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change attention logic to use tridao flash attention in some cases #1

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change attention logic to use tridao flash attention in some cases #1

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41 changes: 21 additions & 20 deletions src/diffusers/models/attention_processor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@
from ..utils import deprecate, logging
from ..utils.import_utils import is_torch_npu_available, is_xformers_available
from ..utils.torch_utils import is_torch_version, maybe_allow_in_graph

from flash_attn import flash_attn_func

logger = logging.get_logger(__name__) # pylint: disable=invalid-name

Expand Down Expand Up @@ -1880,24 +1880,26 @@ def __call__(

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)

# 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)

if head_dim <= 128 and attention_mask is None:
query = query.reshape(batch_size, -1, attn.heads, head_dim)
key = key.reshape(batch_size, -1, attn.heads, head_dim)
value = value.reshape(batch_size, -1, attn.heads, head_dim)
hidden_states = flash_attn_func(query, key, value, dropout_p=0.0, causal=False)
hidden_states = hidden_states.reshape(batch_size, -1, attn.heads * head_dim)
else:
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)
# 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
Expand All @@ -1915,7 +1917,6 @@ def __call__(

return hidden_states


class StableAudioAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
Expand Down