1435 lines
63 KiB
Python
1435 lines
63 KiB
Python
# coding=utf-8
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# Copyright 2018 Hao Tan, Mohit Bansal, and the HuggingFace 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 LXMERT model."""
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import math
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import os
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import warnings
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from dataclasses import dataclass
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from typing import Dict, Optional, Tuple, Union
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import torch
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from torch import nn
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from torch.nn import CrossEntropyLoss, SmoothL1Loss
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from ...activations import ACT2FN, gelu
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from ...modeling_utils import PreTrainedModel
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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_lxmert import LxmertConfig
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "unc-nlp/lxmert-base-uncased"
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_CONFIG_FOR_DOC = "LxmertConfig"
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class GeLU(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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return gelu(x)
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@dataclass
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class LxmertModelOutput(ModelOutput):
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"""
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Lxmert's outputs that contain the last hidden states, pooled outputs, and attention probabilities for the language,
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visual, and, cross-modality encoders. (note: the visual encoder in Lxmert is referred to as the "relation-ship"
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encoder")
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Args:
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language_output (`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 language encoder.
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vision_output (`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 visual encoder.
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pooled_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, CLS, token) further processed
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by a Linear layer and a Tanh activation function. The Linear
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language_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 input features + one for the output of each cross-modality layer) of
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shape `(batch_size, sequence_length, hidden_size)`.
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vision_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 input features + one for the output of each cross-modality layer) of
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shape `(batch_size, sequence_length, hidden_size)`.
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language_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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vision_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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cross_encoder_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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language_output: Optional[torch.FloatTensor] = None
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vision_output: Optional[torch.FloatTensor] = None
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pooled_output: Optional[torch.FloatTensor] = None
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language_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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vision_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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language_attentions: Optional[Tuple[torch.FloatTensor]] = None
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vision_attentions: Optional[Tuple[torch.FloatTensor]] = None
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cross_encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
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@dataclass
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class LxmertForQuestionAnsweringOutput(ModelOutput):
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"""
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Output type of [`LxmertForQuestionAnswering`].
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Args:
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loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
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Total loss as the sum of the masked language modeling loss and the next sequence prediction
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(classification) loss.k.
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question_answering_score (`torch.FloatTensor` of shape `(batch_size, n_qa_answers)`, *optional*):
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Prediction scores of question answering objective (classification).
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language_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 input features + one for the output of each cross-modality layer) of
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shape `(batch_size, sequence_length, hidden_size)`.
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vision_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 input features + one for the output of each cross-modality layer) of
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shape `(batch_size, sequence_length, hidden_size)`.
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language_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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vision_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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cross_encoder_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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question_answering_score: Optional[torch.FloatTensor] = None
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language_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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vision_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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language_attentions: Optional[Tuple[torch.FloatTensor]] = None
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vision_attentions: Optional[Tuple[torch.FloatTensor]] = None
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cross_encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
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@dataclass
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class LxmertForPreTrainingOutput(ModelOutput):
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"""
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Output type of [`LxmertForPreTraining`].
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Args:
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loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
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Total loss as the sum of the masked language modeling loss and the next sequence prediction
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(classification) loss.
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prediction_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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cross_relationship_score (`torch.FloatTensor` of shape `(batch_size, 2)`):
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Prediction scores of the textual matching objective (classification) head (scores of True/False
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continuation before SoftMax).
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question_answering_score (`torch.FloatTensor` of shape `(batch_size, n_qa_answers)`):
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Prediction scores of question answering objective (classification).
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language_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 input features + one for the output of each cross-modality layer) of
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shape `(batch_size, sequence_length, hidden_size)`.
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vision_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 input features + one for the output of each cross-modality layer) of
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shape `(batch_size, sequence_length, hidden_size)`.
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language_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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vision_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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cross_encoder_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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prediction_logits: Optional[torch.FloatTensor] = None
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cross_relationship_score: Optional[torch.FloatTensor] = None
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question_answering_score: Optional[torch.FloatTensor] = None
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language_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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vision_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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language_attentions: Optional[Tuple[torch.FloatTensor]] = None
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vision_attentions: Optional[Tuple[torch.FloatTensor]] = None
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cross_encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
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def load_tf_weights_in_lxmert(model, config, tf_checkpoint_path):
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"""Load tf checkpoints in a pytorch model."""
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try:
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import re
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import numpy as np
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import tensorflow as tf
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except ImportError:
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logger.error(
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"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
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"https://www.tensorflow.org/install/ for installation instructions."
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)
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raise
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tf_path = os.path.abspath(tf_checkpoint_path)
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logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
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# Load weights from TF model
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init_vars = tf.train.list_variables(tf_path)
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names = []
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arrays = []
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for name, shape in init_vars:
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logger.info(f"Loading TF weight {name} with shape {shape}")
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array = tf.train.load_variable(tf_path, name)
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names.append(name)
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arrays.append(array)
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for name, array in zip(names, arrays):
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name = name.split("/")
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# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
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# which are not required for using pretrained model
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if any(
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n
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in [
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"adam_v",
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"adam_m",
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"AdamWeightDecayOptimizer",
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"AdamWeightDecayOptimizer_1",
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"global_step",
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]
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for n in name
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):
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logger.info(f"Skipping {'/'.join(name)}")
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continue
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pointer = model
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for m_name in name:
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if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
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scope_names = re.split(r"_(\d+)", m_name)
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else:
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scope_names = [m_name]
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if scope_names[0] == "kernel" or scope_names[0] == "gamma":
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pointer = getattr(pointer, "weight")
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elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
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pointer = getattr(pointer, "bias")
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elif scope_names[0] == "output_weights":
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pointer = getattr(pointer, "weight")
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elif scope_names[0] == "squad":
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pointer = getattr(pointer, "classifier")
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else:
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try:
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pointer = getattr(pointer, scope_names[0])
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except AttributeError:
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logger.info(f"Skipping {'/'.join(name)}")
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continue
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if len(scope_names) >= 2:
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num = int(scope_names[1])
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pointer = pointer[num]
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if m_name[-11:] == "_embeddings":
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pointer = getattr(pointer, "weight")
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elif m_name == "kernel":
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array = np.transpose(array)
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try:
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assert pointer.shape == array.shape
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except AssertionError as e:
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e.args += (pointer.shape, array.shape)
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raise
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logger.info(f"Initialize PyTorch weight {name}")
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pointer.data = torch.from_numpy(array)
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return model
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class LxmertEmbeddings(nn.Module):
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"""Construct the embeddings from word, position and token_type embeddings."""
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def __init__(self, config):
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super().__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=0)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size, padding_idx=0)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size, padding_idx=0)
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# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
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# any TensorFlow checkpoint file
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, input_ids, token_type_ids=None, inputs_embeds=None):
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if input_ids is not None:
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input_shape = input_ids.size()
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device = input_ids.device
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else:
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input_shape = inputs_embeds.size()[:-1]
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device = inputs_embeds.device
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seq_length = input_shape[1]
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position_ids = torch.arange(seq_length, dtype=torch.long, device=device)
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position_ids = position_ids.unsqueeze(0).expand(input_shape)
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if token_type_ids is None:
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token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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position_embeddings = self.position_embeddings(position_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = inputs_embeds + position_embeddings + token_type_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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class LxmertAttention(nn.Module):
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def __init__(self, config, ctx_dim=None):
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0:
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raise ValueError(
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f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
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f"heads ({config.num_attention_heads})"
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)
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.head_size = self.num_attention_heads * self.attention_head_size
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# visual_dim = 2048
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if ctx_dim is None:
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ctx_dim = config.hidden_size
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self.query = nn.Linear(config.hidden_size, self.head_size)
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self.key = nn.Linear(ctx_dim, self.head_size)
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self.value = nn.Linear(ctx_dim, self.head_size)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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def transpose_for_scores(self, x):
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new_x_shape = x.size()[:-1] + (
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self.num_attention_heads,
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self.attention_head_size,
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)
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x = x.view(new_x_shape)
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return x.permute(0, 2, 1, 3)
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def forward(self, hidden_states, context, attention_mask=None, output_attentions=False):
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mixed_query_layer = self.query(hidden_states)
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mixed_key_layer = self.key(context)
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mixed_value_layer = self.value(context)
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query_layer = self.transpose_for_scores(mixed_query_layer)
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key_layer = self.transpose_for_scores(mixed_key_layer)
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value_layer = self.transpose_for_scores(mixed_value_layer)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
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attention_scores = attention_scores / math.sqrt(self.attention_head_size)
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# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
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if attention_mask is not None:
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attention_scores = attention_scores + attention_mask
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# Normalize the attention scores to probabilities.
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attention_probs = nn.functional.softmax(attention_scores, dim=-1)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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context_layer = torch.matmul(attention_probs, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (self.head_size,)
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context_layer = context_layer.view(new_context_layer_shape)
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outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
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return outputs
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class LxmertAttentionOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class LxmertCrossAttentionLayer(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.att = LxmertAttention(config)
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self.output = LxmertAttentionOutput(config)
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def forward(self, input_tensor, ctx_tensor, ctx_att_mask=None, output_attentions=False):
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output = self.att(input_tensor, ctx_tensor, ctx_att_mask, output_attentions=output_attentions)
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if output_attentions:
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attention_probs = output[1]
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attention_output = self.output(output[0], input_tensor)
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outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
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return outputs
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class LxmertSelfAttentionLayer(nn.Module):
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def __init__(self, config):
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super().__init__()
|
|
self.self = LxmertAttention(config)
|
|
self.output = LxmertAttentionOutput(config)
|
|
|
|
def forward(self, input_tensor, attention_mask, output_attentions=False):
|
|
# Self attention attends to itself, thus keys and queries are the same (input_tensor).
|
|
output = self.self(
|
|
input_tensor,
|
|
input_tensor,
|
|
attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
if output_attentions:
|
|
attention_probs = output[1]
|
|
attention_output = self.output(output[0], input_tensor)
|
|
outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
|
|
return outputs
|
|
|
|
|
|
class LxmertIntermediate(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
|
|
self.intermediate_act_fn = ACT2FN[config.hidden_act]
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class LxmertOutput(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=1e-12)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states, input_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 LxmertLayer(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.attention = LxmertSelfAttentionLayer(config)
|
|
self.intermediate = LxmertIntermediate(config)
|
|
self.output = LxmertOutput(config)
|
|
|
|
def forward(self, hidden_states, attention_mask=None, output_attentions=False):
|
|
outputs = self.attention(hidden_states, attention_mask, output_attentions=output_attentions)
|
|
attention_output = outputs[0]
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
outputs = (layer_output,) + outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
class LxmertXLayer(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
# The cross-attention Layer
|
|
self.visual_attention = LxmertCrossAttentionLayer(config)
|
|
|
|
# Self-attention Layers
|
|
self.lang_self_att = LxmertSelfAttentionLayer(config)
|
|
self.visn_self_att = LxmertSelfAttentionLayer(config)
|
|
|
|
# Intermediate and Output Layers (FFNs)
|
|
self.lang_inter = LxmertIntermediate(config)
|
|
self.lang_output = LxmertOutput(config)
|
|
self.visn_inter = LxmertIntermediate(config)
|
|
self.visn_output = LxmertOutput(config)
|
|
|
|
def cross_att(
|
|
self,
|
|
lang_input,
|
|
lang_attention_mask,
|
|
visual_input,
|
|
visual_attention_mask,
|
|
output_x_attentions=False,
|
|
):
|
|
# Cross Attention
|
|
lang_att_output = self.visual_attention(
|
|
lang_input,
|
|
visual_input,
|
|
ctx_att_mask=visual_attention_mask,
|
|
output_attentions=output_x_attentions,
|
|
)
|
|
visual_att_output = self.visual_attention(
|
|
visual_input,
|
|
lang_input,
|
|
ctx_att_mask=lang_attention_mask,
|
|
output_attentions=False,
|
|
)
|
|
return lang_att_output, visual_att_output
|
|
|
|
def self_att(self, lang_input, lang_attention_mask, visual_input, visual_attention_mask):
|
|
# Self Attention
|
|
lang_att_output = self.lang_self_att(lang_input, lang_attention_mask, output_attentions=False)
|
|
visual_att_output = self.visn_self_att(visual_input, visual_attention_mask, output_attentions=False)
|
|
return lang_att_output[0], visual_att_output[0]
|
|
|
|
def output_fc(self, lang_input, visual_input):
|
|
# FC layers
|
|
lang_inter_output = self.lang_inter(lang_input)
|
|
visual_inter_output = self.visn_inter(visual_input)
|
|
|
|
# Layer output
|
|
lang_output = self.lang_output(lang_inter_output, lang_input)
|
|
visual_output = self.visn_output(visual_inter_output, visual_input)
|
|
|
|
return lang_output, visual_output
|
|
|
|
def forward(
|
|
self,
|
|
lang_feats,
|
|
lang_attention_mask,
|
|
visual_feats,
|
|
visual_attention_mask,
|
|
output_attentions=False,
|
|
):
|
|
lang_att_output, visual_att_output = self.cross_att(
|
|
lang_input=lang_feats,
|
|
lang_attention_mask=lang_attention_mask,
|
|
visual_input=visual_feats,
|
|
visual_attention_mask=visual_attention_mask,
|
|
output_x_attentions=output_attentions,
|
|
)
|
|
attention_probs = lang_att_output[1:]
|
|
lang_att_output, visual_att_output = self.self_att(
|
|
lang_att_output[0],
|
|
lang_attention_mask,
|
|
visual_att_output[0],
|
|
visual_attention_mask,
|
|
)
|
|
|
|
lang_output, visual_output = self.output_fc(lang_att_output, visual_att_output)
|
|
return (
|
|
(
|
|
lang_output,
|
|
visual_output,
|
|
attention_probs[0],
|
|
)
|
|
if output_attentions
|
|
else (lang_output, visual_output)
|
|
)
|
|
|
|
|
|
class LxmertVisualFeatureEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
feat_dim = config.visual_feat_dim
|
|
pos_dim = config.visual_pos_dim
|
|
|
|
# Object feature encoding
|
|
self.visn_fc = nn.Linear(feat_dim, config.hidden_size)
|
|
self.visn_layer_norm = nn.LayerNorm(config.hidden_size, eps=1e-12)
|
|
|
|
# Box position encoding
|
|
self.box_fc = nn.Linear(pos_dim, config.hidden_size)
|
|
self.box_layer_norm = nn.LayerNorm(config.hidden_size, eps=1e-12)
|
|
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, visual_feats, visual_pos):
|
|
x = self.visn_fc(visual_feats)
|
|
x = self.visn_layer_norm(x)
|
|
y = self.box_fc(visual_pos)
|
|
y = self.box_layer_norm(y)
|
|
output = (x + y) / 2
|
|
|
|
output = self.dropout(output)
|
|
return output
|
|
|
|
|
|
class LxmertEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
# Obj-level image embedding layer
|
|
self.visn_fc = LxmertVisualFeatureEncoder(config)
|
|
self.config = config
|
|
|
|
# Number of layers
|
|
self.num_l_layers = config.l_layers
|
|
self.num_x_layers = config.x_layers
|
|
self.num_r_layers = config.r_layers
|
|
|
|
# Layers
|
|
# Using self.layer instead of self.l_layer to support loading BERT weights.
|
|
self.layer = nn.ModuleList([LxmertLayer(config) for _ in range(self.num_l_layers)])
|
|
self.x_layers = nn.ModuleList([LxmertXLayer(config) for _ in range(self.num_x_layers)])
|
|
self.r_layers = nn.ModuleList([LxmertLayer(config) for _ in range(self.num_r_layers)])
|
|
|
|
def forward(
|
|
self,
|
|
lang_feats,
|
|
lang_attention_mask,
|
|
visual_feats,
|
|
visual_pos,
|
|
visual_attention_mask=None,
|
|
output_attentions=None,
|
|
):
|
|
vision_hidden_states = ()
|
|
language_hidden_states = ()
|
|
vision_attentions = () if output_attentions or self.config.output_attentions else None
|
|
language_attentions = () if output_attentions or self.config.output_attentions else None
|
|
cross_encoder_attentions = () if output_attentions or self.config.output_attentions else None
|
|
|
|
visual_feats = self.visn_fc(visual_feats, visual_pos)
|
|
|
|
# Run language layers
|
|
for layer_module in self.layer:
|
|
l_outputs = layer_module(lang_feats, lang_attention_mask, output_attentions=output_attentions)
|
|
lang_feats = l_outputs[0]
|
|
language_hidden_states = language_hidden_states + (lang_feats,)
|
|
if language_attentions is not None:
|
|
language_attentions = language_attentions + (l_outputs[1],)
|
|
|
|
# Run relational layers
|
|
for layer_module in self.r_layers:
|
|
v_outputs = layer_module(visual_feats, visual_attention_mask, output_attentions=output_attentions)
|
|
visual_feats = v_outputs[0]
|
|
vision_hidden_states = vision_hidden_states + (visual_feats,)
|
|
if vision_attentions is not None:
|
|
vision_attentions = vision_attentions + (v_outputs[1],)
|
|
|
|
# Run cross-modality layers
|
|
for layer_module in self.x_layers:
|
|
x_outputs = layer_module(
|
|
lang_feats,
|
|
lang_attention_mask,
|
|
visual_feats,
|
|
visual_attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
lang_feats, visual_feats = x_outputs[:2]
|
|
vision_hidden_states = vision_hidden_states + (visual_feats,)
|
|
language_hidden_states = language_hidden_states + (lang_feats,)
|
|
if cross_encoder_attentions is not None:
|
|
cross_encoder_attentions = cross_encoder_attentions + (x_outputs[2],)
|
|
visual_encoder_outputs = (
|
|
vision_hidden_states,
|
|
vision_attentions if output_attentions else None,
|
|
)
|
|
lang_encoder_outputs = (
|
|
language_hidden_states,
|
|
language_attentions if output_attentions else None,
|
|
)
|
|
return (
|
|
visual_encoder_outputs,
|
|
lang_encoder_outputs,
|
|
cross_encoder_attentions if output_attentions else None,
|
|
)
|
|
|
|
|
|
class LxmertPooler(nn.Module):
|
|
def __init__(self, config):
|
|
super(LxmertPooler, self).__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = nn.Tanh()
|
|
|
|
def forward(self, hidden_states):
|
|
# 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 LxmertPredictionHeadTransform(nn.Module):
|
|
def __init__(self, config):
|
|
super(LxmertPredictionHeadTransform, self).__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.transform_act_fn = ACT2FN[config.hidden_act]
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
|
|
|
|
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 LxmertLMPredictionHead(nn.Module):
|
|
def __init__(self, config, lxmert_model_embedding_weights):
|
|
super(LxmertLMPredictionHead, self).__init__()
|
|
self.transform = LxmertPredictionHeadTransform(config)
|
|
|
|
# The output weights are the same as the input embeddings, but there is
|
|
# an output-only bias for each token.
|
|
self.decoder = nn.Linear(
|
|
lxmert_model_embedding_weights.size(1),
|
|
lxmert_model_embedding_weights.size(0),
|
|
bias=False,
|
|
)
|
|
self.decoder.weight = lxmert_model_embedding_weights
|
|
self.bias = nn.Parameter(torch.zeros(lxmert_model_embedding_weights.size(0)))
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.transform(hidden_states)
|
|
hidden_states = self.decoder(hidden_states) + self.bias
|
|
return hidden_states
|
|
|
|
|
|
class LxmertVisualAnswerHead(nn.Module):
|
|
def __init__(self, config, num_labels):
|
|
super().__init__()
|
|
hid_dim = config.hidden_size
|
|
self.logit_fc = nn.Sequential(
|
|
nn.Linear(hid_dim, hid_dim * 2),
|
|
GeLU(),
|
|
nn.LayerNorm(hid_dim * 2, eps=1e-12),
|
|
nn.Linear(hid_dim * 2, num_labels),
|
|
)
|
|
|
|
def forward(self, hidden_states):
|
|
return self.logit_fc(hidden_states)
|
|
|
|
|
|
class LxmertVisualObjHead(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.transform = LxmertPredictionHeadTransform(config)
|
|
# Decide the use of visual losses
|
|
visual_losses = {}
|
|
if config.visual_obj_loss:
|
|
visual_losses["obj"] = {"shape": (-1,), "num": config.num_object_labels}
|
|
if config.visual_attr_loss:
|
|
visual_losses["attr"] = {"shape": (-1,), "num": config.num_attr_labels}
|
|
if config.visual_feat_loss:
|
|
visual_losses["feat"] = {
|
|
"shape": (-1, config.visual_feat_dim),
|
|
"num": config.visual_feat_dim,
|
|
}
|
|
self.visual_losses = visual_losses
|
|
|
|
# The output weights are the same as the input embeddings, but there is
|
|
# an output-only bias for each token.
|
|
self.decoder_dict = nn.ModuleDict(
|
|
{key: nn.Linear(config.hidden_size, self.visual_losses[key]["num"]) for key in self.visual_losses}
|
|
)
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.transform(hidden_states)
|
|
output = {}
|
|
for key in self.visual_losses:
|
|
output[key] = self.decoder_dict[key](hidden_states)
|
|
return output
|
|
|
|
|
|
class LxmertPreTrainingHeads(nn.Module):
|
|
def __init__(self, config, lxmert_model_embedding_weights):
|
|
super(LxmertPreTrainingHeads, self).__init__()
|
|
self.predictions = LxmertLMPredictionHead(config, lxmert_model_embedding_weights)
|
|
self.seq_relationship = nn.Linear(config.hidden_size, 2)
|
|
|
|
def forward(self, sequence_output, pooled_output):
|
|
prediction_scores = self.predictions(sequence_output)
|
|
seq_relationship_score = self.seq_relationship(pooled_output)
|
|
return prediction_scores, seq_relationship_score
|
|
|
|
|
|
class LxmertPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = LxmertConfig
|
|
load_tf_weights = load_tf_weights_in_lxmert
|
|
base_model_prefix = "lxmert"
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, nn.Linear):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
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):
|
|
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)
|
|
|
|
|
|
LXMERT_START_DOCSTRING = r"""
|
|
|
|
The LXMERT model was proposed in [LXMERT: Learning Cross-Modality Encoder Representations from
|
|
Transformers](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal. It's a vision and language transformer
|
|
model, pretrained on a variety of multi-modal datasets comprising of GQA, VQAv2.0, MSCOCO captions, and Visual
|
|
genome, using a combination of masked language modeling, region of interest feature regression, cross entropy loss
|
|
for question answering attribute prediction, and object tag prediction.
|
|
|
|
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 ([`LxmertConfig`]): 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.
|
|
"""
|
|
|
|
LXMERT_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)
|
|
visual_feats (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_feat_dim)`):
|
|
This input represents visual features. They ROI pooled object features from bounding boxes using a
|
|
faster-RCNN model)
|
|
|
|
These are currently not provided by the transformers library.
|
|
visual_pos (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_pos_dim)`):
|
|
This input represents spacial features corresponding to their relative (via index) visual features. The
|
|
pre-trained LXMERT model expects these spacial features to be normalized bounding boxes on a scale of 0 to
|
|
1.
|
|
|
|
These are currently not provided by the transformers library.
|
|
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)
|
|
visual_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)
|
|
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.
|
|
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 Lxmert Model transformer outputting raw hidden-states without any specific head on top.",
|
|
LXMERT_START_DOCSTRING,
|
|
)
|
|
class LxmertModel(LxmertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.embeddings = LxmertEmbeddings(config)
|
|
self.encoder = LxmertEncoder(config)
|
|
self.pooler = LxmertPooler(config)
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, new_embeddings):
|
|
self.embeddings.word_embeddings = new_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=LxmertModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
visual_feats: Optional[torch.FloatTensor] = None,
|
|
visual_pos: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
visual_attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[LxmertModelOutput, Tuple[torch.FloatTensor]]:
|
|
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")
|
|
|
|
if visual_feats is None:
|
|
raise ValueError("`visual_feats` cannot be `None`")
|
|
if visual_pos is None:
|
|
raise ValueError("`visual_pos` cannot be `None`")
|
|
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(input_shape, device=device)
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
# We create a 3D attention mask from a 2D tensor mask.
|
|
# Sizes are [batch_size, 1, 1, to_seq_length]
|
|
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
|
|
# this attention mask is more simple than the triangular masking of causal attention
|
|
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
|
|
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
|
|
|
|
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
|
|
# masked positions, this operation will create a tensor which is 0.0 for
|
|
# positions we want to attend and the dtype's smallest value for masked positions.
|
|
# Since we are adding it to the raw scores before the softmax, this is
|
|
# effectively the same as removing these entirely.
|
|
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
|
|
extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min
|
|
|
|
# Process the visual attention mask
|
|
if visual_attention_mask is not None:
|
|
extended_visual_attention_mask = visual_attention_mask.unsqueeze(1).unsqueeze(2)
|
|
extended_visual_attention_mask = extended_visual_attention_mask.to(dtype=self.dtype)
|
|
extended_visual_attention_mask = (1.0 - extended_visual_attention_mask) * torch.finfo(self.dtype).min
|
|
else:
|
|
extended_visual_attention_mask = None
|
|
|
|
# Positional Word Embeddings
|
|
embedding_output = self.embeddings(input_ids, token_type_ids, inputs_embeds)
|
|
|
|
# Run Lxmert encoder
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
extended_attention_mask,
|
|
visual_feats=visual_feats,
|
|
visual_pos=visual_pos,
|
|
visual_attention_mask=extended_visual_attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
|
|
visual_encoder_outputs, lang_encoder_outputs = encoder_outputs[:2]
|
|
vision_hidden_states = visual_encoder_outputs[0]
|
|
language_hidden_states = lang_encoder_outputs[0]
|
|
|
|
all_attentions = ()
|
|
if output_attentions:
|
|
language_attentions = lang_encoder_outputs[1]
|
|
vision_attentions = visual_encoder_outputs[1]
|
|
cross_encoder_attentions = encoder_outputs[2]
|
|
all_attentions = (
|
|
language_attentions,
|
|
vision_attentions,
|
|
cross_encoder_attentions,
|
|
)
|
|
|
|
hidden_states = (language_hidden_states, vision_hidden_states) if output_hidden_states else ()
|
|
|
|
visual_output = vision_hidden_states[-1]
|
|
lang_output = language_hidden_states[-1]
|
|
pooled_output = self.pooler(lang_output)
|
|
|
|
if not return_dict:
|
|
return (lang_output, visual_output, pooled_output) + hidden_states + all_attentions
|
|
|
|
return LxmertModelOutput(
|
|
pooled_output=pooled_output,
|
|
language_output=lang_output,
|
|
vision_output=visual_output,
|
|
language_hidden_states=language_hidden_states if output_hidden_states else None,
|
|
vision_hidden_states=vision_hidden_states if output_hidden_states else None,
|
|
language_attentions=language_attentions if output_attentions else None,
|
|
vision_attentions=vision_attentions if output_attentions else None,
|
|
cross_encoder_attentions=cross_encoder_attentions if output_attentions else None,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""Lxmert Model with a specified pretraining head on top.""",
|
|
LXMERT_START_DOCSTRING,
|
|
)
|
|
class LxmertForPreTraining(LxmertPreTrainedModel):
|
|
_tied_weights_keys = ["cls.predictions.decoder.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
# Configuration
|
|
self.config = config
|
|
self.num_qa_labels = config.num_qa_labels
|
|
self.visual_loss_normalizer = config.visual_loss_normalizer
|
|
|
|
# Use of pretraining tasks
|
|
self.task_mask_lm = config.task_mask_lm
|
|
self.task_obj_predict = config.task_obj_predict
|
|
self.task_matched = config.task_matched
|
|
self.task_qa = config.task_qa
|
|
|
|
# Lxmert backbone
|
|
self.lxmert = LxmertModel(config)
|
|
|
|
# Pre-training heads
|
|
self.cls = LxmertPreTrainingHeads(config, self.lxmert.embeddings.word_embeddings.weight)
|
|
if self.task_obj_predict:
|
|
self.obj_predict_head = LxmertVisualObjHead(config)
|
|
if self.task_qa:
|
|
self.answer_head = LxmertVisualAnswerHead(config, self.num_qa_labels)
|
|
|
|
# Weight initialization
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
# Loss functions
|
|
self.loss_fcts = {
|
|
"l2": SmoothL1Loss(reduction="none"),
|
|
"visual_ce": CrossEntropyLoss(reduction="none"),
|
|
"ce": CrossEntropyLoss(),
|
|
}
|
|
|
|
visual_losses = {}
|
|
if config.visual_obj_loss:
|
|
visual_losses["obj"] = {
|
|
"shape": (-1,),
|
|
"num": config.num_object_labels,
|
|
"loss": "visual_ce",
|
|
}
|
|
if config.visual_attr_loss:
|
|
visual_losses["attr"] = {
|
|
"shape": (-1,),
|
|
"num": config.num_attr_labels,
|
|
"loss": "visual_ce",
|
|
}
|
|
if config.visual_feat_loss:
|
|
visual_losses["feat"] = {
|
|
"shape": (-1, config.visual_feat_dim),
|
|
"num": config.visual_feat_dim,
|
|
"loss": "l2",
|
|
}
|
|
self.visual_losses = visual_losses
|
|
|
|
def resize_num_qa_labels(self, num_labels):
|
|
"""
|
|
Build a resized question answering linear layer Module from a provided new linear layer. Increasing the size
|
|
will add newly initialized weights. Reducing the size will remove weights from the end
|
|
|
|
Args:
|
|
num_labels (`int`, *optional*):
|
|
New number of labels in the linear layer weight matrix. Increasing the size will add newly initialized
|
|
weights at the end. Reducing the size will remove weights from the end. If not provided or `None`, just
|
|
returns a pointer to the qa labels ``torch.nn.Linear``` module of the model without doing anything.
|
|
|
|
Return:
|
|
`torch.nn.Linear`: Pointer to the resized Linear layer or the old Linear layer
|
|
"""
|
|
|
|
cur_qa_logit_layer = self.get_qa_logit_layer()
|
|
if num_labels is None or cur_qa_logit_layer is None:
|
|
return
|
|
new_qa_logit_layer = self._resize_qa_labels(num_labels)
|
|
self.config.num_qa_labels = num_labels
|
|
self.num_qa_labels = num_labels
|
|
|
|
return new_qa_logit_layer
|
|
|
|
def _resize_qa_labels(self, num_labels):
|
|
cur_qa_logit_layer = self.get_qa_logit_layer()
|
|
new_qa_logit_layer = self._get_resized_qa_labels(cur_qa_logit_layer, num_labels)
|
|
self._set_qa_logit_layer(new_qa_logit_layer)
|
|
return self.get_qa_logit_layer()
|
|
|
|
def get_qa_logit_layer(self) -> nn.Module:
|
|
"""
|
|
Returns the linear layer that produces question answering logits.
|
|
|
|
Returns:
|
|
`nn.Module`: A torch module mapping the question answering prediction hidden states or `None` if LXMERT
|
|
does not have a visual answering head.
|
|
"""
|
|
if hasattr(self, "answer_head"):
|
|
return self.answer_head.logit_fc[-1]
|
|
|
|
def _set_qa_logit_layer(self, qa_logit_layer):
|
|
self.answer_head.logit_fc[-1] = qa_logit_layer
|
|
|
|
def _get_resized_qa_labels(self, cur_qa_logit_layer, num_labels):
|
|
if num_labels is None:
|
|
return cur_qa_logit_layer
|
|
|
|
cur_qa_labels, hidden_dim = cur_qa_logit_layer.weight.size()
|
|
if cur_qa_labels == num_labels:
|
|
return cur_qa_logit_layer
|
|
|
|
# Build new linear output
|
|
if getattr(cur_qa_logit_layer, "bias", None) is not None:
|
|
new_qa_logit_layer = nn.Linear(hidden_dim, num_labels)
|
|
else:
|
|
new_qa_logit_layer = nn.Linear(hidden_dim, num_labels, bias=False)
|
|
|
|
new_qa_logit_layer.to(cur_qa_logit_layer.weight.device)
|
|
|
|
# initialize all new labels
|
|
self._init_weights(new_qa_logit_layer)
|
|
|
|
# Copy labels from the previous weights
|
|
num_labels_to_copy = min(cur_qa_labels, num_labels)
|
|
new_qa_logit_layer.weight.data[:num_labels_to_copy, :] = cur_qa_logit_layer.weight.data[:num_labels_to_copy, :]
|
|
if getattr(cur_qa_logit_layer, "bias", None) is not None:
|
|
new_qa_logit_layer.bias.data[:num_labels_to_copy] = cur_qa_logit_layer.bias.data[:num_labels_to_copy]
|
|
|
|
return new_qa_logit_layer
|
|
|
|
@add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@replace_return_docstrings(output_type=LxmertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
visual_feats: Optional[torch.FloatTensor] = None,
|
|
visual_pos: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
visual_attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
obj_labels: Optional[Dict[str, Tuple[torch.FloatTensor, torch.FloatTensor]]] = None,
|
|
matched_label: Optional[torch.LongTensor] = None,
|
|
ans: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
**kwargs,
|
|
) -> Union[LxmertForPreTrainingOutput, Tuple[torch.FloatTensor]]:
|
|
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]`
|
|
obj_labels (`Dict[Str: Tuple[Torch.FloatTensor, Torch.FloatTensor]]`, *optional*):
|
|
each key is named after each one of the visual losses and each element of the tuple is of the shape
|
|
`(batch_size, num_features)` and `(batch_size, num_features, visual_feature_dim)` for each the label id and
|
|
the label score respectively
|
|
matched_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the whether or not the text input matches the image (classification) loss. Input
|
|
should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`:
|
|
|
|
- 0 indicates that the sentence does not match the image,
|
|
- 1 indicates that the sentence does match the image.
|
|
ans (`Torch.Tensor` of shape `(batch_size)`, *optional*):
|
|
a one hot representation hof the correct answer *optional*
|
|
|
|
Returns:
|
|
"""
|
|
|
|
if "masked_lm_labels" in kwargs:
|
|
warnings.warn(
|
|
"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels`"
|
|
" instead.",
|
|
FutureWarning,
|
|
)
|
|
labels = kwargs.pop("masked_lm_labels")
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
lxmert_output = self.lxmert(
|
|
input_ids=input_ids,
|
|
visual_feats=visual_feats,
|
|
visual_pos=visual_pos,
|
|
token_type_ids=token_type_ids,
|
|
attention_mask=attention_mask,
|
|
visual_attention_mask=visual_attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_hidden_states=output_hidden_states,
|
|
output_attentions=output_attentions,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
lang_output, visual_output, pooled_output = (
|
|
lxmert_output[0],
|
|
lxmert_output[1],
|
|
lxmert_output[2],
|
|
)
|
|
lang_prediction_scores, cross_relationship_score = self.cls(lang_output, pooled_output)
|
|
if self.task_qa:
|
|
answer_score = self.answer_head(pooled_output)
|
|
else:
|
|
answer_score = pooled_output[0][0]
|
|
|
|
total_loss = (
|
|
None
|
|
if (labels is None and matched_label is None and obj_labels is None and ans is None)
|
|
else torch.tensor(0.0, device=device)
|
|
)
|
|
if labels is not None and self.task_mask_lm:
|
|
masked_lm_loss = self.loss_fcts["ce"](
|
|
lang_prediction_scores.view(-1, self.config.vocab_size),
|
|
labels.view(-1),
|
|
)
|
|
total_loss += masked_lm_loss
|
|
if matched_label is not None and self.task_matched:
|
|
matched_loss = self.loss_fcts["ce"](cross_relationship_score.view(-1, 2), matched_label.view(-1))
|
|
total_loss += matched_loss
|
|
if obj_labels is not None and self.task_obj_predict:
|
|
total_visual_loss = torch.tensor(0.0, device=input_ids.device)
|
|
visual_prediction_scores_dict = self.obj_predict_head(visual_output)
|
|
for key, key_info in self.visual_losses.items():
|
|
label, mask_conf = obj_labels[key]
|
|
output_dim = key_info["num"]
|
|
loss_fct_name = key_info["loss"]
|
|
label_shape = key_info["shape"]
|
|
weight = self.visual_loss_normalizer
|
|
visual_loss_fct = self.loss_fcts[loss_fct_name]
|
|
visual_prediction_scores = visual_prediction_scores_dict[key]
|
|
visual_loss = visual_loss_fct(
|
|
visual_prediction_scores.view(-1, output_dim),
|
|
label.view(label_shape),
|
|
)
|
|
if visual_loss.dim() > 1: # Regression Losses
|
|
visual_loss = visual_loss.mean(1)
|
|
visual_loss = (visual_loss * mask_conf.view(-1)).mean() * weight
|
|
total_visual_loss += visual_loss
|
|
total_loss += total_visual_loss
|
|
if ans is not None and self.task_qa:
|
|
answer_loss = self.loss_fcts["ce"](answer_score.view(-1, self.num_qa_labels), ans.view(-1))
|
|
total_loss += answer_loss
|
|
|
|
if not return_dict:
|
|
output = (
|
|
lang_prediction_scores,
|
|
cross_relationship_score,
|
|
answer_score,
|
|
) + lxmert_output[3:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return LxmertForPreTrainingOutput(
|
|
loss=total_loss,
|
|
prediction_logits=lang_prediction_scores,
|
|
cross_relationship_score=cross_relationship_score,
|
|
question_answering_score=answer_score,
|
|
language_hidden_states=lxmert_output.language_hidden_states,
|
|
vision_hidden_states=lxmert_output.vision_hidden_states,
|
|
language_attentions=lxmert_output.language_attentions,
|
|
vision_attentions=lxmert_output.vision_attentions,
|
|
cross_encoder_attentions=lxmert_output.cross_encoder_attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""Lxmert Model with a visual-answering head on top for downstream QA tasks""",
|
|
LXMERT_START_DOCSTRING,
|
|
)
|
|
class LxmertForQuestionAnswering(LxmertPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
# Configuration
|
|
self.config = config
|
|
self.num_qa_labels = config.num_qa_labels
|
|
self.visual_loss_normalizer = config.visual_loss_normalizer
|
|
|
|
# Lxmert backbone
|
|
self.lxmert = LxmertModel(config)
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self.answer_head = LxmertVisualAnswerHead(config, self.num_qa_labels)
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# Weight initialization
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# Initialize weights and apply final processing
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self.post_init()
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# Loss function
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self.loss = CrossEntropyLoss()
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def resize_num_qa_labels(self, num_labels):
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"""
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Build a resized question answering linear layer Module from a provided new linear layer. Increasing the size
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will add newly initialized weights. Reducing the size will remove weights from the end
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Args:
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num_labels (`int`, *optional*):
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New number of labels in the linear layer weight matrix. Increasing the size will add newly initialized
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weights at the end. Reducing the size will remove weights from the end. If not provided or `None`, just
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returns a pointer to the qa labels ``torch.nn.Linear``` module of the model without doing anything.
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Return:
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`torch.nn.Linear`: Pointer to the resized Linear layer or the old Linear layer
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"""
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cur_qa_logit_layer = self.get_qa_logit_layer()
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if num_labels is None or cur_qa_logit_layer is None:
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return
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new_qa_logit_layer = self._resize_qa_labels(num_labels)
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self.config.num_qa_labels = num_labels
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self.num_qa_labels = num_labels
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return new_qa_logit_layer
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def _resize_qa_labels(self, num_labels):
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cur_qa_logit_layer = self.get_qa_logit_layer()
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new_qa_logit_layer = self._get_resized_qa_labels(cur_qa_logit_layer, num_labels)
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self._set_qa_logit_layer(new_qa_logit_layer)
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return self.get_qa_logit_layer()
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def get_qa_logit_layer(self) -> nn.Module:
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"""
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Returns the linear layer that produces question answering logits
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|
|
|
Returns:
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`nn.Module`: A torch module mapping the question answering prediction hidden states. `None`: A NoneType
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object if Lxmert does not have the visual answering head.
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"""
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if hasattr(self, "answer_head"):
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return self.answer_head.logit_fc[-1]
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def _set_qa_logit_layer(self, qa_logit_layer):
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self.answer_head.logit_fc[-1] = qa_logit_layer
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|
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def _get_resized_qa_labels(self, cur_qa_logit_layer, num_labels):
|
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if num_labels is None:
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return cur_qa_logit_layer
|
|
|
|
cur_qa_labels, hidden_dim = cur_qa_logit_layer.weight.size()
|
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if cur_qa_labels == num_labels:
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return cur_qa_logit_layer
|
|
|
|
# Build new linear output
|
|
if getattr(cur_qa_logit_layer, "bias", None) is not None:
|
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new_qa_logit_layer = nn.Linear(hidden_dim, num_labels)
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else:
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new_qa_logit_layer = nn.Linear(hidden_dim, num_labels, bias=False)
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|
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new_qa_logit_layer.to(cur_qa_logit_layer.weight.device)
|
|
|
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# initialize all new labels
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|
self._init_weights(new_qa_logit_layer)
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|
|
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# Copy labels from the previous weights
|
|
num_labels_to_copy = min(cur_qa_labels, num_labels)
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new_qa_logit_layer.weight.data[:num_labels_to_copy, :] = cur_qa_logit_layer.weight.data[:num_labels_to_copy, :]
|
|
if getattr(cur_qa_logit_layer, "bias", None) is not None:
|
|
new_qa_logit_layer.bias.data[:num_labels_to_copy] = cur_qa_logit_layer.bias.data[:num_labels_to_copy]
|
|
|
|
return new_qa_logit_layer
|
|
|
|
@add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=LxmertForQuestionAnsweringOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
visual_feats: Optional[torch.FloatTensor] = None,
|
|
visual_pos: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
visual_attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[LxmertForQuestionAnsweringOutput, Tuple[torch.FloatTensor]]:
|
|
r"""
|
|
labels (`Torch.Tensor` of shape `(batch_size)`, *optional*):
|
|
A one-hot representation of the correct answer
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
lxmert_output = self.lxmert(
|
|
input_ids=input_ids,
|
|
visual_feats=visual_feats,
|
|
visual_pos=visual_pos,
|
|
token_type_ids=token_type_ids,
|
|
attention_mask=attention_mask,
|
|
visual_attention_mask=visual_attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_hidden_states=output_hidden_states,
|
|
output_attentions=output_attentions,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = lxmert_output[2]
|
|
answer_score = self.answer_head(pooled_output)
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss(answer_score.view(-1, self.num_qa_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (answer_score,) + lxmert_output[3:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return LxmertForQuestionAnsweringOutput(
|
|
loss=loss,
|
|
question_answering_score=answer_score,
|
|
language_hidden_states=lxmert_output.language_hidden_states,
|
|
vision_hidden_states=lxmert_output.vision_hidden_states,
|
|
language_attentions=lxmert_output.language_attentions,
|
|
vision_attentions=lxmert_output.vision_attentions,
|
|
cross_encoder_attentions=lxmert_output.cross_encoder_attentions,
|
|
)
|