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fix: get boft params from weight shape

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v0xie 2024-02-08 11:55:05 -08:00
parent 2f1073dc6e
commit 325eaeb584
1 changed files with 9 additions and 7 deletions
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16
extensions-builtin/Lora/network_oft.py
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@ -1,6 +1,6 @@
import torch import torch
import network import network
from lyco_helpers import factorization, butterfly_factor from lyco_helpers import factorization
from einops import rearrange from einops import rearrange
@ -37,10 +37,8 @@ class NetworkModuleOFT(network.NetworkModule):
self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size) self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size)
self.is_boft = False self.is_boft = False
if "boft" in weights.w.keys(): if weights.w["oft_diag"].dim() == 4:
self.is_boft = True self.is_boft = True
self.boft_b = weights.w["boft_b"]
self.boft_m = weights.w["boft_m"]
is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear] is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear]
is_conv = type(self.sd_module) in [torch.nn.Conv2d] is_conv = type(self.sd_module) in [torch.nn.Conv2d]
@ -59,7 +57,11 @@ class NetworkModuleOFT(network.NetworkModule):
self.block_size = self.out_dim // self.dim self.block_size = self.out_dim // self.dim
elif self.is_boft: elif self.is_boft:
self.constraint = None self.constraint = None
self.block_size, self.block_num = butterfly_factor(self.out_dim, self.dim) self.boft_m = weights.w["oft_diag"].shape[0]
self.block_num = weights.w["oft_diag"].shape[1]
self.block_size = weights.w["oft_diag"].shape[2]
self.boft_b = self.block_size
#self.block_size, self.block_num = butterfly_factor(self.out_dim, self.dim)
else: else:
self.constraint = None self.constraint = None
self.block_size, self.num_blocks = factorization(self.out_dim, self.dim) self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
@ -88,8 +90,8 @@ class NetworkModuleOFT(network.NetworkModule):
merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...') merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
else: else:
scale = 1.0 scale = 1.0
m = self.boft_m.to(device=oft_blocks.device, dtype=oft_blocks.dtype) m = self.boft_m
b = self.boft_b.to(device=oft_blocks.device, dtype=oft_blocks.dtype) b = self.boft_b
r_b = b // 2 r_b = b // 2
inp = orig_weight inp = orig_weight
for i in range(m): for i in range(m):