import torch from torch import einsum, nn from einops import rearrange, repeat from einops_exts import rearrange_many from einops import rearrange from typing import List, Optional, Tuple, Union import torch.nn.functional as F from transformers.modeling_outputs import CausalLMOutputWithPast from dataclasses import dataclass from transformers import CLIPVisionModel import transformers from .utils import num_params, getattr_recursive, stack_with_padding, get_anyres_image_grid_shape, unpad_image class VisionTokenizer(nn.Module): def __init__(self, dim_media, num_tokens_per_media): super().__init__() self.dim_media = dim_media self.num_tokens_per_media = num_tokens_per_media class PerceiverAttention(nn.Module): def __init__(self, *, dim, dim_head=64, heads=8): super().__init__() self.scale = dim_head**-0.5 self.heads = heads inner_dim = dim_head * heads self.norm_media = nn.LayerNorm(dim) self.norm_latents = nn.LayerNorm(dim) self.to_q = nn.Linear(dim, inner_dim, bias=False) self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) self.to_out = nn.Linear(inner_dim, dim, bias=False) def forward(self, x, latents, vision_attn_masks=None): """ Args: x (torch.Tensor): image features shape (b, T, n1, D) latent (torch.Tensor): latent features shape (b, T, n2, D) """ x = self.norm_media(x) latents = self.norm_latents(latents) h = self.heads q = self.to_q(latents) kv_input = torch.cat((x, latents), dim=-2) # TODO: Change the shape of vision attention mask according to this. if vision_attn_masks is not None: vision_attn_masks = torch.cat((vision_attn_masks, torch.ones((latents.shape[0], latents.shape[-2]), dtype=latents.dtype, device=latents.device)), dim=-1) k, v = self.to_kv(kv_input).chunk(2, dim=-1) q, k, v = rearrange_many((q, k, v), "b t n (h d) -> b h t n d", h=h) q = q * self.scale # attention sim = einsum("... i d, ... j d -> ... i j", q, k) # Apply vision attention mask here. # Reference: https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html#torch.nn.functional.scaled_dot_product_attention if vision_attn_masks is not None: attn_bias = torch.zeros((q.size(0), 1, 1, q.size(-2), k.size(-2)), dtype=q.dtype, device=q.device) vision_attn_masks = repeat(vision_attn_masks, 'b n -> b 1 1 l n', l=q.size(-2)) attn_bias.masked_fill_(vision_attn_masks.logical_not(), float("-inf")) sim += attn_bias sim = sim - sim.amax(dim=-1, keepdim=True).detach() attn = sim.softmax(dim=-1) out = einsum("... i j, ... j d -> ... i d", attn, v) out = rearrange(out, "b h t n d -> b t n (h d)", h=h) return self.to_out(out) def FeedForward(dim, mult=4): inner_dim = int(dim * mult) return nn.Sequential( nn.LayerNorm(dim), nn.Linear(dim, inner_dim, bias=False), nn.GELU(), nn.Linear(inner_dim, dim, bias=False), ) class PerceiverResampler(VisionTokenizer): def __init__( self, *, dim, dim_inner=None, depth=6, dim_head=96, heads=16, num_latents=128, max_num_media=None, max_num_frames=None, ff_mult=4, ): """ Perceiver module which takes in image features and outputs image tokens. Args: dim (int): dimension of the incoming image features dim_inner (int, optional): final dimension to project the incoming image features to; also the final dimension of the outputted features. If None, no projection is used, and dim_inner = dim. depth (int, optional): number of layers. Defaults to 6. dim_head (int, optional): dimension of each head. Defaults to 64. heads (int, optional): number of heads. Defaults to 8. num_latents (int, optional): number of latent tokens to use in the Perceiver; also corresponds to number of tokens per sequence to output. Defaults to 64. max_num_media (int, optional): maximum number of media per sequence to input into the Perceiver and keep positional embeddings for. If None, no positional embeddings are used. max_num_frames (int, optional): maximum number of frames to input into the Perceiver and keep positional embeddings for. If None, no positional embeddings are used. ff_mult (int, optional): dimension multiplier for the feedforward network. Defaults to 4. """ if dim_inner is not None: projection = nn.Linear(dim, dim_inner) else: projection = None dim_inner = dim super().__init__(dim_media=dim, num_tokens_per_media=num_latents) self.projection = projection self.latents = nn.Parameter(torch.randn(num_latents, dim)) # positional embeddings self.frame_embs = ( nn.Parameter(torch.randn(max_num_frames, dim)) if exists(max_num_frames) else None ) self.media_time_embs = ( nn.Parameter(torch.randn(max_num_media, 1, dim)) if exists(max_num_media) else None ) self.layers = nn.ModuleList([]) for _ in range(depth): self.layers.append( nn.ModuleList( [ PerceiverAttention( dim=dim, dim_head=dim_head, heads=heads ), FeedForward(dim=dim, mult=ff_mult), ] ) ) self.norm = nn.LayerNorm(dim) def forward(self, x): """ Args: x (torch.Tensor): image features shape (b, T, F, v, D) Returns: shape (b, T, n, D) where n is self.num_latents """ b, T, F, v = x.shape[:4] # frame and media time embeddings if exists(self.frame_embs): frame_embs = repeat(self.frame_embs[:F], "F d -> b T F v d", b=b, T=T, v=v) x = x + frame_embs x = rearrange( x, "b T F v d -> b T (F v) d" ) # flatten the frame and spatial dimensions if exists(self.media_time_embs): x = x + self.media_time_embs[:T] # blocks latents = repeat(self.latents, "n d -> b T n d", b=b, T=T) for attn, ff in self.layers: latents = attn(x, latents) + latents latents = ff(latents) + latents if exists(self.projection): return self.projection(self.norm(latents)) else: return self.norm(latents) class DecoupledEmbedding(nn.Embedding): # Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/sparse.html#Embedding """ Implements a decoupling of parameters to allow freezing (or not) a subset of the embeddings. In practise, the regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `num_additional_embeddings` > 0, then it will create `num_additional_embeddings` additional parameters that are always trained. If `num_additional_embeddings=0`, then the module defaults back to the regular behavior of `nn.Embedding`. """ def __init__( self, max_original_id: int, num_additional_embeddings: int = 0, _weight: torch.Tensor = None, num_original_embeddings: int = None, embedding_dim: int = None, partially_freeze=True, device=None, dtype=None, pad_token_id=None, ) -> None: """ Args: max_original_id (`int`): The largest token id that should be embedded using the regular embedding (regular `weight`). This is usually len(tokenizer) - 1 before additional tokens are added. Note that this may not equal self.weight.shape[0] num_additional_embeddings (`int`): Number of additional tokens to initialize an Embedding matrix for (`additional_weight`). _weight (`torch.Tensor`, *optional*, defaults to `None`): The regular weight tensor. If provided, this sets the `num_original_embeddings` and `embedding_dim` parameters. num_original_embeddings (`int`): self.weight.shape[0] embedding_dim (`int`): The size of each embedding vector partially_freeze: (`bool`, *optional*, defaults to `True`): If `True`, the regular `weight` will be frozen. `additional_weight` is never frozen. padding_idx (`int`, *optional*): The padding index (needs to be less than num_embeddings) Note: there are a lot of other parameters to initialize a standard `nn.Embedding` such as `padding_idx`, `max_norm` or `norm_type`. We are not supporting these. """ # validate args if pad_token_id is not None and pad_token_id > max_original_id: raise ValueError( f"pad_token_id must be <= max_original_id. Got {pad_token_id} and {max_original_id}." + "If the original tokenizer does not have a pad_token_id, use pad_token_id=None." ) if _weight is not None: assert (num_original_embeddings is None) or ( _weight.shape[0] == num_original_embeddings ), f"num_original_embeddings={num_original_embeddings} but _weight.shape[0]={_weight.shape[0]}" assert (embedding_dim is None) or ( _weight.shape[1] == embedding_dim ), f"embedding_dim={embedding_dim} but _weight.shape[1]={_weight.shape[1]}" num_original_embeddings = _weight.shape[0] embedding_dim = _weight.shape[1] else: assert ( num_original_embeddings is not None ), "num_original_embeddings must be provided if _weight is not provided" assert ( embedding_dim is not None ), "embedding_dim must be provided if _weight is not provided" super().__init__( num_embeddings=num_original_embeddings, embedding_dim=embedding_dim, device=device, dtype=dtype, padding_idx=pad_token_id, _weight=_weight, ) self.max_original_id = max_original_id self.padding_idx = pad_token_id self.num_additional_embeddings = num_additional_embeddings if self.num_additional_embeddings > 0: self.additional_embedding = nn.Embedding( num_embeddings=self.num_additional_embeddings, embedding_dim=embedding_dim, device=device, dtype=dtype, ) self.set_requires_grad( require_regular_grad=not partially_freeze, require_additional_grad=True ) def set_requires_grad(self, require_regular_grad, require_additional_grad): """ Helper function to separately set the requires_grad flag for the regular weight and the additional weight. """ self.weight.requires_grad_(require_regular_grad) self.additional_embedding.requires_grad_(require_additional_grad) def forward(self, input_ids): """ we have 2 embeddings, with different indices - one pretrained self.weight and another self.additional_embedding.weight that is being trained. in order to make a lookup of the input ids, we: 1. find out the indices of the entries belonging to the 2nd embedding 2. extract those values while subtracting the size of the first embedding (num_embeddings), since the 2nd embedding starts from 0 and not num_embeddings 3. perform the 2nd embedding lookup 4. now we handle the 1st embedding, we overwrite indices belonging to the 2nd embedding with a padding index 5. perform the 1st embedding lookup 6. now we overwrite the values in the 1st embedding lookup with the values of the 2nd embedding lookup note: for the 1st embedding lookup we could have looked up only the low indices and not do the padding, but then we have to create a new tensor and populate it with 2 tensors that are spread out across various indices - i.e. not a simple concat - I haven't benchmarked the complex case if it's any faster, given that seqlens are usually relatively short it's probably not faster or if faster not by much - but might be a good idea to measure. """ if self.num_additional_embeddings == 0: return F.embedding(input_ids, self.weight) # Clone so that we don't modify the original input_ids later on input_ids = input_ids.clone() additional_vocab_indices = torch.where(input_ids > self.max_original_id) input_ids_additional_vocab = input_ids[additional_vocab_indices] additional_embeddings = self.additional_embedding( input_ids_additional_vocab - self.max_original_id - 1 ) # for successful lookup replace input_ids with 0, the results of these will be discarded anyway input_ids[additional_vocab_indices] = 0 full_vector = F.embedding(input_ids, self.weight) # overwrite the records with high indices full_vector[additional_vocab_indices] = additional_embeddings return full_vector def extra_repr(self) -> str: return "num_original_embeddings={}, num_additional_embeddings={}, embedding_dim={}, partially_freeze={}".format( self.max_original_id + 1, self.num_additional_embeddings, self.embedding_dim, (not self.weight.requires_grad), ) class DecoupledLinear(nn.Linear): # Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/linear.html#Linear """ Implements a decoupling of parameters to allow freezing (or not) a subset of the parameters. In practise, the regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `additional_out_features` > 0, then it will create `additional_out_features * in_features` additional parameters that are always trained. If `additional_out_features=0`, then the module defaults back to the regular behavior of `nn.Linear`. """ def __init__( self, max_original_id: int, additional_out_features: int = 0, _weight: torch.Tensor = None, _bias: torch.Tensor = None, in_features: int = None, original_out_features: int = None, bias: bool = True, partially_freeze: bool = True, device=None, dtype=None, ) -> None: """ Args: max_original_id (`int`): The largest token id that should be extracted from the regular weight. This is usually len(tokenizer) - 1 before additional tokens are added. Note that this may not equal original_out_features - 1 _weight: torch.Tensor, *optional*, defaults to `None`. The regular weight tensor. If provided, this sets the `in_features` and `original_out_features` parameters. _bias: torch.Tensor, *optional*, defaults to `None`. The regular bias tensor. in_features: int. Input hidden size. original_out_features: int. Original out_features of the language model's get_output_embeddings() function. additional_out_features: int. Number of additional trainable dimensions. bias: bool. Whether to include a bias term. partially_freeze: bool, *optional*, defaults to `True`): If `True`, the regular `weight` will be frozen. """ # argument validation if _weight is not None: assert (_weight.shape[0] == original_out_features) or ( original_out_features is None ), f"original_out_features={original_out_features} but _weight.shape[0]={_weight.shape[0]}" assert (_weight.shape[1] == in_features) or ( in_features is None ), f"in_features={in_features} but _weight.shape[1]={_weight.shape[1]}" in_features = _weight.shape[1] original_out_features = _weight.shape[0] else: assert ( in_features is not None ), "in_features must be provided if _weight is not provided" assert ( original_out_features is not None ), "original_out_features must be provided if _weight is not provided" if _bias is not None: assert bias is True, "bias must be True if _bias is provided" # initialize original linear super().__init__( in_features, original_out_features, bias, device, dtype) # set weight and bias manually if _weight is not None: self.weight = nn.Parameter(_weight) if _bias is not None: self.bias = nn.Parameter(_bias) self.in_features = in_features self.original_out_features = original_out_features self.max_original_id = max_original_id # initialize additional linear self.additional_out_features = additional_out_features self.has_bias = bias if additional_out_features > 0: self.additional_fc = nn.Linear( in_features=in_features, out_features=additional_out_features, bias=self.has_bias, device=device, dtype=dtype, ) self.set_requires_grad( require_regular_grad=not partially_freeze, require_additional_grad=True ) def set_requires_grad(self, require_regular_grad, require_additional_grad): """ Helper function to separately set the requires_grad flag for the regular weight and the additional weight. """ self.weight.requires_grad_(require_regular_grad) if self.has_bias: self.bias.requires_grad_(require_regular_grad) self.additional_fc.requires_grad_(require_additional_grad) def forward(self, input: torch.Tensor) -> torch.Tensor: output = F.linear(input, self.weight, self.bias) output = output[..., : self.max_original_id + 1] if self.additional_out_features > 0: additional_features = F.linear( input, self.additional_fc.weight, self.additional_fc.bias ) output = torch.cat((output, additional_features), -1) return output def extra_repr(self) -> str: """Overwriting `nn.Linear.extra_repr` to include new parameters.""" return "in_features={}, out_features={}, additional_out_features={}, bias={}, partially_freeze={}".format( self.in_features, self.max_original_id + 1, self.additional_out_features, self.bias is not None, (not self.weight.requires_grad or not self.bias.requires_grad), ) class VLM(nn.Module): """ Generic vision-language model (VLM) class. A VLM consists of four components: 1. A vision encoder that extracts features from pixels, e.g. CLIP input: (B, T_img, F, C, H, W) output: (B, T_img, F, v, d) 2. A vision tokenizer that converts these features to visual token-like embeddings, e.g. Perceiver, or a linear projection head input: (B, T_img, F, v, d) output: (B, T_img, n, d) 3. A fusion method that allows the language model to attend to these tokens, e.g. cross-attention, or placing the tokens directly in the language model's input sequence 4. A language model """ def __init__( self, vision_encoder: nn.Module, vision_tokenizer: nn.Module, lang_model: nn.Module, initial_tokenizer_len: int, pad_token_id: int, gradient_checkpointing: bool = False, ): """ Args: vision_encoder (nn.Module): e.g. CLIP vision_tokenizer (nn.Module): e.g. PerceiverResampler lang_model (nn.Module): e.g. MPT initial_tokenizer_len (int): size of the original tokenizer vocab pad_token_id (int): id of the pad token gradient_checkpointing (bool, optional): Whether to use gradient checkpointing. Defaults to False. """ super().__init__() # save dimension information self.lang_embedding_dim = lang_model.get_input_embeddings().weight.shape[1] if hasattr(lang_model.config, "d_model"): self.lang_hidden_dim = lang_model.config.d_model # mpt uses d_model else: self.lang_hidden_dim = lang_model.config.hidden_size self.vis_embedding_dim = vision_tokenizer.dim_media self.num_tokens_per_vis = vision_tokenizer.num_tokens_per_media # core components self.vision_encoder = vision_encoder self.vision_tokenizer = vision_tokenizer self.lang_model = lang_model # lm embeddings self.pad_token_id = pad_token_id self.initial_tokenizer_len = initial_tokenizer_len input_embeds = DecoupledEmbedding( max_original_id=initial_tokenizer_len - 1, num_additional_embeddings=len(self.special_tokens), _weight=self.lang_model.get_input_embeddings().weight, pad_token_id=self.pad_token_id, ) if hasattr(input_embeds, "additional_embedding"): input_embeds.additional_embedding.weight.data.normal_( mean=0.0, std=self.lang_model.config.initializer_range if hasattr(self.lang_model.config, "initializer_range") else 0.02, ) self.lang_model.set_input_embeddings(input_embeds) out_embeds = DecoupledLinear( max_original_id=initial_tokenizer_len - 1, additional_out_features=len(self.special_tokens), _weight=self.lang_model.get_output_embeddings().weight, _bias=self.lang_model.get_output_embeddings().bias if hasattr(self.lang_model.get_output_embeddings(), "bias") else None, ) if hasattr(out_embeds, "additional_fc"): out_embeds.additional_fc.weight.data.normal_( mean=0.0, std=self.lang_model.config.initializer_range if hasattr(self.lang_model.config, "initializer_range") else 0.02, ) self.lang_model.set_output_embeddings(out_embeds) # gradient checkpointing self.vision_tokenizer._use_gradient_checkpointing = gradient_checkpointing def forward( self, vision_x: Optional[torch.Tensor], lang_x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, past_key_values: Optional[ List[Union[torch.Tensor, Tuple[torch.Tensor]]] ] = None, past_media_locations: Optional[torch.Tensor] = None, past_vision_tokens: Optional[torch.Tensor] = None, use_cache: Optional[bool] = False, **kwargs, ): """ Args: vision_x: Vision input shape (B, T_img, F, C, H, W) with F=1 only F = 1 is supported (single-frame videos) if T_img > the number of media tokens in the corresponding input_ids (lang_x), only the first number of media tokens in lang_x are used lang_x: Language input ids, with media tokens denoting where visual media should be inserted. shape (B, T_txt) attention_mask: Attention mask. Defaults to None. labels: Labels. Defaults to None. shape (B, T_txt) past_key_values (Tuple[torch.Tensor]], optional): Past key value pairs for each of the T_txt previous tokens in the language model. Defaults to None. list of length = number of decoder layers in the LM exact implementation depends on LM, see Hugging Face docs past_media_locations (torch.Tensor, optional): boolean mask denoting which of the previous T_txt tokens were media tokens. Defaults to None. shape (B, T_txt) past_vision_tokens (torch.Tensor, optional): Previous vision tokens. Defaults to None. use_cache (Optional[bool], optional): Whether to use cache. Defaults to False. If True, includes key_values, media_locations, and vision_tokens in the output. """ assert not (past_vision_tokens is None) ^ ( past_media_locations is None ), "past_vision_tokens and past_media_locations must both be None or both be not None" # convert pixels to vision tokens if vision_x is not None: vision_features = self._encode_vision_x(vision_x=vision_x) vision_tokens = self.vision_tokenizer(vision_features) else: vision_tokens = None # fuse the vision and language tokens new_inputs = self._prepare_inputs_for_forward( vision_tokens=vision_tokens, lang_x=lang_x, attention_mask=attention_mask, labels=labels, past_key_values=past_key_values, past_media_locations=past_media_locations, padding_side="right", past_vision_tokens=past_vision_tokens, ) output = self.lang_model( **new_inputs, use_cache=use_cache, past_key_values=past_key_values, **kwargs, ) # postprocessing may be needed, e.g. to remove extra tokens from logits that were inserted into the language stream # or to add the past_vision_tokens and past_media_locations to the output output = self._postprocess_outputs_from_forward( output=output, lang_x=lang_x, vision_tokens=vision_tokens, use_cache=use_cache, past_vision_tokens=past_vision_tokens, past_media_locations=past_media_locations, ) # postforward hooks self._post_forward_hook() return output def _encode_vision_x_anyres(self, samples, device): image_raw = samples["image"] # list of patch list in of shape [1, N_patch, C, H, W] image_sizes = samples["image_size"] # concate list of patches into one big patch for any res encoding. images = [x.squeeze(0) for x in image_raw] # [N_patch, C, H, W] image = torch.cat(images, dim=0) # [\sum{B}{N_patch_i}, C, H, W] image = image.to(device) with torch.no_grad(): if self.vision_encoder.__class__.__name__ == "TimmModel": image_embeds = self.vision_encoder.trunk.forward_features(image) elif self.vision_encoder.__class__.__name__ == 'CLIPVisionModel': image_embeds = self.vision_encoder(image).last_hidden_state else: image_embeds = self.vision_encoder(image)[1] # OpenCLIP returns tuples if isinstance(self.vision_encoder, CLIPVisionModel): base_img_size = self.vision_encoder.config.image_size else: base_img_size = self.vision_encoder.image_size[0] if self.vision_encoder.__class__.__name__ == "TimmModel": grid_size = self.vision_encoder.trunk.patch_embed.grid_size elif self.vision_encoder.__class__.__name__ == 'CLIPVisionModel': grid_size_base = self.vision_encoder.config.image_size // self.vision_encoder.config.patch_size grid_size = (grid_size_base, grid_size_base) else: grid_size = self.vision_encoder.grid_size height, width = grid_size if not image_embeds.shape[1] == height * width: assert image_embeds.shape[1] == height * width + 1 # For vision encoders that has [CLS] token. image_embeds = image_embeds[:, 1:, :] # Drop the cls token for each patch. n_vis_token_per_patch = image_embeds.shape[1] # Split encoded patches and merge patch features # 1. Get the raw sizes from samples, and split the image embeds [\sum_{B}(N_patch_i), N_tok(16*16), C] split_sizes = [image.shape[0] for image in images] image_embeds = torch.split(image_embeds, split_sizes, dim=0) # 2. For each image (consist of a list of patches), merge the patches spatially (of shape [C, n_patch_height, n_patch_width]) new_image_embeds = [] patch_attn_masks = [] max_n_img_token = -1 for idx, patch_embeds in enumerate(image_embeds): if patch_embeds.shape[0] > 1: # 3. Flatten the patch features and get [C, n_patch_height * (n_patch_width+1)] base_patch_embeds = patch_embeds[0] # TODO: prepend the CLS token for th base patch embeds (of the resized entire image). patch_embeds = patch_embeds[1:] assert height * width == base_patch_embeds.shape[0] num_patch_width, num_patch_height = get_anyres_image_grid_shape(image_sizes[idx], [[base_img_size,base_img_size*2], [base_img_size*2,base_img_size], [base_img_size*2,base_img_size*2], [base_img_size*3,base_img_size], [base_img_size,base_img_size*3]], base_img_size) # Hardcoded grid_pinpoints. patch_embeds = patch_embeds.view(num_patch_height, num_patch_width, height, width, -1) patch_embeds = patch_embeds.permute(4, 0, 2, 1, 3).contiguous() patch_embeds = patch_embeds.flatten(1, 2).flatten(2, 3) # TODO: add an option that return masked patch_embeds instead of trimmed. patch_embeds, patch_attn_mask = unpad_image(patch_embeds, image_sizes[idx], self.anyres_patch_sampling) if hasattr(self, 'image_newline'): patch_embeds = torch.cat(( patch_embeds, self.image_newline[:, None, None].expand(*patch_embeds.shape[:-1], 1) ), dim=-1) if self.anyres_patch_sampling: patch_embeds = patch_embeds.view(-1, num_patch_height, num_patch_width, height*width) patch_embeds = patch_embeds.flatten(1, 2).permute(1, 2, 0) assert patch_attn_mask is not None patch_attn_mask = patch_attn_mask.view(num_patch_height, num_patch_width, height*width) patch_attn_mask = patch_attn_mask.flatten(0, 1) patch_embeds = torch.cat((base_patch_embeds.unsqueeze(0), patch_embeds), dim=0) patch_attn_mask = torch.cat((torch.ones(n_vis_token_per_patch, device=patch_embeds.device).unsqueeze(0), patch_attn_mask), dim=0) else: patch_embeds = patch_embeds.flatten(1, 2).transpose(0, 1) patch_embeds = torch.cat((base_patch_embeds, patch_embeds), dim=0) else: patch_embeds = patch_embeds[0].unsqueeze(0) if self.anyres_patch_sampling else patch_embeds[0] patch_attn_mask = torch.ones(n_vis_token_per_patch, device=patch_embeds.device).unsqueeze(0) if self.anyres_patch_sampling else None if hasattr(self, 'image_newline'): patch_embeds = torch.cat(( patch_embeds, self.image_newline[None] ), dim=0) if not self.anyres_patch_sampling: max_n_img_token = max(patch_embeds.shape[0], max_n_img_token) new_image_embeds.append(patch_embeds) patch_attn_masks.append(patch_attn_mask) if self.anyres_patch_sampling: # Return individual patches for independent token downsampling. return new_image_embeds, patch_attn_masks # 4. Pad and concat the list of image_embeds [N_tok_i, C] together into a batch. Also modify the query attention mask. image_embeds = [] image_atts = [] for image_embed in new_image_embeds: n_img_token = image_embed.shape[0] img_attn = torch.ones((max_n_img_token), dtype=torch.long, device=image_embed.device) if n_img_token < max_n_img_token: padded_embed = torch.zeros((max_n_img_token, image_embed.shape[-1]), dtype=image_embed.dtype, device=image_embed.device) padded_embed[:n_img_token, :] = image_embed img_attn[n_img_token:] = 0 # Mask out the padded entries. else: padded_embed = image_embed image_embeds.append(padded_embed) image_atts.append(img_attn) image_embeds = torch.stack(image_embeds, dim=0) # Shape [B, N_tok_longest, C_dim] image_atts = torch.stack(image_atts, dim=0) # Shape [B, N_tok_longest, C_dim] # TODO: reshape image_embeds and image_atts to "b T F v d" image_embeds = image_embeds[:, None, None, :, :] # image_atts = image_atts[:, None, None, :, :] return image_embeds, image_atts 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: 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] vision_x = rearrange(vision_x, "b T F c h w -> (b T F) c h w") with torch.no_grad(): if self.vision_encoder.__class__.__name__ == "TimmModel": vision_x = self.vision_encoder.trunk.forward_features(vision_x) elif self.vision_encoder.__class__.__name__ == 'CLIPVisionModel': vision_x = self.vision_encoder(vision_x).last_hidden_state else: vision_x = self.vision_encoder(vision_x)[1] # OpenCLIP returns tuples vision_x = rearrange(vision_x, "(b T F) v d -> b T F v d", b=b, T=T, F=F) return vision_x def _concat_vision_cache( self, lang_x, vision_tokens, past_vision_tokens, past_media_locations, use_cache ): """ Helper function to include the past vision tokens and past media locations in the output. """ if use_cache: if past_media_locations is not None and past_vision_tokens is not None: if vision_tokens is not None: updated_vision_tokens = torch.cat( [ past_vision_tokens, vision_tokens, ], dim=1, ) else: updated_vision_tokens = past_vision_tokens updated_media_locations = torch.cat( [ past_media_locations, lang_x == self.media_token_id, ], dim=1, ) else: updated_vision_tokens = vision_tokens updated_media_locations = lang_x == self.media_token_id else: updated_vision_tokens = None updated_media_locations = None return updated_vision_tokens, updated_media_locations def generate( self, vision_x: torch.Tensor, lang_x: torch.Tensor, attention_mask: torch.Tensor = None, past_key_values: Optional[ List[Union[torch.Tensor, Tuple[torch.Tensor]]] ] = None, past_media_locations: Optional[torch.Tensor] = None, past_vision_tokens: Optional[torch.Tensor] = None, **kwargs, ): """ Generate text conditioned on vision and language inputs. Args: vision_x (torch.Tensor): Vision input shape (B, T_img, F, C, H, W) see documentation for forward lang_x (torch.Tensor): Language input shape (B, T_txt) attention_mask (torch.Tensor, optional): Attention mask. Defaults to None. **kwargs: see generate documentation in Hugging Face CausalLM models. Returns: torch.Tensor: lang_x with generated tokens appended to it """ num_beams = kwargs.pop("num_beams", 1) # convert pixels to vision tokens if vision_x is not None: vision_features = self._encode_vision_x(vision_x=vision_x) vision_tokens = self.vision_tokenizer(vision_features) else: vision_tokens = None # fuse the vision and language tokens # for xattn, vision_x and media_location are repeat_interleaved s.t. # the total batch size is B * num_beams new_inputs = self._prepare_inputs_for_forward( vision_tokens=vision_tokens, lang_x=lang_x, attention_mask=attention_mask, past_key_values=past_key_values, past_media_locations=past_media_locations, past_vision_tokens=past_vision_tokens, padding_side="left", num_beams=num_beams, ) output = self.lang_model.generate( **new_inputs, past_key_values=past_key_values, num_beams=num_beams, use_cache=True, **kwargs, ) self._post_forward_hook() return output @property def num_trainable_params(self): """Print the number of trainable parameters""" return num_params(self, filter_to_trainable=True) def set_trainable(self): """ Freeze appropriate parameters in the model. """ raise NotImplementedError def group_params_by_weight_decay(self): """ Return a tuple of (params to optimize w/ weight decay, params to optimize w/o weight decay) """ params_with_wd, params_without_wd = [], [] for n, p in self.named_parameters(): if p.requires_grad: if self._should_apply_weight_decay(n): params_with_wd.append(p) else: params_without_wd.append(p) return params_with_wd, params_without_wd def _should_apply_weight_decay(self, parameter_name): """ Return whether weight decay should be applied to a parameter. """ raise NotImplementedError @property def special_tokens(self): """ Returns a dict mapping from the attribute name of a special token to its string format, e.g. "media_token": "" """ assert ( "media_token" in self._special_tokens ), "VLMs need to request that the tokenizer add a media_token and call set_special_token_ids to set self.media_token_id" return self._special_tokens @property def special_token_ids(self): """ Returns a list of the special token ids """ return [getattr(self, f"{att_name}_id") for att_name in self.special_tokens] def set_special_token_ids(self, string_to_ids): """ Args: string_to_ids (dict): mapping from token string to id """ assert set(self.special_tokens.values()).issubset(set(string_to_ids.keys())) for att_name, token_str in self.special_tokens.items(): token_id = string_to_ids[token_str] setattr(self, f"{att_name}_id", token_id) setattr(self.lang_model, f"{att_name}_id", token_id) def init_gradient_checkpointing(self): from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import ( checkpoint_wrapper, CheckpointWrapper, CheckpointImpl, apply_activation_checkpointing, ) from functools import partial non_reentrant_wrapper = partial( checkpoint_wrapper, checkpoint_impl=CheckpointImpl.NO_REENTRANT, ) apply_activation_checkpointing( self, checkpoint_wrapper_fn=non_reentrant_wrapper, check_fn=lambda m: getattr(m, "_use_gradient_checkpointing", False) and not isinstance(m, CheckpointWrapper), ) @dataclass class VLMOutputWithPast(CausalLMOutputWithPast): """ VLMOutputWithPast is a wrapper around CausalLMOutputWithPast that adds the following attributes: past_media_locations: Optional[torch.Tensor] = None, past_vision_tokens: Optional[torch.Tensor] = None, """ past_media_locations: Optional[torch.Tensor] = None past_vision_tokens: Optional[torch.Tensor] = None def exists(val): return val is not None def FeedForward(dim, mult=4): inner_dim = int(dim * mult) return nn.Sequential( nn.LayerNorm(dim), nn.Linear(dim, inner_dim, bias=False), nn.GELU(), nn.Linear(inner_dim, dim, bias=False), ) class VLMWithLanguageStream(VLM): """ VLM that fuses modalities by inserting vision tokens directly into the language stream. """ def __init__( self, vision_encoder: nn.Module, vision_tokenizer: nn.Module, lang_model: nn.Module, initial_tokenizer_len: int, pad_token_id: int, decoder_layers_attr_name: str = None, gradient_checkpointing: bool = False, ): super().__init__( vision_encoder=vision_encoder, vision_tokenizer=vision_tokenizer, lang_model=lang_model, initial_tokenizer_len=initial_tokenizer_len, pad_token_id=pad_token_id, gradient_checkpointing=gradient_checkpointing, ) self.decoder_layers_attr_name = decoder_layers_attr_name if decoder_layers_attr_name is not None: for block in getattr_recursive(self.lang_model, self.decoder_layers_attr_name): block._use_gradient_checkpointing = gradient_checkpointing def _prepare_inputs_for_forward( self, vision_tokens: torch.Tensor, lang_x: torch.Tensor, attention_mask: torch.Tensor, labels: torch.Tensor = None, past_key_values=None, past_media_locations: torch.Tensor = None, past_vision_tokens: torch.Tensor = None, padding_side: str = "left", num_beams: int = 1, ): """ Insert the vision tokens directly into the language stream/ This requires us to modify the input_ids, attention_mask, and labels. """ if past_key_values is not None: past_len = past_key_values[0][0].shape[2] assert attention_mask.shape[1] == past_len + lang_x.shape[1], ( "Attention_mask must be as long as the entire past len (including image tokens) and current input IDs. " + "Check that you've expanded the attention mask to account for past image tokens." ) if vision_tokens is None: return { "input_ids": lang_x, "attention_mask": attention_mask, "labels": labels, } # get the language embeddings lang_embeds = self.lang_model.get_input_embeddings()(lang_x) # build up the multimodal embeddings B = lang_x.shape[0] has_labels = labels is not None multimodal_embeds = [] multimodal_attention_mask = [] multimodal_labels = [] if has_labels else None for i in range(B): # get index of tokens in lang_x[i] image_token_idxs = torch.where(lang_x[i] == self.media_token_id)[0] if len(image_token_idxs) == 0: multimodal_embeds.append(lang_embeds[i].clone()) multimodal_attention_mask.append(attention_mask[i].clone()) if has_labels: multimodal_labels.append(labels[i].clone()) continue # # since an image is represented by self.num_tokens_per_vis tokens, we need to offset the image_token_idxs # for j, img_idx in enumerate(image_token_idxs): # image_token_idxs[j] += (self.num_tokens_per_vis - 1) * j # loop through the image_token_idxs and insert the vision tokens new_embed = lang_embeds[i].clone() new_attention_mask = ( attention_mask[i].clone() if attention_mask is not None else None ) if has_labels: new_label = labels[i].clone() for img_num, img_idx in enumerate(image_token_idxs): new_embed = torch.cat( ( new_embed[:img_idx], vision_tokens[i][img_num], new_embed[img_idx + self.num_tokens_per_vis :], ), dim=0, ) new_attention_mask = torch.cat( ( new_attention_mask[:img_idx], torch.ones(self.num_tokens_per_vis, dtype=torch.long).to( attention_mask.device ), new_attention_mask[img_idx + self.num_tokens_per_vis :], ), dim=0, ) if has_labels: new_label = torch.cat( ( new_label[:img_idx], torch.ones(self.num_tokens_per_vis, dtype=torch.long).to( labels.device ) * -100, new_label[img_idx + self.num_tokens_per_vis :], ), dim=0, ) multimodal_embeds.append(new_embed) multimodal_attention_mask.append(new_attention_mask) if has_labels: multimodal_labels.append(new_label) # stack multimodal_embeds = stack_with_padding( multimodal_embeds, padding_value=self.pad_token_id, padding_side=padding_side, ) multimodal_attention_mask = stack_with_padding( multimodal_attention_mask, padding_value=0, padding_side=padding_side, ) if has_labels: multimodal_labels = stack_with_padding( multimodal_labels, padding_value=-100, padding_side=padding_side, ) return { "inputs_embeds": multimodal_embeds, "attention_mask": multimodal_attention_mask, "labels": multimodal_labels, } def _postprocess_outputs_from_forward( self, output: CausalLMOutputWithPast, lang_x: torch.Tensor, vision_tokens: torch.Tensor, past_vision_tokens: torch.Tensor, past_media_locations: torch.Tensor, use_cache: bool = False, ): # Include the past vision tokens and past media locations in the output updated_vision_tokens, updated_media_locations = self._concat_vision_cache( lang_x=lang_x, vision_tokens=vision_tokens, past_vision_tokens=past_vision_tokens, past_media_locations=past_media_locations, use_cache=use_cache, ) # return logits that are the same shape as the original input_ids logits = output.logits batch_logits = [] B, T_txt = lang_x.shape for i in range(B): sequence_logits = [] logits_j = 0 for j in range(T_txt): if lang_x[i, j] != self.media_token_id: sequence_logits.append(logits[i, logits_j]) logits_j += 1 else: # append the logit for the first image token, then skip over the rest # note: the model actually learns to predict , not sequence_logits.append(logits[i, logits_j]) logits_j += self.num_tokens_per_vis sequence_logits = torch.stack(sequence_logits, dim=0) # (B, vocab_size) batch_logits.append(sequence_logits) batch_logits = torch.stack(batch_logits, dim=0) # (B, T_txt, vocab_size) # The final logits shape should be the same as the original input_ids shape assert batch_logits.shape[:2] == (B, T_txt) # assemble the output output = VLMOutputWithPast( loss=output.loss, logits=batch_logits, past_key_values=output.past_key_values, hidden_states=output.hidden_states, attentions=output.attentions, past_media_locations=updated_media_locations, past_vision_tokens=updated_vision_tokens, ) return output def _post_forward_hook(self): pass @property def num_params_per_module(self): """Print the number of parameters per module in the model""" return "\n".join( [ f"Vision encoder: {num_params(self.vision_encoder):,} parameters", f"Vision tokenizer: {num_params(self.vision_tokenizer):,} parameters", f"Language model: {num_params(self.lang_model):,} parameters", ] ) @property def num_trainable_params_per_module(self): """Print the number of trainable parameters per module in the model""" return "\n".join( [ f"Vision encoder: {num_params(self.vision_encoder, filter_to_trainable=True):,} trainable parameters", f"Vision tokenizer: {num_params(self.vision_tokenizer, filter_to_trainable=True):,} trainable parameters", f"Language model: {num_params(self.lang_model, filter_to_trainable=True):,} trainable parameters", ] ) class Kosmos(VLMWithLanguageStream): def __init__( self, vision_encoder: nn.Module, vision_tokenizer: nn.Module, lang_model: nn.Module, initial_tokenizer_len: int, pad_token_id: int, decoder_layers_attr_name: str = None, gradient_checkpointing: bool = False, ): """ Args: vision_encoder (nn.Module): HF CLIPModel lang_encoder (nn.Module): HF causal language model vis_feature_dim (int): final dimension of the visual features outputted by the vision_encoder initial_tokenizer_len (int): size of the tokenizer vocab padding_token_id (int): id of the padding token. None if no padding token; then a padding token will be inserted into self.special_tokens, which factory.py fills after creating new tokens decoder_layers_attr_name (str, optional): name of the decoder layers attribute. Defaults to None. gradient_checkpointing (bool, optional): whether to use gradient checkpointing. Defaults to False. """ self._special_tokens = { "media_token": "", "image_placeholder_token": "", "end_of_trunk_token": "<|endofchunk|>" } super().__init__( vision_encoder=vision_encoder, vision_tokenizer=vision_tokenizer, lang_model=lang_model, initial_tokenizer_len=initial_tokenizer_len, gradient_checkpointing=gradient_checkpointing, decoder_layers_attr_name=decoder_layers_attr_name, pad_token_id=pad_token_id ) # def set_trainable(self): # """ # Unfreeze everything except the vision_encoder # """ # self.requires_grad_(True) # self.vision_encoder.requires_grad_(False) def set_trainable(self, unfreeze_vision_encoder: bool = False): """ Unfreeze everything except the vision_encoder """ self.requires_grad_(True) self.vision_encoder.requires_grad_(unfreeze_vision_encoder) def _should_apply_weight_decay(self, parameter_name): """ Kosmos applies 0.01 weight deacy to everything """ return True def generate( self, vision_x: torch.Tensor, lang_x: torch.Tensor, attention_mask: torch.Tensor = None, past_key_values: Optional[ List[Union[torch.Tensor, Tuple[torch.Tensor]]] ] = None, past_media_locations: Optional[torch.Tensor] = None, past_vision_tokens: Optional[torch.Tensor] = None, **kwargs ): """ Generate text conditioned on vision and language inputs. Args: vision_x (torch.Tensor): Vision input shape (B, T_img, F, C, H, W) see documentation for forward lang_x (torch.Tensor): Language input shape (B, T_txt) attention_mask (torch.Tensor, optional): Attention mask. Defaults to None. **kwargs: see generate documentation in Hugging Face CausalLM models. Returns: torch.Tensor: lang_x with generated tokens appended to it """ num_beams = kwargs.pop("num_beams", 1) # convert pixels to vision tokens if vision_x is not None: vision_features = self._encode_vision_x(vision_x=vision_x) vision_tokens = self.vision_tokenizer(vision_features) else: vision_tokens = None # fuse the vision and language tokens # for xattn, vision_x and media_location are repeat_interleaved s.t. # the total batch size is B * num_beams new_inputs = self._prepare_inputs_for_forward( vision_tokens=vision_tokens, lang_x=lang_x, attention_mask=attention_mask, past_key_values=past_key_values, past_media_locations=past_media_locations, past_vision_tokens=past_vision_tokens, padding_side="left", num_beams=num_beams, ) if transformers.__version__ == '4.41.0.dev0': output = self.lang_model.generate( **new_inputs, num_beams=num_beams, use_cache=True, eos_token_id=self.end_of_trunk_token_id, **kwargs) else: output = self.lang_model.generate( **new_inputs, past_key_values=past_key_values, num_beams=num_beams, use_cache=True, eos_token_id=self.end_of_trunk_token_id, **kwargs) return output