mindspore/model_zoo/official/nlp/gnmt_v2

• GNMT v2 For MindSpore
• Model Structure
• Dataset
• Environment Requirements
• • Platform
  • Software
• Quick Start
• Script Description
• • Dataset Preparation
  • Configuration File
  • Training Process
  • Evaluation Process
• Model Description
• • Performance
  • • Result
    • • Training Performance
      • Inference Performance
  • Practice
  • • Dataset Preprocessing
    • Training
    • Inference
• Random Situation Description
• Others
• ModelZoo 主页

• GNMT v2 For MindSpore
• Model Structure
• Dataset
• Environment Requirements
  • Platform
  • Software
• Quick Start
• Script Description
  • Dataset Preparation
  • Configuration File
  • Training Process
  • Evaluation Process
• Model Description
  • Performance
    • Result
      • Training Performance
      • Inference Performance
  • Practice
    • Dataset Preprocessing
    • Training
    • Inference
• Random Situation Description
• Others
• ModelZoo

GNMT v2 For MindSpore

The GNMT v2 model is similar to the model described in Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, which is mainly used for corpus translation.

Model Structure

The GNMTv2 model mainly consists of an encoder, a decoder, and an attention mechanism, where the encoder and the decoder use a shared word embedding vector.
Encoder: consists of four long short-term memory (LSTM) layers. The first LSTM layer is bidirectional, while the other three layers are unidirectional.
Decoder: consists of four unidirectional LSTM layers and a fully connected classifier. The output embedding dimension of LSTM is 1024.
Attention mechanism: uses the standardized Bahdanau attention mechanism. First, the first layer output of the decoder is used as the input of the attention mechanism. Then, the computing result of the attention mechanism is connected to the input of the decoder LSTM, which is used as the input of the subsequent LSTM layer.

Dataset

Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.

• WMT Englis-German for training.
• WMT newstest2014 for evaluation.

Environment Requirements

Platform

• Hardware (Ascend)
  • Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the application form to ascend@huawei.com. Once approved, you could get the resources for trial.
• Framework
  • Install MindSpore.
• For more information, please check the resources below:
  • MindSpore tutorials
  • MindSpore API

Software

numpy
sacrebleu==1.2.10
sacremoses==0.0.19
subword_nmt==0.3.7

Quick Start

After dataset preparation, you can start training and evaluation as follows:

# run training example
python train.py --config /home/workspace/gnmt_v2/config/config.json

# run distributed training example
cd ./scripts
sh run_distributed_train_ascend.sh

# run evaluation example
cd ./scripts
sh run_standalone_eval_ascend.sh

Script Description

The GNMT network script and code result are as follows:

├── gnmt
  ├── README.md                              // Introduction of GNMTv2 model.
  ├── config
  │   ├──__init__.py                         // User interface.  
  │   ├──config.py                           // Configuration instance definition.
  │   ├──config.json                         // Configuration file for pre-train or finetune.
  │   ├──config_test.json                    // Configuration file for test.
  ├── src
  │   ├──__init__.py                         // User interface.  
  │   ├──dataset   
  │      ├──__init__.py                      // User interface.   
  │      ├──base.py                          // Base class of data loader.                       
  │      ├──bi_data_loader.py                // Bilingual data loader.
  │      ├──load_dataset.py                  // Dataset loader to feed into model.
  │      ├──schema.py                        // Define schema of mindrecord.
  │      ├──tokenizer.py                     // Tokenizer class.
  │   ├──gnmt_model
  │      ├──__init__.py                      // User interface.
  │      ├──attention.py                     // Bahdanau attention mechanism.
  │      ├──beam_search.py                   // Beam search decoder for inferring.
  │      ├──bleu_calculate.py                // Calculat the blue accuracy.
  │      ├──components.py                    // Components.
  │      ├──create_attention.py              // Recurrent attention.
  │      ├──create_attn_padding.py           // Create attention paddings from input paddings.
  │      ├──decoder.py                       // GNMT decoder component.
  │      ├──decoder_beam_infer.py            // GNMT decoder component for beam search.
  │      ├──dynamic_rnn.py                   // DynamicRNN.
  │      ├──embedding.py                     // Embedding component.
  │      ├──encoder.py                       // GNMT encoder component.
  │      ├──gnmt.py                          // GNMT model architecture.
  │      ├──gnmt_for_infer.py                // Use GNMT to infer.
  │      ├──gnmt_for_train.py                // Use GNMT to train.
  │      ├──grad_clip.py                     // Gradient clip
  │   ├──utils
  │      ├──__init__.py                      // User interface.
  │      ├──initializer.py                   // Parameters initializer.
  │      ├──load_weights.py                  // Load weights from a checkpoint or NPZ file.
  │      ├──loss_moniter.py                  // Callback of monitering loss during training step.
  │      ├──lr_scheduler.py                  // Learning rate scheduler.
  │      ├──optimizer.py                     // Optimizer.
  ├── scripts
  │   ├──run_distributed_train_ascend.sh     // shell script for distributed train on ascend.
  │   ├──run_standalone_eval_ascend.sh       // shell script for standalone eval on ascend.
  │   ├──run_standalone_train_ascend.sh      // shell script for standalone eval on ascend.
  ├── create_dataset.py                      // dataset preparation.
  ├── eval.py                                // Infer API entry.
  ├── requirements.txt                       // Requirements of third party package.
  ├── train.py                               // Train API entry.

Dataset Preparation

You may use this shell script to download and preprocess WMT English-German dataset. Assuming you get the following files:

• train.tok.clean.bpe.32000.en

• train.tok.clean.bpe.32000.de

• vocab.bpe.32000

• bpe.32000

• newstest2014.en

• newstest2014.de

• Convert the original data to tfrecord for training and evaluation:

  python create_dataset.py --src_folder /home/workspace/wmt16_de_en --output_folder /home/workspace/dataset_menu
  

Configuration File

The JSON file in the config/ directory is the template configuration file.
Almost all required options and parameters can be easily assigned, including the training platform, dataset and model configuration, and optimizer parameters. By setting the corresponding options, you can also obtain optional functions such as loss scale and checkpoint.
For more information about attributes, see the config/config.py file.

Training Process

The model training requires the shell script scripts/run_standalone_train_ascend.sh. In this script, set environment variables and the training script train.py to be executed in gnmt_v2/.
Start task training on a single device and run the following command in bash:

cd ./scripts
sh run_standalone_train_ascend.sh

or multiple devices
Task training on multiple devices and run the following command in bash to be executed in scripts/.:

cd ./scripts
sh run_distributed_train_ascend.sh

Note: Ensure that the hccl_json file is assigned when distributed training is running.
Currently, inconsecutive device IDs are not supported in scripts/run_distributed_train_ascend.sh. The device ID must start from 0 in the distribute_script/rank_table_8p.json file.

Evaluation Process

Set options in config/config_test.json. Make sure the 'existed_ckpt', 'dataset_schema' and 'test_dataset' are set to your own path.

Run scripts/run_standalone_eval_ascend.sh to process the output token ids to get the BLEU scores.

cd ./scripts
sh run_standalone_eval_ascend.sh

Model Description

Performance

Result

Training Performance

Parameters	Ascend
Resource	Ascend 910
uploaded Date	11/06/2020 (month/day/year)
MindSpore Version	1.0.0
Dataset	WMT Englis-German
Training Parameters	epoch=6, batch_size=128
Optimizer	Adam
Loss Function	Softmax Cross Entropy
BLEU Score	24.05
Speed	344ms/step (8pcs)
Loss	63.35
Params (M)	613
Checkpoint for inference	1.8G (.ckpt file)
Scripts	gnmt_v2

Inference Performance

Parameters	Ascend
Resource	Ascend 910
Uploaded Date	11/06/2020 (month/day/year)
MindSpore Version	1.0.0
Dataset	WMT newstest2014
batch_size	128
outputs	BLEU score
Accuracy	BLEU= 24.05

Practice

The process of GNMTv2 performing the text translation task is as follows:

1. Download the wmt16 data corpus and extract the dataset. For details, see the chapter "Dataset" above.
2. Dataset preprocessing.
3. Perform training.
4. Perform inference.

Dataset Preprocessing

For a pre-trained model, configure the following options in the config.json file:

python create_dataset.py --src_folder /home/work_space/wmt16_de_en  --output_folder /home/work_space/dataset_menu

Training

For a pre-trained model, configure the following options in the config/config.json file:

• Assign pre_train_dataset and dataset_schema to the training dataset path.
• Select an optimizer ('momentum/adam/lamb' is available).
• Specify ckpt_prefix and ckpt_path in checkpoint_path to save the model file.
• Set other parameters, including dataset configuration and network configuration.
• If a pre-trained model exists, assign existed_ckpt to the path of the existing model during fine-tuning.

Run the shell script run.sh:

cd ./scripts
sh run_standalone_train_ascend.sh

Inference

For inference using a trained model on multiple hardware platforms, such as GPU, Ascend 910, and Ascend 310, see Network Migration.
For inference interruption, configure the following options in the config/config.json file:

• Assign test_dataset and the dataset_schema to the inference dataset path.
• Assign existed_ckpt and the checkpoint_path to the path of the model file generated during training.
• Set other parameters, including dataset configuration and network configuration.

Run the shell script run.sh:

cd ./scripts
sh run_standalone_eval_ascend.sh

Random Situation Description

There are three random situations:

• Shuffle of the dataset.
• Initialization of some model weights.
• Dropout operations.
  Some seeds have already been set in train.py to avoid the randomness of dataset shuffle and weight initialization. If you want to disable dropout, please set the corresponding dropout_prob parameter to 0 in config/config.json.

Others

This model has been validated in the Ascend environment and is not validated on the CPU and GPU.

ModelZoo 主页

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