Function _tie_or_clone_weights

modelscope/utils/checkpoint.py:516–539

(output_embeddings,
                              input_embeddings,
                              torchscript=False)

        return retrieved_modules

    def _tie_or_clone_weights(output_embeddings,
                              input_embeddings,
                              torchscript=False):
        if torchscript:
            output_embeddings.weight = nn.Parameter(
                input_embeddings.weight.clone())
        else:
            output_embeddings.weight = input_embeddings.weight

        if getattr(output_embeddings, 'bias', None) is not None:
            output_embeddings.bias.data = nn.functional.pad(
                output_embeddings.bias.data,
                (
                    0,
                    output_embeddings.weight.shape[0]
                    - output_embeddings.bias.shape[0],
                ),
                'constant',
                0,
            )

        if hasattr(output_embeddings, 'out_features') and hasattr(
                input_embeddings, 'num_embeddings'):
            output_embeddings.out_features = input_embeddings.num_embeddings

    def tie_weights(model, tie_word_embeddings=False):
        if tie_word_embeddings: