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Merge branch 'dev' into test-fp8

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Kohaku-Blueleaf
2023-12-14 16:54:45 +08:00
parent 39ebd5684b aeaf1c510f
commit 0fb34b57b8
17 changed files with 993 additions and 91 deletions
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37
extensions-builtin/Lora/network_oft.py
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@@ -21,6 +21,8 @@ class NetworkModuleOFT(network.NetworkModule):
self.lin_module = None
self.org_module: list[torch.Module] = [self.sd_module]
self.scale = 1.0
# kohya-ss
if "oft_blocks" in weights.w.keys():
self.is_kohya = True
@@ -53,12 +55,18 @@ class NetworkModuleOFT(network.NetworkModule):
self.constraint = None
self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
def calc_updown_kb(self, orig_weight, multiplier):
def calc_updown(self, orig_weight):
oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
oft_blocks = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix
eye = torch.eye(self.block_size, device=self.oft_blocks.device)
if self.is_kohya:
block_Q = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix
norm_Q = torch.norm(block_Q.flatten())
new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse())
R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
R = R * multiplier + torch.eye(self.block_size, device=orig_weight.device)
# This errors out for MultiheadAttention, might need to be handled up-stream
merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
@@ -72,26 +80,3 @@ class NetworkModuleOFT(network.NetworkModule):
updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
output_shape = orig_weight.shape
return self.finalize_updown(updown, orig_weight, output_shape)
def calc_updown(self, orig_weight):
# if alpha is a very small number as in coft, calc_scale() will return a almost zero number so we ignore it
multiplier = self.multiplier()
return self.calc_updown_kb(orig_weight, multiplier)
# override to remove the multiplier/scale factor; it's already multiplied in get_weight
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)
updown = updown.reshape(output_shape)
if len(output_shape) == 4:
updown = updown.reshape(output_shape)
if orig_weight.size().numel() == updown.size().numel():
updown = updown.reshape(orig_weight.shape)
if ex_bias is not None:
ex_bias = ex_bias * self.multiplier()
return updown, ex_bias

3
extensions-builtin/Lora/networks.py
View File

@@ -159,7 +159,8 @@ def load_network(name, network_on_disk):
bundle_embeddings = {}
for key_network, weight in sd.items():
key_network_without_network_parts, network_part = key_network.split(".", 1)
key_network_without_network_parts, _, network_part = key_network.partition(".")
if key_network_without_network_parts == "bundle_emb":
emb_name, vec_name = network_part.split(".", 1)
emb_dict = bundle_embeddings.get(emb_name, {})