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| import inspect | |
| import math | |
| from typing import Callable, List, Optional, Tuple, Union | |
| import torch | |
| import torch.nn.functional as F | |
| from torch import nn | |
| from diffusers.models.attention_processor import Attention, AttnProcessor2_0 | |
| try: | |
| import flash_attn_interface | |
| IS_FLASH3_AVAILABLE = True | |
| except Exception as e: | |
| print(f"flash_attn3 load fail: {e}") | |
| IS_FLASH3_AVAILABLE = False | |
| class CogVideoXFlashAttn3ControlnetXsProcessor: | |
| def __init__(self): | |
| pass | |
| def __call__( | |
| self, | |
| attn: Attention, | |
| hidden_states: torch.Tensor, | |
| encoder_hidden_states = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| image_rotary_emb: Optional[torch.Tensor] = None, | |
| ) -> torch.Tensor: | |
| batch_size, sequence_length, _ = hidden_states.shape | |
| if attention_mask is not None: | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) | |
| query = attn.to_q(hidden_states) | |
| key = attn.to_k(hidden_states) | |
| value = attn.to_v(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) | |
| if attn.norm_q is not None: | |
| query = attn.norm_q(query) | |
| if attn.norm_k is not None: | |
| key = attn.norm_k(key) | |
| # Apply RoPE if needed | |
| if image_rotary_emb is not None: | |
| from diffusers.models.embeddings import apply_rotary_emb | |
| query = apply_rotary_emb(query, image_rotary_emb) | |
| if not attn.is_cross_attention: | |
| key = apply_rotary_emb(key, image_rotary_emb) | |
| hidden_states = flash_attn_interface.flash_attn_func( | |
| query.transpose(1, 2), key.transpose(1, 2), value.transpose(1, 2) | |
| ) | |
| hidden_states = hidden_states[0] | |
| hidden_states = hidden_states.reshape(batch_size, -1, attn.heads * head_dim) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| return hidden_states | |
| class CogVideoXFlashAttn3Processor: | |
| def __init__(self): | |
| pass | |
| def __call__( | |
| self, | |
| attn: Attention, | |
| hidden_states: torch.Tensor, | |
| encoder_hidden_states: torch.Tensor, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| image_rotary_emb: Optional[torch.Tensor] = None, | |
| ) -> torch.Tensor: | |
| text_seq_length = encoder_hidden_states.size(1) | |
| hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1) | |
| 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) | |
| attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) | |
| query = attn.to_q(hidden_states) | |
| key = attn.to_k(hidden_states) | |
| value = attn.to_v(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) | |
| if attn.norm_q is not None: | |
| query = attn.norm_q(query) | |
| if attn.norm_k is not None: | |
| key = attn.norm_k(key) | |
| # Apply RoPE if needed | |
| if image_rotary_emb is not None: | |
| from diffusers.models.embeddings import apply_rotary_emb | |
| query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb) | |
| if not attn.is_cross_attention: | |
| key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb) | |
| hidden_states = flash_attn_interface.flash_attn_func( | |
| query.transpose(1, 2), key.transpose(1, 2), value.transpose(1, 2) | |
| ) | |
| hidden_states = hidden_states[0] | |
| hidden_states = hidden_states.reshape(batch_size, -1, attn.heads * head_dim) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| encoder_hidden_states, hidden_states = hidden_states.split( | |
| [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1 | |
| ) | |
| return hidden_states, encoder_hidden_states | |