use TFRecordDataset in bert ci script and add absolute position embedding code in bert model

mindspore/model_zoo/Bert_NEZHA/bert_model.py

@@ -165,6 +165,7 @@ class EmbeddingPostprocessor(nn.Cell):
     def __init__(self,
                  embedding_size,
                  embedding_shape,
+                 use_relative_positions=False,
                  use_token_type=False,
                  token_type_vocab_size=16,
                  use_one_hot_embeddings=False,
@@ -192,6 +193,13 @@ class EmbeddingPostprocessor(nn.Cell):
         self.layernorm = nn.LayerNorm(embedding_size)
         self.dropout = nn.Dropout(1 - dropout_prob)
         self.gather = P.GatherV2()
+        self.use_relative_positions = use_relative_positions
+        self.slice = P.Slice()
+        self.full_position_embeddings = Parameter(initializer
+                                                  (TruncatedNormal(initializer_range),
+                                                   [max_position_embeddings,
+                                                    embedding_size]),
+                                                  name='full_position_embeddings')
 
     def construct(self, token_type_ids, word_embeddings):
         output = word_embeddings
@@ -206,6 +214,11 @@ class EmbeddingPostprocessor(nn.Cell):
                 token_type_embeddings = self.gather(self.embedding_table, flat_ids, 0)
             token_type_embeddings = self.reshape(token_type_embeddings, self.shape)
             output += token_type_embeddings
+        if not self.use_relative_positions:
+            _, seq, width = self.shape
+            position_embeddings = self.slice(self.full_position_embeddings, [0, 0], [seq, width])
+            position_embeddings = self.reshape(position_embeddings, (1, seq, width))
+            output += position_embeddings
         output = self.layernorm(output)
         output = self.dropout(output)
         return output
@@ -853,6 +866,7 @@ class BertModel(nn.Cell):
         self.bert_embedding_postprocessor = EmbeddingPostprocessor(
             embedding_size=self.embedding_size,
             embedding_shape=output_embedding_shape,
+            use_relative_positions=config.use_relative_positions,
             use_token_type=True,
             token_type_vocab_size=config.type_vocab_size,
             use_one_hot_embeddings=use_one_hot_embeddings,

tests/st/networks/models/bert/bert_tdt_no_lossscale.py

@@ -103,9 +103,9 @@ def me_de_train_dataset():
     """test me de train dataset"""
     # apply repeat operations
     repeat_count = 1
-    ds = de.StorageDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids",
+    ds = de.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids",
                                                                "next_sentence_labels", "masked_lm_positions",
-                                                               "masked_lm_ids", "masked_lm_weights"])
+                                                               "masked_lm_ids", "masked_lm_weights"], shuffle=False)
     type_cast_op = C.TypeCast(mstype.int32)
     ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op)
     ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op)