from typing import Optional import numpy as np import torch from torch import nn from transformers import GPT2Config, GPT2LMHeadModel from transformers.modeling_utils import ModuleUtilsMixin from ...configuration_utils import ConfigMixin, register_to_config from ...models import ModelMixin # Modified from ClipCaptionModel in https://github.com/thu-ml/unidiffuser/blob/main/libs/caption_decoder.py class UniDiffuserTextDecoder(ModelMixin, ConfigMixin, ModuleUtilsMixin): """ Text decoder model for a image-text [UniDiffuser](https://arxiv.org/pdf/2303.06555.pdf) model. This is used to generate text from the UniDiffuser image-text embedding. Parameters: prefix_length (`int`): Max number of prefix tokens that will be supplied to the model. prefix_inner_dim (`int`): The hidden size of the incoming prefix embeddings. For UniDiffuser, this would be the hidden dim of the CLIP text encoder. prefix_hidden_dim (`int`, *optional*): Hidden dim of the MLP if we encode the prefix. vocab_size (`int`, *optional*, defaults to 50257): Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`GPT2Model`] or [`TFGPT2Model`]. n_positions (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). n_embd (`int`, *optional*, defaults to 768): Dimensionality of the embeddings and hidden states. n_layer (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. n_inner (`int`, *optional*, defaults to None): Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd activation_function (`str`, *optional*, defaults to `"gelu"`): Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`. resid_pdrop (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the embeddings. attn_pdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention. layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): The epsilon to use in the layer normalization layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. scale_attn_weights (`bool`, *optional*, defaults to `True`): Scale attention weights by dividing by sqrt(hidden_size).. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`): Whether to additionally scale attention weights by `1 / layer_idx + 1`. reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`): Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention dot-product/softmax to float() when training with mixed precision. """ _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"] @register_to_config def __init__( self, prefix_length: int, prefix_inner_dim: int, prefix_hidden_dim: Optional[int] = None, vocab_size: int = 50257, # Start of GPT2 config args n_positions: int = 1024, n_embd: int = 768, n_layer: int = 12, n_head: int = 12, n_inner: Optional[int] = None, activation_function: str = "gelu_new", resid_pdrop: float = 0.1, embd_pdrop: float = 0.1, attn_pdrop: float = 0.1, layer_norm_epsilon: float = 1e-5, initializer_range: float = 0.02, scale_attn_weights: bool = True, use_cache: bool = True, scale_attn_by_inverse_layer_idx: bool = False, reorder_and_upcast_attn: bool = False, ): super().__init__() self.prefix_length = prefix_length if prefix_inner_dim != n_embd and prefix_hidden_dim is None: raise ValueError( f"`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and" f" `n_embd`: {n_embd} are not equal." ) self.prefix_inner_dim = prefix_inner_dim self.prefix_hidden_dim = prefix_hidden_dim self.encode_prefix = ( nn.Linear(self.prefix_inner_dim, self.prefix_hidden_dim) if self.prefix_hidden_dim is not None else nn.Identity() ) self.decode_prefix = ( nn.Linear(self.prefix_hidden_dim, n_embd) if self.prefix_hidden_dim is not None else nn.Identity() ) gpt_config = GPT2Config( vocab_size=vocab_size, n_positions=n_positions, n_embd=n_embd, n_layer=n_layer, n_head=n_head, n_inner=n_inner, activation_function=activation_function, resid_pdrop=resid_pdrop, embd_pdrop=embd_pdrop, attn_pdrop=attn_pdrop, layer_norm_epsilon=layer_norm_epsilon, initializer_range=initializer_range, scale_attn_weights=scale_attn_weights, use_cache=use_cache, scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx, reorder_and_upcast_attn=reorder_and_upcast_attn, ) self.transformer = GPT2LMHeadModel(gpt_config) def forward( self, input_ids: torch.Tensor, prefix_embeds: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, ): """ Args: input_ids (`torch.Tensor` of shape `(N, max_seq_len)`): Text tokens to use for inference. prefix_embeds (`torch.Tensor` of shape `(N, prefix_length, 768)`): Prefix embedding to preprend to the embedded tokens. attention_mask (`torch.Tensor` of shape `(N, prefix_length + max_seq_len, 768)`, *optional*): Attention mask for the prefix embedding. labels (`torch.Tensor`, *optional*): Labels to use for language modeling. """ embedding_text = self.transformer.transformer.wte(input_ids) hidden = self.encode_prefix(prefix_embeds) prefix_embeds = self.decode_prefix(hidden) embedding_cat = torch.cat((prefix_embeds, embedding_text), dim=1) if labels is not None: dummy_token = self.get_dummy_token(input_ids.shape[0], input_ids.device) labels = torch.cat((dummy_token, input_ids), dim=1) out = self.transformer(inputs_embeds=embedding_cat, labels=labels, attention_mask=attention_mask) if self.prefix_hidden_dim is not None: return out, hidden else: return out def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor: return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device) def encode(self, prefix): return self.encode_prefix(prefix) @torch.no_grad() def generate_captions(self, features, eos_token_id, device): """ Generate captions given text embedding features. Returns list[L]. Args: features (`torch.Tensor` of shape `(B, L, D)`): Text embedding features to generate captions from. eos_token_id (`int`): The token ID of the EOS token for the text decoder model. device: Device to perform text generation on. Returns: `List[str]`: A list of strings generated from the decoder model. """ features = torch.split(features, 1, dim=0) generated_tokens = [] generated_seq_lengths = [] for feature in features: feature = self.decode_prefix(feature.to(device)) # back to the clip feature # Only support beam search for now output_tokens, seq_lengths = self.generate_beam( input_embeds=feature, device=device, eos_token_id=eos_token_id ) generated_tokens.append(output_tokens[0]) generated_seq_lengths.append(seq_lengths[0]) generated_tokens = torch.stack(generated_tokens) generated_seq_lengths = torch.stack(generated_seq_lengths) return generated_tokens, generated_seq_lengths @torch.no_grad() def generate_beam( self, input_ids=None, input_embeds=None, device=None, beam_size: int = 5, entry_length: int = 67, temperature: float = 1.0, eos_token_id: Optional[int] = None, ): """ Generates text using the given tokenizer and text prompt or token embedding via beam search. This implementation is based on the beam search implementation from the [original UniDiffuser code](https://github.com/thu-ml/unidiffuser/blob/main/libs/caption_decoder.py#L89). Args: eos_token_id (`int`, *optional*): The token ID of the EOS token for the text decoder model. input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*): Tokenizer indices of input sequence tokens in the vocabulary. One of `input_ids` and `input_embeds` must be supplied. input_embeds (`torch.Tensor` of shape `(batch_size, seq_len, hidden_size)`, *optional*): An embedded representation to directly pass to the transformer as a prefix for beam search. One of `input_ids` and `input_embeds` must be supplied. device: The device to perform beam search on. beam_size (`int`, *optional*, defaults to `5`): The number of best states to store during beam search. entry_length (`int`, *optional*, defaults to `67`): The number of iterations to run beam search. temperature (`float`, *optional*, defaults to 1.0): The temperature to use when performing the softmax over logits from the decoding model. Returns: `Tuple(torch.Tensor, torch.Tensor)`: A tuple of tensors where the first element is a tensor of generated token sequences sorted by score in descending order, and the second element is the sequence lengths corresponding to those sequences. """ # Generates text until stop_token is reached using beam search with the desired beam size. stop_token_index = eos_token_id tokens = None scores = None seq_lengths = torch.ones(beam_size, device=device, dtype=torch.int) is_stopped = torch.zeros(beam_size, device=device, dtype=torch.bool) if input_embeds is not None: generated = input_embeds else: generated = self.transformer.transformer.wte(input_ids) for i in range(entry_length): outputs = self.transformer(inputs_embeds=generated) logits = outputs.logits logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0) logits = logits.softmax(-1).log() if scores is None: scores, next_tokens = logits.topk(beam_size, -1) generated = generated.expand(beam_size, *generated.shape[1:]) next_tokens, scores = next_tokens.permute(1, 0), scores.squeeze(0) if tokens is None: tokens = next_tokens else: tokens = tokens.expand(beam_size, *tokens.shape[1:]) tokens = torch.cat((tokens, next_tokens), dim=1) else: logits[is_stopped] = -float(np.inf) logits[is_stopped, 0] = 0 scores_sum = scores[:, None] + logits seq_lengths[~is_stopped] += 1 scores_sum_average = scores_sum / seq_lengths[:, None] scores_sum_average, next_tokens = scores_sum_average.view(-1).topk(beam_size, -1) next_tokens_source = next_tokens // scores_sum.shape[1] seq_lengths = seq_lengths[next_tokens_source] next_tokens = next_tokens % scores_sum.shape[1] next_tokens = next_tokens.unsqueeze(1) tokens = tokens[next_tokens_source] tokens = torch.cat((tokens, next_tokens), dim=1) generated = generated[next_tokens_source] scores = scores_sum_average * seq_lengths is_stopped = is_stopped[next_tokens_source] next_token_embed = self.transformer.transformer.wte(next_tokens.squeeze()).view(generated.shape[0], 1, -1) generated = torch.cat((generated, next_token_embed), dim=1) is_stopped = is_stopped + next_tokens.eq(stop_token_index).squeeze() if is_stopped.all(): break scores = scores / seq_lengths order = scores.argsort(descending=True) # tokens tensors are already padded to max_seq_length output_texts = [tokens[i] for i in order] output_texts = torch.stack(output_texts, dim=0) seq_lengths = torch.tensor([seq_lengths[i] for i in order], dtype=seq_lengths.dtype) return output_texts, seq_lengths