Spaces:
Running
Running
| from typing import TYPE_CHECKING | |
| import torch | |
| import torch.nn.functional as F | |
| if TYPE_CHECKING: | |
| from .attention import Attention | |
| class AttnProcessor: | |
| r""" | |
| Default processor for performing attention-related computations. | |
| """ | |
| def __call__( | |
| self, | |
| attn: "Attention", | |
| hidden_states: torch.FloatTensor, | |
| encoder_hidden_states, | |
| attention_mask, | |
| temb = None, | |
| ) -> torch.Tensor: | |
| residual = hidden_states | |
| if attn.spatial_norm is not None: | |
| hidden_states = attn.spatial_norm(hidden_states, temb = None) | |
| # B, L, C | |
| assert hidden_states.ndim == 3, f"Hidden states must be 3-dimensional, got {hidden_states.ndim}" | |
| batch_size, sequence_length, _ = ( | |
| hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape | |
| ) | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| if attn.group_norm is not None: | |
| hidden_states = attn.group_norm(hidden_states.transpose(1, 2)) | |
| hidden_states = hidden_states.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) | |
| query = attn.head_to_batch_dim(query) | |
| key = attn.head_to_batch_dim(key) | |
| value = attn.head_to_batch_dim(value) | |
| attention_probs = attn.get_attention_scores(query, key, attention_mask) | |
| hidden_states = torch.bmm(attention_probs, value) | |
| hidden_states = attn.batch_to_head_dim(hidden_states) | |
| hidden_states = attn.to_out(hidden_states) | |
| hidden_states = attn.dropout(hidden_states) | |
| if attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| return hidden_states | |
| class AttnProcessor2_0: | |
| r""" | |
| Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). | |
| """ | |
| def __init__(self): | |
| 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: "Attention", | |
| hidden_states: torch.FloatTensor, | |
| encoder_hidden_states, | |
| attention_mask, | |
| temb = None, | |
| ) -> torch.FloatTensor: | |
| residual = hidden_states | |
| if attn.spatial_norm is not None: | |
| hidden_states = attn.spatial_norm(hidden_states, temb = None) | |
| # B, L, C | |
| assert hidden_states.ndim == 3, f"Hidden states must be 3-dimensional, got {hidden_states.ndim}" | |
| 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.nheads, -1, attention_mask.shape[-1]) | |
| if attn.group_norm is not None: | |
| hidden_states = attn.group_norm(hidden_states.transpose(1, 2)) | |
| hidden_states = hidden_states.transpose(1, 2) | |
| query: torch.Tensor = 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: torch.Tensor = attn.to_k(encoder_hidden_states) | |
| value: torch.Tensor = attn.to_v(encoder_hidden_states) | |
| inner_dim = key.shape[-1] | |
| head_dim = inner_dim // attn.nheads | |
| query = query.view(batch_size, -1, attn.nheads, head_dim).transpose(1, 2) | |
| key = key.view(batch_size, -1, attn.nheads, head_dim).transpose(1, 2) | |
| value = value.view(batch_size, -1, attn.nheads, head_dim).transpose(1, 2) | |
| # the output of sdp = (batch, num_heads, seq_len, head_dim) | |
| hidden_states = F.scaled_dot_product_attention( | |
| query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False, scale=attn.scale | |
| ) | |
| hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.nheads * head_dim) | |
| hidden_states = hidden_states.to(query.dtype) | |
| hidden_states = attn.to_out(hidden_states) | |
| hidden_states = attn.dropout(hidden_states) | |
| if attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| return hidden_states | |