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from typing import Callable, Optional, Union
import xformers
import xformers.ops
from diffusers.models.attention_processor import Attention
class XFormersAttnProcessor:
def __init__(self, attention_op: Optional[Callable] = None):
self.attention_op = attention_op
def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
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)
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).contiguous()
key = attn.head_to_batch_dim(key).contiguous()
value = attn.head_to_batch_dim(value).contiguous()
query = query.to(key.dtype)
value = value.to(key.dtype)
hidden_states = xformers.ops.memory_efficient_attention(
query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
)
hidden_states = hidden_states.to(query.dtype)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
return hidden_states
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