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"""Modified from https://github.com/mlfoundations/open_flamingo""" | |
import random | |
import torch.nn as nn | |
from .helpers import GatedCrossAttentionBlock | |
from .utils import getattr_recursive, setattr_recursive | |
class FlamingoLayer(nn.Module): | |
def __init__(self, gated_cross_attn_layer, decoder_layer): | |
super().__init__() | |
self.gated_cross_attn_layer = gated_cross_attn_layer | |
self.decoder_layer = decoder_layer | |
self.vis_x = None | |
self.media_locations = None | |
self.only_lang_x = False | |
def is_conditioned(self) -> bool: | |
"""Check whether the layer is conditioned.""" | |
return self.vis_x is not None | |
# Used this great idea from this implementation of Flamingo (https://github.com/dhansmair/flamingo-mini/) | |
def condition_vis_x(self, vis_x): | |
self.vis_x = vis_x | |
def condition_only_lang_x(self, only_lang_x=False): | |
self.only_lang_x = only_lang_x | |
def condition_media_locations(self, media_locations): | |
self.media_locations = media_locations | |
def condition_attend_previous(self, attend_previous): | |
self.attend_previous = attend_previous | |
def forward( | |
self, | |
lang_x, | |
attention_mask=None, | |
**decoder_layer_kwargs, | |
): | |
if self.gated_cross_attn_layer is None or self.only_lang_x: | |
return self.decoder_layer(lang_x, attention_mask=attention_mask, **decoder_layer_kwargs) | |
if self.vis_x is None: | |
raise ValueError("vis_x must be conditioned before forward pass") | |
if self.media_locations is None: | |
raise ValueError("media_locations must be conditioned before forward pass") | |
lang_x = self.gated_cross_attn_layer( | |
lang_x, | |
self.vis_x, | |
media_locations=self.media_locations, | |
attend_previous=self.attend_previous, | |
) | |
lang_x = self.decoder_layer(lang_x, attention_mask=attention_mask, **decoder_layer_kwargs) | |
return lang_x | |
class FlamingoLMMixin(nn.Module): | |
""" | |
Mixin to add cross-attention layers to a language model. | |
""" | |
def set_decoder_layers_attr_name(self, decoder_layers_attr_name): | |
self.decoder_layers_attr_name = decoder_layers_attr_name | |
def _get_decoder_layers(self): | |
return getattr_recursive(self, self.decoder_layers_attr_name) | |
def _set_decoder_layers(self, value): | |
setattr_recursive(self, self.decoder_layers_attr_name, value) | |
def init_flamingo( | |
self, | |
media_token_id, | |
vis_hidden_size, | |
cross_attn_every_n_layers, | |
use_media_placement_augmentation, | |
): | |
""" | |
Initialize Flamingo by adding a new gated cross attn to the decoder. Store the media token id for computing the media locations. | |
""" | |
self.gated_cross_attn_layers = nn.ModuleList( | |
[ | |
GatedCrossAttentionBlock(dim=self.config.hidden_size, dim_visual=vis_hidden_size) | |
if (layer_idx + 1) % cross_attn_every_n_layers == 0 | |
else None | |
for layer_idx, _ in enumerate(self._get_decoder_layers()) | |
] | |
) | |
self._set_decoder_layers( | |
nn.ModuleList( | |
[ | |
FlamingoLayer(gated_cross_attn_layer, decoder_layer) | |
for gated_cross_attn_layer, decoder_layer in zip( | |
self.gated_cross_attn_layers, self._get_decoder_layers() | |
) | |
] | |
) | |
) | |
self.media_token_id = media_token_id | |
self.use_media_placement_augmentation = use_media_placement_augmentation | |
self.initialized_flamingo = True | |
def forward(self, *input, **kwargs): | |
"""Condition the Flamingo layers on the media locations before forward()""" | |
if not self.initialized_flamingo: | |
raise ValueError("Flamingo layers are not initialized. Please call `init_flamingo` first.") | |
input_ids = kwargs["input_ids"] if "input_ids" in kwargs else input[0] | |
media_locations = input_ids == self.media_token_id | |
attend_previous = (random.random() < 0.5) if self.use_media_placement_augmentation else False | |
for layer in self.get_decoder().layers: | |
layer.condition_media_locations(media_locations) | |
layer.condition_attend_previous(attend_previous) | |
return super().forward(*input, **kwargs) # Call the other parent's forward method | |
def is_conditioned(self) -> bool: | |
"""Check whether all decoder layers are already conditioned.""" | |
return all(l.is_conditioned() for l in self._get_decoder_layers()) | |
def clear_conditioned_layers(self): | |
for layer in self._get_decoder_layers(): | |
layer.condition_vis_x(None) | |
layer.condition_media_locations(None) | |
layer.condition_attend_previous(None) | |