!9016 delete input_mask_from_dataset option in transformer's config

From: @yuchaojie
Reviewed-by: @liangchenghui,@linqingke
Signed-off-by: @linqingke

model_zoo/official/nlp/transformer/README.md

@@ -18,28 +18,24 @@
 - [Description of Random Situation](#description-of-random-situation)
 - [ModelZoo Homepage](#modelzoo-homepage)
 
-
+## [Transfomer Description](#contents)
-# [Transfomer Description](#contents)
 
 Transformer was proposed in 2017 and designed to process sequential data. It is adopted mainly in the field of natural language processing(NLP), for tasks like machine translation or text summarization. Unlike traditional recurrent neural network(RNN) which processes data in order, Transformer adopts attention mechanism and improve the parallelism, therefore reduced training times and made training on larger datasets possible. Since Transformer model was introduced, it has been used to tackle many problems in NLP and derives many network models, such as BERT(Bidirectional Encoder Representations from Transformers) and GPT(Generative Pre-trained Transformer).
 
 [Paper](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.
 
-
+## [Model Architecture](#contents)
-# [Model Architecture](#contents)
 
 Specifically, Transformer contains six encoder modules and six decoder modules. Each encoder module consists of a self-attention layer and a feed forward layer, each decoder module consists of a self-attention layer, a encoder-decoder-attention layer and a feed forward layer.
 
-
+## [Dataset](#contents)
-# [Dataset](#contents)
 
 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](#contents)
-# [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
     - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](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) to ascend@huawei.com. Once approved, you can get the resources.
@@ -49,8 +45,7 @@ Note that you can run the scripts based on the dataset mentioned in original pap
     - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
     - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
 
-
+## [Quick Start](#contents)
-# [Quick Start](#contents)
 
 After dataset preparation, you can start training and evaluation as follows:
 
@@ -65,10 +60,9 @@ sh scripts/run_distribute_train_ascend.sh 8 52 /path/ende-l128-mindrecord rank_t
 python eval.py > eval.log 2>&1 &
 ```
 
+## [Script Description](#contents)
 
-# [Script Description](#contents)
+### [Script and Sample Code](#contents)
-
-## [Script and Sample Code](#contents)
 
 ```shell
 .
@@ -96,10 +90,11 @@ python eval.py > eval.log 2>&1 &
   └─train.py
 ```
 
-## [Script Parameters](#contents)
+### [Script Parameters](#contents)
 
-### Training Script Parameters
+#### Training Script Parameters
-```
+
+```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]
@@ -123,8 +118,9 @@ options:
     --bucket_boundaries        sequence lengths for different bucket: LIST, default is [16, 32, 48, 64, 128]
 ```
 
-### Running Options
+#### Running Options
-```
+
+```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
@@ -139,8 +135,9 @@ eval_config.py:
     output_file                     output file of evaluation: PATH
 ```
 
-### Network Parameters
+#### Network Parameters
-```
+
+```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
@@ -155,7 +152,6 @@ Parameters for dataset and network (Training/Evaluation):
     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
@@ -169,6 +165,7 @@ Parameters for learning rate:
 ```
 
 ## [Dataset Preparation](#contents)
+
 - You may use this [shell script](https://github.com/tensorflow/nmt/blob/master/nmt/scripts/wmt16_en_de.sh) 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
@@ -183,6 +180,7 @@ Parameters for learning rate:
     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]
     ```
+
 - Convert the original data to mindrecord for evaluation:
 
     ``` bash
@@ -190,7 +188,6 @@ Parameters for learning rate:
     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]
     ```
 
-
 ## [Training Process](#contents)
 
 - Set options in `config.py`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html) for more information about dataset.
@@ -200,13 +197,13 @@ Parameters for learning rate:
     ``` bash
     sh scripts/run_standalone_train.sh DEVICE_TARGET DEVICE_ID EPOCH_SIZE DATA_PATH
     ```
+
 - Run `run_distribute_train_ascend.sh` for distributed training of Transformer model.
 
     ``` bash
     sh scripts/run_distribute_train_ascend.sh DEVICE_NUM EPOCH_SIZE DATA_PATH RANK_TABLE_FILE
     ```
 
-
 ## [Evaluation Process](#contents)
 
 - Set options in `eval_config.py`. Make sure the 'data_file', 'model_file' and 'output_file' are set to your own path.
@@ -222,6 +219,7 @@ Parameters for learning rate:
     ```bash
     sh scripts/process_output.sh REF_DATA EVAL_OUTPUT VOCAB_FILE
     ```
+
     You will get two files, REF_DATA.forbleu and EVAL_OUTPUT.forbleu, for BLEU score calculation.
 
 - Calculate BLEU score, you may use this [perl script](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/generic/multi-bleu.perl) and run following command to get the BLEU score.
@@ -230,10 +228,11 @@ Parameters for learning rate:
     perl multi-bleu.perl REF_DATA.forbleu < EVAL_OUTPUT.forbleu
     ```
 
-# [Model Description](#contents)
+## [Model Description](#contents)
-## [Performance](#contents)
 
-### Training Performance 
+### [Performance](#contents)
+
+#### Training Performance
 
 | Parameters                 | Ascend                                                         |
 | -------------------------- | -------------------------------------------------------------- |
@@ -249,10 +248,9 @@ Parameters for learning rate:
 | Loss                       | 2.8                                                            |
 | Params (M)                 | 213.7                                                          |
 | Checkpoint for inference   | 2.4G (.ckpt file)                                              |
-| Scripts                    | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/nlp/transformer |
+| Scripts                    | [Transformer scripts](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/nlp/transformer) |
 
-
+#### Evaluation Performance
-### Evaluation Performance
 
 | Parameters          | Ascend                      |
 | ------------------- | --------------------------- |
@@ -264,17 +262,16 @@ Parameters for learning rate:
 | outputs             | BLEU score                  |
 | Accuracy            | BLEU=28.7                   |
 
-
+## [Description of Random Situation](#contents)
-# [Description of Random Situation](#contents)
 
 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 src/config.py.
 
-
+## [ModelZoo Homepage](#contents)
-# [ModelZoo Homepage](#contents)
 
 Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

model_zoo/official/nlp/transformer/mindspore_hub_conf.py

@@ -32,7 +32,6 @@ transformer_net_cfg_large = TransformerConfig(
     max_position_embeddings=128,
     initializer_range=0.02,
     label_smoothing=0.1,
-    input_mask_from_dataset=True,
     dtype=mstype.float32,
     compute_type=mstype.float16
 )

model_zoo/official/nlp/transformer/src/config.py

@@ -49,7 +49,6 @@ if cfg.transformer_network == 'large':
         max_position_embeddings=128,
         initializer_range=0.02,
         label_smoothing=0.1,
-        input_mask_from_dataset=True,
         dtype=mstype.float32,
         compute_type=mstype.float16)
     transformer_net_cfg_gpu = TransformerConfig(
@@ -66,7 +65,6 @@ if cfg.transformer_network == 'large':
         max_position_embeddings=128,
         initializer_range=0.02,
         label_smoothing=0.1,
-        input_mask_from_dataset=True,
         dtype=mstype.float32,
         compute_type=mstype.float16)
 if cfg.transformer_network == 'base':
@@ -84,6 +82,5 @@ if cfg.transformer_network == 'base':
         max_position_embeddings=128,
         initializer_range=0.02,
         label_smoothing=0.1,
-        input_mask_from_dataset=True,
         dtype=mstype.float32,
         compute_type=mstype.float16)

model_zoo/official/nlp/transformer/src/eval_config.py

@@ -41,7 +41,6 @@ if cfg.transformer_network == 'large':
         attention_probs_dropout_prob=0.0,
         max_position_embeddings=128,
         label_smoothing=0.1,
-        input_mask_from_dataset=True,
         beam_width=4,
         max_decode_length=80,
         length_penalty_weight=1.0,
@@ -61,7 +60,6 @@ if cfg.transformer_network == 'base':
         attention_probs_dropout_prob=0.0,
         max_position_embeddings=128,
         label_smoothing=0.1,
-        input_mask_from_dataset=True,
         beam_width=4,
         max_decode_length=80,
         length_penalty_weight=1.0,

model_zoo/official/nlp/transformer/src/transformer_model.py

@@ -51,8 +51,6 @@ class TransformerConfig:
                                  model. Default: 128.
         initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
         label_smoothing (float): label smoothing setting. Default: 0.1
-        input_mask_from_dataset (bool): Specifies whether to use the input mask that loaded from
-                                 dataset. Default: True.
         beam_width (int): beam width setting. Default: 4
         max_decode_length (int): max decode length in evaluation. Default: 80
         length_penalty_weight (float): normalize scores of translations according to their length. Default: 1.0
@@ -73,7 +71,6 @@ class TransformerConfig:
                  max_position_embeddings=128,
                  initializer_range=0.02,
                  label_smoothing=0.1,
-                 input_mask_from_dataset=True,
                  beam_width=4,
                  max_decode_length=80,
                  length_penalty_weight=1.0,
@@ -92,7 +89,6 @@ class TransformerConfig:
         self.max_position_embeddings = max_position_embeddings
         self.initializer_range = initializer_range
         self.label_smoothing = label_smoothing
-        self.input_mask_from_dataset = input_mask_from_dataset
         self.beam_width = beam_width
         self.max_decode_length = max_decode_length
         self.length_penalty_weight = length_penalty_weight
@@ -1014,7 +1010,6 @@ class TransformerModel(nn.Cell):
             config.hidden_dropout_prob = 0.0
             config.attention_probs_dropout_prob = 0.0
 
-        self.input_mask_from_dataset = config.input_mask_from_dataset
         self.batch_size = config.batch_size
         self.hidden_size = config.hidden_size
         self.num_hidden_layers = config.num_hidden_layers

tests/st/model_zoo_tests/transformer/test_transformer.py

@@ -52,7 +52,6 @@ def get_config(version='base', batch_size=1):
             max_position_embeddings=128,
             initializer_range=0.02,
             label_smoothing=0.1,
-            input_mask_from_dataset=True,
             dtype=mstype.float32,
             compute_type=mstype.float16)
     elif version == 'base':
@@ -70,7 +69,6 @@ def get_config(version='base', batch_size=1):
             max_position_embeddings=128,
             initializer_range=0.02,
             label_smoothing=0.1,
-            input_mask_from_dataset=True,
             dtype=mstype.float32,
             compute_type=mstype.float16)
     else: