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r"""
A textual head accepts visual features from the visual backbone, and performs
task specific modeling (captioning, classification etc.) to predict an output
distribution over vocabulary tokens for one or multiple time-steps in the batch.
"""
import torch
from torch import nn
from typing import Optional
from virtex.modules.embedding import WordAndPositionalEmbedding
from virtex.modules.transformer import (
PreNormTransformerEncoderLayer,
PreNormTransformerDecoderLayer,
)
class TextualHead(nn.Module):
r"""
Base class for all textual heads. All child classes can simply inherit
from :class:`~torch.nn.Module`, however this is kept here for uniform
type annotations.
Parameters
----------
visual_feature_size: int
Size (number of channels) of the input features from the visual backbone.
vocab_size: int
Number of tokens in the output vocabulary.
hidden_size: int
Size of the token embedding vectors, or hidden state vector of the
language model.
"""
def __init__(self, visual_feature_size: int, vocab_size: int, hidden_size: int):
super().__init__()
self.visual_feature_size = visual_feature_size
self.vocab_size = vocab_size
self.hidden_size = hidden_size
@property
def textual_feature_size(self):
r"""
Size of the last dimension of output right before the output linear
layer (which predicts a distribution over vocabulary tokens). This is
typically same as :attr:`hidden_size` for most modules. This property
is used to add more modules on top of this.
"""
return self.hidden_size
class LinearTextualHead(TextualHead):
r"""
A textual head containing a single linear layer projecting from the visual
feature size to the output vocabulary size.
Parameters
----------
visual_feature_size: int
Size (number of channels) of the input features from the visual backbone.
vocab_size: int
Number of tokens in the output vocabulary.
"""
def __init__(self, visual_feature_size: int, vocab_size: int, **kwargs):
# For API consistency.
hidden_size = visual_feature_size
super().__init__(visual_feature_size, vocab_size, hidden_size)
self.output = nn.Linear(visual_feature_size, vocab_size)
def forward(
self,
visual_features: torch.Tensor,
caption_tokens: Optional[torch.Tensor] = None,
caption_lengths: Optional[torch.Tensor] = None,
) -> torch.Tensor:
r"""
Project visual features directly to predict a distribution over
vocabulary tokens through a single linear layer. This textual head
ignores arguments ``caption_tokens`` and ``caption_lengths``, they
are here for API consistency.
Parameters
----------
visual_features: torch.Tensor
A tensor of shape ``(batch_size, channels, height, width)`` containing
features from visual backbone.
Returns
-------
torch.Tensor
A tensor of shape ``(batch_size, vocab_size)`` containing output
vocabulary logits.
"""
# Convert to NHWC and project visual features to textual feature size.
batch_size, channels, height, width = visual_features.size()
visual_features = visual_features.view(batch_size, channels, -1)
visual_features = visual_features.permute(0, 2, 1)
# Perform global average pooling of visual features.
# shape: (batch_size, channels)
visual_features = visual_features.mean(dim=1)
# shape: (batch_size, max_caption_length, vocab_size)
output_logits = self.output(visual_features)
return output_logits
class TransformerDecoderTextualHead(TextualHead):
r"""
A textual head composed of four main modules: (1) input projection (linear
layer) for visual features to match size with textual features, (2) word
and positional embedding for input captions, (3) a unidirectional transformer
decoder, and (4) and output projection (linear layer) to predict a
distribution over vocabulary tokens. The word embedding weights are tied
with output projection; the latter still has its own learnable bias.
.. note::
For the "bicaptioning" pretraining task, our *textual head* (as defined
in the paper) must have two transformer decoders: one each to decode
caption in either direction. This class however will always have one
transformer per object.
Refer :class:`~virtex.models.captioning.BidirectionalCaptioningModel`
source to understand how an object of this class is cloned, along with
tying embedding and output weights, for bicaptioning.
Hence, while there are *two objects* of this class, it is pragmatically
a *single* textual head as a whole, according to the terminology used
in paper.
Parameters
----------
visual_feature_size: int
Size (number of channels) of the input features from the visual backbone.
vocab_size: int
Number of tokens in the output vocabulary.
hidden_size: int
Size of the token embedding vectors, or hidden state vector of the
language model.
num_layers: int
Number of layers in the transformer.
attention_heads: int
Number of attention heads in the transformer.
feedforward_size: int
Size of feedforward layers in the transformer.
dropout: float, optional (default = 0.1)
Dropout probability for transformer (applied after layer normalization).
norm_type: str, optional (default = "post")
Type of transformer layer: pre-normalization (like GPT-2) or
post-normalization (like BERT). One of ``{"pre", "post"}``.
mask_future_positions: bool, optional (default = True)
Whether to mask future positions for self-attention over caption tokens.
This must be ``True`` for captioning (and bicaptioning) tasks to prevent
the language model from cheating, and ``False`` for masked language
modeling, as the self-attention should consider all tokens.
max_caption_length: int, optional (default = 30)
Maximum length of input captions; this is used to create a fixed
positional embedding lookup table.
padding_idx: int, optional (default = 0)
Token index of ``[PAD]`` token, word embedding for these tokens will
be a vector of zeroes (and not trainable).
"""
def __init__(
self,
visual_feature_size: int,
vocab_size: int,
hidden_size: int,
num_layers: int,
attention_heads: int,
feedforward_size: int,
dropout: float = 0.1,
norm_type: str = "post",
mask_future_positions: bool = True,
max_caption_length: int = 30,
padding_idx: int = 0,
):
super().__init__(visual_feature_size, vocab_size, hidden_size)
self.num_layers = num_layers
self.attention_heads = attention_heads
self.feedforward_size = feedforward_size
self.dropout = dropout
self.mask_future_positions = mask_future_positions
self.padding_idx = padding_idx
self.visual_projection = nn.Linear(
visual_feature_size, self.textual_feature_size
)
self.embedding = WordAndPositionalEmbedding(
self.vocab_size,
self.textual_feature_size,
dropout=dropout,
max_caption_length=max_caption_length,
padding_idx=padding_idx,
)
# Make decoder layer depending on whether it's a Pre-Norm or Post-Norm.
LayerClass = (
nn.TransformerDecoderLayer
if norm_type == "post"
else PreNormTransformerDecoderLayer
)
_layer = LayerClass(
self.textual_feature_size,
self.attention_heads,
dim_feedforward=self.feedforward_size,
dropout=dropout,
activation="gelu",
)
self.transformer = nn.TransformerDecoder(_layer, self.num_layers)
self.apply(self._init_weights)
# Create an output linear layer and tie the input and output word
# embeddings to reduce parameters.
self.output = nn.Linear(self.textual_feature_size, vocab_size)
self.output.weight = self.embedding.words.weight
@staticmethod
def _init_weights(module):
r"""Initialize weights like BERT - N(0.0, 0.02), bias = 0."""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=0.02)
elif isinstance(module, nn.MultiheadAttention):
module.in_proj_weight.data.normal_(mean=0.0, std=0.02)
module.out_proj.weight.data.normal_(mean=0.0, std=0.02)
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=0.02)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def forward(
self,
visual_features: torch.Tensor,
caption_tokens: torch.Tensor,
caption_lengths: torch.Tensor,
) -> torch.Tensor:
r"""
Given (projected) visual features from visual backbone and caption
tokens, predict the output logits for next time-step.
Parameters
----------
visual_features: torch.Tensor
A tensor of shape ``(batch_size, channels, height, width)`` containing
features from visual backbone.
caption_tokens: torch.Tensor
A tensor of shape ``(batch_size, max_caption_length)`` of caption
tokens padded to the right by ``padding_idx``.
caption_lengths: torch.Tensor
A tensor of shape ``(batch_size, )`` containing lengths of caption
tokens in the batch.
Returns
-------
torch.Tensor
A tensor of shape ``(batch_size, max_caption_length, vocab_size)``
containing output vocabulary logits for each time-step.
"""
# Convert to NHWC and project visual features to textual feature size.
batch_size, channels, height, width = visual_features.size()
visual_features = visual_features.view(batch_size, channels, -1)
visual_features = visual_features.permute(0, 2, 1)
# shape: (batch_size, height * width, textual_feature_size)
projected_visual_features = self.visual_projection(visual_features)
# Now visual and textual features are of same size.
# Note that `max_caption_length` here may be less than the
# `max_caption_length` passed in `__init__`, but it does not matter.
batch_size, max_caption_length = caption_tokens.size()
# Create a mask based on caption lengths, shape: (batch_size, )
# Form a binary mask: it is True for padding positions.
# These positions will be ignored for multi-headed attention.
ones = torch.ones_like(caption_tokens)
caption_mask = caption_lengths.unsqueeze(1) < ones.cumsum(dim=1)
# shape: (batch_size, max_caption_length, textual_feature_size)
caption_embeddings = self.embedding(caption_tokens)
if self.mask_future_positions:
# An additive mask for masking the future (one direction).
unidirectional_mask = self._generate_future_mask(
max_caption_length, caption_embeddings.dtype, caption_embeddings.device
)
else:
unidirectional_mask = None
# We transpose the first two dimensions of tokens embeddings and visual
# features, as required by decoder.
caption_embeddings = caption_embeddings.transpose(0, 1)
projected_visual_features = projected_visual_features.transpose(0, 1)
# shape: (max_caption_length, batch_size, hidden_size)
textual_features = self.transformer(
caption_embeddings,
projected_visual_features,
tgt_mask=unidirectional_mask,
tgt_key_padding_mask=caption_mask,
)
# Undo the transpose and bring batch to dim 0.
# shape: (batch_size, max_caption_length, hidden_size)
textual_features = textual_features.transpose(0, 1)
# shape: (batch_size, max_caption_length, vocab_size)
output_logits = self.output(textual_features)
return output_logits
def _generate_future_mask(
self, size: int, dtype: torch.dtype, device: torch.device
) -> torch.Tensor:
r"""
Generate a mask for "future" positions, useful when using this module
for language modeling.
Parameters
----------
size: int
"""
# Default mask is for forward direction. Flip for backward direction.
mask = torch.triu(
torch.ones(size, size, device=device, dtype=dtype), diagonal=1
)
mask = mask.masked_fill(mask == 1, float("-inf"))
return mask
class TransformerEncoderTextualHead(TextualHead):
def __init__(
self,
visual_feature_size: int,
vocab_size: int,
hidden_size: int,
num_layers: int,
attention_heads: int,
feedforward_size: int,
dropout: float = 0.1,
norm_type: str = "pre",
mask_future_positions: bool = True,
max_caption_length: int = 30,
padding_idx: int = 0,
):
super().__init__(visual_feature_size, vocab_size, hidden_size)
self.num_layers = num_layers
self.attention_heads = attention_heads
self.feedforward_size = feedforward_size
self.dropout = dropout
self.mask_future_positions = mask_future_positions
self.padding_idx = padding_idx
self.embedding = WordAndPositionalEmbedding(
self.vocab_size,
self.textual_feature_size,
dropout=dropout,
max_caption_length=max_caption_length,
padding_idx=padding_idx,
)
# Make decoder layer depending on whether it's a Pre-Norm or Post-Norm.
LayerClass = (
nn.TransformerEncoderLayer
if norm_type == "post"
else PreNormTransformerEncoderLayer
)
_layer = LayerClass(
self.textual_feature_size,
self.attention_heads,
dim_feedforward=self.feedforward_size,
dropout=dropout,
activation="gelu",
)
self.transformer = nn.TransformerEncoder(_layer, self.num_layers)
self.final_ln = nn.LayerNorm(self.textual_feature_size)
self._init_weights()
def _init_weights(self):
nn.init.normal_(self.embedding.words.weight, std=0.02)
nn.init.normal_(self.embedding.positions.weight, std=0.01)
proj_std = (self.hidden_size ** -0.5) * ((2 * self.num_layers) ** -0.5)
for layer in self.transformer.layers:
nn.init.normal_(layer.self_attn.in_proj_weight, std=self.hidden_size ** -0.5)
nn.init.normal_(layer.self_attn.out_proj.weight, std=proj_std)
nn.init.normal_(layer.linear1.weight, std=(2 * self.hidden_size) ** -0.5)
nn.init.normal_(layer.linear2.weight, std=proj_std)
def forward(
self,
caption_tokens: torch.Tensor,
caption_lengths: torch.Tensor,
) -> torch.Tensor:
# Note that `max_caption_length` here may be less than the
# `max_caption_length` passed in `__init__`, but it does not matter.
batch_size, max_caption_length = caption_tokens.size()
# Create a mask based on caption lengths, shape: (batch_size, )
# Form a binary mask: it is True for padding positions.
# These positions will be ignored for multi-headed attention.
ones = torch.ones_like(caption_tokens)
caption_mask = caption_lengths.unsqueeze(1) < ones.cumsum(dim=1)
# shape: (batch_size, max_caption_length, textual_feature_size)
caption_embeddings = self.embedding(caption_tokens)
if self.mask_future_positions:
# An additive mask for masking the future (one direction).
unidirectional_mask = self._generate_future_mask(
max_caption_length, caption_embeddings.dtype, caption_embeddings.device
)
else:
unidirectional_mask = None
# We transpose the first two dimensions of tokens embeddings and visual
# features, as required by decoder.
caption_embeddings = caption_embeddings.transpose(0, 1)
# shape: (max_caption_length, batch_size, hidden_size)
textual_features = self.transformer(
caption_embeddings,
mask=unidirectional_mask,
src_key_padding_mask=caption_mask,
)
# Undo the transpose and bring batch to dim 0.
# shape: (batch_size, max_caption_length, hidden_size)
textual_features = textual_features.transpose(0, 1)
textual_features = self.final_ln(textual_features)
return textual_features
@staticmethod
def _generate_future_mask(
size: int, dtype: torch.dtype, device: torch.device
) -> torch.Tensor:
r"""
Generate a mask for "future" positions, useful when using this module
for language modeling.
Parameters
----------
size: int
"""
# Default mask is for forward direction. Flip for backward direction.
mask = torch.triu(
torch.ones(size, size, device=device, dtype=dtype), diagonal=1
)
mask = mask.masked_fill(mask == 1, float("-inf"))
return mask
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