1754 lines
110 KiB
Python
1754 lines
110 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import warnings
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from dataclasses import dataclass
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from typing import Optional, Tuple
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import torch
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from .utils import ModelOutput
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@dataclass
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class BaseModelOutput(ModelOutput):
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"""
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Base class for model's outputs, with potential hidden states and attentions.
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Args:
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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"""
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last_hidden_state: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class BaseModelOutputWithNoAttention(ModelOutput):
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"""
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Base class for model's outputs, with potential hidden states.
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Args:
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
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Sequence of hidden-states at the output of the last layer of the model.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, num_channels, height, width)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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"""
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last_hidden_state: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class BaseModelOutputWithPooling(ModelOutput):
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"""
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Base class for model's outputs that also contains a pooling of the last hidden states.
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Args:
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
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Last layer hidden-state of the first token of the sequence (classification token) after further processing
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through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
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the classification token after processing through a linear layer and a tanh activation function. The linear
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layer weights are trained from the next sentence prediction (classification) objective during pretraining.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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"""
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last_hidden_state: torch.FloatTensor = None
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pooler_output: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class BaseModelOutputWithPoolingAndNoAttention(ModelOutput):
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"""
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Base class for model's outputs that also contains a pooling of the last hidden states.
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Args:
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
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Sequence of hidden-states at the output of the last layer of the model.
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pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
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Last layer hidden-state after a pooling operation on the spatial dimensions.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, num_channels, height, width)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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"""
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last_hidden_state: torch.FloatTensor = None
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pooler_output: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class BaseModelOutputWithPast(ModelOutput):
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"""
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Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
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Args:
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
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hidden_size)` is output.
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past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
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`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
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`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
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encoder_sequence_length, embed_size_per_head)`.
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Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
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`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
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input) to speed up sequential decoding.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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"""
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last_hidden_state: torch.FloatTensor = None
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past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class BaseModelOutputWithCrossAttentions(ModelOutput):
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"""
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Base class for model's outputs, with potential hidden states and attentions.
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Args:
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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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`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
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weighted average in the cross-attention heads.
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"""
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last_hidden_state: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class BaseModelOutputWithPoolingAndCrossAttentions(ModelOutput):
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"""
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Base class for model's outputs that also contains a pooling of the last hidden states.
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Args:
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
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Last layer hidden-state of the first token of the sequence (classification token) after further processing
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|
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
|
|
the classification token after processing through a linear layer and a tanh activation function. The linear
|
|
layer weights are trained from the next sentence prediction (classification) objective during pretraining.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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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`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
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weighted average in the cross-attention heads.
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past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
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`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
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`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
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encoder_sequence_length, embed_size_per_head)`.
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Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
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`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
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input) to speed up sequential decoding.
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"""
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last_hidden_state: torch.FloatTensor = None
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pooler_output: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class BaseModelOutputWithPastAndCrossAttentions(ModelOutput):
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"""
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Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
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Args:
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
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hidden_size)` is output.
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past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
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`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
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`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
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encoder_sequence_length, embed_size_per_head)`.
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Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
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|
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
|
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input) to speed up sequential decoding.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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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`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
|
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weighted average in the cross-attention heads.
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"""
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last_hidden_state: torch.FloatTensor = None
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past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class MoECausalLMOutputWithPast(ModelOutput):
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"""
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Base class for causal language model (or autoregressive) outputs as well as Mixture of Expert's router hidden
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states terms, to train a MoE model.
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Args:
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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Language modeling loss (for next-token prediction).
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logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
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Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
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past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
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`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
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Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
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`past_key_values` input) to speed up sequential decoding.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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z_loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided):
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z_loss for the sparse modules.
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aux_loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided):
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aux_loss for the sparse modules.
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router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
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Router logits of the encoder model, useful to compute the auxiliary loss and the z_loss for the sparse
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modules.
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"""
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loss: Optional[torch.FloatTensor] = None
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logits: torch.FloatTensor = None
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past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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z_loss: torch.FloatTensor = None
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aux_loss: torch.FloatTensor = None
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router_logits: Optional[Tuple[torch.FloatTensor]] = None
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@dataclass
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class MoEModelOutput(ModelOutput):
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"""
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Base class for model's outputs, with potential hidden states and attentions.
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Args:
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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|
Sequence of hidden-states at the output of the last layer of the model.
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|
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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router_probs (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_probs=True` and `config.add_router_probs=True` is passed or when `config.output_router_probs=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
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Raw router probabilities that are computed by MoE routers, these terms are used to compute the auxiliary
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loss and the z_loss for Mixture of Experts models.
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"""
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last_hidden_state: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
router_probs: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
@dataclass
|
|
class MoeModelOutputWithPast(ModelOutput):
|
|
"""
|
|
Base class for model's outputs, with potential hidden states and attentions.
|
|
|
|
Args:
|
|
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
|
Sequence of hidden-states at the output of the last layer of the model.
|
|
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.
|
|
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.
|
|
router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_probs=True` and `config.add_router_probs=True` is passed or when `config.output_router_probs=True`):
|
|
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
|
|
|
|
Raw router logtis (post-softmax) that are computed by MoE routers, these terms are used to compute the auxiliary
|
|
loss for Mixture of Experts models.
|
|
"""
|
|
|
|
last_hidden_state: 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
|
|
router_logits: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
@dataclass
|
|
class MoeCausalLMOutputWithPast(ModelOutput):
|
|
"""
|
|
Base class for causal language model (or autoregressive) with mixture of experts outputs.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Language modeling loss (for next-token prediction).
|
|
|
|
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).
|
|
|
|
aux_loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided):
|
|
aux_loss for the sparse modules.
|
|
|
|
router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_probs=True` and `config.add_router_probs=True` is passed or when `config.output_router_probs=True`):
|
|
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
|
|
|
|
Raw router logtis (post-softmax) that are computed by MoE routers, these terms are used to compute the auxiliary
|
|
loss for Mixture of Experts models.
|
|
|
|
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
|
|
aux_loss: 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
|
|
router_logits: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
@dataclass
|
|
class MoEModelOutputWithPastAndCrossAttentions(ModelOutput):
|
|
"""
|
|
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding) as well as
|
|
Mixture of Expert's router hidden states terms, to train a MoE model.
|
|
|
|
Args:
|
|
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
|
Sequence of hidden-states at the output of the last layer of the model.
|
|
|
|
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
|
|
hidden_size)` is output.
|
|
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.
|
|
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.
|
|
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.
|
|
router_probs (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_probs=True` and `config.add_router_probs=True` is passed or when `config.output_router_probs=True`):
|
|
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
|
|
|
|
Raw router probabilities that are computed by MoE routers, these terms are used to compute the auxiliary
|
|
loss and the z_loss for Mixture of Experts models.
|
|
"""
|
|
|
|
last_hidden_state: 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
|
|
cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
router_probs: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
@dataclass
|
|
class Seq2SeqModelOutput(ModelOutput):
|
|
"""
|
|
Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
|
|
decoding.
|
|
|
|
Args:
|
|
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
|
Sequence of hidden-states at the output of the last layer of the decoder of the model.
|
|
|
|
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
|
|
hidden_size)` is output.
|
|
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 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 in the cross-attention
|
|
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
decoder_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 decoder at the output of each layer plus the optional initial embedding outputs.
|
|
decoder_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 of the decoder, 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` 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.
|
|
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder of the model.
|
|
encoder_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 encoder at the output of each layer plus the optional initial embedding outputs.
|
|
encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the
|
|
self-attention heads.
|
|
"""
|
|
|
|
last_hidden_state: torch.FloatTensor = None
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
|
|
decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
|
|
encoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class Seq2SeqMoEModelOutput(ModelOutput):
|
|
"""
|
|
Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
|
|
decoding.
|
|
|
|
Args:
|
|
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
|
Sequence of hidden-states at the output of the last layer of the decoder of the model.
|
|
|
|
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
|
|
hidden_size)` is output.
|
|
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 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 in the cross-attention
|
|
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
decoder_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 decoder at the output of each layer plus the optional initial embedding outputs.
|
|
decoder_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 of the decoder, after the attention softmax, used to compute the weighted average in the
|
|
self-attention heads.
|
|
decoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`):
|
|
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
|
|
|
|
Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models.
|
|
cross_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 of the decoder's cross-attention layer, after the attention softmax, used to compute the
|
|
weighted average in the cross-attention heads.
|
|
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder of the model.
|
|
encoder_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 encoder at the output of each layer plus the optional initial embedding outputs.
|
|
encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the
|
|
self-attention heads.
|
|
encoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`):
|
|
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
|
|
|
|
Router logits of the encoder model, useful to compute the auxiliary loss and the z_loss for the sparse
|
|
modules.
|
|
"""
|
|
|
|
last_hidden_state: torch.FloatTensor = None
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
|
|
decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
decoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None
|
|
cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
|
|
encoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
@dataclass
|
|
class CausalLMOutput(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).
|
|
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).
|
|
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
|
|
logits: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class CausalLMOutputWithPast(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).
|
|
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
|
|
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
|
|
|
|
|
|
@dataclass
|
|
class CausalLMOutputWithCrossAttentions(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).
|
|
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).
|
|
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.
|
|
cross_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)`.
|
|
|
|
Cross attentions weights 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 `torch.FloatTensor` tuples of length `config.n_layers`, with each tuple containing the cached key,
|
|
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
|
|
setting. Only relevant if `config.is_decoder = True`.
|
|
|
|
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
|
|
`past_key_values` input) to speed up sequential decoding.
|
|
"""
|
|
|
|
loss: 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
|
|
cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class SequenceClassifierOutputWithPast(ModelOutput):
|
|
"""
|
|
Base class for outputs of sentence classification models.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Classification (or regression if config.num_labels==1) loss.
|
|
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
|
|
Classification (or regression if config.num_labels==1) scores (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
|
|
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
|
|
|
|
|
|
@dataclass
|
|
class MaskedLMOutput(ModelOutput):
|
|
"""
|
|
Base class for masked language models outputs.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Masked language modeling (MLM) loss.
|
|
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).
|
|
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
|
|
logits: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class Seq2SeqLMOutput(ModelOutput):
|
|
"""
|
|
Base class for sequence-to-sequence language models outputs.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Language modeling loss.
|
|
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)`) and 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 in the cross-attention
|
|
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
decoder_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 decoder at the output of each layer plus the initial embedding outputs.
|
|
decoder_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 of the decoder, 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` 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.
|
|
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder of the model.
|
|
encoder_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 encoder at the output of each layer plus the initial embedding outputs.
|
|
encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the
|
|
self-attention heads.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
logits: torch.FloatTensor = None
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
|
|
decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
|
|
encoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class Seq2SeqMoEOutput(ModelOutput):
|
|
"""
|
|
Base class for sequence-to-sequence language models outputs.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Language modeling loss.
|
|
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)`) and 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 in the cross-attention
|
|
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
decoder_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 decoder at the output of each layer plus the initial embedding outputs.
|
|
decoder_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 of the decoder, after the attention softmax, used to compute the weighted average in the
|
|
self-attention heads.
|
|
decoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`):
|
|
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
|
|
|
|
Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models.
|
|
cross_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 of the decoder's cross-attention layer, after the attention softmax, used to compute the
|
|
weighted average in the cross-attention heads.
|
|
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder of the model.
|
|
encoder_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 encoder at the output of each layer plus the initial embedding outputs.
|
|
encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the
|
|
self-attention heads.
|
|
encoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`):
|
|
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
|
|
|
|
Router logits of the encoder model, useful to compute the auxiliary loss and z_loss for Mixture of Experts
|
|
models.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
logits: torch.FloatTensor = None
|
|
encoder_z_loss: torch.FloatTensor = None
|
|
decoder_z_loss: torch.FloatTensor = None
|
|
encoder_aux_loss: torch.FloatTensor = None
|
|
decoder_aux_loss: torch.FloatTensor = None
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
|
|
decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
decoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None
|
|
cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
|
|
encoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
@dataclass
|
|
class NextSentencePredictorOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of models predicting if two sentences are consecutive or not.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `next_sentence_label` is provided):
|
|
Next sequence prediction (classification) loss.
|
|
logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
|
|
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
|
|
before SoftMax).
|
|
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
|
|
logits: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class SequenceClassifierOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of sentence classification models.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Classification (or regression if config.num_labels==1) loss.
|
|
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
|
|
Classification (or regression if config.num_labels==1) scores (before SoftMax).
|
|
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
|
|
logits: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class Seq2SeqSequenceClassifierOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of sequence-to-sequence sentence classification models.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `label` is provided):
|
|
Classification (or regression if config.num_labels==1) loss.
|
|
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
|
|
Classification (or regression if config.num_labels==1) scores (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)`) and 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 in the cross-attention
|
|
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
decoder_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 decoder at the output of each layer plus the initial embedding outputs.
|
|
decoder_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 of the decoder, 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` 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.
|
|
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder of the model.
|
|
encoder_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 encoder at the output of each layer plus the initial embedding outputs.
|
|
encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the
|
|
self-attention heads.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
logits: torch.FloatTensor = None
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
|
|
decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
|
|
encoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class MultipleChoiceModelOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of multiple choice models.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
|
|
Classification loss.
|
|
logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`):
|
|
*num_choices* is the second dimension of the input tensors. (see *input_ids* above).
|
|
|
|
Classification scores (before SoftMax).
|
|
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
|
|
logits: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class TokenClassifierOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of token classification models.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) :
|
|
Classification loss.
|
|
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
|
|
Classification scores (before SoftMax).
|
|
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
|
|
logits: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class QuestionAnsweringModelOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of question answering models.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
|
|
start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
|
|
Span-start scores (before SoftMax).
|
|
end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
|
|
Span-end scores (before SoftMax).
|
|
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
|
|
start_logits: torch.FloatTensor = None
|
|
end_logits: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class Seq2SeqQuestionAnsweringModelOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of sequence-to-sequence question answering models.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
|
|
start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
|
|
Span-start scores (before SoftMax).
|
|
end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
|
|
Span-end scores (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)`) and 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 in the cross-attention
|
|
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
decoder_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 decoder at the output of each layer plus the initial embedding outputs.
|
|
decoder_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 of the decoder, 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` 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.
|
|
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder of the model.
|
|
encoder_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 encoder at the output of each layer plus the initial embedding outputs.
|
|
encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the
|
|
self-attention heads.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
start_logits: torch.FloatTensor = None
|
|
end_logits: torch.FloatTensor = None
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
|
|
decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
|
|
encoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class SemanticSegmenterOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of semantic segmentation models.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Classification (or regression if config.num_labels==1) loss.
|
|
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`):
|
|
Classification scores for each pixel.
|
|
|
|
<Tip warning={true}>
|
|
|
|
The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is
|
|
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
|
|
original image size as post-processing. You should always check your logits shape and resize as needed.
|
|
|
|
</Tip>
|
|
|
|
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, patch_size, 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, patch_size,
|
|
sequence_length)`.
|
|
|
|
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
|
|
heads.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
logits: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class ImageClassifierOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of image classification models.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Classification (or regression if config.num_labels==1) loss.
|
|
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
|
|
Classification (or regression if config.num_labels==1) scores (before SoftMax).
|
|
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 stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states
|
|
(also called feature maps) of the model at the output of each stage.
|
|
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, patch_size,
|
|
sequence_length)`.
|
|
|
|
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
|
|
heads.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
logits: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class ImageClassifierOutputWithNoAttention(ModelOutput):
|
|
"""
|
|
Base class for outputs of image classification models.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Classification (or regression if config.num_labels==1) loss.
|
|
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
|
|
Classification (or regression if config.num_labels==1) scores (before SoftMax).
|
|
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 stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also
|
|
called feature maps) of the model at the output of each stage.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
logits: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class DepthEstimatorOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of depth estimation models.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Classification (or regression if config.num_labels==1) loss.
|
|
predicted_depth (`torch.FloatTensor` of shape `(batch_size, height, width)`):
|
|
Predicted depth for each pixel.
|
|
|
|
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, num_channels, height, width)`.
|
|
|
|
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, patch_size,
|
|
sequence_length)`.
|
|
|
|
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
|
|
heads.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
predicted_depth: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class ImageSuperResolutionOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of image super resolution models.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Reconstruction loss.
|
|
reconstruction (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Reconstructed images, possibly upscaled.
|
|
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 stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states
|
|
(also called feature maps) of the model at the output of each stage.
|
|
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, patch_size,
|
|
sequence_length)`.
|
|
|
|
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
|
|
heads.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
reconstruction: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class Wav2Vec2BaseModelOutput(ModelOutput):
|
|
"""
|
|
Base class for models that have been trained with the Wav2Vec2 loss objective.
|
|
|
|
Args:
|
|
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
|
Sequence of hidden-states at the output of the last layer of the model.
|
|
extract_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, conv_dim[-1])`):
|
|
Sequence of extracted feature vectors of the last convolutional layer of the model.
|
|
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 + 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 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.
|
|
"""
|
|
|
|
last_hidden_state: torch.FloatTensor = None
|
|
extract_features: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class XVectorOutput(ModelOutput):
|
|
"""
|
|
Output type of [`Wav2Vec2ForXVector`].
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Classification loss.
|
|
logits (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_dim)`):
|
|
Classification hidden states before AMSoftmax.
|
|
embeddings (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_dim)`):
|
|
Utterance embeddings used for vector similarity-based retrieval.
|
|
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 + 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 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
|
|
logits: torch.FloatTensor = None
|
|
embeddings: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class BackboneOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of backbones.
|
|
|
|
Args:
|
|
feature_maps (`tuple(torch.FloatTensor)` of shape `(batch_size, num_channels, height, width)`):
|
|
Feature maps of the stages.
|
|
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 + one for the output of each layer) of
|
|
shape `(batch_size, sequence_length, hidden_size)` or `(batch_size, num_channels, height, width)`,
|
|
depending on the backbone.
|
|
|
|
Hidden-states of the model at the output of each stage plus the 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)`. Only applicable if the backbone uses attention.
|
|
|
|
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
|
|
heads.
|
|
"""
|
|
|
|
feature_maps: Tuple[torch.FloatTensor] = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class BaseModelOutputWithPoolingAndProjection(ModelOutput):
|
|
"""
|
|
Base class for model's outputs that also contains a pooling of the last hidden states.
|
|
|
|
Args:
|
|
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
|
Sequence of hidden-states at the output of the last layer of the model.
|
|
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
|
|
Last layer hidden-state of the first token of the sequence (classification token) after further processing
|
|
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
|
|
the classification token after processing through a linear layer and a tanh activation function. The linear
|
|
layer weights are trained from the next sentence prediction (classification) objective during pretraining.
|
|
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.
|
|
projection_state (`tuple(torch.FloatTensor)`, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `torch.FloatTensor` of shape `(batch_size,config.project_dim)`.
|
|
|
|
Text embeddings before the projection layer, used to mimic the last hidden state of the teacher encoder.
|
|
"""
|
|
|
|
last_hidden_state: torch.FloatTensor = None
|
|
pooler_output: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
projection_state: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
@dataclass
|
|
class Seq2SeqSpectrogramOutput(ModelOutput):
|
|
"""
|
|
Base class for sequence-to-sequence spectrogram outputs.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Spectrogram generation loss.
|
|
spectrogram (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_bins)`):
|
|
The predicted spectrogram.
|
|
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 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 in the cross-attention
|
|
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
decoder_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 decoder at the output of each layer plus the initial embedding outputs.
|
|
decoder_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 of the decoder, 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` 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.
|
|
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder of the model.
|
|
encoder_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 encoder at the output of each layer plus the initial embedding outputs.
|
|
encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the
|
|
self-attention heads.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
spectrogram: torch.FloatTensor = None
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
|
|
decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
|
|
encoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
@dataclass
|
|
class Seq2SeqTSModelOutput(ModelOutput):
|
|
"""
|
|
Base class for time series model's encoder outputs that also contains pre-computed hidden states that can speed up
|
|
sequential decoding.
|
|
|
|
Args:
|
|
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
|
Sequence of hidden-states at the output of the last layer of the decoder of the model.
|
|
|
|
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
|
|
hidden_size)` is output.
|
|
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 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 in the cross-attention
|
|
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
decoder_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 decoder at the output of each layer plus the optional initial embedding outputs.
|
|
decoder_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 of the decoder, 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` 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.
|
|
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder of the model.
|
|
encoder_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 encoder at the output of each layer plus the optional initial embedding outputs.
|
|
encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the
|
|
self-attention heads.
|
|
loc (`torch.FloatTensor` of shape `(batch_size,)` or `(batch_size, input_size)`, *optional*):
|
|
Shift values of each time series' context window which is used to give the model inputs of the same
|
|
magnitude and then used to shift back to the original magnitude.
|
|
scale (`torch.FloatTensor` of shape `(batch_size,)` or `(batch_size, input_size)`, *optional*):
|
|
Scaling values of each time series' context window which is used to give the model inputs of the same
|
|
magnitude and then used to rescale back to the original magnitude.
|
|
static_features (`torch.FloatTensor` of shape `(batch_size, feature size)`, *optional*):
|
|
Static features of each time series' in a batch which are copied to the covariates at inference time.
|
|
"""
|
|
|
|
last_hidden_state: torch.FloatTensor = None
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
|
|
decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
|
|
encoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
loc: Optional[torch.FloatTensor] = None
|
|
scale: Optional[torch.FloatTensor] = None
|
|
static_features: Optional[torch.FloatTensor] = None
|
|
|
|
|
|
@dataclass
|
|
class Seq2SeqTSPredictionOutput(ModelOutput):
|
|
"""
|
|
Base class for time series model's decoder outputs that also contain the loss as well as the parameters of the
|
|
chosen distribution.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when a `future_values` is provided):
|
|
Distributional loss.
|
|
params (`torch.FloatTensor` of shape `(batch_size, num_samples, num_params)`):
|
|
Parameters of the chosen distribution.
|
|
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 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 in the cross-attention
|
|
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
decoder_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 decoder at the output of each layer plus the initial embedding outputs.
|
|
decoder_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 of the decoder, 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` 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.
|
|
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder of the model.
|
|
encoder_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 encoder at the output of each layer plus the initial embedding outputs.
|
|
encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the
|
|
self-attention heads.
|
|
loc (`torch.FloatTensor` of shape `(batch_size,)` or `(batch_size, input_size)`, *optional*):
|
|
Shift values of each time series' context window which is used to give the model inputs of the same
|
|
magnitude and then used to shift back to the original magnitude.
|
|
scale (`torch.FloatTensor` of shape `(batch_size,)` or `(batch_size, input_size)`, *optional*):
|
|
Scaling values of each time series' context window which is used to give the model inputs of the same
|
|
magnitude and then used to rescale back to the original magnitude.
|
|
static_features (`torch.FloatTensor` of shape `(batch_size, feature size)`, *optional*):
|
|
Static features of each time series' in a batch which are copied to the covariates at inference time.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
params: Optional[Tuple[torch.FloatTensor]] = None
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
|
|
decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
|
|
encoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
encoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
loc: Optional[torch.FloatTensor] = None
|
|
scale: Optional[torch.FloatTensor] = None
|
|
static_features: Optional[torch.FloatTensor] = None
|
|
|
|
|
|
@dataclass
|
|
class SampleTSPredictionOutput(ModelOutput):
|
|
"""
|
|
Base class for time series model's predictions outputs that contains the sampled values from the chosen
|
|
distribution.
|
|
|
|
Args:
|
|
sequences (`torch.FloatTensor` of shape `(batch_size, num_samples, prediction_length)` or `(batch_size, num_samples, prediction_length, input_size)`):
|
|
Sampled values from the chosen distribution.
|
|
"""
|
|
|
|
sequences: torch.FloatTensor = None
|
|
|
|
|
|
@dataclass
|
|
class MaskedImageModelingOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of masked image completion / in-painting models.
|
|
|
|
Args:
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
|
|
Reconstruction loss.
|
|
reconstruction (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Reconstructed / completed images.
|
|
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 stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states
|
|
(also called feature maps) of the model at the output of each stage.
|
|
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, patch_size,
|
|
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
|
|
the self-attention heads.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
reconstruction: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
@property
|
|
def logits(self):
|
|
warnings.warn(
|
|
"logits attribute is deprecated and will be removed in version 5 of Transformers."
|
|
" Please use the reconstruction attribute to retrieve the final output instead.",
|
|
FutureWarning,
|
|
)
|
|
return self.reconstruction
|