Class AttentionBackboneModelOutput

modelscope/outputs/nlp_outputs.py:29–67

The output class for backbones of attention based models. Args: attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of

@dataclass
class AttentionBackboneModelOutput(BackboneModelOutput):
    """The output class for backbones of attention based models.

    Args:
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when
        `output_attentions=True` is passed or when
        `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape
            `(batch_size, num_heads, sequence_length, sequence_length)`.

            Attentions weights after the attention softmax, used to compute the
            weighted average in the self-attention heads.
        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when
        `output_attentions=True` and `config.add_cross_attention=True` is passed
        or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape
            `(batch_size, num_heads, sequence_length, sequence_length)`.

            Attentions weights of the decoder's cross-attention layer, after the
            attention softmax, used to compute the weighted average in the
            cross-attention heads.
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned
        when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`,
            with each tuple having 2 tensors of shape `(batch_size, num_heads,
            sequence_length, embed_size_per_head)`) and optionally if
            `config.is_encoder_decoder=True` 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length,
            embed_size_per_head)`.

            Contains pre-computed hidden-states (key and values in the
            self-attention blocks and optionally if
            `config.is_encoder_decoder=True` in the cross-attention blocks) that
            can be used (see `past_key_values` input) to speed up sequential
            decoding.
    """
    attentions: Tensor = None
    past_key_values: Tensor = None
    cross_attentions: Tensor = None


@dataclass