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# Modified by Jialian Wu from
# https://github.com/microsoft/GenerativeImage2Text/blob/main/generativeimage2text/layers/decoder.py
# and https://github.com/kdexd/virtex
from torch import nn
import torch
import functools
from torch.nn import functional as F
import warnings
class TextualHead(nn.Module):
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):
return self.hidden_size
class WordAndPositionalEmbedding(nn.Module):
def __init__(
self,
vocab_size: int,
hidden_size: int,
dropout: float = 0.0,
max_caption_length: int = 30,
padding_idx: int = 0,
):
super().__init__()
self.vocab_size = vocab_size
self.padding_idx = padding_idx
#self.words = nn.Embedding(vocab_size, hidden_size, padding_idx=padding_idx)
self.words = nn.Embedding(vocab_size, hidden_size)
# We provide no "padding index" for positional embeddings. We zero out
# the positional embeddings of padded positions as a post-processing.
self.positions = nn.Embedding(max_caption_length, hidden_size)
self.layer_norm = nn.LayerNorm(
hidden_size, eps=1e-8, elementwise_affine=True
)
self.dropout = nn.Dropout(p=dropout)
def forward(self, tokens: torch.Tensor):
position_indices = self._create_position_indices(tokens)
# shape: (batch_size, max_caption_length, hidden_size)
word_embeddings = self.words(tokens)
position_embeddings = self.positions(position_indices)
# shape: (batch_size, max_caption_length, hidden_size)
embeddings = self.layer_norm(word_embeddings + position_embeddings)
embeddings = self.dropout(embeddings)
return embeddings
@functools.lru_cache(maxsize=128)
def _create_position_indices(self, tokens: torch.Tensor):
# Create position indices of the same size as token indices.
batch_size, max_caption_length = tokens.size()
positions = torch.arange(
max_caption_length, dtype=tokens.dtype, device=tokens.device
)
# shape: (batch_size, max_caption_length)
positions = positions.unsqueeze(0).expand(batch_size, max_caption_length)
return positions
class BertEncoderAsDecoder(nn.Module):
def __init__(self, encoder):
super().__init__()
self.encoder = encoder
def forward(self, tgt, memory,
tgt_mask=None,
tgt_key_padding_mask=None,
memory_key_padding_mask=None,
tgt_bi_valid_mask=None,
encoder_history_states=None,
):
assert tgt_key_padding_mask is None, 'not supported'
assert tgt_mask.dim() == 2
assert tgt_mask.shape[0] == tgt_mask.shape[1]
# tgt_mask should always be 0/negative infinity
tgt = tgt.transpose(0, 1)
memory = memory.transpose(0, 1)
hidden_states = torch.cat((memory, tgt), dim=1)
num_tgt = tgt.shape[1]
num_memory = memory.shape[1]
device = tgt.device
dtype = tgt.dtype
top_left = torch.zeros((num_memory, num_memory), device=device, dtype=dtype)
top_right = torch.full((num_memory, num_tgt), float('-inf'), device=tgt.device, dtype=dtype,)
bottom_left = torch.zeros((num_tgt, num_memory), dtype=dtype, device=tgt_mask.device,)
left = torch.cat((top_left, bottom_left), dim=0)
right = torch.cat((top_right, tgt_mask.to(dtype)), dim=0)
full_attention_mask = torch.cat((left, right), dim=1)[None, :]
if memory_key_padding_mask is None:
memory_key_padding_mask = torch.full((memory.shape[0], memory.shape[1]), fill_value=False, device=device)
# if it is False, it means valid. That is, it is not a padding
assert memory_key_padding_mask.dtype == torch.bool
zero_negative_infinity = torch.zeros_like(memory_key_padding_mask, dtype=tgt.dtype)
zero_negative_infinity[memory_key_padding_mask] = float('-inf')
full_attention_mask = full_attention_mask.expand((memory_key_padding_mask.shape[0], num_memory + num_tgt, num_memory + num_tgt))
full_attention_mask = full_attention_mask.clone()
origin_left = full_attention_mask[:, :, :num_memory]
update = zero_negative_infinity[:, None, :]
full_attention_mask[:, :, :num_memory] = origin_left + update
if tgt_bi_valid_mask is not None:
# verify the correctness
bs = full_attention_mask.shape[0]
# during inference, tgt_bi_valid_mask's length is not changed, but
# num_tgt can be increased
max_valid_target = tgt_bi_valid_mask.shape[1]
mask = tgt_bi_valid_mask[:, None, :].expand((bs, num_memory+num_tgt, max_valid_target))
full_attention_mask[:, :, num_memory:(num_memory+max_valid_target)][mask] = 0
# add axis for multi-head
full_attention_mask = full_attention_mask[:, None, :, :]
if encoder_history_states is None:
result = self.encoder(
hidden_states=hidden_states,
attention_mask=full_attention_mask,
encoder_history_states=encoder_history_states,
)
result = list(result)
result[0] = result[0][:, num_memory:].transpose(0, 1)
if self.encoder.output_hidden_states:
return result[0], result[1]
else:
# make it back-compatible
return result[0]
else:
encoder_out = self.encoder(
hidden_states=hidden_states[:, -1:],
attention_mask=full_attention_mask[:, :, -1:],
encoder_history_states=encoder_history_states,
)
result = encoder_out[0].transpose(0, 1)
if self.encoder.output_hidden_states:
return result, encoder_out[1]
else:
return result
def create_transformer(decoder_type, norm_type,
textual_feature_size,
attention_heads,
feedforward_size,
dropout,
num_layers,
output_hidden_states=False,
use_mlp_wrapper=None,
use_act_checkpoint=True,
):
assert norm_type in ['post', 'pre']
if decoder_type is None:
LayerClass = (
nn.TransformerDecoderLayer
if norm_type == "post"
else PreNormTransformerDecoderLayer
)
_layer = LayerClass(
textual_feature_size,
attention_heads,
dim_feedforward=feedforward_size,
dropout=dropout,
activation="gelu",
)
return nn.TransformerDecoder(_layer, num_layers)
elif decoder_type == 'bert_en':
from .modeling_bert import BertConfig, BertEncoder
config = BertConfig(
vocab_size_or_config_json_file=30522,
hidden_size=textual_feature_size,
num_hidden_layers=num_layers,
num_attention_heads=attention_heads,
intermediate_size=feedforward_size,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
layer_norm_eps=1e-12,
)
config.pre_norm = (norm_type == 'pre')
config.use_mlp_wrapper = use_mlp_wrapper
config.output_hidden_states = output_hidden_states
encoder = BertEncoder(config, use_act_checkpoint=use_act_checkpoint)
return BertEncoderAsDecoder(encoder)
class PreNormTransformerDecoderLayer(nn.TransformerDecoderLayer):
def forward(self, tgt, memory, tgt_mask=None, memory_mask=None,
tgt_key_padding_mask=None, memory_key_padding_mask=None):
# fmt: off
# We use the members (modules) from super-class, just the order of
# operations is changed here. First layernorm, then attention.
tgt2 = self.norm1(tgt)
tgt2, _ = self.self_attn(
tgt2, tgt2, tgt2, attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask
)
tgt = tgt + self.dropout1(tgt2)
# Layernorm first, then decoder attention.
tgt2 = self.norm2(tgt)
tgt2, _ = self.multihead_attn(
tgt2, memory, memory, attn_mask=memory_mask,
key_padding_mask=memory_key_padding_mask
)
tgt = tgt + self.dropout2(tgt2)
# Layernorm first, then transformation through feedforward network.
tgt2 = self.norm3(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
tgt = tgt + self.dropout3(tgt2)
return tgt
class TransformerDecoderTextualHead(TextualHead):
def __init__(
self,
object_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 = 1024,
padding_idx: int = 0,
decoder_type=None,
not_tie_weight=None,
output_hidden_states=None,
use_mlp_wrapper=None,
use_act_checkpoint=True,
):
super().__init__(object_feature_size, vocab_size, hidden_size)
self.num_layers = num_layers
self.attention_heads = attention_heads
self.feedforward_size = feedforward_size
self.dropout = dropout
assert mask_future_positions
self.padding_idx = padding_idx
self.object_feature_projection = nn.Sequential(
nn.Linear(object_feature_size, self.textual_feature_size),
nn.LayerNorm(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,
)
self.transformer = create_transformer(
decoder_type=decoder_type,
norm_type=norm_type,
textual_feature_size=self.textual_feature_size,
attention_heads=self.attention_heads,
feedforward_size=self.feedforward_size,
dropout=dropout,
num_layers=self.num_layers,
output_hidden_states=output_hidden_states,
use_mlp_wrapper=use_mlp_wrapper,
use_act_checkpoint=use_act_checkpoint,
)
self.apply(self._init_weights)
# Create an output linear layer and tie the input and output word
# embeddings to reduce parametejs.
self.output = nn.Linear(self.textual_feature_size, vocab_size)
if not not_tie_weight:
self.output.weight = self.embedding.words.weight
@staticmethod
def _init_weights(module):
"""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,
hidden_states,
text_tokens,
):
projected_object_features = self.object_feature_projection(hidden_states) if hidden_states is not None else None
batch_size, max_text_length = text_tokens.size()
text_embeddings = self.embedding(text_tokens)
# An additive mask for masking the future (one direction).
uni_mask_zero_neg = self._generate_future_mask(
max_text_length, text_embeddings.dtype, text_embeddings.device
)
# We transpose the first two dimensions of tokens embeddings and visual
# features, as required by decoder.
text_embeddings = text_embeddings.transpose(0, 1)
projected_object_features = projected_object_features.transpose(0, 1)
# if transformer here is the pytorch/decoder, there is no chance, the
# output is always tensor
trans_out = self.transformer(
text_embeddings,
projected_object_features,
tgt_mask=uni_mask_zero_neg,
)
if isinstance(trans_out, tuple):
textual_features = trans_out[0]
else:
assert isinstance(trans_out, torch.Tensor)
textual_features = trans_out
# 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)
if isinstance(trans_out, tuple):
return output_logits, trans_out[1]
else:
return output_logits
def _generate_future_mask(
self, size: int, dtype: torch.dtype, device: torch.device
):
# 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 AutoRegressiveBeamSearch(object):
def __init__(
self,
end_token_id: int,
max_steps: int = 50,
beam_size: int = 5,
objectdet=True,
per_node_beam_size: int = 2,
):
self._eos_index = end_token_id
self.max_steps = max_steps
self.beam_size = beam_size
self.objectdet = objectdet
self.per_node_beam_size = per_node_beam_size or beam_size
def search(self, begin_tokens, step):
if self.beam_size > 1 and self.objectdet:
only_return_best = False
else:
only_return_best = True
batch_size = begin_tokens.size()[0]
predictions = begin_tokens.unsqueeze(1).expand((batch_size, self.beam_size, begin_tokens.shape[-1]))
# Calculate the first timestep. This is done outside the main loop
# because we are going from a single decoder input (the output from the
# encoder) to the top `beam_size` decoder outputs. On the other hand,
# within the main loop we are going from the `beam_size` elements of the
# beam to `beam_size`^2 candidates from which we will select the top
# `beam_size` elements for the next iteration.
# shape: (batch_size, num_classes)
start_class_logits = step(begin_tokens)
# Convert logits to logprobs.
# shape: (batch_size * beam_size, vocab_size)
start_class_logprobs = F.log_softmax(start_class_logits, dim=1)
num_classes = start_class_logprobs.size()[1]
# shape: (batch_size, beam_size), (batch_size, beam_size)
start_top_logprobs, start_predicted_classes = start_class_logprobs.topk(
self.beam_size
)
if (
self.beam_size == 1
and (start_predicted_classes == self._eos_index).all()
):
warnings.warn(
"Empty object description predicted. You may want to increase beam"
"size or ensure your step function is working properly.",
RuntimeWarning,
)
if only_return_best:
return start_predicted_classes, start_top_logprobs
else:
return start_predicted_classes.unsqueeze(-1), start_top_logprobs
# The log probs for the last time step.
# shape: (batch_size, beam_size)
last_logprobs = start_top_logprobs
# shape: (batch_size, beam_size, sequence_length)
predictions = torch.cat([predictions, start_predicted_classes.unsqueeze(-1)], dim=-1)
# Log probability tensor that mandates that the end token is selected.
# shape: (batch_size * beam_size, num_classes)
logprobs_after_end = start_class_logprobs.new_full(
(batch_size * self.beam_size, num_classes), float("-inf")
)
logprobs_after_end[:, self._eos_index] = 0.0
logits_after_end = start_class_logprobs.new_full(
(batch_size * self.beam_size, num_classes), float("-inf")
)
logits_after_end[:, self._eos_index] = 0
while predictions.shape[-1] < self.max_steps:
# shape: (batch_size * beam_size,)
last_predictions = predictions[:, :, -1].reshape(batch_size * self.beam_size)
# If every predicted token from the last step is `self._eos_index`,
# then we can stop early.
if (last_predictions == self._eos_index).all():
break
predictions_so_far = predictions.view(
batch_size * self.beam_size, -1
)
# shape: (batch_size * beam_size, num_classes)
class_logits = step(predictions_so_far)
# Set logprobs of last predicted tokens as high negative value to avoid
# repetition in description.
class_logits = class_logits.scatter(1, predictions_so_far[:, -1].view((-1, 1)), -10000)
# shape: (batch_size * beam_size, num_classes)
last_predictions_expanded = last_predictions.unsqueeze(-1).expand(
batch_size * self.beam_size, num_classes
)
# Here we are finding any beams where we predicted the end token in
# the previous timestep and replacing the distribution with a
# one-hot distribution, forcing the beam to predict the end token
# this timestep as well.
class_logits = torch.where(
last_predictions_expanded == self._eos_index,
logits_after_end,
class_logits,
)
# Convert logits to logprobs.
# shape: (batch_size * beam_size, vocab_size)
class_logprobs = F.log_softmax(class_logits, dim=1)
# shape (both): (batch_size * beam_size, per_node_beam_size)
top_logprobs, predicted_classes = class_logprobs.topk(
self.per_node_beam_size
)
# Here we expand the last log probs to `(batch_size * beam_size,
# per_node_beam_size)` so that we can add them to the current log
# probs for this timestep. This lets us maintain the log
# probability of each element on the beam.
# shape: (batch_size * beam_size, per_node_beam_size)
expanded_last_logprobs = (
last_logprobs.unsqueeze(2)
.expand(batch_size, self.beam_size, self.per_node_beam_size)
.reshape(batch_size * self.beam_size, self.per_node_beam_size)
)
# shape: (batch_size * beam_size, per_node_beam_size)
summed_top_logprobs = top_logprobs + expanded_last_logprobs
# shape: (batch_size, beam_size * per_node_beam_size)
reshaped_summed = summed_top_logprobs.reshape(
batch_size, self.beam_size * self.per_node_beam_size
)
# shape: (batch_size, beam_size * per_node_beam_size)
reshaped_predicted_classes = predicted_classes.reshape(
batch_size, self.beam_size * self.per_node_beam_size
)
# Append the predictions to the current beam.
reshaped_beam = (
predictions.view(batch_size * self.beam_size, 1, -1)
.repeat(1, self.per_node_beam_size, 1)
.reshape(batch_size, self.beam_size * self.per_node_beam_size, -1)
)
# batch_size, (beam_size * per_node_beach_size), #token
reshaped_beam = torch.cat([reshaped_beam, reshaped_predicted_classes.unsqueeze(-1)], dim=-1)
# Keep only the top `beam_size` beam indices.
# shape: (batch_size, beam_size), (batch_size, beam_size)
restricted_beam_logprobs, restricted_beam_indices = reshaped_summed.topk(
self.beam_size
)
predictions = reshaped_beam.gather(
1, restricted_beam_indices.unsqueeze(-1).repeat(1,1,reshaped_beam.shape[-1])
)
# shape: (batch_size, beam_size)
last_logprobs = restricted_beam_logprobs
if not torch.isfinite(last_logprobs).all():
warnings.warn(
"Infinite log probs encountered. Some final descriptions may not "
"make sense. This can happen when the beam size is larger than"
" the number of valid (non-zero probability) transitions that "
"the step function produces.",
RuntimeWarning,
)
# Optionally select best beam and its logprobs.
if only_return_best:
# shape: (batch_size, sequence_length)
predictions = predictions[:, 0, :]
last_logprobs = last_logprobs[:, 0]
num_valid = (predictions != self._eos_index).sum(dim=-1)
num_valid += (predictions == self._eos_index).sum(dim=-1) > 0
num_valid = num_valid - begin_tokens.shape[1]
num_valid = num_valid.clip(min=1)
last_logprobs = last_logprobs / num_valid
return predictions, last_logprobs
class GRiTTextDecoder(nn.Module):
def __init__(
self,
transformer,
begin_token_id=101,
beamsearch_decode=None,
loss_type=None,
tokenizer=None,
):
super().__init__()
self.textual = transformer
self.padding_idx = self.textual.padding_idx
self.begin_token_id = begin_token_id
self.beamsearch_decode = beamsearch_decode
self.tokenizer = tokenizer
if loss_type is None:
self.loss = nn.CrossEntropyLoss(ignore_index=self.padding_idx)
elif loss_type == 'smooth':
self.loss = SmoothLabelCrossEntropyLoss(ignore_index=self.padding_idx)
else:
raise NotImplementedError(loss_type)
def forward(self, batch):
object_features = batch['object_features']
if self.training:
caption_token_input = batch["text_tokens"]
output_logits = self.textual(
object_features,
caption_token_input,
)
if 'need_predict' in batch:
# in place should also be good, but we do not choose that for
# safety as we may use it in prediction results in future
target = batch["text_tokens"].clone()
target[batch['need_predict'] == 0] = self.padding_idx
else:
target = batch["text_tokens"]
feat = output_logits[:, :-1].contiguous()
target = target[:, 1:].contiguous()
feat = feat.view(-1, self.textual.vocab_size)
target = target.view(-1)
valid_mask = target != self.padding_idx
target = target[valid_mask]
feat = feat[valid_mask]
loss = self.loss(feat, target)
return loss
else:
output_dict = self.infer(object_features)
return output_dict
def infer(self, object_features):
batch_size = object_features.size(0)
begin_tokens = object_features.new_full(
(batch_size, 1), self.begin_token_id
).long()
decoding_step = functools.partial(
self.decoding_step, object_features
)
object_description_tokens, logprobs = self.beamsearch_decode.search(
begin_tokens, decoding_step
)
output_dict = {
'predictions': object_description_tokens,
'logprobs': logprobs,
}
return output_dict
def decoding_step(self, object_features, partial_text):
batch_size = object_features.shape[0]
beam_size = int(partial_text.size(0) / batch_size)
if beam_size > 1:
batch_size, num_token, channels = object_features.size()
object_features = object_features.unsqueeze(1).repeat(1, beam_size, 1, 1)
object_features = object_features.view(
batch_size * beam_size, num_token, channels
)
text_lengths = torch.ones_like(partial_text)
if len(text_lengths.size()) != 2:
partial_text = partial_text.unsqueeze(1)
# shape: (batch_size * beam_size, partial_caption_length, vocab_size)
logits = self.textual(
object_features,
partial_text,
)
return logits[:, -1, :].float()
class SmoothLabelCrossEntropyLoss(nn.Module):
def __init__(self, eps=0.1, log_prefix='', ignore_index=None):
super().__init__()
self.eps = eps
self.log_soft = nn.LogSoftmax(dim=1)
self.kl = nn.KLDivLoss(reduction='none')
self.iter = 0
self.max_loss = 0
self.min_loss = 0
self.log_prefix = log_prefix
self.ignore_index = ignore_index
def forward(self, feature, target):
feature = feature.float()
if self.ignore_index is not None:
valid_mask = target != self.ignore_index
target = target[valid_mask]
feature = feature[valid_mask]
assert target.numel() > 0
self.iter += 1
eps = self.eps
n_class = feature.size(1)
one_hot = torch.zeros_like(feature).scatter(1, target.view(-1, 1), 1)
one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
log_prb = self.log_soft(feature)
loss = self.kl(log_prb, one_hot)
return loss.sum(dim=1).mean()
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