### 运行选项
```text
config.py:
transformer_network version of Transformer model: base | large, default is large
init_loss_scale_value initial value of loss scale: N, default is 2^10
scale_factor factor used to update loss scale: N, default is 2
scale_window steps for once updatation of loss scale: N, default is 2000
optimizer optimizer used in the network: Adam, default is "Adam"
eval_config.py:
transformer_network version of Transformer model: base | large, default is large
data_file data file: PATH
model_file checkpoint file to be loaded: PATH
output_file output file of evaluation: PATH
```
#### 网络参数
```text
Parameters for dataset and network (Training/Evaluation):
batch_size batch size of input dataset: N, default is 96
seq_length max length of input sequence: N, default is 128
vocab_size size of each embedding vector: N, default is 36560
hidden_size size of Transformer encoder layers: N, default is 1024
num_hidden_layers number of hidden layers: N, default is 6
num_attention_heads number of attention heads: N, default is 16
intermediate_size size of intermediate layer: N, default is 4096
hidden_act activation function used: ACTIVATION, default is "relu"
hidden_dropout_prob dropout probability for TransformerOutput: Q, default is 0.3
attention_probs_dropout_prob dropout probability for TransformerAttention: Q, default is 0.3
max_position_embeddings maximum length of sequences: N, default is 128
initializer_range initialization value of TruncatedNormal: Q, default is 0.02
label_smoothing label smoothing setting: Q, default is 0.1
input_mask_from_dataset use the input mask loaded form dataset or not: True | False, default is True
beam_width beam width setting: N, default is 4
max_decode_length max decode length in evaluation: N, default is 80
length_penalty_weight normalize scores of translations according to their length: Q, default is 1.0
compute_type compute type in Transformer: mstype.float16 | mstype.float32, default is mstype.float16
Parameters for learning rate:
learning_rate value of learning rate: Q
warmup_steps steps of the learning rate warm up: N
start_decay_step step of the learning rate to decay: N
min_lr minimal learning rate: Q
```
### 准备数据集
- 您可以使用[Shell脚本](https://github.com/tensorflow/nmt/blob/master/nmt/scripts/wmt16_en_de.sh)下载并预处理WMT英-德翻译数据集。假设您已获得下列文件：
- train.tok.clean.bpe.32000.en
- train.tok.clean.bpe.32000.de
- vocab.bpe.32000
- newstest2014.tok.bpe.32000.en
- newstest2014.tok.bpe.32000.de
- newstest2014.tok.de
- 将原数据转换为MindRecord数据格式进行训练：
``` bash
paste train.tok.clean.bpe.32000.en train.tok.clean.bpe.32000.de > train.all
python create_data.py --input_file train.all --vocab_file vocab.bpe.32000 --output_file /path/ende-l128-mindrecord --max_seq_length 128 --bucket [16,32,48,64,128]
```
- 将原数据转化为MindRecord数据格式进行评估：
``` bash
paste newstest2014.tok.bpe.32000.en newstest2014.tok.bpe.32000.de > test.all
python create_data.py --input_file test.all --vocab_file vocab.bpe.32000 --output_file /path/newstest2014-l128-mindrecord --num_splits 1 --max_seq_length 128 --clip_to_max_len True --bucket [128]
```
### 训练过程
- 在`config.py`中设置选项，包括loss_scale、学习率和网络超参数。点击[这里](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html)查看更多数据集信息。
- 运行`run_standalone_train.sh`，进行Transformer模型的非分布式训练。
``` bash
sh scripts/run_standalone_train.sh DEVICE_TARGET DEVICE_ID EPOCH_SIZE DATA_PATH
```
- 运行`run_distribute_train_ascend.sh`，进行Transformer模型的非分布式训练。
``` bash
sh scripts/run_distribute_train_ascend.sh DEVICE_NUM EPOCH_SIZE DATA_PATH RANK_TABLE_FILE
```
### 评估过程
- 在`eval_config.py`中设置选项。确保已设置了‘data_file'、'model_file’和'output_file'文件路径。
- 运行`eval.py`，评估Transformer模型。
```bash
python eval.py
```
- 运行`process_output.sh`，处理输出标记ids，获得真实翻译结果。
```bash
sh scripts/process_output.sh REF_DATA EVAL_OUTPUT VOCAB_FILE
```
您将会获得REF_DATA.forbleu和EVAL_OUTPUT.forbleu两个文件来进行BLEU分数计算。
- 如需计算BLEU分数，详情参见[perl脚本](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/generic/multi-bleu.perl)，并运行一下命令获得BLEU分数。
```bash
perl multi-bleu.perl REF_DATA.forbleu < EVAL_OUTPUT.forbleu
```
## 模型描述
### 性能
#### 训练性能
| 参数 | Transformer |
| -------------------------- | -------------------------------------------------------------- |
| 资源 | Ascend 910 |
| 上传日期 | 2020-06-09 |
| MindSpore版本 | 0.5.0-beta |
| 数据集 | WMT英-德翻译数据集 |
| 训练参数 | epoch=52, batch_size=96 |
| 优化器 | Adam |
| 损失函数 | Softmax Cross Entropy |
| BLEU分数 | 28.7 |
| 速度 | 400毫秒/步(8卡) |
| 损失 | 2.8 |
| 参数 (M) | 213.7 |
| 推理检查点 | 2.4G （.ckpt文件） |
| 脚本 | <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/nlp/transformer> |
#### 评估性能
| 参数 | GoogleNet |
| ------------------- | --------------------------- |
|资源| Ascend 910 |
| 上传日期 | 2020-06-09 |
| MindSpore版本 | 0.5.0-beta |
| 数据集 | WMT newstest2014 |
| batch_size | 1 |
| 输出 | BLEU score |
| 准确率 | BLEU=28.7 |
## 随机情况说明
以下三种随机情况：
- 轮换数据集
- 初始化部分模型权重
- 随机失活运行
train.py已经设置了一些种子，避免数据集轮换和权重初始化的随机性。若需关闭随机失活，将src/config.py中相应的dropout_prob参数设置为0。
## ModelZoo主页
请浏览官网[主页](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)。

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