Add test_util

tests/test_util.py

@@ -0,0 +1,34 @@
+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