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import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
import weakref
from typing import Union, TYPE_CHECKING, Optional, Tuple
from transformers import T5EncoderModel, CLIPTextModel, CLIPTokenizer, T5Tokenizer
from transformers.models.clip.modeling_clip import CLIPEncoder, CLIPAttention
from toolkit.models.zipper_resampler import ZipperResampler, ZipperModule
from toolkit.paths import REPOS_ROOT
from toolkit.resampler import Resampler
sys.path.append(REPOS_ROOT)
from ipadapter.ip_adapter.attention_processor import AttnProcessor2_0
if TYPE_CHECKING:
from toolkit.stable_diffusion_model import StableDiffusion
from toolkit.custom_adapter import CustomAdapter
class TEAugAdapterCLIPAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, attn_module: 'CLIPAttention', adapter: 'TEAugAdapter'):
super().__init__()
self.adapter_ref: weakref.ref = weakref.ref(adapter)
self.attn_module_ref: weakref.ref = weakref.ref(attn_module)
self.k_proj_adapter = nn.Linear(attn_module.embed_dim, attn_module.embed_dim)
self.v_proj_adapter = nn.Linear(attn_module.embed_dim, attn_module.embed_dim)
# copy the weights from the original module
self.k_proj_adapter.weight.data = attn_module.k_proj.weight.data.clone() * 0.01
self.v_proj_adapter.weight.data = attn_module.v_proj.weight.data.clone() * 0.01
#reset the bias
self.k_proj_adapter.bias.data = attn_module.k_proj.bias.data.clone() * 0.001
self.v_proj_adapter.bias.data = attn_module.v_proj.bias.data.clone() * 0.001
self.zipper = ZipperModule(
in_size=attn_module.embed_dim,
in_tokens=77 * 2,
out_size=attn_module.embed_dim,
out_tokens=77,
hidden_size=attn_module.embed_dim,
hidden_tokens=77,
)
# self.k_proj_adapter.weight.data = torch.zeros_like(attn_module.k_proj.weight.data)
# self.v_proj_adapter.weight.data = torch.zeros_like(attn_module.v_proj.weight.data)
# #reset the bias
# self.k_proj_adapter.bias.data = torch.zeros_like(attn_module.k_proj.bias.data)
# self.v_proj_adapter.bias.data = torch.zeros_like(attn_module.v_proj.bias.data)
# replace the original forward with our forward
self.original_forward = attn_module.forward
attn_module.forward = self.forward
@property
def is_active(self):
return self.adapter_ref().is_active
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
attn_module = self.attn_module_ref()
bsz, tgt_len, embed_dim = hidden_states.size()
# get query proj
query_states = attn_module.q_proj(hidden_states) * attn_module.scale
key_states = attn_module._shape(attn_module.k_proj(hidden_states), -1, bsz)
value_states = attn_module._shape(attn_module.v_proj(hidden_states), -1, bsz)
proj_shape = (bsz * attn_module.num_heads, -1, attn_module.head_dim)
query_states = attn_module._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * attn_module.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * attn_module.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
# apply the causal_attention_mask first
if causal_attention_mask is not None:
if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {causal_attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, attn_module.num_heads, tgt_len, src_len) + causal_attention_mask
attn_weights = attn_weights.view(bsz * attn_module.num_heads, tgt_len, src_len)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, attn_module.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * attn_module.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if output_attentions:
# this operation is a bit akward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, attn_module.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * attn_module.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=attn_module.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * attn_module.num_heads, tgt_len, attn_module.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, attn_module.num_heads, tgt_len, attn_module.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, attn_module.num_heads, tgt_len, attn_module.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
adapter: 'CustomAdapter' = self.adapter_ref().adapter_ref()
if self.adapter_ref().is_active and adapter.conditional_embeds is not None:
# apply the adapter
if adapter.is_unconditional_run:
embeds = adapter.unconditional_embeds
else:
embeds = adapter.conditional_embeds
# if the shape is not the same on batch, we are doing cfg and need to concat unconditional as well
if embeds.size(0) != bsz:
embeds = torch.cat([adapter.unconditional_embeds, embeds], dim=0)
key_states_raw = self.k_proj_adapter(embeds)
key_states = attn_module._shape(key_states_raw, -1, bsz)
value_states_raw = self.v_proj_adapter(embeds)
value_states = attn_module._shape(value_states_raw, -1, bsz)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
attn_probs = nn.functional.dropout(attn_weights, p=attn_module.dropout, training=self.training)
attn_output_adapter = torch.bmm(attn_probs, value_states)
if attn_output_adapter.size() != (bsz * attn_module.num_heads, tgt_len, attn_module.head_dim):
raise ValueError(
f"`attn_output_adapter` should be of size {(bsz, attn_module.num_heads, tgt_len, attn_module.head_dim)}, but is"
f" {attn_output_adapter.size()}"
)
attn_output_adapter = attn_output_adapter.view(bsz, attn_module.num_heads, tgt_len, attn_module.head_dim)
attn_output_adapter = attn_output_adapter.transpose(1, 2)
attn_output_adapter = attn_output_adapter.reshape(bsz, tgt_len, embed_dim)
attn_output_adapter = self.zipper(torch.cat([attn_output_adapter, attn_output], dim=1))
# attn_output_adapter = attn_module.out_proj(attn_output_adapter)
attn_output = attn_output + attn_output_adapter
attn_output = attn_module.out_proj(attn_output)
return attn_output, attn_weights_reshaped
class TEAugAdapter(torch.nn.Module):
def __init__(
self,
adapter: 'CustomAdapter',
sd: 'StableDiffusion',
):
super(TEAugAdapter, self).__init__()
self.adapter_ref: weakref.ref = weakref.ref(adapter)
self.sd_ref: weakref.ref = weakref.ref(sd)
if isinstance(sd.text_encoder, list):
raise ValueError("Dual text encoders is not yet supported")
# dim will come from text encoder
# dim = sd.unet.config['cross_attention_dim']
text_encoder: CLIPTextModel = sd.text_encoder
dim = text_encoder.config.hidden_size
clip_encoder: CLIPEncoder = text_encoder.text_model.encoder
# dim = clip_encoder.layers[-1].self_attn
if hasattr(adapter.vision_encoder.config, 'hidden_sizes'):
embedding_dim = adapter.vision_encoder.config.hidden_sizes[-1]
else:
embedding_dim = adapter.vision_encoder.config.hidden_size
image_encoder_state_dict = adapter.vision_encoder.state_dict()
# max_seq_len = CLIP tokens + CLS token
in_tokens = 257
if "vision_model.embeddings.position_embedding.weight" in image_encoder_state_dict:
# clip
in_tokens = int(image_encoder_state_dict["vision_model.embeddings.position_embedding.weight"].shape[0])
if adapter.config.image_encoder_arch.startswith('convnext'):
in_tokens = 16 * 16
embedding_dim = adapter.vision_encoder.config.hidden_sizes[-1]
out_tokens = adapter.config.num_tokens if adapter.config.num_tokens > 0 else in_tokens
self.image_proj_model = ZipperModule(
in_size=embedding_dim,
in_tokens=in_tokens,
out_size=dim,
out_tokens=out_tokens,
hidden_size=dim,
hidden_tokens=out_tokens,
)
# init adapter modules
attn_procs = {}
for idx, layer in enumerate(clip_encoder.layers):
name = f"clip_attention.{idx}"
attn_procs[name] = TEAugAdapterCLIPAttention(
layer.self_attn,
self
)
self.adapter_modules = torch.nn.ModuleList(list(attn_procs.values()))
# make a getter to see if is active
@property
def is_active(self):
return self.adapter_ref().is_active
def forward(self, input):
# # apply the adapter
input = self.image_proj_model(input)
# self.embeds = input
return input
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