Move test util to library

tests/library/test_lora_util.py

@@ -1,7 +1,7 @@
 import torch
 import pytest
 from library.lora_util import initialize_pissa
-from ..test_util import generate_synthetic_weights
+from library.test_util import generate_synthetic_weights
 
 
 def test_initialize_pissa_basic():

tests/networks/test_lora_flux.py

@@ -2,7 +2,7 @@ import pytest
 import torch
 import torch.nn as nn
 from networks.lora_flux import LoRAModule, LoRANetwork, create_network
-from ..test_util import generate_synthetic_weights
+from library.test_util import generate_synthetic_weights
 from unittest.mock import MagicMock
 
 

tests/test_util.py

@@ -1,34 +0,0 @@
-import torch 
-
-def generate_synthetic_weights(org_weight, seed=42):
-    generator = torch.manual_seed(seed)
-    
-    # Base random normal distribution
-    weights = torch.randn_like(org_weight)
-    
-    # Add structured variance to mimic real-world weight matrices
-    # Techniques to create more realistic weight distributions:
-    
-    # 1. Block-wise variation
-    block_size = max(1, org_weight.shape[0] // 4)
-    for i in range(0, org_weight.shape[0], block_size):
-        block_end = min(i + block_size, org_weight.shape[0])
-        block_variation = torch.randn(1, generator=generator) * 0.3  # Local scaling
-        weights[i:block_end, :] *= (1 + block_variation)
-    
-    # 2. Sparse connectivity simulation
-    sparsity_mask = torch.rand(org_weight.shape, generator=generator) > 0.2  # 20% sparsity
-    weights *= sparsity_mask.float()
-    
-    # 3. Magnitude decay
-    magnitude_decay = torch.linspace(1.0, 0.5, org_weight.shape[0]).unsqueeze(1)
-    weights *= magnitude_decay
-    
-    # 4. Add structured noise
-    structural_noise = torch.randn_like(org_weight) * 0.1
-    weights += structural_noise
-    
-    # Normalize to have similar statistical properties to trained weights
-    weights = (weights - weights.mean()) / weights.std()
-    
-    return weights