| import torch |
| import warnings |
| from dataclasses import dataclass |
| from typing import Optional, Tuple, Dict, List |
| from transformers.utils import ModelOutput |
|
|
| @dataclass |
| class CausalLMOutputWithPastAndLabel(ModelOutput): |
| """ |
| Base class for causal language model (or autoregressive) outputs. |
| |
| Args: |
| loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): |
| Language modeling loss (for next-token prediction). |
| labels (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*, returned when `labels` is provided): |
| logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): |
| Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). |
| 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)`) |
| |
| Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see |
| `past_key_values` input) to speed up sequential decoding. |
| hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): |
| Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + |
| one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. |
| |
| Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. |
| 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. |
| """ |
|
|
| loss: Optional[torch.FloatTensor] = None |
| labels: Optional[torch.FloatTensor] = None |
| logits: torch.FloatTensor = None |
| past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None |
| hidden_states: Optional[Tuple[torch.FloatTensor]] = None |
| attentions: Optional[Tuple[torch.FloatTensor]] = None |
| bs2imgs_token_list: List[List[int]] = None |
