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"""Modified from https://github.com/mlfoundations/open_flamingo"""
import torch
from einops import rearrange
from torch import nn
from .helpers import PerceiverResampler
class Flamingo(nn.Module):
def __init__(
self,
vision_encoder: nn.Module,
lang_encoder: nn.Module,
eoc_token_id: int,
media_token_id: int,
vis_dim: int,
cross_attn_every_n_layers: int = 1,
use_media_placement_augmentation: bool = False,
):
"""
Args:
vision_encoder (nn.Module): HF CLIPModel
lang_encoder (nn.Module): HF causal language model
eoc_token_id (int): Token id for <|endofchunk|>
media_token_id (int): Token id for <image>
vis_dim (int): Dimension of the visual features.
Visual features are projected to match this shape along the last dimension.
cross_attn_every_n_layers (int, optional): How often to apply cross attention after transformer layer. Defaults to 1.
use_media_placement_augmentation (bool, optional): Whether to randomly assign images to the preceding or following text in training. Defaults to False.
"""
super().__init__()
self.eoc_token_id = eoc_token_id
self.media_token_id = media_token_id
self.use_media_placement_augmentation = use_media_placement_augmentation
self.vis_dim = vis_dim
self.vision_encoder = vision_encoder
self.perceiver = PerceiverResampler(dim=self.vis_dim)
self.lang_encoder = lang_encoder
self.lang_encoder.init_flamingo(
media_token_id=media_token_id,
vis_hidden_size=self.vis_dim,
cross_attn_every_n_layers=cross_attn_every_n_layers,
use_media_placement_augmentation=self.use_media_placement_augmentation,
)
def forward(
self,
vision_x: torch.Tensor,
lang_x: torch.Tensor,
attention_mask: torch.Tensor = None,
labels: torch.Tensor = None,
use_cached_vision_x: bool = False,
clear_conditioned_layers: bool = True,
past_key_values=None,
use_cache: bool = False,
):
"""
Forward pass of Flamingo.
Args:
vision_x (torch.Tensor): Vision input
shape (B, T_img, F, C, H, W) with F=1
lang_x (torch.Tensor): Language input ids
shape (B, T_txt)
attention_mask (torch.Tensor, optional): Attention mask. Defaults to None.
labels (torch.Tensor, optional): Labels. Defaults to None.
clear_conditioned_layers: if True, clear the conditioned layers
once the foward pass is completed. Set this to false if the
same set of images will be reused in another subsequent
forward pass.
past_key_values: pre-computed values to pass to language model.
See past_key_values documentation in Hugging Face
CausalLM models.
use_cache: whether to use cached key values. See use_cache
documentation in Hugging Face CausalLM models.
"""
if vision_x is None and use_cached_vision_x is False:
for layer in self.lang_encoder._get_decoder_layers():
layer.condition_only_lang_x(True)
output = self.lang_encoder(
input_ids=lang_x,
attention_mask=attention_mask,
labels=labels,
past_key_values=past_key_values,
use_cache=use_cache,
)
for layer in self.lang_encoder._get_decoder_layers():
layer.condition_only_lang_x(False)
return output
assert (
vision_x is not None
) or use_cached_vision_x, "Must provide either vision_x or use_cached_vision_x to True."
if use_cached_vision_x:
# Case: use cached; vision_x should be cached and other
# vision-related inputs should not be provided.
assert vision_x is None, "Expect vision_x to be None when use_cached_vision_x is True."
assert self.lang_encoder.is_conditioned()
else:
# Case: do not use caching (i.e. this is a standard forward pass);
self._encode_vision_x(vision_x=vision_x)
output = self.lang_encoder(
input_ids=lang_x,
attention_mask=attention_mask,
labels=labels,
past_key_values=past_key_values,
use_cache=use_cache,
)
if clear_conditioned_layers:
self.lang_encoder.clear_conditioned_layers()
return output
def generate(
self,
vision_x: torch.Tensor,
lang_x: torch.Tensor,
attention_mask: torch.Tensor = None,
num_beams=1,
max_new_tokens=None,
temperature=1.0,
top_k=0,
top_p=1.0,
no_repeat_ngram_size=0,
prefix_allowed_tokens_fn=None,
length_penalty=1.0,
num_return_sequences=1,
do_sample=False,
early_stopping=False,
):
"""
Generate text conditioned on vision and language inputs.
Args:
vision_x (torch.Tensor): Vision input
shape (B, T_img, F, C, H, W)
images in the same chunk are collated along T_img, and frames are collated along F
currently only F=1 is supported (single-frame videos)
lang_x (torch.Tensor): Language input
shape (B, T_txt)
max_length (int, optional): Maximum length of the output. Defaults to None.
attention_mask (torch.Tensor, optional): Attention mask. Defaults to None.
num_beams (int, optional): Number of beams. Defaults to 1.
max_new_tokens (int, optional): Maximum new tokens. Defaults to None.
temperature (float, optional): Temperature. Defaults to 1.0.
top_k (int, optional): Top k. Defaults to 0.
top_p (float, optional): Top p. Defaults to 1.0.
no_repeat_ngram_size (int, optional): No repeat ngram size. Defaults to 0.
length_penalty (float, optional): Length penalty. Defaults to 1.0.
num_return_sequences (int, optional): Number of return sequences. Defaults to 1.
do_sample (bool, optional): Do sample. Defaults to False.
early_stopping (bool, optional): Early stopping. Defaults to False.
Returns:
torch.Tensor: lang_x with generated tokens appended to it
"""
if num_beams > 1:
vision_x = vision_x.repeat_interleave(num_beams, dim=0)
self._encode_vision_x(vision_x=vision_x)
output = self.lang_encoder.generate(
lang_x,
attention_mask=attention_mask,
# eos_token_id=self.eoc_token_id,
num_beams=num_beams,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_k=top_k,
top_p=top_p,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
no_repeat_ngram_size=no_repeat_ngram_size,
length_penalty=length_penalty,
num_return_sequences=num_return_sequences,
do_sample=do_sample,
early_stopping=early_stopping,
)
self.lang_encoder.clear_conditioned_layers()
return output
def _encode_vision_x(self, vision_x: torch.Tensor):
"""
Compute media tokens from vision input by passing it through vision encoder and conditioning language model.
Args:
vision_x (torch.Tensor): Vision input
shape (B, T_img, F, C, H, W)
Images in the same chunk are collated along T_img, and frames are collated along F
Currently only F=1 is supported (single-frame videos)
rearrange code based on https://github.com/dhansmair/flamingo-mini
"""
assert vision_x.ndim == 6, "vision_x should be of shape (b, T_img, F, C, H, W)"
b, T, F = vision_x.shape[:3]
assert F == 1, "Only single frame supported"
vision_x = rearrange(vision_x, "b T F c h w -> (b T F) c h w")
with torch.no_grad():
vision_x = self.vision_encoder.visual(vision_x)[1]
vision_x = rearrange(vision_x, "(b T F) v d -> b T F v d", b=b, T=T, F=F)
vision_x = self.perceiver(vision_x) # reshapes to (b, T, n, d)
for layer in self.lang_encoder._get_decoder_layers():
layer.condition_vis_x(vision_x)
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