fairseq/models/bart/model.py [170:214]:
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        current_head_names = (
            []
            if not hasattr(self, "classification_heads")
            else self.classification_heads.keys()
        )

        # Handle new classification heads present in the state dict.
        keys_to_delete = []
        for k in state_dict.keys():
            if not k.startswith(prefix + "classification_heads."):
                continue

            head_name = k[len(prefix + "classification_heads.") :].split(".")[0]
            num_classes = state_dict[
                prefix + "classification_heads." + head_name + ".out_proj.weight"
            ].size(0)
            inner_dim = state_dict[
                prefix + "classification_heads." + head_name + ".dense.weight"
            ].size(0)

            if getattr(self.args, "load_checkpoint_heads", False):
                if head_name not in current_head_names:
                    self.register_classification_head(head_name, num_classes, inner_dim)
            else:
                if head_name not in current_head_names:
                    logger.warning(
                        "deleting classification head ({}) from checkpoint "
                        "not present in current model: {}".format(head_name, k)
                    )
                    keys_to_delete.append(k)
                elif (
                    num_classes
                    != self.classification_heads[head_name].out_proj.out_features
                    or inner_dim
                    != self.classification_heads[head_name].dense.out_features
                ):
                    logger.warning(
                        "deleting classification head ({}) from checkpoint "
                        "with different dimensions than current model: {}".format(
                            head_name, k
                        )
                    )
                    keys_to_delete.append(k)
        for k in keys_to_delete:
            del state_dict[k]
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fairseq/models/roberta/model.py [403:445]:
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        current_head_names = (
            []
            if not hasattr(self, "classification_heads")
            else self.classification_heads.keys()
        )
        keys_to_delete = []
        for k in state_dict.keys():
            if not k.startswith(prefix + "classification_heads."):
                continue

            head_name = k[len(prefix + "classification_heads.") :].split(".")[0]
            num_classes = state_dict[
                prefix + "classification_heads." + head_name + ".out_proj.weight"
            ].size(0)
            inner_dim = state_dict[
                prefix + "classification_heads." + head_name + ".dense.weight"
            ].size(0)

            if getattr(self.args, "load_checkpoint_heads", False):
                if head_name not in current_head_names:
                    self.register_classification_head(head_name, num_classes, inner_dim)
            else:
                if head_name not in current_head_names:
                    logger.warning(
                        "deleting classification head ({}) from checkpoint "
                        "not present in current model: {}".format(head_name, k)
                    )
                    keys_to_delete.append(k)
                elif (
                    num_classes
                    != self.classification_heads[head_name].out_proj.out_features
                    or inner_dim
                    != self.classification_heads[head_name].dense.out_features
                ):
                    logger.warning(
                        "deleting classification head ({}) from checkpoint "
                        "with different dimensions than current model: {}".format(
                            head_name, k
                        )
                    )
                    keys_to_delete.append(k)
        for k in keys_to_delete:
            del state_dict[k]
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