Customized load.
(self, state_dict, strict=True)
| 910 | return state_dict_ |
| 911 | |
| 912 | def load_state_dict(self, state_dict, strict=True): |
| 913 | """Customized load.""" |
| 914 | |
| 915 | # Embedding. |
| 916 | if self._embedding_key in state_dict: |
| 917 | state_dict_ = state_dict[self._embedding_key] |
| 918 | else: |
| 919 | # for backward compatibility. |
| 920 | state_dict_ = {} |
| 921 | for key in state_dict.keys(): |
| 922 | if '_embeddings' in key: |
| 923 | state_dict_[key] = state_dict[key] |
| 924 | self.embedding.load_state_dict(state_dict_, strict=strict) |
| 925 | |
| 926 | if self._topQueryEmbedding_key in state_dict: |
| 927 | state_dict_ = state_dict[self._topQueryEmbedding_key] |
| 928 | else: |
| 929 | # for backward compatibility. |
| 930 | state_dict_ = {} |
| 931 | for key in state_dict.keys(): |
| 932 | if '_embeddings' in key: |
| 933 | state_dict_[key] = state_dict[key] |
| 934 | self.topQueryEmbedding.load_state_dict(state_dict_, strict=strict) |
| 935 | |
| 936 | # Transformer. |
| 937 | if self._transformer_key in state_dict: |
| 938 | state_dict_ = state_dict[self._transformer_key] |
| 939 | else: |
| 940 | # for backward compatibility. |
| 941 | state_dict_ = {} |
| 942 | for key in state_dict.keys(): |
| 943 | if 'transformer.' in key: |
| 944 | state_dict_[key.split('transformer.')[1]] = state_dict[key] |
| 945 | self.transformer.load_state_dict(state_dict_, strict=strict) |
| 946 | |
| 947 | |
| 948 | class CodeGeeXModel(torch.nn.Module): |
nothing calls this directly
no test coverage detected