992 lines
55 KiB
Python
992 lines
55 KiB
Python
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# 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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from __future__ import annotations
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import warnings
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from dataclasses import dataclass
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from typing import List, Optional, Tuple
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import tensorflow as tf
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from .utils import ModelOutput
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@dataclass
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class TFBaseModelOutput(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 (`tf.Tensor` 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(tf.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
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`(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `tf.Tensor` (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: tf.Tensor = None
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hidden_states: Tuple[tf.Tensor] | None = None
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attentions: Tuple[tf.Tensor] | None = None
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@dataclass
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class TFBaseModelOutputWithNoAttention(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 (`tf.Tensor` 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(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `tf.Tensor` (one for the output of the embeddings, if the model has an embedding layer, + one for
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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: tf.Tensor = None
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hidden_states: Optional[Tuple[tf.Tensor, ...]] = None
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@dataclass
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class TFBaseModelOutputWithPooling(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 (`tf.Tensor` 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 (`tf.Tensor` of shape `(batch_size, hidden_size)`):
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Last layer hidden-state of the first token of the sequence (classification token) further processed by a
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Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
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prediction (classification) objective during pretraining.
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This output is usually *not* a good summary of the semantic content of the input, you're often better with
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averaging or pooling the sequence of hidden-states for the whole input sequence.
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hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
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`(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `tf.Tensor` (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: tf.Tensor = None
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pooler_output: tf.Tensor = None
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hidden_states: Tuple[tf.Tensor] | None = None
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attentions: Tuple[tf.Tensor] | None = None
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@dataclass
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class TFBaseModelOutputWithPoolingAndNoAttention(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 (`tf.Tensor` 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 (`tf.Tensor` 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(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `tf.Tensor` (one for the output of the embeddings, if the model has an embedding layer, + one for
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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: tf.Tensor = None
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pooler_output: tf.Tensor = None
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hidden_states: Optional[Tuple[tf.Tensor, ...]] = None
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@dataclass
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class TFBaseModelOutputWithPoolingAndCrossAttentions(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 (`tf.Tensor` 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 (`tf.Tensor` of shape `(batch_size, hidden_size)`):
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Last layer hidden-state of the first token of the sequence (classification token) further processed by a
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Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
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prediction (classification) objective during pretraining.
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This output is usually *not* a good summary of the semantic content of the input, you're often better with
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averaging or pooling the sequence of hidden-states for the whole input sequence.
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past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
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sequence_length, embed_size_per_head)`).
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Contains pre-computed hidden-states (key and values in the 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(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
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`(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `tf.Tensor` (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: tf.Tensor = None
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pooler_output: tf.Tensor = None
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past_key_values: List[tf.Tensor] | None = None
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hidden_states: Tuple[tf.Tensor] | None = None
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attentions: Tuple[tf.Tensor] | None = None
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cross_attentions: Tuple[tf.Tensor] | None = None
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@dataclass
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class TFBaseModelOutputWithPast(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 (`tf.Tensor` 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 (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
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sequence_length, embed_size_per_head)`).
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Contains pre-computed hidden-states (key and values in the 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(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
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`(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `tf.Tensor` (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: tf.Tensor = None
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past_key_values: List[tf.Tensor] | None = None
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hidden_states: Tuple[tf.Tensor] | None = None
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attentions: Tuple[tf.Tensor] | None = None
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@dataclass
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class TFBaseModelOutputWithCrossAttentions(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 (`tf.Tensor` 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(tf.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
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`(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `tf.Tensor` (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: tf.Tensor = None
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hidden_states: Tuple[tf.Tensor] | None = None
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attentions: Tuple[tf.Tensor] | None = None
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cross_attentions: Tuple[tf.Tensor] | None = None
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@dataclass
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class TFBaseModelOutputWithPastAndCrossAttentions(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 (`tf.Tensor` 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 (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
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sequence_length, embed_size_per_head)`).
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Contains pre-computed hidden-states (key and values in the 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(tf.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
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`(batch_size, sequence_length, hidden_size)`.
|
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|
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `tf.Tensor` (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: tf.Tensor = None
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past_key_values: List[tf.Tensor] | None = None
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hidden_states: Tuple[tf.Tensor] | None = None
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attentions: Tuple[tf.Tensor] | None = None
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cross_attentions: Tuple[tf.Tensor] | None = None
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@dataclass
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class TFSeq2SeqModelOutput(ModelOutput):
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"""
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Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
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decoding.
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Args:
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last_hidden_state (`tf.Tensor` 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 decoder 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 (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
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sequence_length, embed_size_per_head)`).
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Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
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used (see `past_key_values` input) to speed up sequential decoding.
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decoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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|
|
Tuple of `tf.Tensor` (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 decoder at the output of each layer plus the initial embedding outputs.
|
||
|
|
decoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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 (`tf.Tensor` 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(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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 encoder at the output of each layer plus the initial embedding outputs.
|
||
|
|
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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: tf.Tensor = None
|
||
|
|
past_key_values: List[tf.Tensor] | None = None
|
||
|
|
decoder_hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
decoder_attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
cross_attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
encoder_last_hidden_state: tf.Tensor | None = None
|
||
|
|
encoder_hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
encoder_attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class TFCausalLMOutput(ModelOutput):
|
||
|
|
"""
|
||
|
|
Base class for causal language model (or autoregressive) outputs.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
|
||
|
|
Language modeling loss (for next-token prediction).
|
||
|
|
logits (`tf.Tensor` 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(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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: tf.Tensor | None = None
|
||
|
|
logits: tf.Tensor = None
|
||
|
|
hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class TFCausalLMOutputWithPast(ModelOutput):
|
||
|
|
"""
|
||
|
|
Base class for causal language model (or autoregressive) outputs.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
|
||
|
|
Language modeling loss (for next-token prediction).
|
||
|
|
logits (`tf.Tensor` 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 (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
||
|
|
List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
|
||
|
|
sequence_length, embed_size_per_head)`).
|
||
|
|
|
||
|
|
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.
|
||
|
|
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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: tf.Tensor | None = None
|
||
|
|
logits: tf.Tensor = None
|
||
|
|
past_key_values: List[tf.Tensor] | None = None
|
||
|
|
hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class TFCausalLMOutputWithCrossAttentions(ModelOutput):
|
||
|
|
"""
|
||
|
|
Base class for causal language model (or autoregressive) outputs.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
|
||
|
|
Language modeling loss (for next-token prediction).
|
||
|
|
logits (`tf.Tensor` 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(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
|
||
|
|
sequence_length)`.
|
||
|
|
|
||
|
|
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
|
||
|
|
weighted average in the cross-attention heads.
|
||
|
|
past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
||
|
|
List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
|
||
|
|
sequence_length, embed_size_per_head)`).
|
||
|
|
|
||
|
|
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: tf.Tensor | None = None
|
||
|
|
logits: tf.Tensor = None
|
||
|
|
past_key_values: List[tf.Tensor] | None = None
|
||
|
|
hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
cross_attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class TFMaskedLMOutput(ModelOutput):
|
||
|
|
"""
|
||
|
|
Base class for masked language models outputs.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
|
||
|
|
Masked language modeling (MLM) loss.
|
||
|
|
logits (`tf.Tensor` 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(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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: tf.Tensor | None = None
|
||
|
|
logits: tf.Tensor = None
|
||
|
|
hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class TFSeq2SeqLMOutput(ModelOutput):
|
||
|
|
"""
|
||
|
|
Base class for sequence-to-sequence language models outputs.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
|
||
|
|
Language modeling loss.
|
||
|
|
logits (`tf.Tensor` 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 (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
||
|
|
List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
|
||
|
|
sequence_length, embed_size_per_head)`).
|
||
|
|
|
||
|
|
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
|
||
|
|
used (see `past_key_values` input) to speed up sequential decoding.
|
||
|
|
decoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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 decoder at the output of each layer plus the initial embedding outputs.
|
||
|
|
decoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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 (`tf.Tensor` 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(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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 encoder at the output of each layer plus the initial embedding outputs.
|
||
|
|
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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: tf.Tensor | None = None
|
||
|
|
logits: tf.Tensor = None
|
||
|
|
past_key_values: List[tf.Tensor] | None = None
|
||
|
|
decoder_hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
decoder_attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
cross_attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
encoder_last_hidden_state: tf.Tensor | None = None
|
||
|
|
encoder_hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
encoder_attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class TFNextSentencePredictorOutput(ModelOutput):
|
||
|
|
"""
|
||
|
|
Base class for outputs of models predicting if two sentences are consecutive or not.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `next_sentence_label` is provided):
|
||
|
|
Next sentence prediction loss.
|
||
|
|
logits (`tf.Tensor` of shape `(batch_size, 2)`):
|
||
|
|
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
|
||
|
|
before SoftMax).
|
||
|
|
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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: tf.Tensor | None = None
|
||
|
|
logits: tf.Tensor = None
|
||
|
|
hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class TFSequenceClassifierOutput(ModelOutput):
|
||
|
|
"""
|
||
|
|
Base class for outputs of sentence classification models.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `labels` is provided):
|
||
|
|
Classification (or regression if config.num_labels==1) loss.
|
||
|
|
logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
|
||
|
|
Classification (or regression if config.num_labels==1) scores (before SoftMax).
|
||
|
|
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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: tf.Tensor | None = None
|
||
|
|
logits: tf.Tensor = None
|
||
|
|
hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class TFSeq2SeqSequenceClassifierOutput(ModelOutput):
|
||
|
|
"""
|
||
|
|
Base class for outputs of sequence-to-sequence sentence classification models.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `label` is provided):
|
||
|
|
Classification (or regression if config.num_labels==1) loss.
|
||
|
|
logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
|
||
|
|
Classification (or regression if config.num_labels==1) scores (before SoftMax).
|
||
|
|
past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
||
|
|
List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
|
||
|
|
sequence_length, embed_size_per_head)`).
|
||
|
|
|
||
|
|
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
|
||
|
|
used (see `past_key_values` input) to speed up sequential decoding.
|
||
|
|
decoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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 decoder at the output of each layer plus the initial embedding outputs.
|
||
|
|
decoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
|
||
|
|
sequence_length)`
|
||
|
|
encoder_last_hidden_state (`tf.Tensor` 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(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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 encoder at the output of each layer plus the initial embedding outputs.
|
||
|
|
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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: tf.Tensor | None = None
|
||
|
|
logits: tf.Tensor = None
|
||
|
|
past_key_values: List[tf.Tensor] | None = None
|
||
|
|
decoder_hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
decoder_attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
cross_attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
encoder_last_hidden_state: tf.Tensor | None = None
|
||
|
|
encoder_hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
encoder_attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class TFSemanticSegmenterOutput(ModelOutput):
|
||
|
|
"""
|
||
|
|
Base class for outputs of semantic segmentation models.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
||
|
|
Classification (or regression if config.num_labels==1) loss.
|
||
|
|
logits (`tf.Tensor` 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(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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: tf.Tensor | None = None
|
||
|
|
logits: tf.Tensor = None
|
||
|
|
hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class TFSemanticSegmenterOutputWithNoAttention(ModelOutput):
|
||
|
|
"""
|
||
|
|
Base class for outputs of semantic segmentation models that do not output attention scores.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
||
|
|
Classification (or regression if config.num_labels==1) loss.
|
||
|
|
logits (`tf.Tensor` 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(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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.
|
||
|
|
"""
|
||
|
|
|
||
|
|
loss: tf.Tensor | None = None
|
||
|
|
logits: tf.Tensor = None
|
||
|
|
hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class TFImageClassifierOutput(ModelOutput):
|
||
|
|
"""
|
||
|
|
Base class for outputs of image classification models.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
||
|
|
Classification (or regression if config.num_labels==1) loss.
|
||
|
|
logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
|
||
|
|
Classification (or regression if config.num_labels==1) scores (before SoftMax).
|
||
|
|
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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: tf.Tensor | None = None
|
||
|
|
logits: tf.Tensor = None
|
||
|
|
hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class TFMultipleChoiceModelOutput(ModelOutput):
|
||
|
|
"""
|
||
|
|
Base class for outputs of multiple choice models.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
loss (`tf.Tensor` of shape *(batch_size, )*, *optional*, returned when `labels` is provided):
|
||
|
|
Classification loss.
|
||
|
|
logits (`tf.Tensor` 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(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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: tf.Tensor | None = None
|
||
|
|
logits: tf.Tensor = None
|
||
|
|
hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class TFTokenClassifierOutput(ModelOutput):
|
||
|
|
"""
|
||
|
|
Base class for outputs of token classification models.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of unmasked labels, returned when `labels` is provided) :
|
||
|
|
Classification loss.
|
||
|
|
logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.num_labels)`):
|
||
|
|
Classification scores (before SoftMax).
|
||
|
|
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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: tf.Tensor | None = None
|
||
|
|
logits: tf.Tensor = None
|
||
|
|
hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class TFQuestionAnsweringModelOutput(ModelOutput):
|
||
|
|
"""
|
||
|
|
Base class for outputs of question answering models.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `start_positions` and `end_positions` are provided):
|
||
|
|
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
|
||
|
|
start_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`):
|
||
|
|
Span-start scores (before SoftMax).
|
||
|
|
end_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`):
|
||
|
|
Span-end scores (before SoftMax).
|
||
|
|
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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: tf.Tensor | None = None
|
||
|
|
start_logits: tf.Tensor = None
|
||
|
|
end_logits: tf.Tensor = None
|
||
|
|
hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class TFSeq2SeqQuestionAnsweringModelOutput(ModelOutput):
|
||
|
|
"""
|
||
|
|
Base class for outputs of sequence-to-sequence question answering models.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
loss (`tf.Tensor` 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 (`tf.Tensor` of shape `(batch_size, sequence_length)`):
|
||
|
|
Span-start scores (before SoftMax).
|
||
|
|
end_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`):
|
||
|
|
Span-end scores (before SoftMax).
|
||
|
|
past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
||
|
|
List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
|
||
|
|
sequence_length, embed_size_per_head)`).
|
||
|
|
|
||
|
|
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
|
||
|
|
used (see `past_key_values` input) to speed up sequential decoding.
|
||
|
|
decoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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 decoder at the output of each layer plus the initial embedding outputs.
|
||
|
|
decoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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.
|
||
|
|
encoder_last_hidden_state (`tf.Tensor` 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(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
||
|
|
Tuple of `tf.Tensor` (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 encoder at the output of each layer plus the initial embedding outputs.
|
||
|
|
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
||
|
|
Tuple of `tf.Tensor` (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: tf.Tensor | None = None
|
||
|
|
start_logits: tf.Tensor = None
|
||
|
|
end_logits: tf.Tensor = None
|
||
|
|
past_key_values: List[tf.Tensor] | None = None
|
||
|
|
decoder_hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
decoder_attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
encoder_last_hidden_state: tf.Tensor | None = None
|
||
|
|
encoder_hidden_states: Tuple[tf.Tensor] | None = None
|
||
|
|
encoder_attentions: Tuple[tf.Tensor] | None = None
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class TFSequenceClassifierOutputWithPast(ModelOutput):
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"""
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Base class for outputs of sentence classification models.
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Args:
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loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `labels` is provided):
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Classification (or regression if config.num_labels==1) loss.
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logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
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Classification (or regression if config.num_labels==1) scores (before SoftMax).
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past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
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sequence_length, embed_size_per_head)`).
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Contains pre-computed hidden-states (key and values in the 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(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
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`(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `tf.Tensor` (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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loss: tf.Tensor | None = None
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logits: tf.Tensor = None
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past_key_values: List[tf.Tensor] | None = None
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hidden_states: Tuple[tf.Tensor] | None = None
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attentions: Tuple[tf.Tensor] | None = None
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@dataclass
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class TFImageClassifierOutputWithNoAttention(ModelOutput):
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"""
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Base class for outputs of image classification models.
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Args:
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loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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Classification (or regression if config.num_labels==1) loss.
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logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
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Classification (or regression if config.num_labels==1) scores (before SoftMax).
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hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `tf.Tensor` (one for the output of the embeddings, if the model has an embedding layer, + one for
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the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also called
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feature maps) of the model at the output of each stage.
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"""
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loss: tf.Tensor | None = None
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logits: tf.Tensor = None
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hidden_states: Optional[Tuple[tf.Tensor, ...]] = None
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@dataclass
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class TFMaskedImageModelingOutput(ModelOutput):
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"""
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Base class for outputs of masked image completion / in-painting models.
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Args:
|
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loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
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Reconstruction loss.
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reconstruction (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
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Reconstructed / completed images.
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hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when
|
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`config.output_hidden_states=True`):
|
||
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|
Tuple of `tf.Tensor` (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
|
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|
feature maps) of the model at the output of each stage.
|
||
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|
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when
|
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`config.output_attentions=True`):
|
||
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Tuple of `tf.Tensor` (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
|
||
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heads.
|
||
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"""
|
||
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|
||
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loss: tf.Tensor | None = None
|
||
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|
reconstruction: tf.Tensor = None
|
||
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|
hidden_states: Tuple[tf.Tensor] | None = None
|
||
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|
attentions: Tuple[tf.Tensor] | None = None
|
||
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|
@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.",
|
||
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|
FutureWarning,
|
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|
)
|
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return self.reconstruction
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