from dataclasses import dataclass from typing import Optional import torch import torch.nn.functional as F from torch import nn from x3D_utils import BaseModule from basic_transformer_block import BasicTransformerBlock class Transformer1D(BaseModule): @dataclass class Config(BaseModule.Config): num_attention_heads: int = 16 attention_head_dim: int = 88 in_channels: Optional[int] = None out_channels: Optional[int] = None num_layers: int = 1 dropout: float = 0.0 norm_num_groups: int = 32 cross_attention_dim: Optional[int] = None attention_bias: bool = False activation_fn: str = "geglu" only_cross_attention: bool = False double_self_attention: bool = False upcast_attention: bool = False norm_type: str = "layer_norm" norm_elementwise_affine: bool = True gradient_checkpointing: bool = False cfg: Config def configure(self) -> None: self.num_attention_heads = self.cfg.num_attention_heads self.attention_head_dim = self.cfg.attention_head_dim inner_dim = self.num_attention_heads * self.attention_head_dim linear_cls = nn.Linear # 2. Define input layers self.in_channels = self.cfg.in_channels self.norm = torch.nn.GroupNorm( num_groups=self.cfg.norm_num_groups, num_channels=self.cfg.in_channels, eps=1e-6, affine=True, ) self.proj_in = linear_cls(self.cfg.in_channels, inner_dim) # 3. Define transformers blocks self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock( inner_dim, self.num_attention_heads, self.attention_head_dim, dropout=self.cfg.dropout, cross_attention_dim=self.cfg.cross_attention_dim, activation_fn=self.cfg.activation_fn, attention_bias=self.cfg.attention_bias, only_cross_attention=self.cfg.only_cross_attention, double_self_attention=self.cfg.double_self_attention, upcast_attention=self.cfg.upcast_attention, norm_type=self.cfg.norm_type, norm_elementwise_affine=self.cfg.norm_elementwise_affine, ) for d in range(self.cfg.num_layers) ] ) # 4. Define output layers self.out_channels = ( self.cfg.in_channels if self.cfg.out_channels is None else self.cfg.out_channels ) self.proj_out = linear_cls(inner_dim, self.cfg.in_channels) self.gradient_checkpointing = self.cfg.gradient_checkpointing def forward( self, hidden_states: torch.Tensor, encoder_hidden_states: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, ): """ The [`Transformer1DModel`] forward method. Args: hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous): Input `hidden_states`. encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): Conditional embeddings for cross attention layer. If not given, cross-attention defaults to self-attention. attention_mask ( `torch.Tensor`, *optional*): An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large negative values to the attention scores corresponding to "discard" tokens. encoder_attention_mask ( `torch.Tensor`, *optional*): Cross-attention mask applied to `encoder_hidden_states`. Two formats supported: * Mask `(batch, sequence_length)` True = keep, False = discard. * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard. If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format above. This bias will be added to the cross-attention scores. Returns: torch.FloatTensor """ # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. # expects mask of shape: # [batch, key_tokens] # adds singleton query_tokens dimension: # [batch, 1, key_tokens] # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) if attention_mask is not None and attention_mask.ndim == 2: # assume that mask is expressed as: # (1 = keep, 0 = discard) # convert mask into a bias that can be added to attention scores: # (keep = +0, discard = -10000.0) attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 attention_mask = attention_mask.unsqueeze(1) # convert encoder_attention_mask to a bias the same way we do for attention_mask if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: encoder_attention_mask = ( 1 - encoder_attention_mask.to(hidden_states.dtype) ) * -10000.0 encoder_attention_mask = encoder_attention_mask.unsqueeze(1) # 1. Input batch, _, seq_len = hidden_states.shape residual = hidden_states hidden_states = self.norm(hidden_states) inner_dim = hidden_states.shape[1] hidden_states = hidden_states.permute(0, 2, 1).reshape( batch, seq_len, inner_dim ) hidden_states = self.proj_in(hidden_states) # 2. Blocks for block in self.transformer_blocks: if self.training and self.gradient_checkpointing: hidden_states = torch.utils.checkpoint.checkpoint( block, hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, use_reentrant=False, ) else: hidden_states = block( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) # 3. Output hidden_states = self.proj_out(hidden_states) hidden_states = ( hidden_states.reshape(batch, seq_len, inner_dim) .permute(0, 2, 1) .contiguous() ) output = hidden_states + residual return output