adding tests

pytorch_transformers/modeling_auto.py

@@ -393,6 +393,8 @@ class AutoModelWithLMHead(DerivedAutoModel):
 
     def __init__(self, base_model):
         super(AutoModelWithLMHead, self).__init__(base_model)
+        config = base_model.config
+
         self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
 
         self.apply(self.init_weights)
@@ -426,6 +428,17 @@ class AutoModelWithLMHead(DerivedAutoModel):
         return outputs  # (loss), lm_logits, presents, (all hidden_states), (attentions)
 
 
+AUTO_MODEL_SEQUENCE_SUMMARY_DEFAULTS = {
+     'num_labels': 2,
+     'summary_type': 'first',
+     'summary_use_proj': True,
+     'summary_activation': None,
+     'summary_proj_to_labels': True,
+     'summary_first_dropout': 0.1
+}
+
+
+
 class AutoModelForSequenceClassification(DerivedAutoModel):
     r"""
         :class:`~pytorch_transformers.AutoModelForSequenceClassification` is a class for sequence classification
@@ -451,8 +464,18 @@ class AutoModelForSequenceClassification(DerivedAutoModel):
 
     def __init__(self, base_model):
         super(AutoModelForSequenceClassification, self).__init__(base_model)
-        self.num_labels = base_model.config.num_labels
-        self.sequence_summary = SequenceSummary(base_model.config)
+        # Complete configuration with defaults if necessary
+        config = base_model.config
+        for key, value in AUTO_MODEL_SEQUENCE_SUMMARY_DEFAULTS.items():
+            if not hasattr(config, key):
+                setattr(config, key, value)
+
+        # Update base model and derived model config
+        self.transformer.config = config
+        self.config = config
+
+        self.num_labels = config.num_labels
+        self.sequence_summary = SequenceSummary(config)
 
         self.apply(self.init_weights)