Support ofa export for bert (PaddlePaddle#1326)

* fix ofa export bug

* support bert export

Author: LiuChiachi

examples/model_compression/ofa/export_model.py

@@ -27,35 +27,53 @@
 import paddle.nn.functional as F
 
 from paddlenlp.transformers import BertModel, BertForSequenceClassification, BertTokenizer
-from paddlenlp.transformers import TinyBertModel, TinyBertForSequenceClassification, TinyBertTokenizer
-from paddlenlp.transformers import TinyBertForSequenceClassification, TinyBertTokenizer
-from paddlenlp.transformers import RobertaForSequenceClassification, RobertaTokenizer
 from paddlenlp.utils.log import logger
 from paddleslim.nas.ofa import OFA, utils
 from paddleslim.nas.ofa.convert_super import Convert, supernet
 from paddleslim.nas.ofa.layers import BaseBlock
 
-MODEL_CLASSES = {
-    "bert": (BertForSequenceClassification, BertTokenizer),
-    "roberta": (RobertaForSequenceClassification, RobertaTokenizer),
-    "tinybert": (TinyBertForSequenceClassification, TinyBertTokenizer),
-}
+MODEL_CLASSES = {"bert": (BertForSequenceClassification, BertTokenizer), }
 
 
-def tinybert_forward(self, input_ids, token_type_ids=None, attention_mask=None):
+def bert_forward(self,
+                 input_ids,
+                 token_type_ids=None,
+                 position_ids=None,
+                 attention_mask=None,
+                 output_hidden_states=False):
     wtype = self.pooler.dense.fn.weight.dtype if hasattr(
         self.pooler.dense, 'fn') else self.pooler.dense.weight.dtype
     if attention_mask is None:
         attention_mask = paddle.unsqueeze(
             (input_ids == self.pad_token_id).astype(wtype) * -1e9, axis=[1, 2])
-    embedding_output = self.embeddings(input_ids, token_type_ids)
-    encoded_layer = self.encoder(embedding_output, attention_mask)
-    pooled_output = self.pooler(encoded_layer)
-
-    return encoded_layer, pooled_output
+    else:
+        if attention_mask.ndim == 2:
+            # attention_mask [batch_size, sequence_length] -> [batch_size, 1, 1, sequence_length]
+            attention_mask = attention_mask.unsqueeze(axis=[1, 2])
+
+    embedding_output = self.embeddings(
+        input_ids=input_ids,
+        position_ids=position_ids,
+        token_type_ids=token_type_ids)
+    if output_hidden_states:
+        output = embedding_output
+        encoder_outputs = []
+        for mod in self.encoder.layers:
+            output = mod(output, src_mask=attention_mask)
+            encoder_outputs.append(output)
+        if self.encoder.norm is not None:
+            encoder_outputs[-1] = self.encoder.norm(encoder_outputs[-1])
+        pooled_output = self.pooler(encoder_outputs[-1])
+    else:
+        sequence_output = self.encoder(embedding_output, attention_mask)
+        pooled_output = self.pooler(sequence_output)
+    if output_hidden_states:
+        return encoder_outputs, pooled_output
+    else:
+        return sequence_output, pooled_output
 
 
-TinyBertModel.forward = tinybert_forward
+BertModel.forward = bert_forward
 
 
 def parse_args():