add initializing from pretrained BERT

src/swag_transformers/swag_bert/__init__.py

@@ -1,10 +1,12 @@
 """PyTorch SWAG wrapper for BERT"""
 
 import logging
+from typing import Union
 
 import torch
 
-from transformers import PreTrainedModel, PretrainedConfig, BertConfig, BertModel, BertForSequenceClassification
+from transformers import PreTrainedModel, PretrainedConfig, BertConfig, BertModel, \
+    BertPreTrainedModel, BertForSequenceClassification
 
 from swag.posteriors.swag import SWAG
 
@@ -31,6 +33,13 @@ def __init__(
         self.var_clamp = var_clamp
         self.internal_model_config = internal_config.to_dict()
 
+    @classmethod
+    def from_config(cls, base_config: BertConfig, **kwargs):
+        """Initialize from existing BertConfig"""
+        config = cls(**kwargs)
+        config.internal_model_config = base_config.to_dict()
+        return config
+
 
 class SwagBertPreTrainedModel(PreTrainedModel):
 
@@ -49,6 +58,14 @@ def __init__(self, config):
             device='cpu'  # FIXME: how to deal with device
         )
 
+    @classmethod
+    def from_base(cls, base_model: BertPreTrainedModel, **kwargs):
+        """Initialize from existing BertPreTrainedModel"""
+        config = SwagBertConfig.from_config(base_model.config, **kwargs)
+        swag_model = cls(config)
+        swag_model.model.base = base_model
+        return swag_model
+
     def _init_weights(self, module):
         # FIXME: What should be here?
         raise NotImplementedError("TODO")

tests/test_swag_bert.py

@@ -3,13 +3,16 @@
 
 import torch
 
-from transformers import AutoConfig
+from transformers import AutoConfig, AutoModel, AutoModelForSequenceClassification, AutoTokenizer
 
-from swag_transformers.swag_bert import *
+from swag_transformers.swag_bert import SwagBertConfig, SwagBertModel, SwagBertPreTrainedModel, \
+    SwagBertForSequenceClassification
 
 
 class TestSwagBert(unittest.TestCase):
 
+    pretrained_model_name = 'prajjwal1/bert-tiny'
+
     def test_untrained(self):
         hidden_size = 240
         config = SwagBertConfig(no_cov_mat=False, hidden_size=hidden_size)
@@ -18,7 +21,10 @@ def test_untrained(self):
         logging.debug(swag_model)
         swag_model = SwagBertModel(config)
         logging.debug(swag_model)
-        out = swag_model.forward(input_ids=torch.tensor([[3, 14]]))
+        with self.assertLogs(level='WARNING') as cm:
+            out = swag_model.forward(input_ids=torch.tensor([[3, 14]]))
+            # Warning from using forward before sampling parameters
+            self.assertTrue(any(msg.startswith('WARNING') for msg in cm.output))
         logging.debug(out)
         swag_model.model.sample()
         out = swag_model.forward(input_ids=torch.tensor([[3, 14]]))
@@ -38,7 +44,27 @@ def test_untrained_classifier(self):
         logging.debug(out)
         self.assertEqual(out.logits.shape, (1, num_labels))
 
+    def test_pretrained_bert_tiny(self):
+        model = AutoModel.from_pretrained(self.pretrained_model_name)
+        hidden_size = model.config.hidden_size
+        config = SwagBertConfig.from_config(model.config, no_cov_mat=False)
+        logging.debug(config)
+        swag_model = SwagBertModel.from_base(model)
+        logging.debug(swag_model)
+        swag_model.model.sample()
+        out = swag_model.forward(input_ids=torch.tensor([[3, 14]]))
+        logging.debug(out)
+        self.assertEqual(out.last_hidden_state.shape, (1, 2, hidden_size))
 
+    def test_pretrained_bert_tiny_classifier(self):
+        num_labels = 4
+        model = AutoModelForSequenceClassification.from_pretrained(self.pretrained_model_name, num_labels=num_labels)
+        swag_model = SwagBertForSequenceClassification.from_base(model)
+        logging.debug(swag_model)
+        swag_model.model.sample()
+        out = swag_model.forward(input_ids=torch.tensor([[3, 14]]))
+        logging.debug(out)
+        self.assertEqual(out.logits.shape, (1, num_labels))
 
 
 if __name__ == "__main__":