From 47a0a9fa9f88a6f79e80e91ec45e8bd4e3930d3c Mon Sep 17 00:00:00 2001
From: rockerBOO <rockerboo@gmail.com>
Date: Wed, 18 Jun 2025 16:46:52 -0400
Subject: [PATCH] Remove random pruning

---
 library/network_utils.py | 21 ++-------------------
 1 file changed, 2 insertions(+), 19 deletions(-)

diff --git a/library/network_utils.py b/library/network_utils.py
index 184dd74a..cfb62565 100644
--- a/library/network_utils.py
+++ b/library/network_utils.py
@@ -87,20 +87,6 @@ def maybe_pruned_save(model, optimizer, enable_pruning=False, pruning_ratio=0.1)
         # Complete fallback: generate a random variance
         return torch.rand_like(param)
 
-    # If variance extraction consistently fails, use random pruning
-    def random_pruning(param, pruning_ratio):
-        mask = torch.ones_like(param, dtype=torch.bool)
-        num_to_prune = int(param.numel() * pruning_ratio)
-        
-        # Create a flat tensor of all indices and shuffle
-        indices = torch.randperm(param.numel())[:num_to_prune]
-        
-        # Create a flattened mask and set selected indices to False
-        flat_mask = mask.view(-1)
-        flat_mask[indices] = False
-        
-        return mask
-
     # Track parameters with gradients
     gradients_exist = False
     for param in model.parameters():
@@ -121,12 +107,9 @@ def maybe_pruned_save(model, optimizer, enable_pruning=False, pruning_ratio=0.1)
         yield
         return
 
-    # Fallback to random pruning if no variance info found
+    # No pruning if no variance info found
     if not param_variances:
-        logger.info("No variance info found, using random pruning")
-        for param in model.parameters():
-            if param.grad is not None and param.requires_grad:
-                pruning_mask[id(param)] = random_pruning(param, pruning_ratio)
+        logger.info("No variance info found, skipping pruning")
         yield
         return