fix doc & code bugs for gnmt

model_zoo/official/nlp/gnmt_v2/config/config.json

@@ -1,7 +1,4 @@
 {
-  "training_platform": {
-    "modelarts": false
-  },
   "dataset_config": {
     "random_seed": 50,
     "epochs": 6,
@@ -9,10 +6,8 @@
     "dataset_schema": "/home/workspace/dataset_menu/train.tok.clean.bpe.32000.en.json",
     "pre_train_dataset": "/home/workspace/dataset_menu/train.tok.clean.bpe.32000.en.tfrecord-001-of-001",
     "fine_tune_dataset": null,
-    "test_dataset": null,
     "valid_dataset": null,
-    "dataset_sink_mode": true,
+    "dataset_sink_mode": true
-    "dataset_sink_step": 2
   },
   "model_config": {
     "seq_length": 51,

model_zoo/official/nlp/gnmt_v2/config/config.py

@@ -53,7 +53,6 @@ def get_source_list(folder: str) -> List:
 
 
 PARAM_NODES = {"dataset_config",
-               "training_platform",
                "model_config",
                "loss_scale_config",
                "learn_rate_config",
@@ -65,88 +64,99 @@ class GNMTConfig:
     Configuration for `GNMT`.
 
     Args:
-        random_seed (int): Random seed.
+        random_seed (int): Random seed, it can be changed.
-        batch_size (int): Batch size of input dataset.
         epochs (int): Epoch number.
-        dataset_sink_mode (bool): Whether enable dataset sink mode.
+        batch_size (int): Batch size of input dataset.
-        dataset_sink_step (int): Dataset sink step.
-        lr_scheduler (str): Whether use lr_scheduler, only support "ISR" now.
-        lr (float): Initial learning rate.
-        min_lr (float): Minimum learning rate.
-        decay_start_step (int): Step to decay.
-        warmup_steps (int): Warm up steps.
         dataset_schema (str): Path of dataset schema file.
         pre_train_dataset (str): Path of pre-training dataset file or folder.
         fine_tune_dataset (str): Path of fine-tune dataset file or folder.
         test_dataset (str): Path of test dataset file or folder.
         valid_dataset (str): Path of validation dataset file or folder.
-        ckpt_path (str): Checkpoints save path.
+        dataset_sink_mode (bool): Whether enable dataset sink mode.
-        save_ckpt_steps (int): Interval of saving ckpt.
+        seq_length (int): Length of input sequence.
-        ckpt_prefix (str): Prefix of ckpt file.
+        vocab_size (int): The shape of each embedding vector.
-        keep_ckpt_max (int): Max ckpt files number.
+        hidden_size (int): Size of embedding, attention, dim.
-        seq_length (int): Length of input sequence. Default: 64.
-        vocab_size (int): The shape of each embedding vector. Default: 46192.
-        hidden_size (int): Size of embedding, attention, dim. Default: 512.
         num_hidden_layers (int): Encoder, Decoder layers.
-
         intermediate_size (int): Size of intermediate layer in the Transformer
-            encoder/decoder cell. Default: 4096.
+            encoder/decoder cell.
         hidden_act (str): Activation function used in the Transformer encoder/decoder
-            cell. Default: "relu".
+            cell.
+        hidden_dropout_prob (float): The dropout probability for hidden outputs.
+        attention_dropout_prob (float): The dropout probability for Attention module.
+        initializer_range (float): Initialization value of TruncatedNormal.
+        label_smoothing (float): Label smoothing setting.
+        beam_width (int): Beam width for beam search in inferring.
+        length_penalty_weight (float): Penalty for sentence length.
+        max_decode_length (int): Max decode length for inferring.
+        input_mask_from_dataset (bool): Specifies whether to use the input mask that loaded from
+            dataset.
         init_loss_scale (int): Initialized loss scale.
         loss_scale_factor (int): Loss scale factor.
         scale_window (int): Window size of loss scale.
-        beam_width (int): Beam width for beam search in inferring. Default: 4.
+        lr_scheduler (str): Whether use lr_scheduler, only support "ISR" now.
-        length_penalty_weight (float): Penalty for sentence length. Default: 1.0.
+        optimizer (str): Optimizer for training, e.g. Adam, Lamb, momentum. Default: Adam.
-        label_smoothing (float): Label smoothing setting. Default: 0.1.
+        lr (float): Initial learning rate.
-        input_mask_from_dataset (bool): Specifies whether to use the input mask that loaded from
+        min_lr (float): Minimum learning rate.
-            dataset. Default: True.
+        decay_steps (int): Decay steps.
+        lr_scheduler_power(float): A value used to calculate decayed learning rate.
+        warmup_lr_remain_steps (int or float): Start decay at 'remain_steps' iteration.
+        warmup_lr_decay_interval (int):interval between LR decay steps.
+        decay_start_step (int): Step to decay.
+        warmup_steps (int): Warm up steps.
+        existed_ckpt (str): Using existed checkpoint to keep training or not.
+        save_ckpt_steps (int): Interval of saving ckpt.
+        keep_ckpt_max (int): Max ckpt files number.
+        ckpt_prefix (str): Prefix of ckpt file.
+        ckpt_path (str): Checkpoints save path.
         save_graphs (bool): Whether to save graphs, please set to True if mindinsight
             is wanted.
-        dtype (mstype): Data type of the input. Default: mstype.float32.
+        dtype (mstype): Data type of the input.
-        max_decode_length (int): Max decode length for inferring. Default: 64.
+
-        hidden_dropout_prob (float): The dropout probability for hidden outputs. Default: 0.1.
+    Note:
-        attention_dropout_prob (float): The dropout probability for
+        There are three types of learning rate scheduler, square root scheduler, polynomial
-            Multi-head Self-Attention. Default: 0.1.
+        decay scheduler and warmup multistep learning rate scheduler.
-        initializer_range (float): Initialization value of TruncatedNormal. Default: 0.02.
+        In square root scheduler, the following parameters can be used, lr, decay_start_step,
+            warmup_steps and min_lr.
+        In polynomial decay scheduler, the following parameters can be used, lr, min_lr, decay_steps,
+            warmup_steps, lr_scheduler_power.
+        In warmmup multistep learning rate scheduler, the following parameters can be used, lr, warmup_steps,
+            warmup_lr_remain_steps, warmup_lr_decay_interval, decay_steps, lr_scheduler_power.
     """
 
     def __init__(self,
-                 modelarts=False, random_seed=74,
+                 random_seed=50,
-                 epochs=6, batch_size=64,
+                 epochs=6, batch_size=128,
                  dataset_schema: str = None,
                  pre_train_dataset: str = None,
                  fine_tune_dataset: str = None,
                  test_dataset: str = None,
                  valid_dataset: str = None,
-                 dataset_sink_mode=True, dataset_sink_step=1,
+                 dataset_sink_mode=True,
                  seq_length=51, vocab_size=32320, hidden_size=1024,
                  num_hidden_layers=4, intermediate_size=4096,
                  hidden_act="tanh",
                  hidden_dropout_prob=0.2, attention_dropout_prob=0.2,
                  initializer_range=0.1,
                  label_smoothing=0.1,
-                 beam_width=5,
+                 beam_width=2,
-                 length_penalty_weight=1.0,
+                 length_penalty_weight=0.6,
                  max_decode_length=50,
                  input_mask_from_dataset=False,
-                 init_loss_scale=2 ** 10,
+                 init_loss_scale=65536,
-                 loss_scale_factor=2, scale_window=128,
+                 loss_scale_factor=2, scale_window=1000,
-                 lr_scheduler="", optimizer="adam",
+                 lr_scheduler="WarmupMultiStepLR",
-                 lr=1e-4, min_lr=1e-6,
+                 optimizer="adam",
-                 decay_steps=4, lr_scheduler_power=1,
+                 lr=2e-3, min_lr=1e-6,
+                 decay_steps=4, lr_scheduler_power=0.5,
                  warmup_lr_remain_steps=0.666, warmup_lr_decay_interval=-1,
                  decay_start_step=-1, warmup_steps=200,
-                 existed_ckpt="", save_ckpt_steps=2000, keep_ckpt_max=20,
+                 existed_ckpt="", save_ckpt_steps=3452, keep_ckpt_max=6,
                  ckpt_prefix="gnmt", ckpt_path: str = None,
-                 save_step=10000,
                  save_graphs=False,
                  dtype=mstype.float32):
 
         self.save_graphs = save_graphs
         self.random_seed = random_seed
-        self.modelarts = modelarts
-        self.save_step = save_step
         self.dataset_schema = dataset_schema
         self.pre_train_dataset = get_source_list(pre_train_dataset)  # type: List[str]
         self.fine_tune_dataset = get_source_list(fine_tune_dataset)  # type: List[str]
@@ -158,7 +168,6 @@ class GNMTConfig:
 
         self.epochs = epochs
         self.dataset_sink_mode = dataset_sink_mode
-        self.dataset_sink_step = dataset_sink_step
 
         self.ckpt_path = ckpt_path
         self.keep_ckpt_max = keep_ckpt_max
@@ -201,8 +210,6 @@ class GNMTConfig:
         self.decay_start_step = decay_start_step
         self.warmup_steps = warmup_steps
 
-        self.train_url = ""
-
     @classmethod
     def from_dict(cls, json_object: dict):
         """Constructs a `TransformerConfig` from a Python dictionary of parameters."""

model_zoo/official/nlp/gnmt_v2/config/config_test.json

@@ -1,7 +1,4 @@
 {
-  "training_platform": {
-    "modelarts": false
-  },
   "dataset_config": {
     "random_seed": 50,
     "epochs": 6,
@@ -11,8 +8,7 @@
     "fine_tune_dataset": null,
     "test_dataset":  "/home/workspace/dataset_menu/newstest2014.en.tfrecord-001-of-001",
     "valid_dataset": null,
-    "dataset_sink_mode": true,
+    "dataset_sink_mode": true
-    "dataset_sink_step": 2
   },
   "model_config": {
     "seq_length": 107,
@@ -29,9 +25,9 @@
     "max_decode_length": 80
   },
   "loss_scale_config": {
-    "init_loss_scale": 8192,
+    "init_loss_scale": 65536,
     "loss_scale_factor": 2,
-    "scale_window": 128
+    "scale_window": 1000
   },
   "learn_rate_config": {
     "optimizer": "adam",

model_zoo/official/nlp/gnmt_v2/create_dataset.py

@@ -49,11 +49,12 @@ if __name__ == '__main__':
         schema_address=args.output_folder + "/" + test_src_file + ".json"
     )
     print(f" | It's writing, please wait a moment.")
-    test.write_to_tfrecord(
+    test.write_to_mindrecord(
         path=os.path.join(
             args.output_folder,
-            os.path.basename(test_src_file) + ".tfrecord"
+            os.path.basename(test_src_file) + ".mindrecord"
-        )
+        ),
+        train_mode=False
     )
 
     train = BiLingualDataLoader(
@@ -65,11 +66,12 @@ if __name__ == '__main__':
         schema_address=args.output_folder + "/" + train_src_file + ".json"
     )
     print(f" | It's writing, please wait a moment.")
-    train.write_to_tfrecord(
+    train.write_to_mindrecord(
         path=os.path.join(
             args.output_folder,
-            os.path.basename(train_src_file) + ".tfrecord"
+            os.path.basename(train_src_file) + ".mindrecord"
-        )
+        ),
+        train_mode=True
     )
 
     print(f" | Vocabulary size: {tokenizer.vocab_size}.")

model_zoo/official/nlp/gnmt_v2/src/dataset/base.py

@@ -14,16 +14,16 @@
 # ============================================================================
 """Base class of data loader."""
 import os
-import collections
 import numpy as np
 
 from mindspore.mindrecord import FileWriter
-from .schema import SCHEMA
+from .schema import SCHEMA, TEST_SCHEMA
 
 
 class DataLoader:
     """Data loader for dataset."""
     _SCHEMA = SCHEMA
+    _TEST_SCHEMA = TEST_SCHEMA
 
     def __init__(self):
         self._examples = []
@@ -41,7 +41,7 @@ class DataLoader:
         new_sen[:sen.shape[0]] = sen[:]
         return new_sen
 
-    def write_to_mindrecord(self, path, shard_num=1, desc=""):
+    def write_to_mindrecord(self, path, train_mode, shard_num=1, desc="gnmt"):
         """
         Write mindrecord file.
 
@@ -54,7 +54,10 @@ class DataLoader:
             path = os.path.abspath(path)
 
         writer = FileWriter(file_name=path, shard_num=shard_num)
-        writer.add_schema(self._SCHEMA, desc)
+        if train_mode:
+            writer.add_schema(self._SCHEMA, desc)
+        else:
+            writer.add_schema(self._TEST_SCHEMA, desc)
         if not self._examples:
             self._load()
 
@@ -62,41 +65,5 @@ class DataLoader:
         writer.commit()
         print(f"| Wrote to {path}.")
 
-    def write_to_tfrecord(self, path, shard_num=1):
-        """
-        Write to tfrecord.
-
-        Args:
-            path (str): Output file path.
-            shard_num (int): Shard num.
-        """
-        import tensorflow as tf
-        if not os.path.isabs(path):
-            path = os.path.abspath(path)
-        output_files = []
-        for i in range(shard_num):
-            output_file = path + "-%03d-of-%03d" % (i + 1, shard_num)
-            output_files.append(output_file)
-        # create writers
-        writers = []
-        for output_file in output_files:
-            writers.append(tf.io.TFRecordWriter(output_file))
-
-        if not self._examples:
-            self._load()
-
-        # create feature
-        features = collections.OrderedDict()
-        for example in self._examples:
-            for key in example:
-                features[key] = tf.train.Feature(int64_list=tf.train.Int64List(value=example[key].tolist()))
-            tf_example = tf.train.Example(features=tf.train.Features(feature=features))
-            for writer in writers:
-                writer.write(tf_example.SerializeToString())
-        for writer in writers:
-            writer.close()
-        for p in output_files:
-            print(f" | Write to {p}.")
-
     def _add_example(self, example):
         self._examples.append(example)

model_zoo/official/nlp/gnmt_v2/src/dataset/load_dataset.py

@@ -19,9 +19,9 @@ import mindspore.dataset.engine as de
 import mindspore.dataset.transforms.c_transforms as deC
 
 
-def _load_dataset(input_files, schema_file, batch_size, epoch_count=1,
+def _load_dataset(input_files, schema_file, batch_size, sink_mode=False,
-                  sink_mode=False, sink_step=1, rank_size=1, rank_id=0, shuffle=True,
+                  rank_size=1, rank_id=0, shuffle=True, drop_remainder=True,
-                  drop_remainder=True, is_translate=False):
+                  is_translate=False):
     """
     Load dataset according to passed in params.
 
@@ -29,9 +29,7 @@ def _load_dataset(input_files, schema_file, batch_size, epoch_count=1,
         input_files (list): Data files.
         schema_file (str): Schema file path.
         batch_size (int): Batch size.
-        epoch_count (int): Epoch count.
         sink_mode (bool): Whether enable sink mode.
-        sink_step (int): Step to sink.
         rank_size (int): Rank size.
         rank_id (int): Rank id.
         shuffle (bool): Whether shuffle dataset.
@@ -57,15 +55,14 @@ def _load_dataset(input_files, schema_file, batch_size, epoch_count=1,
         print(f" | Loading {datafile}.")
 
     if not is_translate:
-        ds = de.TFRecordDataset(
+        ds = de.MindDataset(
-            input_files, schema_file,
+            input_files, columns_list=[
-            columns_list=[
                 "src", "src_padding",
                 "prev_opt",
                 "target", "tgt_padding"
-            ],
+            ], shuffle=False, num_shards=rank_size, shard_id=rank_id,
-            shuffle=False, num_shards=rank_size, shard_id=rank_id,
+            num_parallel_workers=8
-            shard_equal_rows=True, num_parallel_workers=8)
+        )
 
         ori_dataset_size = ds.get_dataset_size()
         print(f" | Dataset size: {ori_dataset_size}.")
@@ -92,13 +89,13 @@ def _load_dataset(input_files, schema_file, batch_size, epoch_count=1,
         )
         ds = ds.batch(batch_size, drop_remainder=drop_remainder)
     else:
-        ds = de.TFRecordDataset(
+        ds = de.MindDataset(
-            input_files, schema_file,
+            input_files, columns_list=[
-            columns_list=[
                 "src", "src_padding"
             ],
             shuffle=False, num_shards=rank_size, shard_id=rank_id,
-            shard_equal_rows=True, num_parallel_workers=8)
+            num_parallel_workers=8
+        )
 
         ori_dataset_size = ds.get_dataset_size()
         print(f" | Dataset size: {ori_dataset_size}.")
@@ -119,7 +116,7 @@ def _load_dataset(input_files, schema_file, batch_size, epoch_count=1,
     return ds
 
 
-def load_dataset(data_files: list, schema: str, batch_size: int, epoch_count: int, sink_mode: bool, sink_step: int = 1,
+def load_dataset(data_files: list, schema: str, batch_size: int, sink_mode: bool,
                  rank_size: int = 1, rank_id: int = 0, shuffle=True, drop_remainder=True, is_translate=False):
     """
     Load dataset.
@@ -128,9 +125,7 @@ def load_dataset(data_files: list, schema: str, batch_size: int, epoch_count: in
         data_files (list): Data files.
         schema (str): Schema file path.
         batch_size (int): Batch size.
-        epoch_count (int): Epoch count.
         sink_mode (bool): Whether enable sink mode.
-        sink_step (int): Step to sink.
         rank_size (int): Rank size.
         rank_id (int): Rank id.
         shuffle (bool): Whether shuffle dataset.
@@ -138,6 +133,5 @@ def load_dataset(data_files: list, schema: str, batch_size: int, epoch_count: in
     Returns:
         Dataset, dataset instance.
     """
-    return _load_dataset(data_files, schema, batch_size, epoch_count, sink_mode,
+    return _load_dataset(data_files, schema, batch_size, sink_mode, rank_size, rank_id, shuffle=shuffle,
-                         sink_step, rank_size, rank_id, shuffle=shuffle,
                          drop_remainder=drop_remainder, is_translate=is_translate)

model_zoo/official/nlp/gnmt_v2/src/dataset/schema.py

@@ -21,3 +21,8 @@ SCHEMA = {
     "target": {"type": "int64", "shape": [-1]},
     "tgt_padding": {"type": "int64", "shape": [-1]},
 }
+
+TEST_SCHEMA = {
+    "src": {"type": "int64", "shape": [-1]},
+    "src_padding": {"type": "int64", "shape": [-1]},
+}

model_zoo/official/nlp/gnmt_v2/src/gnmt_model/gnmt_for_infer.py

@@ -189,7 +189,6 @@ def infer(config):
     eval_dataset = load_dataset(data_files=config.test_dataset,
                                 schema=config.dataset_schema,
                                 batch_size=config.batch_size,
-                                epoch_count=1,
                                 sink_mode=config.dataset_sink_mode,
                                 drop_remainder=False,
                                 is_translate=True,

model_zoo/official/nlp/gnmt_v2/src/utils/loss_monitor.py

@@ -16,8 +16,6 @@
 import time
 
 from mindspore.train.callback import Callback
-from mindspore.communication.management import get_rank
-
 from config import GNMTConfig
 
 
@@ -51,9 +49,6 @@ class LossCallBack(Callback):
         """step end."""
         cb_params = run_context.original_args()
         file_name = "./loss.log"
-        if self.config.modelarts:
-            import os
-            file_name = "/home/work/workspace/loss/loss_{}.log".format(os.getenv('DEVICE_ID'))
         with open(file_name, "a+") as f:
             time_stamp_current = self._get_ms_timestamp()
             f.write("time: {}, epoch: {}, step: {}, outputs: [loss: {}, overflow: {}, loss scale value: {} ].\n".format(
@@ -65,14 +60,6 @@ class LossCallBack(Callback):
                 str(cb_params.net_outputs[2].asnumpy())
             ))
 
-        if self.config.modelarts:
-            from modelarts.data_util import upload_output
-            rank_id = get_rank()
-            if cb_params.cur_step_num % self.config.save_step == 1 \
-                    and cb_params.cur_step_num != 1 and rank_id in [0, 8]:
-                upload_output("/home/work/workspace/loss", self.config.train_url)
-                upload_output("/cache/ckpt_0", self.config.train_url)
-
     @staticmethod
     def _get_ms_timestamp():
         t = time.time()

model_zoo/official/nlp/gnmt_v2/src/utils/optimizer.py

@@ -87,10 +87,7 @@ def _check_param_value(beta1, beta2, eps, weight_decay, prim_name):
     validator.check_value_type("beta2", beta2, [float], prim_name)
     validator.check_value_type("eps", eps, [float], prim_name)
     validator.check_value_type("weight_dacay", weight_decay, [float], prim_name)
-    # validator.check_number_range("beta1", beta1, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
+
-    # validator.check_number_range("beta2", beta2, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
-    # validator.check_number_range("eps", eps, 0.0, float("inf"), Rel.INC_NEITHER, prim_name)
-    # validator.check_number_range("weight_decay", weight_decay, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
 
 
 def _check_learning_rate_value(learning_rate, end_learning_rate, decay_steps, power, prim_name):

model_zoo/official/nlp/gnmt_v2/train.py

@@ -169,9 +169,7 @@ def _get_optimizer(config, network, lr):
     if config.optimizer.lower() == "adam":
         optimizer = Adam(network.trainable_params(), lr, beta1=0.9, beta2=0.98)
     elif config.optimizer.lower() == "lamb":
-        optimizer = Lamb(network.trainable_params(), decay_steps=12000,
+        optimizer = Lamb(network.trainable_params(), learning_rate=lr,
-                         start_learning_rate=config.lr, end_learning_rate=config.min_lr,
-                         power=10.0, warmup_steps=config.warmup_steps, weight_decay=0.01,
                          eps=1e-6)
     elif config.optimizer.lower() == "momentum":
         optimizer = Momentum(network.trainable_params(), lr, momentum=0.9)
@@ -277,25 +275,21 @@ def train_parallel(config: GNMTConfig):
         data_files=config.pre_train_dataset,
         schema=config.dataset_schema,
         batch_size=config.batch_size,
-        epoch_count=config.epochs,
         sink_mode=config.dataset_sink_mode,
-        sink_step=config.dataset_sink_step,
         rank_size=MultiAscend.get_group_size(),
         rank_id=MultiAscend.get_rank()
     ) if config.pre_train_dataset else None
     fine_tune_dataset = load_dataset(
         data_files=config.fine_tune_dataset, schema=config.dataset_schema,
-        batch_size=config.batch_size, epoch_count=config.epochs,
+        batch_size=config.batch_size,
         sink_mode=config.dataset_sink_mode,
-        sink_step=config.dataset_sink_step,
         rank_size=MultiAscend.get_group_size(),
         rank_id=MultiAscend.get_rank()
     ) if config.fine_tune_dataset else None
     test_dataset = load_dataset(
         data_files=config.test_dataset, schema=config.dataset_schema,
-        batch_size=config.batch_size, epoch_count=config.epochs,
+        batch_size=config.batch_size,
         sink_mode=config.dataset_sink_mode,
-        sink_step=config.dataset_sink_step,
         rank_size=MultiAscend.get_group_size(),
         rank_id=MultiAscend.get_rank()
     ) if config.test_dataset else None
@@ -318,21 +312,15 @@ def train_single(config: GNMTConfig):
     pre_train_dataset = load_dataset(data_files=config.pre_train_dataset,
                                      schema=config.dataset_schema,
                                      batch_size=config.batch_size,
-                                     epoch_count=config.epochs,
+                                     sink_mode=config.dataset_sink_mode) if config.pre_train_dataset else None
-                                     sink_mode=config.dataset_sink_mode,
-                                     sink_step=config.dataset_sink_step) if config.pre_train_dataset else None
     fine_tune_dataset = load_dataset(data_files=config.fine_tune_dataset,
                                      schema=config.dataset_schema,
                                      batch_size=config.batch_size,
-                                     epoch_count=config.epochs,
+                                     sink_mode=config.dataset_sink_mode) if config.fine_tune_dataset else None
-                                     sink_mode=config.dataset_sink_mode,
-                                     sink_step=config.dataset_sink_step) if config.fine_tune_dataset else None
     test_dataset = load_dataset(data_files=config.test_dataset,
                                 schema=config.dataset_schema,
                                 batch_size=config.batch_size,
-                                epoch_count=config.epochs,
+                                sink_mode=config.dataset_sink_mode) if config.test_dataset else None
-                                sink_mode=config.dataset_sink_mode,
-                                sink_step=config.dataset_sink_step) if config.test_dataset else None
 
     _build_training_pipeline(config=config,
                              pre_training_dataset=pre_train_dataset,