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[Bugfix] fix error of peft lora when xformers enabled #5697

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merged 3 commits into from
Nov 8, 2023
Merged

[Bugfix] fix error of peft lora when xformers enabled #5697

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f441b9f
bugfix peft lor
okotaku Nov 8, 2023
b24144f
Apply suggestions from code review
patrickvonplaten Nov 8, 2023
c25e3bc
Merge branch 'main' into bugfix/peft_lora
patrickvonplaten Nov 8, 2023
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40 changes: 21 additions & 19 deletions src/diffusers/models/attention_processor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -879,6 +879,9 @@ def __call__(
scale: float = 1.0,
) -> torch.Tensor:
residual = hidden_states

args = () if USE_PEFT_BACKEND else (scale,)

hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
batch_size, sequence_length, _ = hidden_states.shape

Expand All @@ -891,17 +894,17 @@ def __call__(

hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

query = attn.to_q(hidden_states, scale=scale)
query = attn.to_q(hidden_states, *args)
query = attn.head_to_batch_dim(query)

encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states, scale=scale)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states, scale=scale)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states, *args)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states, *args)
encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)

if not attn.only_cross_attention:
key = attn.to_k(hidden_states, scale=scale)
value = attn.to_v(hidden_states, scale=scale)
key = attn.to_k(hidden_states, *args)
value = attn.to_v(hidden_states, *args)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
Expand All @@ -915,7 +918,7 @@ def __call__(
hidden_states = attn.batch_to_head_dim(hidden_states)

# linear proj
hidden_states = attn.to_out[0](hidden_states, scale=scale)
hidden_states = attn.to_out[0](hidden_states, *args)
# dropout
hidden_states = attn.to_out[1](hidden_states)

Expand Down Expand Up @@ -946,6 +949,9 @@ def __call__(
scale: float = 1.0,
) -> torch.Tensor:
residual = hidden_states

args = () if USE_PEFT_BACKEND else (scale,)

hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
batch_size, sequence_length, _ = hidden_states.shape

Expand All @@ -958,7 +964,7 @@ def __call__(

hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

query = attn.to_q(hidden_states, scale=scale)
query = attn.to_q(hidden_states, *args)
query = attn.head_to_batch_dim(query, out_dim=4)

encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
Expand All @@ -967,8 +973,8 @@ def __call__(
encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj, out_dim=4)

if not attn.only_cross_attention:
key = attn.to_k(hidden_states, scale=scale)
value = attn.to_v(hidden_states, scale=scale)
key = attn.to_k(hidden_states, *args)
value = attn.to_v(hidden_states, *args)
key = attn.head_to_batch_dim(key, out_dim=4)
value = attn.head_to_batch_dim(value, out_dim=4)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
Expand All @@ -985,7 +991,7 @@ def __call__(
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, residual.shape[1])

# linear proj
hidden_states = attn.to_out[0](hidden_states, scale=scale)
hidden_states = attn.to_out[0](hidden_states, *args)
# dropout
hidden_states = attn.to_out[1](hidden_states)

Expand Down Expand Up @@ -1177,6 +1183,8 @@ def __call__(
) -> torch.FloatTensor:
residual = hidden_states

args = () if USE_PEFT_BACKEND else (scale,)

if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)

Expand Down Expand Up @@ -1207,12 +1215,8 @@ def __call__(
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

key = (
attn.to_k(encoder_hidden_states, scale=scale) if not USE_PEFT_BACKEND else attn.to_k(encoder_hidden_states)
)
value = (
attn.to_v(encoder_hidden_states, scale=scale) if not USE_PEFT_BACKEND else attn.to_v(encoder_hidden_states)
)
key = attn.to_k(encoder_hidden_states, *args)
value = attn.to_v(encoder_hidden_states, *args)

inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
Expand All @@ -1232,9 +1236,7 @@ def __call__(
hidden_states = hidden_states.to(query.dtype)

# linear proj
hidden_states = (
attn.to_out[0](hidden_states, scale=scale) if not USE_PEFT_BACKEND else attn.to_out[0](hidden_states)
)
hidden_states = attn.to_out[0](hidden_states, *args)
# dropout
hidden_states = attn.to_out[1](hidden_states)

Expand Down
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