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| from torch.nn import functional as F | |
| import torch | |
| class SkipAttnProcessor(torch.nn.Module): | |
| def __init__(self, *args, **kwargs) -> None: | |
| super().__init__() | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| attention_mask=None, | |
| temb=None, | |
| ): | |
| return hidden_states | |
| class AttnProcessor2_0(torch.nn.Module): | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
| """ | |
| def __init__( | |
| self, | |
| hidden_size=None, | |
| cross_attention_dim=None, | |
| **kwargs | |
| ): | |
| super().__init__() | |
| if not hasattr(F, "scaled_dot_product_attention"): | |
| raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| attention_mask=None, | |
| temb=None, | |
| *args, | |
| **kwargs, | |
| ): | |
| residual = hidden_states | |
| if attn.spatial_norm is not None: | |
| hidden_states = attn.spatial_norm(hidden_states, temb) | |
| input_ndim = hidden_states.ndim | |
| if input_ndim == 4: | |
| batch_size, channel, height, width = hidden_states.shape | |
| hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) | |
| batch_size, sequence_length, _ = ( | |
| hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape | |
| ) | |
| if attention_mask is not None: | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| # scaled_dot_product_attention expects attention_mask shape to be | |
| # (batch, heads, source_length, target_length) | |
| attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) | |
| if attn.group_norm is not None: | |
| hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) | |
| query = attn.to_q(hidden_states) | |
| if encoder_hidden_states is None: | |
| encoder_hidden_states = hidden_states | |
| elif attn.norm_cross: | |
| encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) | |
| key = attn.to_k(encoder_hidden_states) | |
| value = attn.to_v(encoder_hidden_states) | |
| inner_dim = key.shape[-1] | |
| head_dim = inner_dim // attn.heads | |
| query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) | |
| # the output of sdp = (batch, num_heads, seq_len, head_dim) | |
| # TODO: add support for attn.scale when we move to Torch 2.1 | |
| hidden_states = F.scaled_dot_product_attention( | |
| query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False | |
| ) | |
| hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) | |
| hidden_states = hidden_states.to(query.dtype) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| if input_ndim == 4: | |
| hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
| if attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| return hidden_states | |