ALBERT passes all tests

transformers/configuration_albert.py

@@ -7,7 +7,7 @@ class AlbertConfig(PretrainedConfig):
     """
 
     def __init__(self,
-                 vocab_size_or_config_json_file,
+                 vocab_size_or_config_json_file=30000,
                  embedding_size=128,
                  hidden_size=4096,
                  num_hidden_layers=12,
@@ -15,7 +15,6 @@ class AlbertConfig(PretrainedConfig):
                  num_attention_heads=64,
                  intermediate_size=16384,
                  inner_group_num=1,
-                 down_scale_factor=1,
                  hidden_act="gelu_new",
                  hidden_dropout_prob=0,
                  attention_probs_dropout_prob=0,
@@ -61,7 +60,6 @@ class AlbertConfig(PretrainedConfig):
         self.num_hidden_groups = num_hidden_groups
         self.num_attention_heads = num_attention_heads
         self.inner_group_num = inner_group_num
-        self.down_scale_factor = down_scale_factor
         self.hidden_act = hidden_act
         self.intermediate_size = intermediate_size
         self.hidden_dropout_prob = hidden_dropout_prob

transformers/modeling_albert.py

@@ -202,9 +202,6 @@ class AlbertLayerGroup(nn.Module):
         layer_attentions = ()
 
         for albert_layer in self.albert_layers:
-            if self.output_hidden_states:
-                layer_hidden_states = layer_hidden_states + (hidden_states,)
-
             layer_output = albert_layer(hidden_states, attention_mask, head_mask)
             hidden_states = layer_output[0]
 
@@ -247,7 +244,7 @@ class AlbertTransformer(nn.Module):
             hidden_states = layer_group_output[0]
 
             if self.output_attentions:
-                all_attentions = all_attentions + layer_group_output[1]
+                all_attentions = all_attentions + layer_group_output[-1]
 
             if self.output_hidden_states:
                 all_hidden_states = all_hidden_states + (hidden_states,)

transformers/tests/tokenization_albert_test.py

@@ -22,7 +22,7 @@ from transformers.tokenization_albert import (AlbertTokenizer, SPIECE_UNDERLINE)
 from .tokenization_tests_commons import CommonTestCases
 
 SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)),
-                    'fixtures/30k-clean.model')
+                    'fixtures/spiece.model')
 
 class AlbertTokenizationTest(CommonTestCases.CommonTokenizerTester):