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
  • Inference Process
• Model Description
  • Performance
    • Result
      • Training Performance
      • Inference Performance
• 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

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 preparation and configuration.
3. Training.
4. Inference.

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

# run training example
cd ./scripts
sh run_standalone_train_ascend.sh DATASET_SCHEMA_TRAIN PRE_TRAIN_DATASET

# run distributed training example
cd ./scripts
sh run_distributed_train_ascend.sh RANK_TABLE_ADDR DATASET_SCHEMA_TRAIN PRE_TRAIN_DATASET

# run evaluation example
cd ./scripts
sh run_standalone_eval_ascend.sh DATASET_SCHEMA_TEST TEST_DATASET EXISTED_CKPT_PATH \
  VOCAB_ADDR BPE_CODE_ADDR TEST_TARGET

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, model configuration, and optimizer parameters.

• config for GNMTv2

  'random_seed': 50         # global random seed
  'epochs':6                # total training epochs
  'batch_size': 128         # training batch size
  'dataset_sink_mode': true # whether use dataset sink mode
  'seq_length': 51          # max length of source sentences
  'vocab_size': 32320       # vocabulary size
  'hidden_size': 125        # the output's last dimension of dynamicRNN
  'initializer_range': 0.1  # initializer range
  'max_decode_length': 125  # max length of decoder
  'lr': 0.1                 # initial learning rate
  'lr_scheduler': 'WarmupMultiStepLR'  # learning rate scheduler
  'existed_ckpt': ''        # the absolute full path to save the checkpoint file
  

For more configuration details, please refer the script config/config.py file.

Training Process

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

• 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.

Start task training on a single device and run the shell script scripts/run_standalone_train_ascend.sh:

cd ./scripts
sh run_standalone_train_ascend.sh DATASET_SCHEMA_TRAIN PRE_TRAIN_DATASET

In this script, the DATASET_SCHEMA_TRAIN and PRE_TRAIN_DATASET are the dataset schema and dataset address.

Run scripts/run_distributed_train_ascend.sh for distributed training of GNMTv2 model. 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 RANK_TABLE_ADDR DATASET_SCHEMA_TRAIN PRE_TRAIN_DATASET

Note: the RANK_TABLE_ADDR is the hccl_json file 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 RANK_TABLE_ADDR file.

Inference Process

For inference using a trained model on multiple hardware platforms, such as Ascend 910. Set options in config/config_test.json.

Run the shell script 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
sh run_standalone_eval_ascend.sh DATASET_SCHEMA_TEST TEST_DATASET EXISTED_CKPT_PATH \
  VOCAB_ADDR BPE_CODE_ADDR TEST_TARGET

The DATASET_SCHEMA_TEST and the TEST_DATASET are the schema and address of inference dataset respectively, and EXISTED_CKPT_PATH is the path of the model file generated during training process. The VOCAB_ADDR is the vocabulary address, BPE_CODE_ADDR is the bpe code address and the TEST_TARGET are the path of answers.

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
outputs	probability
Speed	344ms/step (8pcs)
Total Time	7800s (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
Total Time	1560s
outputs	probability
Accuracy	BLEU Score= 24.05
Model for inference	1.8G (.ckpt file)

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.

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