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import torch
from transformers import T5EncoderModel, T5Config
from .sd_text_encoder import SDTextEncoder
from .sdxl_text_encoder import SDXLTextEncoder2, SDXLTextEncoder2StateDictConverter
class SD3TextEncoder1(SDTextEncoder):
def __init__(self, vocab_size=49408):
super().__init__(vocab_size=vocab_size)
def forward(self, input_ids, clip_skip=2):
embeds = self.token_embedding(input_ids) + self.position_embeds
attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
for encoder_id, encoder in enumerate(self.encoders):
embeds = encoder(embeds, attn_mask=attn_mask)
if encoder_id + clip_skip == len(self.encoders):
hidden_states = embeds
embeds = self.final_layer_norm(embeds)
pooled_embeds = embeds[torch.arange(embeds.shape[0]), input_ids.to(dtype=torch.int).argmax(dim=-1)]
return pooled_embeds, hidden_states
@staticmethod
def state_dict_converter():
return SD3TextEncoder1StateDictConverter()
class SD3TextEncoder2(SDXLTextEncoder2):
def __init__(self):
super().__init__()
@staticmethod
def state_dict_converter():
return SD3TextEncoder2StateDictConverter()
class SD3TextEncoder3(T5EncoderModel):
def __init__(self):
config = T5Config(
_name_or_path = ".",
architectures = ["T5EncoderModel"],
classifier_dropout = 0.0,
d_ff = 10240,
d_kv = 64,
d_model = 4096,
decoder_start_token_id = 0,
dense_act_fn = "gelu_new",
dropout_rate = 0.1,
eos_token_id = 1,
feed_forward_proj = "gated-gelu",
initializer_factor = 1.0,
is_encoder_decoder = True,
is_gated_act = True,
layer_norm_epsilon = 1e-06,
model_type = "t5",
num_decoder_layers = 24,
num_heads = 64,
num_layers = 24,
output_past = True,
pad_token_id = 0,
relative_attention_max_distance = 128,
relative_attention_num_buckets = 32,
tie_word_embeddings = False,
torch_dtype = torch.float16,
transformers_version = "4.41.2",
use_cache = True,
vocab_size = 32128
)
super().__init__(config)
self.eval()
def forward(self, input_ids):
outputs = super().forward(input_ids=input_ids)
prompt_emb = outputs.last_hidden_state
return prompt_emb
@staticmethod
def state_dict_converter():
return SD3TextEncoder3StateDictConverter()
class SD3TextEncoder1StateDictConverter:
def __init__(self):
pass
def from_diffusers(self, state_dict):
rename_dict = {
"text_model.embeddings.token_embedding.weight": "token_embedding.weight",
"text_model.embeddings.position_embedding.weight": "position_embeds",
"text_model.final_layer_norm.weight": "final_layer_norm.weight",
"text_model.final_layer_norm.bias": "final_layer_norm.bias",
}
attn_rename_dict = {
"self_attn.q_proj": "attn.to_q",
"self_attn.k_proj": "attn.to_k",
"self_attn.v_proj": "attn.to_v",
"self_attn.out_proj": "attn.to_out",
"layer_norm1": "layer_norm1",
"layer_norm2": "layer_norm2",
"mlp.fc1": "fc1",
"mlp.fc2": "fc2",
}
state_dict_ = {}
for name in state_dict:
if name in rename_dict:
param = state_dict[name]
if name == "text_model.embeddings.position_embedding.weight":
param = param.reshape((1, param.shape[0], param.shape[1]))
state_dict_[rename_dict[name]] = param
elif name.startswith("text_model.encoder.layers."):
param = state_dict[name]
names = name.split(".")
layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1]
name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail])
state_dict_[name_] = param
return state_dict_
def from_civitai(self, state_dict):
rename_dict = {
"text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight": "position_embeds",
"text_encoders.clip_l.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias",
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"text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias",
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"text_encoders.clip_l.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.1.layer_norm1.weight": "encoders.1.layer_norm1.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias",
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"text_encoders.clip_l.transformer.text_model.encoder.layers.4.self_attn.q_proj.bias": "encoders.4.attn.to_q.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.4.self_attn.q_proj.weight": "encoders.4.attn.to_q.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.4.self_attn.v_proj.bias": "encoders.4.attn.to_v.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.4.self_attn.v_proj.weight": "encoders.4.attn.to_v.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.5.layer_norm1.bias": "encoders.5.layer_norm1.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.5.layer_norm1.weight": "encoders.5.layer_norm1.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.5.layer_norm2.bias": "encoders.5.layer_norm2.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.5.layer_norm2.weight": "encoders.5.layer_norm2.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.5.mlp.fc1.bias": "encoders.5.fc1.bias",
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"text_encoders.clip_l.transformer.text_model.encoder.layers.7.layer_norm1.bias": "encoders.7.layer_norm1.bias",
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"text_encoders.clip_l.transformer.text_model.encoder.layers.7.self_attn.q_proj.bias": "encoders.7.attn.to_q.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.7.self_attn.q_proj.weight": "encoders.7.attn.to_q.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.7.self_attn.v_proj.bias": "encoders.7.attn.to_v.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.7.self_attn.v_proj.weight": "encoders.7.attn.to_v.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.8.layer_norm1.bias": "encoders.8.layer_norm1.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.8.layer_norm1.weight": "encoders.8.layer_norm1.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.8.layer_norm2.bias": "encoders.8.layer_norm2.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.8.layer_norm2.weight": "encoders.8.layer_norm2.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.8.mlp.fc1.bias": "encoders.8.fc1.bias",
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"text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.k_proj.bias": "encoders.8.attn.to_k.bias",
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"text_encoders.clip_l.transformer.text_model.encoder.layers.9.layer_norm1.bias": "encoders.9.layer_norm1.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.9.layer_norm1.weight": "encoders.9.layer_norm1.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.9.layer_norm2.bias": "encoders.9.layer_norm2.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.9.layer_norm2.weight": "encoders.9.layer_norm2.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc1.bias": "encoders.9.fc1.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc1.weight": "encoders.9.fc1.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc2.bias": "encoders.9.fc2.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc2.weight": "encoders.9.fc2.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.k_proj.bias": "encoders.9.attn.to_k.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.k_proj.weight": "encoders.9.attn.to_k.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.out_proj.bias": "encoders.9.attn.to_out.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.out_proj.weight": "encoders.9.attn.to_out.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.q_proj.bias": "encoders.9.attn.to_q.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight",
"text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias",
"text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight",
"text_encoders.clip_l.transformer.text_model.final_layer_norm.bias": "final_layer_norm.bias",
"text_encoders.clip_l.transformer.text_model.final_layer_norm.weight": "final_layer_norm.weight",
}
state_dict_ = {}
for name in state_dict:
if name in rename_dict:
param = state_dict[name]
if name == "text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight":
param = param.reshape((1, param.shape[0], param.shape[1]))
state_dict_[rename_dict[name]] = param
return state_dict_
class SD3TextEncoder2StateDictConverter(SDXLTextEncoder2StateDictConverter):
def __init__(self):
pass
def from_diffusers(self, state_dict):
return super().from_diffusers(state_dict)
def from_civitai(self, state_dict):
rename_dict = {
"text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight": "position_embeds",
"text_encoders.clip_g.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight": "encoders.0.attn.to_q.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight": "encoders.0.attn.to_v.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm1.weight": "encoders.1.layer_norm1.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm2.weight": "encoders.1.layer_norm2.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc1.bias": "encoders.1.fc1.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc1.weight": "encoders.1.fc1.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc2.bias": "encoders.1.fc2.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc2.weight": "encoders.1.fc2.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.k_proj.bias": "encoders.1.attn.to_k.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.k_proj.weight": "encoders.1.attn.to_k.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.out_proj.bias": "encoders.1.attn.to_out.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.out_proj.weight": "encoders.1.attn.to_out.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.q_proj.bias": "encoders.1.attn.to_q.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.q_proj.weight": "encoders.1.attn.to_q.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.v_proj.bias": "encoders.1.attn.to_v.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.v_proj.weight": "encoders.1.attn.to_v.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.10.layer_norm1.bias": "encoders.10.layer_norm1.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.10.layer_norm1.weight": "encoders.10.layer_norm1.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.10.layer_norm2.bias": "encoders.10.layer_norm2.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.10.layer_norm2.weight": "encoders.10.layer_norm2.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.10.mlp.fc1.bias": "encoders.10.fc1.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.10.mlp.fc1.weight": "encoders.10.fc1.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.10.mlp.fc2.bias": "encoders.10.fc2.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.10.mlp.fc2.weight": "encoders.10.fc2.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.10.self_attn.k_proj.bias": "encoders.10.attn.to_k.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.10.self_attn.k_proj.weight": "encoders.10.attn.to_k.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.10.self_attn.out_proj.bias": "encoders.10.attn.to_out.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.10.self_attn.out_proj.weight": "encoders.10.attn.to_out.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.10.self_attn.q_proj.bias": "encoders.10.attn.to_q.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.10.self_attn.q_proj.weight": "encoders.10.attn.to_q.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.10.self_attn.v_proj.bias": "encoders.10.attn.to_v.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.10.self_attn.v_proj.weight": "encoders.10.attn.to_v.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.11.layer_norm1.bias": "encoders.11.layer_norm1.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.11.layer_norm1.weight": "encoders.11.layer_norm1.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.11.layer_norm2.bias": "encoders.11.layer_norm2.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.11.layer_norm2.weight": "encoders.11.layer_norm2.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.11.mlp.fc1.bias": "encoders.11.fc1.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.11.mlp.fc1.weight": "encoders.11.fc1.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.11.mlp.fc2.bias": "encoders.11.fc2.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.11.mlp.fc2.weight": "encoders.11.fc2.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.11.self_attn.k_proj.bias": "encoders.11.attn.to_k.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.11.self_attn.k_proj.weight": "encoders.11.attn.to_k.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.11.self_attn.out_proj.bias": "encoders.11.attn.to_out.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.11.self_attn.out_proj.weight": "encoders.11.attn.to_out.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.11.self_attn.q_proj.bias": "encoders.11.attn.to_q.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.11.self_attn.q_proj.weight": "encoders.11.attn.to_q.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.11.self_attn.v_proj.bias": "encoders.11.attn.to_v.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.11.self_attn.v_proj.weight": "encoders.11.attn.to_v.weight",
"text_encoders.clip_g.transformer.text_model.encoder.layers.12.layer_norm1.bias": "encoders.12.layer_norm1.bias",
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"text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias",
"text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight",
"text_encoders.clip_g.transformer.text_model.final_layer_norm.bias": "final_layer_norm.bias",
"text_encoders.clip_g.transformer.text_model.final_layer_norm.weight": "final_layer_norm.weight",
"text_encoders.clip_g.transformer.text_projection.weight": "text_projection.weight",
}
state_dict_ = {}
for name in state_dict:
if name in rename_dict:
param = state_dict[name]
if name == "text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight":
param = param.reshape((1, param.shape[0], param.shape[1]))
state_dict_[rename_dict[name]] = param
return state_dict_
class SD3TextEncoder3StateDictConverter():
def __init__(self):
pass
def from_diffusers(self, state_dict):
state_dict_ = state_dict
return state_dict_
def from_civitai(self, state_dict):
prefix = "text_encoders.t5xxl.transformer."
state_dict_ = {name[len(prefix):]: param for name, param in state_dict.items() if name.startswith(prefix)}
if len(state_dict_) > 0:
return self.from_diffusers(state_dict_)
name_list = [
"encoder.block.0.layer.0.SelfAttention.k.weight",
"encoder.block.0.layer.0.SelfAttention.o.weight",
"encoder.block.0.layer.0.SelfAttention.q.weight",
"encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight",
"encoder.block.0.layer.0.SelfAttention.v.weight",
"encoder.block.0.layer.0.layer_norm.weight",
"encoder.block.0.layer.1.DenseReluDense.wi_0.weight",
"encoder.block.0.layer.1.DenseReluDense.wi_1.weight",
"encoder.block.0.layer.1.DenseReluDense.wo.weight",
"encoder.block.0.layer.1.layer_norm.weight",
"encoder.block.1.layer.0.SelfAttention.k.weight",
"encoder.block.1.layer.0.SelfAttention.o.weight",
"encoder.block.1.layer.0.SelfAttention.q.weight",
"encoder.block.1.layer.0.SelfAttention.v.weight",
"encoder.block.1.layer.0.layer_norm.weight",
"encoder.block.1.layer.1.DenseReluDense.wi_0.weight",
"encoder.block.1.layer.1.DenseReluDense.wi_1.weight",
"encoder.block.1.layer.1.DenseReluDense.wo.weight",
"encoder.block.1.layer.1.layer_norm.weight",
"encoder.block.10.layer.0.SelfAttention.k.weight",
"encoder.block.10.layer.0.SelfAttention.o.weight",
"encoder.block.10.layer.0.SelfAttention.q.weight",
"encoder.block.10.layer.0.SelfAttention.v.weight",
"encoder.block.10.layer.0.layer_norm.weight",
"encoder.block.10.layer.1.DenseReluDense.wi_0.weight",
"encoder.block.10.layer.1.DenseReluDense.wi_1.weight",
"encoder.block.10.layer.1.DenseReluDense.wo.weight",
"encoder.block.10.layer.1.layer_norm.weight",
"encoder.block.11.layer.0.SelfAttention.k.weight",
"encoder.block.11.layer.0.SelfAttention.o.weight",
"encoder.block.11.layer.0.SelfAttention.q.weight",
"encoder.block.11.layer.0.SelfAttention.v.weight",
"encoder.block.11.layer.0.layer_norm.weight",
"encoder.block.11.layer.1.DenseReluDense.wi_0.weight",
"encoder.block.11.layer.1.DenseReluDense.wi_1.weight",
"encoder.block.11.layer.1.DenseReluDense.wo.weight",
"encoder.block.11.layer.1.layer_norm.weight",
"encoder.block.12.layer.0.SelfAttention.k.weight",
"encoder.block.12.layer.0.SelfAttention.o.weight",
"encoder.block.12.layer.0.SelfAttention.q.weight",
"encoder.block.12.layer.0.SelfAttention.v.weight",
"encoder.block.12.layer.0.layer_norm.weight",
"encoder.block.12.layer.1.DenseReluDense.wi_0.weight",
"encoder.block.12.layer.1.DenseReluDense.wi_1.weight",
"encoder.block.12.layer.1.DenseReluDense.wo.weight",
"encoder.block.12.layer.1.layer_norm.weight",
"encoder.block.13.layer.0.SelfAttention.k.weight",
"encoder.block.13.layer.0.SelfAttention.o.weight",
"encoder.block.13.layer.0.SelfAttention.q.weight",
"encoder.block.13.layer.0.SelfAttention.v.weight",
"encoder.block.13.layer.0.layer_norm.weight",
"encoder.block.13.layer.1.DenseReluDense.wi_0.weight",
"encoder.block.13.layer.1.DenseReluDense.wi_1.weight",
"encoder.block.13.layer.1.DenseReluDense.wo.weight",
"encoder.block.13.layer.1.layer_norm.weight",
"encoder.block.14.layer.0.SelfAttention.k.weight",
"encoder.block.14.layer.0.SelfAttention.o.weight",
"encoder.block.14.layer.0.SelfAttention.q.weight",
"encoder.block.14.layer.0.SelfAttention.v.weight",
"encoder.block.14.layer.0.layer_norm.weight",
"encoder.block.14.layer.1.DenseReluDense.wi_0.weight",
"encoder.block.14.layer.1.DenseReluDense.wi_1.weight",
"encoder.block.14.layer.1.DenseReluDense.wo.weight",
"encoder.block.14.layer.1.layer_norm.weight",
"encoder.block.15.layer.0.SelfAttention.k.weight",
"encoder.block.15.layer.0.SelfAttention.o.weight",
"encoder.block.15.layer.0.SelfAttention.q.weight",
"encoder.block.15.layer.0.SelfAttention.v.weight",
"encoder.block.15.layer.0.layer_norm.weight",
"encoder.block.15.layer.1.DenseReluDense.wi_0.weight",
"encoder.block.15.layer.1.DenseReluDense.wi_1.weight",
"encoder.block.15.layer.1.DenseReluDense.wo.weight",
"encoder.block.15.layer.1.layer_norm.weight",
"encoder.block.16.layer.0.SelfAttention.k.weight",
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state_dict_ = {}
for name, param in state_dict.items():
if name in name_list:
state_dict_[name] = param
return state_dict_
|