701 lines
41 KiB
Python
701 lines
41 KiB
Python
# Copyright 2021 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 typing import Dict, Optional, Tuple
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import flax
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import jax.numpy as jnp
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from .utils import ModelOutput
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@flax.struct.dataclass
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class FlaxBaseModelOutput(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 (`jnp.ndarray` 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(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `jnp.ndarray` (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: jnp.ndarray = None
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hidden_states: Optional[Tuple[jnp.ndarray]] = None
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attentions: Optional[Tuple[jnp.ndarray]] = None
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@flax.struct.dataclass
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class FlaxBaseModelOutputWithNoAttention(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 (`jnp.ndarray` 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(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `jnp.ndarray` (one for the output of the embeddings, if the model has an embedding layer, + one
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for the output of each layer) of shape `(batch_size, num_channels, height, width)`. Hidden-states of the
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model at the output of each layer plus the optional initial embedding outputs.
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"""
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last_hidden_state: jnp.ndarray = None
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hidden_states: Optional[Tuple[jnp.ndarray]] = None
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@flax.struct.dataclass
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class FlaxBaseModelOutputWithPoolingAndNoAttention(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 (`jnp.ndarray` 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 (`jnp.ndarray` 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(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `jnp.ndarray` (one for the output of the embeddings, if the model has an embedding layer, + one
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for the output of each layer) of shape `(batch_size, num_channels, height, width)`. Hidden-states of the
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model at the output of each layer plus the optional initial embedding outputs.
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"""
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last_hidden_state: jnp.ndarray = None
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pooler_output: jnp.ndarray = None
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hidden_states: Optional[Tuple[jnp.ndarray]] = None
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@flax.struct.dataclass
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class FlaxImageClassifierOutputWithNoAttention(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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logits (`jnp.ndarray` 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(jnp.ndarray)`, *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 `jnp.ndarray` (one for the output of the embeddings, if the model has an embedding layer, + one
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for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also
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called feature maps) of the model at the output of each stage.
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"""
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logits: jnp.ndarray = None
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hidden_states: Optional[Tuple[jnp.ndarray]] = None
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@flax.struct.dataclass
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class FlaxBaseModelOutputWithPast(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 (`jnp.ndarray` 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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past_key_values (`Dict[str, jnp.ndarray]`):
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Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
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auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
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hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `jnp.ndarray` (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: jnp.ndarray = None
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past_key_values: Optional[Dict[str, jnp.ndarray]] = None
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hidden_states: Optional[Tuple[jnp.ndarray]] = None
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attentions: Optional[Tuple[jnp.ndarray]] = None
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@flax.struct.dataclass
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class FlaxBaseModelOutputWithPooling(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 (`jnp.ndarray` 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 (`jnp.ndarray` 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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hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `jnp.ndarray` (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: jnp.ndarray = None
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pooler_output: jnp.ndarray = None
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hidden_states: Optional[Tuple[jnp.ndarray]] = None
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attentions: Optional[Tuple[jnp.ndarray]] = None
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@flax.struct.dataclass
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class FlaxBaseModelOutputWithPoolingAndCrossAttentions(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 (`jnp.ndarray` 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 (`jnp.ndarray` 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(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `jnp.ndarray` (one for the output of the embeddings, if the model has an embedding layer, + one
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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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *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 `jnp.ndarray` (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(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(jnp.ndarray)` 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: jnp.ndarray = None
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pooler_output: jnp.ndarray = None
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hidden_states: Optional[Tuple[jnp.ndarray]] = None
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past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None
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attentions: Optional[Tuple[jnp.ndarray]] = None
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cross_attentions: Optional[Tuple[jnp.ndarray]] = None
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@flax.struct.dataclass
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class FlaxBaseModelOutputWithPastAndCrossAttentions(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 (`jnp.ndarray` 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(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(jnp.ndarray)` 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(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *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 `jnp.ndarray` (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: jnp.ndarray = None
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past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None
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hidden_states: Optional[Tuple[jnp.ndarray]] = None
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attentions: Optional[Tuple[jnp.ndarray]] = None
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cross_attentions: Optional[Tuple[jnp.ndarray]] = None
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@flax.struct.dataclass
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class FlaxSeq2SeqModelOutput(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 (`jnp.ndarray` 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 (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(jnp.ndarray)` 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 2 additional tensors of shape
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`(batch_size, num_heads, 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 in the cross-attention
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blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
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decoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `jnp.ndarray` (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 decoder at the output of each layer plus the initial embedding outputs.
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decoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `jnp.ndarray` (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, after the attention softmax, used to compute the weighted average in the
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self-attention heads.
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cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `jnp.ndarray` (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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encoder_last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
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Sequence of hidden-states at the output of the last layer of the encoder of the model.
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encoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `jnp.ndarray` (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 encoder at the output of each layer plus the initial embedding outputs.
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encoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `jnp.ndarray` (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 encoder, after the attention softmax, used to compute the weighted average in the
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self-attention heads.
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"""
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last_hidden_state: jnp.ndarray = None
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past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None
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decoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None
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decoder_attentions: Optional[Tuple[jnp.ndarray]] = None
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cross_attentions: Optional[Tuple[jnp.ndarray]] = None
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encoder_last_hidden_state: Optional[jnp.ndarray] = None
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encoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None
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encoder_attentions: Optional[Tuple[jnp.ndarray]] = None
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@flax.struct.dataclass
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class FlaxCausalLMOutputWithCrossAttentions(ModelOutput):
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"""
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Base class for causal language model (or autoregressive) outputs.
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Args:
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logits (`jnp.ndarray` 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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hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Cross attentions weights after the attention softmax, used to compute the weighted average in the
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cross-attention heads.
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past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `jnp.ndarray` tuples of length `config.n_layers`, with each tuple containing the cached key, value
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states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting.
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Only relevant if `config.is_decoder = True`.
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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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"""
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logits: jnp.ndarray = None
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past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None
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hidden_states: Optional[Tuple[jnp.ndarray]] = None
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attentions: Optional[Tuple[jnp.ndarray]] = None
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cross_attentions: Optional[Tuple[jnp.ndarray]] = None
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@flax.struct.dataclass
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class FlaxMaskedLMOutput(ModelOutput):
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"""
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Base class for masked language models outputs.
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|
|
Args:
|
|
logits (`jnp.ndarray` 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(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `jnp.ndarray` (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.
|
|
"""
|
|
|
|
logits: jnp.ndarray = None
|
|
hidden_states: Optional[Tuple[jnp.ndarray]] = None
|
|
attentions: Optional[Tuple[jnp.ndarray]] = None
|
|
|
|
|
|
FlaxCausalLMOutput = FlaxMaskedLMOutput
|
|
|
|
|
|
@flax.struct.dataclass
|
|
class FlaxSeq2SeqLMOutput(ModelOutput):
|
|
"""
|
|
Base class for sequence-to-sequence language models outputs.
|
|
|
|
Args:
|
|
logits (`jnp.ndarray` 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(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
|
Tuple of `tuple(jnp.ndarray)` 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(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `jnp.ndarray` (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 (`jnp.ndarray` 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(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `jnp.ndarray` (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.
|
|
"""
|
|
|
|
logits: jnp.ndarray = None
|
|
past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None
|
|
decoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None
|
|
decoder_attentions: Optional[Tuple[jnp.ndarray]] = None
|
|
cross_attentions: Optional[Tuple[jnp.ndarray]] = None
|
|
encoder_last_hidden_state: Optional[jnp.ndarray] = None
|
|
encoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None
|
|
encoder_attentions: Optional[Tuple[jnp.ndarray]] = None
|
|
|
|
|
|
@flax.struct.dataclass
|
|
class FlaxNextSentencePredictorOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of models predicting if two sentences are consecutive or not.
|
|
|
|
Args:
|
|
logits (`jnp.ndarray` of shape `(batch_size, 2)`):
|
|
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
|
|
before SoftMax).
|
|
hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `jnp.ndarray` (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.
|
|
"""
|
|
|
|
logits: jnp.ndarray = None
|
|
hidden_states: Optional[Tuple[jnp.ndarray]] = None
|
|
attentions: Optional[Tuple[jnp.ndarray]] = None
|
|
|
|
|
|
@flax.struct.dataclass
|
|
class FlaxSequenceClassifierOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of sentence classification models.
|
|
|
|
Args:
|
|
logits (`jnp.ndarray` of shape `(batch_size, config.num_labels)`):
|
|
Classification (or regression if config.num_labels==1) scores (before SoftMax).
|
|
hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `jnp.ndarray` (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.
|
|
"""
|
|
|
|
logits: jnp.ndarray = None
|
|
hidden_states: Optional[Tuple[jnp.ndarray]] = None
|
|
attentions: Optional[Tuple[jnp.ndarray]] = None
|
|
|
|
|
|
@flax.struct.dataclass
|
|
class FlaxSeq2SeqSequenceClassifierOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of sequence-to-sequence sentence classification models.
|
|
|
|
Args:
|
|
logits (`jnp.ndarray` of shape `(batch_size, config.num_labels)`):
|
|
Classification (or regression if config.num_labels==1) scores (before SoftMax).
|
|
past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
|
Tuple of `tuple(jnp.ndarray)` 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(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `jnp.ndarray` (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 (`jnp.ndarray` 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(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `jnp.ndarray` (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.
|
|
"""
|
|
|
|
logits: jnp.ndarray = None
|
|
past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None
|
|
decoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None
|
|
decoder_attentions: Optional[Tuple[jnp.ndarray]] = None
|
|
cross_attentions: Optional[Tuple[jnp.ndarray]] = None
|
|
encoder_last_hidden_state: Optional[jnp.ndarray] = None
|
|
encoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None
|
|
encoder_attentions: Optional[Tuple[jnp.ndarray]] = None
|
|
|
|
|
|
@flax.struct.dataclass
|
|
class FlaxMultipleChoiceModelOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of multiple choice models.
|
|
|
|
Args:
|
|
logits (`jnp.ndarray` 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(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `jnp.ndarray` (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.
|
|
"""
|
|
|
|
logits: jnp.ndarray = None
|
|
hidden_states: Optional[Tuple[jnp.ndarray]] = None
|
|
attentions: Optional[Tuple[jnp.ndarray]] = None
|
|
|
|
|
|
@flax.struct.dataclass
|
|
class FlaxTokenClassifierOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of token classification models.
|
|
|
|
Args:
|
|
logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.num_labels)`):
|
|
Classification scores (before SoftMax).
|
|
hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `jnp.ndarray` (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.
|
|
"""
|
|
|
|
logits: jnp.ndarray = None
|
|
hidden_states: Optional[Tuple[jnp.ndarray]] = None
|
|
attentions: Optional[Tuple[jnp.ndarray]] = None
|
|
|
|
|
|
@flax.struct.dataclass
|
|
class FlaxQuestionAnsweringModelOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of question answering models.
|
|
|
|
Args:
|
|
start_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
|
|
Span-start scores (before SoftMax).
|
|
end_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
|
|
Span-end scores (before SoftMax).
|
|
hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `jnp.ndarray` (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.
|
|
"""
|
|
|
|
start_logits: jnp.ndarray = None
|
|
end_logits: jnp.ndarray = None
|
|
hidden_states: Optional[Tuple[jnp.ndarray]] = None
|
|
attentions: Optional[Tuple[jnp.ndarray]] = None
|
|
|
|
|
|
@flax.struct.dataclass
|
|
class FlaxSeq2SeqQuestionAnsweringModelOutput(ModelOutput):
|
|
"""
|
|
Base class for outputs of sequence-to-sequence question answering models.
|
|
|
|
Args:
|
|
start_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
|
|
Span-start scores (before SoftMax).
|
|
end_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
|
|
Span-end scores (before SoftMax).
|
|
past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
|
Tuple of `tuple(jnp.ndarray)` 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(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `jnp.ndarray` (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 (`jnp.ndarray` 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(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
Tuple of `jnp.ndarray` (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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `jnp.ndarray` (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.
|
|
"""
|
|
|
|
start_logits: jnp.ndarray = None
|
|
end_logits: jnp.ndarray = None
|
|
past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None
|
|
decoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None
|
|
decoder_attentions: Optional[Tuple[jnp.ndarray]] = None
|
|
cross_attentions: Optional[Tuple[jnp.ndarray]] = None
|
|
encoder_last_hidden_state: Optional[jnp.ndarray] = None
|
|
encoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None
|
|
encoder_attentions: Optional[Tuple[jnp.ndarray]] = None
|