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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) | |