Delete bert_embeddings.py

bert_embeddings.py

@@ -1,82 +0,0 @@
-import logging
-from typing import Optional
-
-import torch
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-from transformers import BertPreTrainedModel
-
-from bert_layers_mosa import BertModel
-
-logger = logging.getLogger(__name__)
-
-
-class MosaicBertForEmbeddingGeneration(BertPreTrainedModel):
-
-    def __init__(self, config, add_pooling_layer=False):
-        """
-        Initializes the BertEmbeddings class.
-
-        Args:
-            config (BertConfig): The configuration for the BERT model.
-            add_pooling_layer (bool, optional): Whether to add a pooling layer. Defaults to False.
-        """
-        super().__init__(config)
-        assert (
-            config.num_hidden_layers >= config.num_embedding_layers
-        ), "num_hidden_layers should be greater than or equal to num_embedding_layers"
-        self.config = config
-        self.strategy = config.strategy
-        self.bert = BertModel(config, add_pooling_layer=add_pooling_layer)
-        # this resets the weights
-        self.post_init()
-
-    @classmethod
-    def from_pretrained(
-        cls, pretrained_checkpoint, state_dict=None, config=None, *inputs, **kwargs
-    ):
-        """Load from pre-trained."""
-        # this gets a fresh init model
-        model = cls(config, *inputs, **kwargs)
-
-        # thus we need to load the state_dict
-        state_dict = torch.load(pretrained_checkpoint)
-        # remove `model` prefix to avoid error
-        consume_prefix_in_state_dict_if_present(state_dict, prefix="model.")
-        missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
-
-        if len(missing_keys) > 0:
-            logger.warning(
-                f"Found these missing keys in the checkpoint: {', '.join(missing_keys)}"
-            )
-
-            logger.warning(f"the number of which is equal to {len(missing_keys)}")
-
-        if len(unexpected_keys) > 0:
-            logger.warning(
-                f"Found these unexpected keys in the checkpoint: {', '.join(unexpected_keys)}",
-            )
-            logger.warning(f"the number of which is equal to {len(unexpected_keys)}")
-
-        return model
-
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        subset_mask: Optional[torch.Tensor] = None,
-        output_all_encoded_layers: Book = True,
-    ) -> torch.Tensor:
-
-        embedding_output = self.bert.embeddings(input_ids, token_type_ids, position_ids)
-
-        encoder_outputs_all = self.bert.encoder(
-            embedding_output,
-            attention_mask,
-            output_all_encoded_layers=output_all_encoded_layers,
-            subset_mask=subset_mask,
-        )
-
-        # batch_size, hidden_dim
-        return encoder_outputs_all