| from typing import Optional |
|
|
| import torch |
| import torch.nn.functional as F |
| from diffusers.models.attention import Attention |
| from diffusers.models.embeddings import apply_rotary_emb |
|
|
| from .fuser import (get_sequence_parallel_rank, |
| get_sequence_parallel_world_size, get_sp_group, |
| init_distributed_environment, initialize_model_parallel, |
| xFuserLongContextAttention) |
|
|
| class CogVideoXMultiGPUsAttnProcessor2_0: |
| r""" |
| Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on |
| query and key vectors, but does not include spatial normalization. |
| """ |
|
|
| def __init__(self): |
| if xFuserLongContextAttention is not None: |
| try: |
| self.hybrid_seq_parallel_attn = xFuserLongContextAttention() |
| except Exception: |
| self.hybrid_seq_parallel_attn = None |
| else: |
| self.hybrid_seq_parallel_attn = None |
| if not hasattr(F, "scaled_dot_product_attention"): |
| raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") |
|
|
| 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) |
|
|
| |
| if image_rotary_emb is not None: |
| 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) |
|
|
| if self.hybrid_seq_parallel_attn is None: |
| 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 |
| else: |
| img_q = query[:, :, text_seq_length:].transpose(1, 2) |
| txt_q = query[:, :, :text_seq_length].transpose(1, 2) |
| img_k = key[:, :, text_seq_length:].transpose(1, 2) |
| txt_k = key[:, :, :text_seq_length].transpose(1, 2) |
| img_v = value[:, :, text_seq_length:].transpose(1, 2) |
| txt_v = value[:, :, :text_seq_length].transpose(1, 2) |
|
|
| hidden_states = self.hybrid_seq_parallel_attn( |
| None, |
| img_q, img_k, img_v, dropout_p=0.0, causal=False, |
| joint_tensor_query=txt_q, |
| joint_tensor_key=txt_k, |
| joint_tensor_value=txt_v, |
| joint_strategy='front', |
| ).transpose(1, 2) |
|
|
| hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) |
|
|
| |
| hidden_states = attn.to_out[0](hidden_states) |
| |
| 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 |
|
|
|
|
