2232 lines
101 KiB
Python
2232 lines
101 KiB
Python
# coding=utf-8
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# Copyright Studio Ousia and The HuggingFace Inc. team.
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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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"""PyTorch LUKE model."""
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import math
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from dataclasses import dataclass
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from typing import Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...activations import ACT2FN, gelu
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from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import apply_chunking_to_forward
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from ...utils import (
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ModelOutput,
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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logging,
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replace_return_docstrings,
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)
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from .configuration_luke import LukeConfig
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "LukeConfig"
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_CHECKPOINT_FOR_DOC = "studio-ousia/luke-base"
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from ..deprecated._archive_maps import LUKE_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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@dataclass
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class BaseLukeModelOutputWithPooling(BaseModelOutputWithPooling):
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"""
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Base class for outputs of the LUKE model.
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Args:
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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entity_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, entity_length, hidden_size)`):
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Sequence of entity hidden-states at the output of the last layer of the model.
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pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
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Last layer hidden-state of the first token of the sequence (classification token) further processed by a
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Linear layer and a Tanh activation function.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
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plus the initial embedding outputs.
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entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
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layer plus the initial entity embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length +
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entity_length, sequence_length + entity_length)`. Attentions weights after the attention softmax, used to
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compute the weighted average in the self-attention heads.
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"""
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entity_last_hidden_state: torch.FloatTensor = None
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entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class BaseLukeModelOutput(BaseModelOutput):
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"""
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Base class for model's outputs, with potential hidden states and attentions.
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Args:
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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entity_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, entity_length, hidden_size)`):
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Sequence of entity hidden-states at the output of the last layer of the model.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(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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entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
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layer plus the initial entity embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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"""
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entity_last_hidden_state: torch.FloatTensor = None
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entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class LukeMaskedLMOutput(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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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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The sum of masked language modeling (MLM) loss and entity prediction loss.
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mlm_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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Masked language modeling (MLM) loss.
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mep_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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Masked entity prediction (MEP) loss.
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logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
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Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
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entity_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
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Prediction scores of the entity prediction head (scores for each entity vocabulary token before SoftMax).
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(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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entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
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layer plus the initial entity embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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"""
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loss: Optional[torch.FloatTensor] = None
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mlm_loss: Optional[torch.FloatTensor] = None
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mep_loss: Optional[torch.FloatTensor] = None
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logits: torch.FloatTensor = None
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entity_logits: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class EntityClassificationOutput(ModelOutput):
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"""
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Outputs of entity classification models.
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Args:
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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Classification loss.
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logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
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Classification scores (before SoftMax).
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
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plus the initial embedding outputs.
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entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
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layer plus the initial entity embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
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the self-attention heads.
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"""
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loss: Optional[torch.FloatTensor] = None
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logits: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class EntityPairClassificationOutput(ModelOutput):
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"""
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Outputs of entity pair classification models.
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Args:
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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Classification loss.
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logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
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Classification scores (before SoftMax).
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
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plus the initial embedding outputs.
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entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
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layer plus the initial entity embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
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the self-attention heads.
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"""
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loss: Optional[torch.FloatTensor] = None
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logits: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class EntitySpanClassificationOutput(ModelOutput):
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"""
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Outputs of entity span classification models.
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Args:
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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Classification loss.
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logits (`torch.FloatTensor` of shape `(batch_size, entity_length, config.num_labels)`):
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Classification scores (before SoftMax).
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
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plus the initial embedding outputs.
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entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
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layer plus the initial entity embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
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the self-attention heads.
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"""
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loss: Optional[torch.FloatTensor] = None
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logits: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class LukeSequenceClassifierOutput(ModelOutput):
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"""
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Outputs of sentence classification models.
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Args:
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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Classification (or regression if config.num_labels==1) loss.
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logits (`torch.FloatTensor` 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(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
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layer plus the initial entity embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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"""
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loss: Optional[torch.FloatTensor] = None
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logits: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class LukeTokenClassifierOutput(ModelOutput):
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"""
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Base class for outputs of token classification models.
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Args:
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) :
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Classification loss.
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logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
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Classification scores (before SoftMax).
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
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layer plus the initial entity embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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"""
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loss: Optional[torch.FloatTensor] = None
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logits: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class LukeQuestionAnsweringModelOutput(ModelOutput):
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"""
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Outputs of question answering models.
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Args:
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
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start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
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Span-start scores (before SoftMax).
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end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
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Span-end scores (before SoftMax).
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
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layer plus the initial entity embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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"""
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loss: Optional[torch.FloatTensor] = None
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start_logits: torch.FloatTensor = None
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end_logits: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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@dataclass
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class LukeMultipleChoiceModelOutput(ModelOutput):
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"""
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Outputs of multiple choice models.
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Args:
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loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
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Classification loss.
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logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`):
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*num_choices* is the second dimension of the input tensors. (see *input_ids* above).
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Classification scores (before SoftMax).
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
|
|
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
|
|
shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
|
|
layer plus the initial entity embedding outputs.
|
|
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
|
|
sequence_length)`.
|
|
|
|
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
|
|
heads.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
logits: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
|
|
|
|
|
|
class LukeEmbeddings(nn.Module):
|
|
"""
|
|
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
|
|
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
|
|
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
|
|
|
|
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
|
|
# any TensorFlow checkpoint file
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
# End copy
|
|
self.padding_idx = config.pad_token_id
|
|
self.position_embeddings = nn.Embedding(
|
|
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
):
|
|
if position_ids is None:
|
|
if input_ids is not None:
|
|
# Create the position ids from the input token ids. Any padded tokens remain padded.
|
|
position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx).to(input_ids.device)
|
|
else:
|
|
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
|
|
|
|
if input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
else:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.word_embeddings(input_ids)
|
|
|
|
position_embeddings = self.position_embeddings(position_ids)
|
|
token_type_embeddings = self.token_type_embeddings(token_type_ids)
|
|
|
|
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
|
|
embeddings = self.LayerNorm(embeddings)
|
|
embeddings = self.dropout(embeddings)
|
|
return embeddings
|
|
|
|
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
|
|
"""
|
|
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
|
|
|
|
Args:
|
|
inputs_embeds: torch.Tensor
|
|
|
|
Returns: torch.Tensor
|
|
"""
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
sequence_length = input_shape[1]
|
|
|
|
position_ids = torch.arange(
|
|
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
|
|
)
|
|
return position_ids.unsqueeze(0).expand(input_shape)
|
|
|
|
|
|
class LukeEntityEmbeddings(nn.Module):
|
|
def __init__(self, config: LukeConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
|
|
self.entity_embeddings = nn.Embedding(config.entity_vocab_size, config.entity_emb_size, padding_idx=0)
|
|
if config.entity_emb_size != config.hidden_size:
|
|
self.entity_embedding_dense = nn.Linear(config.entity_emb_size, config.hidden_size, bias=False)
|
|
|
|
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
|
|
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
|
|
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(
|
|
self, entity_ids: torch.LongTensor, position_ids: torch.LongTensor, token_type_ids: torch.LongTensor = None
|
|
):
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros_like(entity_ids)
|
|
|
|
entity_embeddings = self.entity_embeddings(entity_ids)
|
|
if self.config.entity_emb_size != self.config.hidden_size:
|
|
entity_embeddings = self.entity_embedding_dense(entity_embeddings)
|
|
|
|
position_embeddings = self.position_embeddings(position_ids.clamp(min=0))
|
|
position_embedding_mask = (position_ids != -1).type_as(position_embeddings).unsqueeze(-1)
|
|
position_embeddings = position_embeddings * position_embedding_mask
|
|
position_embeddings = torch.sum(position_embeddings, dim=-2)
|
|
position_embeddings = position_embeddings / position_embedding_mask.sum(dim=-2).clamp(min=1e-7)
|
|
|
|
token_type_embeddings = self.token_type_embeddings(token_type_ids)
|
|
|
|
embeddings = entity_embeddings + position_embeddings + token_type_embeddings
|
|
embeddings = self.LayerNorm(embeddings)
|
|
embeddings = self.dropout(embeddings)
|
|
|
|
return embeddings
|
|
|
|
|
|
class LukeSelfAttention(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
|
|
raise ValueError(
|
|
f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
|
|
f"heads {config.num_attention_heads}."
|
|
)
|
|
|
|
self.num_attention_heads = config.num_attention_heads
|
|
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
|
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
|
self.use_entity_aware_attention = config.use_entity_aware_attention
|
|
|
|
self.query = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.key = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.value = nn.Linear(config.hidden_size, self.all_head_size)
|
|
|
|
if self.use_entity_aware_attention:
|
|
self.w2e_query = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.e2w_query = nn.Linear(config.hidden_size, self.all_head_size)
|
|
self.e2e_query = nn.Linear(config.hidden_size, self.all_head_size)
|
|
|
|
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
|
|
|
|
def transpose_for_scores(self, x):
|
|
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
|
|
x = x.view(*new_x_shape)
|
|
return x.permute(0, 2, 1, 3)
|
|
|
|
def forward(
|
|
self,
|
|
word_hidden_states,
|
|
entity_hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
output_attentions=False,
|
|
):
|
|
word_size = word_hidden_states.size(1)
|
|
|
|
if entity_hidden_states is None:
|
|
concat_hidden_states = word_hidden_states
|
|
else:
|
|
concat_hidden_states = torch.cat([word_hidden_states, entity_hidden_states], dim=1)
|
|
|
|
key_layer = self.transpose_for_scores(self.key(concat_hidden_states))
|
|
value_layer = self.transpose_for_scores(self.value(concat_hidden_states))
|
|
|
|
if self.use_entity_aware_attention and entity_hidden_states is not None:
|
|
# compute query vectors using word-word (w2w), word-entity (w2e), entity-word (e2w), entity-entity (e2e)
|
|
# query layers
|
|
w2w_query_layer = self.transpose_for_scores(self.query(word_hidden_states))
|
|
w2e_query_layer = self.transpose_for_scores(self.w2e_query(word_hidden_states))
|
|
e2w_query_layer = self.transpose_for_scores(self.e2w_query(entity_hidden_states))
|
|
e2e_query_layer = self.transpose_for_scores(self.e2e_query(entity_hidden_states))
|
|
|
|
# compute w2w, w2e, e2w, and e2e key vectors used with the query vectors computed above
|
|
w2w_key_layer = key_layer[:, :, :word_size, :]
|
|
e2w_key_layer = key_layer[:, :, :word_size, :]
|
|
w2e_key_layer = key_layer[:, :, word_size:, :]
|
|
e2e_key_layer = key_layer[:, :, word_size:, :]
|
|
|
|
# compute attention scores based on the dot product between the query and key vectors
|
|
w2w_attention_scores = torch.matmul(w2w_query_layer, w2w_key_layer.transpose(-1, -2))
|
|
w2e_attention_scores = torch.matmul(w2e_query_layer, w2e_key_layer.transpose(-1, -2))
|
|
e2w_attention_scores = torch.matmul(e2w_query_layer, e2w_key_layer.transpose(-1, -2))
|
|
e2e_attention_scores = torch.matmul(e2e_query_layer, e2e_key_layer.transpose(-1, -2))
|
|
|
|
# combine attention scores to create the final attention score matrix
|
|
word_attention_scores = torch.cat([w2w_attention_scores, w2e_attention_scores], dim=3)
|
|
entity_attention_scores = torch.cat([e2w_attention_scores, e2e_attention_scores], dim=3)
|
|
attention_scores = torch.cat([word_attention_scores, entity_attention_scores], dim=2)
|
|
|
|
else:
|
|
query_layer = self.transpose_for_scores(self.query(concat_hidden_states))
|
|
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
|
|
|
|
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
|
|
if attention_mask is not None:
|
|
# Apply the attention mask is (precomputed for all layers in LukeModel forward() function)
|
|
attention_scores = attention_scores + attention_mask
|
|
|
|
# Normalize the attention scores to probabilities.
|
|
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
|
|
|
|
# This is actually dropping out entire tokens to attend to, which might
|
|
# seem a bit unusual, but is taken from the original Transformer paper.
|
|
attention_probs = self.dropout(attention_probs)
|
|
|
|
# Mask heads if we want to
|
|
if head_mask is not None:
|
|
attention_probs = attention_probs * head_mask
|
|
|
|
context_layer = torch.matmul(attention_probs, value_layer)
|
|
|
|
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
|
|
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
|
|
context_layer = context_layer.view(*new_context_layer_shape)
|
|
|
|
output_word_hidden_states = context_layer[:, :word_size, :]
|
|
if entity_hidden_states is None:
|
|
output_entity_hidden_states = None
|
|
else:
|
|
output_entity_hidden_states = context_layer[:, word_size:, :]
|
|
|
|
if output_attentions:
|
|
outputs = (output_word_hidden_states, output_entity_hidden_states, attention_probs)
|
|
else:
|
|
outputs = (output_word_hidden_states, output_entity_hidden_states)
|
|
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
|
|
class LukeSelfOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
class LukeAttention(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.self = LukeSelfAttention(config)
|
|
self.output = LukeSelfOutput(config)
|
|
self.pruned_heads = set()
|
|
|
|
def prune_heads(self, heads):
|
|
raise NotImplementedError("LUKE does not support the pruning of attention heads")
|
|
|
|
def forward(
|
|
self,
|
|
word_hidden_states,
|
|
entity_hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
output_attentions=False,
|
|
):
|
|
word_size = word_hidden_states.size(1)
|
|
self_outputs = self.self(
|
|
word_hidden_states,
|
|
entity_hidden_states,
|
|
attention_mask,
|
|
head_mask,
|
|
output_attentions,
|
|
)
|
|
if entity_hidden_states is None:
|
|
concat_self_outputs = self_outputs[0]
|
|
concat_hidden_states = word_hidden_states
|
|
else:
|
|
concat_self_outputs = torch.cat(self_outputs[:2], dim=1)
|
|
concat_hidden_states = torch.cat([word_hidden_states, entity_hidden_states], dim=1)
|
|
|
|
attention_output = self.output(concat_self_outputs, concat_hidden_states)
|
|
|
|
word_attention_output = attention_output[:, :word_size, :]
|
|
if entity_hidden_states is None:
|
|
entity_attention_output = None
|
|
else:
|
|
entity_attention_output = attention_output[:, word_size:, :]
|
|
|
|
# add attentions if we output them
|
|
outputs = (word_attention_output, entity_attention_output) + self_outputs[2:]
|
|
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
|
|
class LukeIntermediate(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.intermediate_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.intermediate_act_fn = config.hidden_act
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertOutput
|
|
class LukeOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
class LukeLayer(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.seq_len_dim = 1
|
|
self.attention = LukeAttention(config)
|
|
self.intermediate = LukeIntermediate(config)
|
|
self.output = LukeOutput(config)
|
|
|
|
def forward(
|
|
self,
|
|
word_hidden_states,
|
|
entity_hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
output_attentions=False,
|
|
):
|
|
word_size = word_hidden_states.size(1)
|
|
|
|
self_attention_outputs = self.attention(
|
|
word_hidden_states,
|
|
entity_hidden_states,
|
|
attention_mask,
|
|
head_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
if entity_hidden_states is None:
|
|
concat_attention_output = self_attention_outputs[0]
|
|
else:
|
|
concat_attention_output = torch.cat(self_attention_outputs[:2], dim=1)
|
|
|
|
outputs = self_attention_outputs[2:] # add self attentions if we output attention weights
|
|
|
|
layer_output = apply_chunking_to_forward(
|
|
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, concat_attention_output
|
|
)
|
|
word_layer_output = layer_output[:, :word_size, :]
|
|
if entity_hidden_states is None:
|
|
entity_layer_output = None
|
|
else:
|
|
entity_layer_output = layer_output[:, word_size:, :]
|
|
|
|
outputs = (word_layer_output, entity_layer_output) + outputs
|
|
|
|
return outputs
|
|
|
|
def feed_forward_chunk(self, attention_output):
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
|
|
class LukeEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([LukeLayer(config) for _ in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
word_hidden_states,
|
|
entity_hidden_states,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
output_attentions=False,
|
|
output_hidden_states=False,
|
|
return_dict=True,
|
|
):
|
|
all_word_hidden_states = () if output_hidden_states else None
|
|
all_entity_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_word_hidden_states = all_word_hidden_states + (word_hidden_states,)
|
|
all_entity_hidden_states = all_entity_hidden_states + (entity_hidden_states,)
|
|
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
layer_module.__call__,
|
|
word_hidden_states,
|
|
entity_hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
output_attentions,
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(
|
|
word_hidden_states,
|
|
entity_hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
output_attentions,
|
|
)
|
|
|
|
word_hidden_states = layer_outputs[0]
|
|
|
|
if entity_hidden_states is not None:
|
|
entity_hidden_states = layer_outputs[1]
|
|
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[2],)
|
|
|
|
if output_hidden_states:
|
|
all_word_hidden_states = all_word_hidden_states + (word_hidden_states,)
|
|
all_entity_hidden_states = all_entity_hidden_states + (entity_hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [
|
|
word_hidden_states,
|
|
all_word_hidden_states,
|
|
all_self_attentions,
|
|
entity_hidden_states,
|
|
all_entity_hidden_states,
|
|
]
|
|
if v is not None
|
|
)
|
|
return BaseLukeModelOutput(
|
|
last_hidden_state=word_hidden_states,
|
|
hidden_states=all_word_hidden_states,
|
|
attentions=all_self_attentions,
|
|
entity_last_hidden_state=entity_hidden_states,
|
|
entity_hidden_states=all_entity_hidden_states,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertPooler
|
|
class LukePooler(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = nn.Tanh()
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
# We "pool" the model by simply taking the hidden state corresponding
|
|
# to the first token.
|
|
first_token_tensor = hidden_states[:, 0]
|
|
pooled_output = self.dense(first_token_tensor)
|
|
pooled_output = self.activation(pooled_output)
|
|
return pooled_output
|
|
|
|
|
|
class EntityPredictionHeadTransform(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.entity_emb_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.transform_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.transform_act_fn = config.hidden_act
|
|
self.LayerNorm = nn.LayerNorm(config.entity_emb_size, eps=config.layer_norm_eps)
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.transform_act_fn(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class EntityPredictionHead(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.transform = EntityPredictionHeadTransform(config)
|
|
self.decoder = nn.Linear(config.entity_emb_size, config.entity_vocab_size, bias=False)
|
|
self.bias = nn.Parameter(torch.zeros(config.entity_vocab_size))
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.transform(hidden_states)
|
|
hidden_states = self.decoder(hidden_states) + self.bias
|
|
|
|
return hidden_states
|
|
|
|
|
|
class LukePreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = LukeConfig
|
|
base_model_prefix = "luke"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["LukeAttention", "LukeEntityEmbeddings"]
|
|
|
|
def _init_weights(self, module: nn.Module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, nn.Linear):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
if module.embedding_dim == 1: # embedding for bias parameters
|
|
module.weight.data.zero_()
|
|
else:
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
LUKE_START_DOCSTRING = r"""
|
|
|
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
|
etc.)
|
|
|
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
|
and behavior.
|
|
|
|
Parameters:
|
|
config ([`LukeConfig`]): Model configuration class with all the parameters of the
|
|
model. Initializing with a config file does not load the weights associated with the model, only the
|
|
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
|
"""
|
|
|
|
LUKE_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `({0})`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
|
|
1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
|
|
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
|
|
Indices of entity tokens in the entity vocabulary.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
|
|
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for entity tokens that are **not masked**,
|
|
- 0 for entity tokens that are **masked**.
|
|
|
|
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
|
|
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
|
|
selected in `[0, 1]`:
|
|
|
|
- 0 corresponds to a *portion A* entity token,
|
|
- 1 corresponds to a *portion B* entity token.
|
|
|
|
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
|
|
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
|
model's internal embedding lookup matrix.
|
|
|
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare LUKE model transformer outputting raw hidden-states for both word tokens and entities without any"
|
|
" specific head on top.",
|
|
LUKE_START_DOCSTRING,
|
|
)
|
|
class LukeModel(LukePreTrainedModel):
|
|
def __init__(self, config: LukeConfig, add_pooling_layer: bool = True):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = LukeEmbeddings(config)
|
|
self.entity_embeddings = LukeEntityEmbeddings(config)
|
|
self.encoder = LukeEncoder(config)
|
|
|
|
self.pooler = LukePooler(config) if add_pooling_layer else None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
def get_entity_embeddings(self):
|
|
return self.entity_embeddings.entity_embeddings
|
|
|
|
def set_entity_embeddings(self, value):
|
|
self.entity_embeddings.entity_embeddings = value
|
|
|
|
def _prune_heads(self, heads_to_prune):
|
|
raise NotImplementedError("LUKE does not support the pruning of attention heads")
|
|
|
|
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=BaseLukeModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
entity_ids: Optional[torch.LongTensor] = None,
|
|
entity_attention_mask: Optional[torch.FloatTensor] = None,
|
|
entity_token_type_ids: Optional[torch.LongTensor] = None,
|
|
entity_position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseLukeModelOutputWithPooling]:
|
|
r"""
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, LukeModel
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-base")
|
|
>>> model = LukeModel.from_pretrained("studio-ousia/luke-base")
|
|
# Compute the contextualized entity representation corresponding to the entity mention "Beyoncé"
|
|
|
|
>>> text = "Beyoncé lives in Los Angeles."
|
|
>>> entity_spans = [(0, 7)] # character-based entity span corresponding to "Beyoncé"
|
|
|
|
>>> encoding = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt")
|
|
>>> outputs = model(**encoding)
|
|
>>> word_last_hidden_state = outputs.last_hidden_state
|
|
>>> entity_last_hidden_state = outputs.entity_last_hidden_state
|
|
# Input Wikipedia entities to obtain enriched contextualized representations of word tokens
|
|
|
|
>>> text = "Beyoncé lives in Los Angeles."
|
|
>>> entities = [
|
|
... "Beyoncé",
|
|
... "Los Angeles",
|
|
... ] # Wikipedia entity titles corresponding to the entity mentions "Beyoncé" and "Los Angeles"
|
|
>>> entity_spans = [
|
|
... (0, 7),
|
|
... (17, 28),
|
|
... ] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
|
|
|
|
>>> encoding = tokenizer(
|
|
... text, entities=entities, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt"
|
|
... )
|
|
>>> outputs = model(**encoding)
|
|
>>> word_last_hidden_state = outputs.last_hidden_state
|
|
>>> entity_last_hidden_state = outputs.entity_last_hidden_state
|
|
```"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
|
|
input_shape = input_ids.size()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
batch_size, seq_length = input_shape
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones((batch_size, seq_length), device=device)
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
if entity_ids is not None:
|
|
entity_seq_length = entity_ids.size(1)
|
|
if entity_attention_mask is None:
|
|
entity_attention_mask = torch.ones((batch_size, entity_seq_length), device=device)
|
|
if entity_token_type_ids is None:
|
|
entity_token_type_ids = torch.zeros((batch_size, entity_seq_length), dtype=torch.long, device=device)
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
# First, compute word embeddings
|
|
word_embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
|
|
# Second, compute extended attention mask
|
|
extended_attention_mask = self.get_extended_attention_mask(attention_mask, entity_attention_mask)
|
|
|
|
# Third, compute entity embeddings and concatenate with word embeddings
|
|
if entity_ids is None:
|
|
entity_embedding_output = None
|
|
else:
|
|
entity_embedding_output = self.entity_embeddings(entity_ids, entity_position_ids, entity_token_type_ids)
|
|
|
|
# Fourth, send embeddings through the model
|
|
encoder_outputs = self.encoder(
|
|
word_embedding_output,
|
|
entity_embedding_output,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
# Fifth, get the output. LukeModel outputs the same as BertModel, namely sequence_output of shape (batch_size, seq_len, hidden_size)
|
|
sequence_output = encoder_outputs[0]
|
|
|
|
# Sixth, we compute the pooled_output, word_sequence_output and entity_sequence_output based on the sequence_output
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
if not return_dict:
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseLukeModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
entity_last_hidden_state=encoder_outputs.entity_last_hidden_state,
|
|
entity_hidden_states=encoder_outputs.entity_hidden_states,
|
|
)
|
|
|
|
def get_extended_attention_mask(
|
|
self, word_attention_mask: torch.LongTensor, entity_attention_mask: Optional[torch.LongTensor]
|
|
):
|
|
"""
|
|
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
|
|
|
|
Arguments:
|
|
word_attention_mask (`torch.LongTensor`):
|
|
Attention mask for word tokens with ones indicating tokens to attend to, zeros for tokens to ignore.
|
|
entity_attention_mask (`torch.LongTensor`, *optional*):
|
|
Attention mask for entity tokens with ones indicating tokens to attend to, zeros for tokens to ignore.
|
|
|
|
Returns:
|
|
`torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`.
|
|
"""
|
|
attention_mask = word_attention_mask
|
|
if entity_attention_mask is not None:
|
|
attention_mask = torch.cat([attention_mask, entity_attention_mask], dim=-1)
|
|
|
|
if attention_mask.dim() == 3:
|
|
extended_attention_mask = attention_mask[:, None, :, :]
|
|
elif attention_mask.dim() == 2:
|
|
extended_attention_mask = attention_mask[:, None, None, :]
|
|
else:
|
|
raise ValueError(f"Wrong shape for attention_mask (shape {attention_mask.shape})")
|
|
|
|
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
|
|
extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min
|
|
return extended_attention_mask
|
|
|
|
|
|
def create_position_ids_from_input_ids(input_ids, padding_idx):
|
|
"""
|
|
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
|
|
are ignored. This is modified from fairseq's `utils.make_positions`.
|
|
|
|
Args:
|
|
x: torch.Tensor x:
|
|
|
|
Returns: torch.Tensor
|
|
"""
|
|
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
|
|
mask = input_ids.ne(padding_idx).int()
|
|
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask)) * mask
|
|
return incremental_indices.long() + padding_idx
|
|
|
|
|
|
# Copied from transformers.models.roberta.modeling_roberta.RobertaLMHead
|
|
class LukeLMHead(nn.Module):
|
|
"""Roberta Head for masked language modeling."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
|
|
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
|
|
self.decoder.bias = self.bias
|
|
|
|
def forward(self, features, **kwargs):
|
|
x = self.dense(features)
|
|
x = gelu(x)
|
|
x = self.layer_norm(x)
|
|
|
|
# project back to size of vocabulary with bias
|
|
x = self.decoder(x)
|
|
|
|
return x
|
|
|
|
def _tie_weights(self):
|
|
# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
|
|
# For accelerate compatibility and to not break backward compatibility
|
|
if self.decoder.bias.device.type == "meta":
|
|
self.decoder.bias = self.bias
|
|
else:
|
|
self.bias = self.decoder.bias
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The LUKE model with a language modeling head and entity prediction head on top for masked language modeling and
|
|
masked entity prediction.
|
|
""",
|
|
LUKE_START_DOCSTRING,
|
|
)
|
|
class LukeForMaskedLM(LukePreTrainedModel):
|
|
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias", "entity_predictions.decoder.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.luke = LukeModel(config)
|
|
|
|
self.lm_head = LukeLMHead(config)
|
|
self.entity_predictions = EntityPredictionHead(config)
|
|
|
|
self.loss_fn = nn.CrossEntropyLoss()
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def tie_weights(self):
|
|
super().tie_weights()
|
|
self._tie_or_clone_weights(self.entity_predictions.decoder, self.luke.entity_embeddings.entity_embeddings)
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head.decoder = new_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=LukeMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
entity_ids: Optional[torch.LongTensor] = None,
|
|
entity_attention_mask: Optional[torch.LongTensor] = None,
|
|
entity_token_type_ids: Optional[torch.LongTensor] = None,
|
|
entity_position_ids: Optional[torch.LongTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
entity_labels: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, LukeMaskedLMOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
|
|
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
|
|
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
|
|
entity_labels (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
|
|
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
|
|
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
|
|
|
|
Returns:
|
|
|
|
"""
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.luke(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
entity_ids=entity_ids,
|
|
entity_attention_mask=entity_attention_mask,
|
|
entity_token_type_ids=entity_token_type_ids,
|
|
entity_position_ids=entity_position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
)
|
|
|
|
loss = None
|
|
|
|
mlm_loss = None
|
|
logits = self.lm_head(outputs.last_hidden_state)
|
|
if labels is not None:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(logits.device)
|
|
mlm_loss = self.loss_fn(logits.view(-1, self.config.vocab_size), labels.view(-1))
|
|
if loss is None:
|
|
loss = mlm_loss
|
|
|
|
mep_loss = None
|
|
entity_logits = None
|
|
if outputs.entity_last_hidden_state is not None:
|
|
entity_logits = self.entity_predictions(outputs.entity_last_hidden_state)
|
|
if entity_labels is not None:
|
|
mep_loss = self.loss_fn(entity_logits.view(-1, self.config.entity_vocab_size), entity_labels.view(-1))
|
|
if loss is None:
|
|
loss = mep_loss
|
|
else:
|
|
loss = loss + mep_loss
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [
|
|
loss,
|
|
mlm_loss,
|
|
mep_loss,
|
|
logits,
|
|
entity_logits,
|
|
outputs.hidden_states,
|
|
outputs.entity_hidden_states,
|
|
outputs.attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
|
|
return LukeMaskedLMOutput(
|
|
loss=loss,
|
|
mlm_loss=mlm_loss,
|
|
mep_loss=mep_loss,
|
|
logits=logits,
|
|
entity_logits=entity_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
entity_hidden_states=outputs.entity_hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The LUKE model with a classification head on top (a linear layer on top of the hidden state of the first entity
|
|
token) for entity classification tasks, such as Open Entity.
|
|
""",
|
|
LUKE_START_DOCSTRING,
|
|
)
|
|
class LukeForEntityClassification(LukePreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.luke = LukeModel(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=EntityClassificationOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
entity_ids: Optional[torch.LongTensor] = None,
|
|
entity_attention_mask: Optional[torch.FloatTensor] = None,
|
|
entity_token_type_ids: Optional[torch.LongTensor] = None,
|
|
entity_position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, EntityClassificationOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, *optional*):
|
|
Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is
|
|
used for the single-label classification. In this case, labels should contain the indices that should be in
|
|
`[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy
|
|
loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0
|
|
and 1 indicate false and true, respectively.
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, LukeForEntityClassification
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-open-entity")
|
|
>>> model = LukeForEntityClassification.from_pretrained("studio-ousia/luke-large-finetuned-open-entity")
|
|
|
|
>>> text = "Beyoncé lives in Los Angeles."
|
|
>>> entity_spans = [(0, 7)] # character-based entity span corresponding to "Beyoncé"
|
|
>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
>>> logits = outputs.logits
|
|
>>> predicted_class_idx = logits.argmax(-1).item()
|
|
>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
|
|
Predicted class: person
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.luke(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
entity_ids=entity_ids,
|
|
entity_attention_mask=entity_attention_mask,
|
|
entity_token_type_ids=entity_token_type_ids,
|
|
entity_position_ids=entity_position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
)
|
|
|
|
feature_vector = outputs.entity_last_hidden_state[:, 0, :]
|
|
feature_vector = self.dropout(feature_vector)
|
|
logits = self.classifier(feature_vector)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# When the number of dimension of `labels` is 1, cross entropy is used as the loss function. The binary
|
|
# cross entropy is used otherwise.
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(logits.device)
|
|
if labels.ndim == 1:
|
|
loss = nn.functional.cross_entropy(logits, labels)
|
|
else:
|
|
loss = nn.functional.binary_cross_entropy_with_logits(logits.view(-1), labels.view(-1).type_as(logits))
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
|
|
if v is not None
|
|
)
|
|
|
|
return EntityClassificationOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
entity_hidden_states=outputs.entity_hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The LUKE model with a classification head on top (a linear layer on top of the hidden states of the two entity
|
|
tokens) for entity pair classification tasks, such as TACRED.
|
|
""",
|
|
LUKE_START_DOCSTRING,
|
|
)
|
|
class LukeForEntityPairClassification(LukePreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.luke = LukeModel(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size * 2, config.num_labels, False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=EntityPairClassificationOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
entity_ids: Optional[torch.LongTensor] = None,
|
|
entity_attention_mask: Optional[torch.FloatTensor] = None,
|
|
entity_token_type_ids: Optional[torch.LongTensor] = None,
|
|
entity_position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, EntityPairClassificationOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, *optional*):
|
|
Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is
|
|
used for the single-label classification. In this case, labels should contain the indices that should be in
|
|
`[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy
|
|
loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0
|
|
and 1 indicate false and true, respectively.
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, LukeForEntityPairClassification
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
|
|
>>> model = LukeForEntityPairClassification.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
|
|
|
|
>>> text = "Beyoncé lives in Los Angeles."
|
|
>>> entity_spans = [
|
|
... (0, 7),
|
|
... (17, 28),
|
|
... ] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
|
|
>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
>>> logits = outputs.logits
|
|
>>> predicted_class_idx = logits.argmax(-1).item()
|
|
>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
|
|
Predicted class: per:cities_of_residence
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.luke(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
entity_ids=entity_ids,
|
|
entity_attention_mask=entity_attention_mask,
|
|
entity_token_type_ids=entity_token_type_ids,
|
|
entity_position_ids=entity_position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
)
|
|
|
|
feature_vector = torch.cat(
|
|
[outputs.entity_last_hidden_state[:, 0, :], outputs.entity_last_hidden_state[:, 1, :]], dim=1
|
|
)
|
|
feature_vector = self.dropout(feature_vector)
|
|
logits = self.classifier(feature_vector)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# When the number of dimension of `labels` is 1, cross entropy is used as the loss function. The binary
|
|
# cross entropy is used otherwise.
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(logits.device)
|
|
if labels.ndim == 1:
|
|
loss = nn.functional.cross_entropy(logits, labels)
|
|
else:
|
|
loss = nn.functional.binary_cross_entropy_with_logits(logits.view(-1), labels.view(-1).type_as(logits))
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
|
|
if v is not None
|
|
)
|
|
|
|
return EntityPairClassificationOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
entity_hidden_states=outputs.entity_hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The LUKE model with a span classification head on top (a linear layer on top of the hidden states output) for tasks
|
|
such as named entity recognition.
|
|
""",
|
|
LUKE_START_DOCSTRING,
|
|
)
|
|
class LukeForEntitySpanClassification(LukePreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.luke = LukeModel(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size * 3, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=EntitySpanClassificationOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
entity_ids: Optional[torch.LongTensor] = None,
|
|
entity_attention_mask: Optional[torch.LongTensor] = None,
|
|
entity_token_type_ids: Optional[torch.LongTensor] = None,
|
|
entity_position_ids: Optional[torch.LongTensor] = None,
|
|
entity_start_positions: Optional[torch.LongTensor] = None,
|
|
entity_end_positions: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, EntitySpanClassificationOutput]:
|
|
r"""
|
|
entity_start_positions (`torch.LongTensor`):
|
|
The start positions of entities in the word token sequence.
|
|
|
|
entity_end_positions (`torch.LongTensor`):
|
|
The end positions of entities in the word token sequence.
|
|
|
|
labels (`torch.LongTensor` of shape `(batch_size, entity_length)` or `(batch_size, entity_length, num_labels)`, *optional*):
|
|
Labels for computing the classification loss. If the shape is `(batch_size, entity_length)`, the cross
|
|
entropy loss is used for the single-label classification. In this case, labels should contain the indices
|
|
that should be in `[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, entity_length,
|
|
num_labels)`, the binary cross entropy loss is used for the multi-label classification. In this case,
|
|
labels should only contain `[0, 1]`, where 0 and 1 indicate false and true, respectively.
|
|
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, LukeForEntitySpanClassification
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")
|
|
>>> model = LukeForEntitySpanClassification.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")
|
|
|
|
>>> text = "Beyoncé lives in Los Angeles"
|
|
# List all possible entity spans in the text
|
|
|
|
>>> word_start_positions = [0, 8, 14, 17, 21] # character-based start positions of word tokens
|
|
>>> word_end_positions = [7, 13, 16, 20, 28] # character-based end positions of word tokens
|
|
>>> entity_spans = []
|
|
>>> for i, start_pos in enumerate(word_start_positions):
|
|
... for end_pos in word_end_positions[i:]:
|
|
... entity_spans.append((start_pos, end_pos))
|
|
|
|
>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
>>> logits = outputs.logits
|
|
>>> predicted_class_indices = logits.argmax(-1).squeeze().tolist()
|
|
>>> for span, predicted_class_idx in zip(entity_spans, predicted_class_indices):
|
|
... if predicted_class_idx != 0:
|
|
... print(text[span[0] : span[1]], model.config.id2label[predicted_class_idx])
|
|
Beyoncé PER
|
|
Los Angeles LOC
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.luke(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
entity_ids=entity_ids,
|
|
entity_attention_mask=entity_attention_mask,
|
|
entity_token_type_ids=entity_token_type_ids,
|
|
entity_position_ids=entity_position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
)
|
|
hidden_size = outputs.last_hidden_state.size(-1)
|
|
|
|
entity_start_positions = entity_start_positions.unsqueeze(-1).expand(-1, -1, hidden_size)
|
|
if entity_start_positions.device != outputs.last_hidden_state.device:
|
|
entity_start_positions = entity_start_positions.to(outputs.last_hidden_state.device)
|
|
start_states = torch.gather(outputs.last_hidden_state, -2, entity_start_positions)
|
|
|
|
entity_end_positions = entity_end_positions.unsqueeze(-1).expand(-1, -1, hidden_size)
|
|
if entity_end_positions.device != outputs.last_hidden_state.device:
|
|
entity_end_positions = entity_end_positions.to(outputs.last_hidden_state.device)
|
|
end_states = torch.gather(outputs.last_hidden_state, -2, entity_end_positions)
|
|
|
|
feature_vector = torch.cat([start_states, end_states, outputs.entity_last_hidden_state], dim=2)
|
|
|
|
feature_vector = self.dropout(feature_vector)
|
|
logits = self.classifier(feature_vector)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(logits.device)
|
|
# When the number of dimension of `labels` is 2, cross entropy is used as the loss function. The binary
|
|
# cross entropy is used otherwise.
|
|
if labels.ndim == 2:
|
|
loss = nn.functional.cross_entropy(logits.view(-1, self.num_labels), labels.view(-1))
|
|
else:
|
|
loss = nn.functional.binary_cross_entropy_with_logits(logits.view(-1), labels.view(-1).type_as(logits))
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
|
|
if v is not None
|
|
)
|
|
|
|
return EntitySpanClassificationOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
entity_hidden_states=outputs.entity_hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The LUKE Model transformer with a sequence classification/regression head on top (a linear layer on top of the
|
|
pooled output) e.g. for GLUE tasks.
|
|
""",
|
|
LUKE_START_DOCSTRING,
|
|
)
|
|
class LukeForSequenceClassification(LukePreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.luke = LukeModel(config)
|
|
self.dropout = nn.Dropout(
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=LukeSequenceClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
entity_ids: Optional[torch.LongTensor] = None,
|
|
entity_attention_mask: Optional[torch.FloatTensor] = None,
|
|
entity_token_type_ids: Optional[torch.LongTensor] = None,
|
|
entity_position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, LukeSequenceClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.luke(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
entity_ids=entity_ids,
|
|
entity_attention_mask=entity_attention_mask,
|
|
entity_token_type_ids=entity_token_type_ids,
|
|
entity_position_ids=entity_position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
)
|
|
|
|
pooled_output = outputs.pooler_output
|
|
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(logits.device)
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
|
|
if v is not None
|
|
)
|
|
|
|
return LukeSequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
entity_hidden_states=outputs.entity_hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The LUKE Model with a token classification head on top (a linear layer on top of the hidden-states output). To
|
|
solve Named-Entity Recognition (NER) task using LUKE, `LukeForEntitySpanClassification` is more suitable than this
|
|
class.
|
|
""",
|
|
LUKE_START_DOCSTRING,
|
|
)
|
|
class LukeForTokenClassification(LukePreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.luke = LukeModel(config, add_pooling_layer=False)
|
|
self.dropout = nn.Dropout(
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=LukeTokenClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
entity_ids: Optional[torch.LongTensor] = None,
|
|
entity_attention_mask: Optional[torch.FloatTensor] = None,
|
|
entity_token_type_ids: Optional[torch.LongTensor] = None,
|
|
entity_position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, LukeTokenClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
|
|
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
|
|
`input_ids` above)
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.luke(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
entity_ids=entity_ids,
|
|
entity_attention_mask=entity_attention_mask,
|
|
entity_token_type_ids=entity_token_type_ids,
|
|
entity_position_ids=entity_position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
)
|
|
|
|
sequence_output = outputs.last_hidden_state
|
|
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(logits.device)
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
|
|
if v is not None
|
|
)
|
|
|
|
return LukeTokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
entity_hidden_states=outputs.entity_hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The LUKE Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
|
|
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
|
|
""",
|
|
LUKE_START_DOCSTRING,
|
|
)
|
|
class LukeForQuestionAnswering(LukePreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
|
|
self.luke = LukeModel(config, add_pooling_layer=False)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=LukeQuestionAnsweringModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.FloatTensor] = None,
|
|
entity_ids: Optional[torch.LongTensor] = None,
|
|
entity_attention_mask: Optional[torch.FloatTensor] = None,
|
|
entity_token_type_ids: Optional[torch.LongTensor] = None,
|
|
entity_position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
start_positions: Optional[torch.LongTensor] = None,
|
|
end_positions: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, LukeQuestionAnsweringModelOutput]:
|
|
r"""
|
|
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.luke(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
entity_ids=entity_ids,
|
|
entity_attention_mask=entity_attention_mask,
|
|
entity_token_type_ids=entity_token_type_ids,
|
|
entity_position_ids=entity_position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
)
|
|
|
|
sequence_output = outputs.last_hidden_state
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1)
|
|
end_logits = end_logits.squeeze(-1)
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if len(start_positions.size()) > 1:
|
|
start_positions = start_positions.squeeze(-1)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1)
|
|
start_positions.clamp_(0, ignored_index)
|
|
end_positions.clamp_(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [
|
|
total_loss,
|
|
start_logits,
|
|
end_logits,
|
|
outputs.hidden_states,
|
|
outputs.entity_hidden_states,
|
|
outputs.attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
|
|
return LukeQuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
entity_hidden_states=outputs.entity_hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The LUKE Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
|
|
softmax) e.g. for RocStories/SWAG tasks.
|
|
""",
|
|
LUKE_START_DOCSTRING,
|
|
)
|
|
class LukeForMultipleChoice(LukePreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.luke = LukeModel(config)
|
|
self.dropout = nn.Dropout(
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.classifier = nn.Linear(config.hidden_size, 1)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=LukeMultipleChoiceModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
entity_ids: Optional[torch.LongTensor] = None,
|
|
entity_attention_mask: Optional[torch.FloatTensor] = None,
|
|
entity_token_type_ids: Optional[torch.LongTensor] = None,
|
|
entity_position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, LukeMultipleChoiceModelOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
|
|
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
|
|
`input_ids` above)
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
|
|
|
|
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
|
|
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
|
|
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
|
|
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
|
|
inputs_embeds = (
|
|
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
|
|
if inputs_embeds is not None
|
|
else None
|
|
)
|
|
|
|
entity_ids = entity_ids.view(-1, entity_ids.size(-1)) if entity_ids is not None else None
|
|
entity_attention_mask = (
|
|
entity_attention_mask.view(-1, entity_attention_mask.size(-1))
|
|
if entity_attention_mask is not None
|
|
else None
|
|
)
|
|
entity_token_type_ids = (
|
|
entity_token_type_ids.view(-1, entity_token_type_ids.size(-1))
|
|
if entity_token_type_ids is not None
|
|
else None
|
|
)
|
|
entity_position_ids = (
|
|
entity_position_ids.view(-1, entity_position_ids.size(-2), entity_position_ids.size(-1))
|
|
if entity_position_ids is not None
|
|
else None
|
|
)
|
|
|
|
outputs = self.luke(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
entity_ids=entity_ids,
|
|
entity_attention_mask=entity_attention_mask,
|
|
entity_token_type_ids=entity_token_type_ids,
|
|
entity_position_ids=entity_position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
)
|
|
|
|
pooled_output = outputs.pooler_output
|
|
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
reshaped_logits = logits.view(-1, num_choices)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(reshaped_logits.device)
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(reshaped_logits, labels)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [
|
|
loss,
|
|
reshaped_logits,
|
|
outputs.hidden_states,
|
|
outputs.entity_hidden_states,
|
|
outputs.attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
|
|
return LukeMultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
entity_hidden_states=outputs.entity_hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|