Chroma / comfy /ldm /ace /attention.py
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# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/attention.py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Tuple, Union, Optional
import torch
import torch.nn.functional as F
from torch import nn
import comfy.model_management
from comfy.ldm.modules.attention import optimized_attention
class Attention(nn.Module):
def __init__(
self,
query_dim: int,
cross_attention_dim: Optional[int] = None,
heads: int = 8,
kv_heads: Optional[int] = None,
dim_head: int = 64,
dropout: float = 0.0,
bias: bool = False,
qk_norm: Optional[str] = None,
added_kv_proj_dim: Optional[int] = None,
added_proj_bias: Optional[bool] = True,
out_bias: bool = True,
scale_qk: bool = True,
only_cross_attention: bool = False,
eps: float = 1e-5,
rescale_output_factor: float = 1.0,
residual_connection: bool = False,
processor=None,
out_dim: int = None,
out_context_dim: int = None,
context_pre_only=None,
pre_only=False,
elementwise_affine: bool = True,
is_causal: bool = False,
dtype=None, device=None, operations=None
):
super().__init__()
self.inner_dim = out_dim if out_dim is not None else dim_head * heads
self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads
self.query_dim = query_dim
self.use_bias = bias
self.is_cross_attention = cross_attention_dim is not None
self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
self.rescale_output_factor = rescale_output_factor
self.residual_connection = residual_connection
self.dropout = dropout
self.fused_projections = False
self.out_dim = out_dim if out_dim is not None else query_dim
self.out_context_dim = out_context_dim if out_context_dim is not None else query_dim
self.context_pre_only = context_pre_only
self.pre_only = pre_only
self.is_causal = is_causal
self.scale_qk = scale_qk
self.scale = dim_head**-0.5 if self.scale_qk else 1.0
self.heads = out_dim // dim_head if out_dim is not None else heads
# for slice_size > 0 the attention score computation
# is split across the batch axis to save memory
# You can set slice_size with `set_attention_slice`
self.sliceable_head_dim = heads
self.added_kv_proj_dim = added_kv_proj_dim
self.only_cross_attention = only_cross_attention
if self.added_kv_proj_dim is None and self.only_cross_attention:
raise ValueError(
"`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`."
)
self.group_norm = None
self.spatial_norm = None
self.norm_q = None
self.norm_k = None
self.norm_cross = None
self.to_q = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device)
if not self.only_cross_attention:
# only relevant for the `AddedKVProcessor` classes
self.to_k = operations.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device)
self.to_v = operations.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device)
else:
self.to_k = None
self.to_v = None
self.added_proj_bias = added_proj_bias
if self.added_kv_proj_dim is not None:
self.add_k_proj = operations.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias, dtype=dtype, device=device)
self.add_v_proj = operations.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias, dtype=dtype, device=device)
if self.context_pre_only is not None:
self.add_q_proj = operations.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias, dtype=dtype, device=device)
else:
self.add_q_proj = None
self.add_k_proj = None
self.add_v_proj = None
if not self.pre_only:
self.to_out = nn.ModuleList([])
self.to_out.append(operations.Linear(self.inner_dim, self.out_dim, bias=out_bias, dtype=dtype, device=device))
self.to_out.append(nn.Dropout(dropout))
else:
self.to_out = None
if self.context_pre_only is not None and not self.context_pre_only:
self.to_add_out = operations.Linear(self.inner_dim, self.out_context_dim, bias=out_bias, dtype=dtype, device=device)
else:
self.to_add_out = None
self.norm_added_q = None
self.norm_added_k = None
self.processor = processor
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
**cross_attention_kwargs,
) -> torch.Tensor:
return self.processor(
self,
hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
**cross_attention_kwargs,
)
class CustomLiteLAProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections. add rms norm for query and key and apply RoPE"""
def __init__(self):
self.kernel_func = nn.ReLU(inplace=False)
self.eps = 1e-15
self.pad_val = 1.0
def apply_rotary_emb(
self,
x: torch.Tensor,
freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings
to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are
reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting
tensors contain rotary embeddings and are returned as real tensors.
Args:
x (`torch.Tensor`):
Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply
freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],)
Returns:
Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
"""
cos, sin = freqs_cis # [S, D]
cos = cos[None, None]
sin = sin[None, None]
cos, sin = cos.to(x.device), sin.to(x.device)
x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2]
x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
return out
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
*args,
**kwargs,
) -> torch.FloatTensor:
hidden_states_len = hidden_states.shape[1]
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
if encoder_hidden_states is not None:
context_input_ndim = encoder_hidden_states.ndim
if context_input_ndim == 4:
batch_size, channel, height, width = encoder_hidden_states.shape
encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size = hidden_states.shape[0]
# `sample` projections.
dtype = hidden_states.dtype
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
# `context` projections.
has_encoder_hidden_state_proj = hasattr(attn, "add_q_proj") and hasattr(attn, "add_k_proj") and hasattr(attn, "add_v_proj")
if encoder_hidden_states is not None and has_encoder_hidden_state_proj:
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
# attention
if not attn.is_cross_attention:
query = torch.cat([query, encoder_hidden_states_query_proj], dim=1)
key = torch.cat([key, encoder_hidden_states_key_proj], dim=1)
value = torch.cat([value, encoder_hidden_states_value_proj], dim=1)
else:
query = hidden_states
key = encoder_hidden_states
value = encoder_hidden_states
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1)
key = key.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1).transpose(-1, -2)
value = value.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1)
# RoPE需要 [B, H, S, D] 输入
# 此时 query是 [B, H, D, S], 需要转成 [B, H, S, D] 才能应用RoPE
query = query.permute(0, 1, 3, 2) # [B, H, S, D] (从 [B, H, D, S])
# Apply query and key normalization if needed
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 rotary_freqs_cis is not None:
query = self.apply_rotary_emb(query, rotary_freqs_cis)
if not attn.is_cross_attention:
key = self.apply_rotary_emb(key, rotary_freqs_cis)
elif rotary_freqs_cis_cross is not None and has_encoder_hidden_state_proj:
key = self.apply_rotary_emb(key, rotary_freqs_cis_cross)
# 此时 query是 [B, H, S, D],需要还原成 [B, H, D, S]
query = query.permute(0, 1, 3, 2) # [B, H, D, S]
if attention_mask is not None:
# attention_mask: [B, S] -> [B, 1, S, 1]
attention_mask = attention_mask[:, None, :, None].to(key.dtype) # [B, 1, S, 1]
query = query * attention_mask.permute(0, 1, 3, 2) # [B, H, S, D] * [B, 1, S, 1]
if not attn.is_cross_attention:
key = key * attention_mask # key: [B, h, S, D] 与 mask [B, 1, S, 1] 相乘
value = value * attention_mask.permute(0, 1, 3, 2) # 如果 value 是 [B, h, D, S],那么需调整mask以匹配S维度
if attn.is_cross_attention and encoder_attention_mask is not None and has_encoder_hidden_state_proj:
encoder_attention_mask = encoder_attention_mask[:, None, :, None].to(key.dtype) # [B, 1, S_enc, 1]
# 此时 key: [B, h, S_enc, D], value: [B, h, D, S_enc]
key = key * encoder_attention_mask # [B, h, S_enc, D] * [B, 1, S_enc, 1]
value = value * encoder_attention_mask.permute(0, 1, 3, 2) # [B, h, D, S_enc] * [B, 1, 1, S_enc]
query = self.kernel_func(query)
key = self.kernel_func(key)
query, key, value = query.float(), key.float(), value.float()
value = F.pad(value, (0, 0, 0, 1), mode="constant", value=self.pad_val)
vk = torch.matmul(value, key)
hidden_states = torch.matmul(vk, query)
if hidden_states.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.float()
hidden_states = hidden_states[:, :, :-1] / (hidden_states[:, :, -1:] + self.eps)
hidden_states = hidden_states.view(batch_size, attn.heads * head_dim, -1).permute(0, 2, 1)
hidden_states = hidden_states.to(dtype)
if encoder_hidden_states is not None:
encoder_hidden_states = encoder_hidden_states.to(dtype)
# Split the attention outputs.
if encoder_hidden_states is not None and not attn.is_cross_attention and has_encoder_hidden_state_proj:
hidden_states, encoder_hidden_states = (
hidden_states[:, : hidden_states_len],
hidden_states[:, hidden_states_len:],
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if encoder_hidden_states is not None and not attn.context_pre_only and not attn.is_cross_attention and hasattr(attn, "to_add_out"):
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if encoder_hidden_states is not None and context_input_ndim == 4:
encoder_hidden_states = encoder_hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if torch.get_autocast_gpu_dtype() == torch.float16:
hidden_states = hidden_states.clip(-65504, 65504)
if encoder_hidden_states is not None:
encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)
return hidden_states, encoder_hidden_states
class CustomerAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
"""
def apply_rotary_emb(
self,
x: torch.Tensor,
freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings
to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are
reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting
tensors contain rotary embeddings and are returned as real tensors.
Args:
x (`torch.Tensor`):
Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply
freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],)
Returns:
Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
"""
cos, sin = freqs_cis # [S, D]
cos = cos[None, None]
sin = sin[None, None]
cos, sin = cos.to(x.device), sin.to(x.device)
x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2]
x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
return out
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
*args,
**kwargs,
) -> torch.Tensor:
residual = hidden_states
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
has_encoder_hidden_state_proj = hasattr(attn, "add_q_proj") and hasattr(attn, "add_k_proj") and hasattr(attn, "add_v_proj")
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
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)
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 rotary_freqs_cis is not None:
query = self.apply_rotary_emb(query, rotary_freqs_cis)
if not attn.is_cross_attention:
key = self.apply_rotary_emb(key, rotary_freqs_cis)
elif rotary_freqs_cis_cross is not None and has_encoder_hidden_state_proj:
key = self.apply_rotary_emb(key, rotary_freqs_cis_cross)
if attn.is_cross_attention and encoder_attention_mask is not None and has_encoder_hidden_state_proj:
# attention_mask: N x S1
# encoder_attention_mask: N x S2
# cross attention 整合attention_mask和encoder_attention_mask
combined_mask = attention_mask[:, :, None] * encoder_attention_mask[:, None, :]
attention_mask = torch.where(combined_mask == 1, 0.0, -torch.inf)
attention_mask = attention_mask[:, None, :, :].expand(-1, attn.heads, -1, -1).to(query.dtype)
elif not attn.is_cross_attention and attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
# the output of sdp = (batch, num_heads, seq_len, head_dim)
hidden_states = optimized_attention(
query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True,
).to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
def val2list(x: list or tuple or any, repeat_time=1) -> list: # type: ignore
"""Repeat `val` for `repeat_time` times and return the list or val if list/tuple."""
if isinstance(x, (list, tuple)):
return list(x)
return [x for _ in range(repeat_time)]
def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1) -> tuple: # type: ignore
"""Return tuple with min_len by repeating element at idx_repeat."""
# convert to list first
x = val2list(x)
# repeat elements if necessary
if len(x) > 0:
x[idx_repeat:idx_repeat] = [x[idx_repeat] for _ in range(min_len - len(x))]
return tuple(x)
def t2i_modulate(x, shift, scale):
return x * (1 + scale) + shift
def get_same_padding(kernel_size: Union[int, Tuple[int, ...]]) -> Union[int, Tuple[int, ...]]:
if isinstance(kernel_size, tuple):
return tuple([get_same_padding(ks) for ks in kernel_size])
else:
assert kernel_size % 2 > 0, f"kernel size {kernel_size} should be odd number"
return kernel_size // 2
class ConvLayer(nn.Module):
def __init__(
self,
in_dim: int,
out_dim: int,
kernel_size=3,
stride=1,
dilation=1,
groups=1,
padding: Union[int, None] = None,
use_bias=False,
norm=None,
act=None,
dtype=None, device=None, operations=None
):
super().__init__()
if padding is None:
padding = get_same_padding(kernel_size)
padding *= dilation
self.in_dim = in_dim
self.out_dim = out_dim
self.kernel_size = kernel_size
self.stride = stride
self.dilation = dilation
self.groups = groups
self.padding = padding
self.use_bias = use_bias
self.conv = operations.Conv1d(
in_dim,
out_dim,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=use_bias,
device=device,
dtype=dtype
)
if norm is not None:
self.norm = operations.RMSNorm(out_dim, elementwise_affine=False, dtype=dtype, device=device)
else:
self.norm = None
if act is not None:
self.act = nn.SiLU(inplace=True)
else:
self.act = None
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.conv(x)
if self.norm:
x = self.norm(x)
if self.act:
x = self.act(x)
return x
class GLUMBConv(nn.Module):
def __init__(
self,
in_features: int,
hidden_features: int,
out_feature=None,
kernel_size=3,
stride=1,
padding: Union[int, None] = None,
use_bias=False,
norm=(None, None, None),
act=("silu", "silu", None),
dilation=1,
dtype=None, device=None, operations=None
):
out_feature = out_feature or in_features
super().__init__()
use_bias = val2tuple(use_bias, 3)
norm = val2tuple(norm, 3)
act = val2tuple(act, 3)
self.glu_act = nn.SiLU(inplace=False)
self.inverted_conv = ConvLayer(
in_features,
hidden_features * 2,
1,
use_bias=use_bias[0],
norm=norm[0],
act=act[0],
dtype=dtype,
device=device,
operations=operations,
)
self.depth_conv = ConvLayer(
hidden_features * 2,
hidden_features * 2,
kernel_size,
stride=stride,
groups=hidden_features * 2,
padding=padding,
use_bias=use_bias[1],
norm=norm[1],
act=None,
dilation=dilation,
dtype=dtype,
device=device,
operations=operations,
)
self.point_conv = ConvLayer(
hidden_features,
out_feature,
1,
use_bias=use_bias[2],
norm=norm[2],
act=act[2],
dtype=dtype,
device=device,
operations=operations,
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x.transpose(1, 2)
x = self.inverted_conv(x)
x = self.depth_conv(x)
x, gate = torch.chunk(x, 2, dim=1)
gate = self.glu_act(gate)
x = x * gate
x = self.point_conv(x)
x = x.transpose(1, 2)
return x
class LinearTransformerBlock(nn.Module):
"""
A Sana block with global shared adaptive layer norm (adaLN-single) conditioning.
"""
def __init__(
self,
dim,
num_attention_heads,
attention_head_dim,
use_adaln_single=True,
cross_attention_dim=None,
added_kv_proj_dim=None,
context_pre_only=False,
mlp_ratio=4.0,
add_cross_attention=False,
add_cross_attention_dim=None,
qk_norm=None,
dtype=None, device=None, operations=None
):
super().__init__()
self.norm1 = operations.RMSNorm(dim, elementwise_affine=False, eps=1e-6)
self.attn = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim,
added_kv_proj_dim=added_kv_proj_dim,
dim_head=attention_head_dim,
heads=num_attention_heads,
out_dim=dim,
bias=True,
qk_norm=qk_norm,
processor=CustomLiteLAProcessor2_0(),
dtype=dtype,
device=device,
operations=operations,
)
self.add_cross_attention = add_cross_attention
self.context_pre_only = context_pre_only
if add_cross_attention and add_cross_attention_dim is not None:
self.cross_attn = Attention(
query_dim=dim,
cross_attention_dim=add_cross_attention_dim,
added_kv_proj_dim=add_cross_attention_dim,
dim_head=attention_head_dim,
heads=num_attention_heads,
out_dim=dim,
context_pre_only=context_pre_only,
bias=True,
qk_norm=qk_norm,
processor=CustomerAttnProcessor2_0(),
dtype=dtype,
device=device,
operations=operations,
)
self.norm2 = operations.RMSNorm(dim, 1e-06, elementwise_affine=False)
self.ff = GLUMBConv(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
use_bias=(True, True, False),
norm=(None, None, None),
act=("silu", "silu", None),
dtype=dtype,
device=device,
operations=operations,
)
self.use_adaln_single = use_adaln_single
if use_adaln_single:
self.scale_shift_table = nn.Parameter(torch.empty(6, dim, dtype=dtype, device=device))
def forward(
self,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: torch.FloatTensor = None,
encoder_attention_mask: torch.FloatTensor = None,
rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
temb: torch.FloatTensor = None,
):
N = hidden_states.shape[0]
# step 1: AdaLN single
if self.use_adaln_single:
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
comfy.model_management.cast_to(self.scale_shift_table[None], dtype=temb.dtype, device=temb.device) + temb.reshape(N, 6, -1)
).chunk(6, dim=1)
norm_hidden_states = self.norm1(hidden_states)
if self.use_adaln_single:
norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
# step 2: attention
if not self.add_cross_attention:
attn_output, encoder_hidden_states = self.attn(
hidden_states=norm_hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
rotary_freqs_cis=rotary_freqs_cis,
rotary_freqs_cis_cross=rotary_freqs_cis_cross,
)
else:
attn_output, _ = self.attn(
hidden_states=norm_hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=None,
encoder_attention_mask=None,
rotary_freqs_cis=rotary_freqs_cis,
rotary_freqs_cis_cross=None,
)
if self.use_adaln_single:
attn_output = gate_msa * attn_output
hidden_states = attn_output + hidden_states
if self.add_cross_attention:
attn_output = self.cross_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
rotary_freqs_cis=rotary_freqs_cis,
rotary_freqs_cis_cross=rotary_freqs_cis_cross,
)
hidden_states = attn_output + hidden_states
# step 3: add norm
norm_hidden_states = self.norm2(hidden_states)
if self.use_adaln_single:
norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
# step 4: feed forward
ff_output = self.ff(norm_hidden_states)
if self.use_adaln_single:
ff_output = gate_mlp * ff_output
hidden_states = hidden_states + ff_output
return hidden_states