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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 | |