Merge pull request #12503 from AUTOMATIC1111/extra-norm-module

Add Norm Module to lora ext and add "bias" support
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AUTOMATIC1111 2023-08-13 08:28:48 +03:00 committed by GitHub
commit da80d649fd
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4 changed files with 104 additions and 11 deletions

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@ -133,7 +133,7 @@ class NetworkModule:
return 1.0
def finalize_updown(self, updown, orig_weight, output_shape):
def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
if self.bias is not None:
updown = updown.reshape(self.bias.shape)
updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
@ -145,7 +145,10 @@ class NetworkModule:
if orig_weight.size().numel() == updown.size().numel():
updown = updown.reshape(orig_weight.shape)
return updown * self.calc_scale() * self.multiplier()
if ex_bias is not None:
ex_bias = ex_bias * self.multiplier()
return updown * self.calc_scale() * self.multiplier(), ex_bias
def calc_updown(self, target):
raise NotImplementedError()

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@ -0,0 +1,28 @@
import network
class ModuleTypeNorm(network.ModuleType):
def create_module(self, net: network.Network, weights: network.NetworkWeights):
if all(x in weights.w for x in ["w_norm", "b_norm"]):
return NetworkModuleNorm(net, weights)
return None
class NetworkModuleNorm(network.NetworkModule):
def __init__(self, net: network.Network, weights: network.NetworkWeights):
super().__init__(net, weights)
self.w_norm = weights.w.get("w_norm")
self.b_norm = weights.w.get("b_norm")
def calc_updown(self, orig_weight):
output_shape = self.w_norm.shape
updown = self.w_norm.to(orig_weight.device, dtype=orig_weight.dtype)
if self.b_norm is not None:
ex_bias = self.b_norm.to(orig_weight.device, dtype=orig_weight.dtype)
else:
ex_bias = None
return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)

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@ -7,6 +7,7 @@ import network_hada
import network_ia3
import network_lokr
import network_full
import network_norm
import torch
from typing import Union
@ -19,6 +20,7 @@ module_types = [
network_ia3.ModuleTypeIa3(),
network_lokr.ModuleTypeLokr(),
network_full.ModuleTypeFull(),
network_norm.ModuleTypeNorm(),
]
@ -31,6 +33,8 @@ suffix_conversion = {
"resnets": {
"conv1": "in_layers_2",
"conv2": "out_layers_3",
"norm1": "in_layers_0",
"norm2": "out_layers_0",
"time_emb_proj": "emb_layers_1",
"conv_shortcut": "skip_connection",
}
@ -258,20 +262,25 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
purge_networks_from_memory()
def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
weights_backup = getattr(self, "network_weights_backup", None)
bias_backup = getattr(self, "network_bias_backup", None)
if weights_backup is None:
if weights_backup is None and bias_backup is None:
return
if isinstance(self, torch.nn.MultiheadAttention):
self.in_proj_weight.copy_(weights_backup[0])
self.out_proj.weight.copy_(weights_backup[1])
else:
self.weight.copy_(weights_backup)
if weights_backup is not None:
if isinstance(self, torch.nn.MultiheadAttention):
self.in_proj_weight.copy_(weights_backup[0])
self.out_proj.weight.copy_(weights_backup[1])
else:
self.weight.copy_(weights_backup)
if bias_backup is not None:
self.bias.copy_(bias_backup)
def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
"""
Applies the currently selected set of networks to the weights of torch layer self.
If weights already have this particular set of networks applied, does nothing.
@ -294,6 +303,11 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
self.network_weights_backup = weights_backup
bias_backup = getattr(self, "network_bias_backup", None)
if bias_backup is None and getattr(self, 'bias', None) is not None:
bias_backup = self.bias.to(devices.cpu, copy=True)
self.network_bias_backup = bias_backup
if current_names != wanted_names:
network_restore_weights_from_backup(self)
@ -301,13 +315,15 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
module = net.modules.get(network_layer_name, None)
if module is not None and hasattr(self, 'weight'):
with torch.no_grad():
updown = module.calc_updown(self.weight)
updown, ex_bias = module.calc_updown(self.weight)
if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
# inpainting model. zero pad updown to make channel[1] 4 to 9
updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
self.weight += updown
if ex_bias is not None and getattr(self, 'bias', None) is not None:
self.bias += ex_bias
continue
module_q = net.modules.get(network_layer_name + "_q_proj", None)
@ -397,6 +413,36 @@ def network_Conv2d_load_state_dict(self, *args, **kwargs):
return torch.nn.Conv2d_load_state_dict_before_network(self, *args, **kwargs)
def network_GroupNorm_forward(self, input):
if shared.opts.lora_functional:
return network_forward(self, input, torch.nn.GroupNorm_forward_before_network)
network_apply_weights(self)
return torch.nn.GroupNorm_forward_before_network(self, input)
def network_GroupNorm_load_state_dict(self, *args, **kwargs):
network_reset_cached_weight(self)
return torch.nn.GroupNorm_load_state_dict_before_network(self, *args, **kwargs)
def network_LayerNorm_forward(self, input):
if shared.opts.lora_functional:
return network_forward(self, input, torch.nn.LayerNorm_forward_before_network)
network_apply_weights(self)
return torch.nn.LayerNorm_forward_before_network(self, input)
def network_LayerNorm_load_state_dict(self, *args, **kwargs):
network_reset_cached_weight(self)
return torch.nn.LayerNorm_load_state_dict_before_network(self, *args, **kwargs)
def network_MultiheadAttention_forward(self, *args, **kwargs):
network_apply_weights(self)

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@ -40,6 +40,18 @@ if not hasattr(torch.nn, 'Conv2d_forward_before_network'):
if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_network'):
torch.nn.Conv2d_load_state_dict_before_network = torch.nn.Conv2d._load_from_state_dict
if not hasattr(torch.nn, 'GroupNorm_forward_before_network'):
torch.nn.GroupNorm_forward_before_network = torch.nn.GroupNorm.forward
if not hasattr(torch.nn, 'GroupNorm_load_state_dict_before_network'):
torch.nn.GroupNorm_load_state_dict_before_network = torch.nn.GroupNorm._load_from_state_dict
if not hasattr(torch.nn, 'LayerNorm_forward_before_network'):
torch.nn.LayerNorm_forward_before_network = torch.nn.LayerNorm.forward
if not hasattr(torch.nn, 'LayerNorm_load_state_dict_before_network'):
torch.nn.LayerNorm_load_state_dict_before_network = torch.nn.LayerNorm._load_from_state_dict
if not hasattr(torch.nn, 'MultiheadAttention_forward_before_network'):
torch.nn.MultiheadAttention_forward_before_network = torch.nn.MultiheadAttention.forward
@ -50,6 +62,10 @@ torch.nn.Linear.forward = networks.network_Linear_forward
torch.nn.Linear._load_from_state_dict = networks.network_Linear_load_state_dict
torch.nn.Conv2d.forward = networks.network_Conv2d_forward
torch.nn.Conv2d._load_from_state_dict = networks.network_Conv2d_load_state_dict
torch.nn.GroupNorm.forward = networks.network_GroupNorm_forward
torch.nn.GroupNorm._load_from_state_dict = networks.network_GroupNorm_load_state_dict
torch.nn.LayerNorm.forward = networks.network_LayerNorm_forward
torch.nn.LayerNorm._load_from_state_dict = networks.network_LayerNorm_load_state_dict
torch.nn.MultiheadAttention.forward = networks.network_MultiheadAttention_forward
torch.nn.MultiheadAttention._load_from_state_dict = networks.network_MultiheadAttention_load_state_dict