[DPR] Correct init (#13796)

* update

* add to docs and init

* make fix-copies

docs/source/model_doc/dpr.rst

@@ -41,6 +41,13 @@ DPRConfig
     :members:
 
 
+DPRPreTrainedModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.DPRPreTrainedModel
+    :members:
+
+
 DPRContextEncoderTokenizer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

src/transformers/__init__.py

@@ -773,6 +773,7 @@ if is_torch_available():
             "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
             "DPRContextEncoder",
             "DPRPretrainedContextEncoder",
+            "DPRPreTrainedModel",
             "DPRPretrainedQuestionEncoder",
             "DPRPretrainedReader",
             "DPRQuestionEncoder",
@@ -2512,6 +2513,7 @@ if TYPE_CHECKING:
             DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
             DPRContextEncoder,
             DPRPretrainedContextEncoder,
+            DPRPreTrainedModel,
             DPRPretrainedQuestionEncoder,
             DPRPretrainedReader,
             DPRQuestionEncoder,

src/transformers/models/dpr/__init__.py

@@ -46,6 +46,7 @@ if is_torch_available():
         "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DPRContextEncoder",
         "DPRPretrainedContextEncoder",
+        "DPRPreTrainedModel",
         "DPRPretrainedQuestionEncoder",
         "DPRPretrainedReader",
         "DPRQuestionEncoder",
@@ -89,6 +90,7 @@ if TYPE_CHECKING:
             DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
             DPRContextEncoder,
             DPRPretrainedContextEncoder,
+            DPRPreTrainedModel,
             DPRPretrainedQuestionEncoder,
             DPRPretrainedReader,
             DPRQuestionEncoder,

src/transformers/models/dpr/modeling_dpr.py

@@ -147,7 +147,29 @@ class DPRReaderOutput(ModelOutput):
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
 
-class DPREncoder(PreTrainedModel):
+class DPRPreTrainedModel(PreTrainedModel):
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, BertEncoder):
+            module.gradient_checkpointing = value
+
+
+class DPREncoder(DPRPreTrainedModel):
 
     base_model_prefix = "bert_model"
 
@@ -200,13 +222,8 @@ class DPREncoder(PreTrainedModel):
             return self.encode_proj.out_features
         return self.bert_model.config.hidden_size
 
-    def init_weights(self):
-        self.bert_model.init_weights()
-        if self.projection_dim > 0:
-            self.encode_proj.apply(self.bert_model._init_weights)
 
-
+class DPRSpanPredictor(DPRPreTrainedModel):
-class DPRSpanPredictor(PreTrainedModel):
 
     base_model_prefix = "encoder"
 
@@ -262,16 +279,13 @@ class DPRSpanPredictor(PreTrainedModel):
             attentions=outputs.attentions,
         )
 
-    def init_weights(self):
-        self.encoder.init_weights()
-
 
 ##################
 # PreTrainedModel
 ##################
 
 
-class DPRPretrainedContextEncoder(PreTrainedModel):
+class DPRPretrainedContextEncoder(DPRPreTrainedModel):
     """
     An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
     models.
@@ -282,11 +296,8 @@ class DPRPretrainedContextEncoder(PreTrainedModel):
     base_model_prefix = "ctx_encoder"
     _keys_to_ignore_on_load_missing = [r"position_ids"]
 
-    def init_weights(self):
-        self.ctx_encoder.init_weights()
 
-
+class DPRPretrainedQuestionEncoder(DPRPreTrainedModel):
-class DPRPretrainedQuestionEncoder(PreTrainedModel):
     """
     An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
     models.
@@ -297,15 +308,8 @@ class DPRPretrainedQuestionEncoder(PreTrainedModel):
     base_model_prefix = "question_encoder"
     _keys_to_ignore_on_load_missing = [r"position_ids"]
 
-    def init_weights(self):
-        self.question_encoder.init_weights()
 
-    def _set_gradient_checkpointing(self, module, value=False):
+class DPRPretrainedReader(DPRPreTrainedModel):
-        if isinstance(module, BertEncoder):
-            module.gradient_checkpointing = value
-
-
-class DPRPretrainedReader(PreTrainedModel):
     """
     An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
     models.
@@ -316,15 +320,6 @@ class DPRPretrainedReader(PreTrainedModel):
     base_model_prefix = "span_predictor"
     _keys_to_ignore_on_load_missing = [r"position_ids"]
 
-    def init_weights(self):
-        self.span_predictor.encoder.init_weights()
-        self.span_predictor.qa_classifier.apply(self.span_predictor.encoder.bert_model._init_weights)
-        self.span_predictor.qa_outputs.apply(self.span_predictor.encoder.bert_model._init_weights)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, BertEncoder):
-            module.gradient_checkpointing = value
-
 
 ###############
 # Actual Models

src/transformers/utils/dummy_pt_objects.py

@@ -1462,6 +1462,15 @@ class DPRPretrainedContextEncoder:
         requires_backends(self, ["torch"])
 
 
+class DPRPreTrainedModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
 class DPRPretrainedQuestionEncoder:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])

tests/test_modeling_dpr.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 
 
+import tempfile
 import unittest
 
 from transformers import DPRConfig, is_torch_available
@@ -213,6 +214,19 @@ class DPRModelTest(ModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_reader(*config_and_inputs)
 
+    def test_init_changed_config(self):
+        config = self.model_tester.prepare_config_and_inputs()[0]
+
+        model = DPRQuestionEncoder(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        with tempfile.TemporaryDirectory() as tmp_dirname:
+            model.save_pretrained(tmp_dirname)
+            model = DPRQuestionEncoder.from_pretrained(tmp_dirname, projection_dim=512)
+
+        self.assertIsNotNone(model)
+
     @slow
     def test_model_from_pretrained(self):
         for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: