Cache weight norms estimate on initialization. Move to update norms every step

networks/lora_flux.py

@@ -15,6 +15,7 @@ from diffusers import AutoencoderKL
 from transformers import CLIPTextModel
 import numpy as np
 import torch
+from torch import Tensor
 import re
 from library.utils import setup_logging
 from library.sdxl_original_unet import SdxlUNet2DConditionModel
@@ -145,8 +146,13 @@ class LoRAModule(torch.nn.Module):
         self.ggpo_sigma = ggpo_sigma
         self.ggpo_beta = ggpo_beta
 
-        self.perturbation_norm_factor = 1.0 / math.sqrt(org_module.weight.shape[0])
+        if self.ggpo_beta is not None and self.ggpo_sigma is not None:
-        self._org_module_weight = self.org_module.weight.detach()
+            self.combined_weight_norms = None
+            self.grad_norms = None
+            self.perturbation_norm_factor = 1.0 / math.sqrt(org_module.weight.shape[0])
+            self.perturbation_seed = torch.randint(0, 2**32 - 1, (1,)).detach().item()
+            self.initialize_norm_cache(org_module.weight)
+            self.org_module_shape: tuple[int] = org_module.weight.shape
 
     def apply_to(self):
         self.org_forward = self.org_module.forward
@@ -187,10 +193,12 @@ class LoRAModule(torch.nn.Module):
             lx = self.lora_up(lx)
 
             # LoRA Gradient-Guided Perturbation Optimization
-            if self.training and hasattr(self, 'perturbation_seed') and self.ggpo_sigma is not None and self.ggpo_beta is not None:
+            if self.training and hasattr(self, 'perturbation_seed') and self.ggpo_sigma is not None and self.ggpo_beta is not None and self.combined_weight_norms is not None and self.grad_norms is not None:
-                with torch.no_grad(), torch.autocast(self.device.type), temp_random_seed(self.perturbation_seed):
+                with torch.no_grad(), temp_random_seed(self.perturbation_seed):
-                    perturbation = torch.randn_like(self._org_module_weight,  dtype=self.dtype, device=self.device)
+                    perturbation_scale = (self.ggpo_sigma * torch.sqrt(self.combined_weight_norms ** 2)) + (self.ggpo_beta * (self.grad_norms ** 2))
-                    perturbation.mul_(self.perturbation_scale_factor)
+                    perturbation_scale_factor = (perturbation_scale * self.perturbation_norm_factor).to(self.device)
+                    perturbation = torch.randn(self.org_module_shape,  dtype=self.dtype, device=self.device)
+                    perturbation.mul_(perturbation_scale_factor)
                     perturbation_output = x @ perturbation.T  # Result: (batch × n)
                 return org_forwarded + (self.multiplier * scale * lx) + perturbation_output
             else:
@@ -221,6 +229,69 @@ class LoRAModule(torch.nn.Module):
 
             return org_forwarded + torch.cat(lxs, dim=-1) * self.multiplier * scale
 
+    @torch.no_grad()
+    def initialize_norm_cache(self, org_module_weight: Tensor):
+        # Choose a reasonable sample size
+        n_rows = org_module_weight.shape[0]
+        sample_size = min(1000, n_rows)  # Cap at 1000 samples or use all if smaller
+        
+        # Sample random indices across all rows
+        indices = torch.randperm(n_rows)[:sample_size]
+        
+        # Convert to a supported data type first, then index
+        # Use float32 for indexing operations
+        weights_float32 = org_module_weight.to(dtype=torch.float32)
+        sampled_weights = weights_float32[indices].to(device=self.device)
+        
+        # Calculate sampled norms
+        sampled_norms = torch.norm(sampled_weights, dim=1, keepdim=True)
+        
+        # Store the mean norm as our estimate
+        self.org_weight_norm_estimate = sampled_norms.mean()
+        
+        # Optional: store standard deviation for confidence intervals
+        self.org_weight_norm_std = sampled_norms.std()
+        
+        # Free memory
+        del sampled_weights, weights_float32
+
+    @torch.no_grad()
+    def validate_norm_approximation(self, org_module_weight: Tensor, verbose=True):
+        # Calculate the true norm (this will be slow but it's just for validation)
+        true_norms = []
+        chunk_size = 1024  # Process in chunks to avoid OOM
+        
+        for i in range(0, org_module_weight.shape[0], chunk_size):
+            end_idx = min(i + chunk_size, org_module_weight.shape[0])
+            chunk = org_module_weight[i:end_idx].to(device=self.device, dtype=self.dtype)
+            chunk_norms = torch.norm(chunk, dim=1, keepdim=True)
+            true_norms.append(chunk_norms.cpu())
+            del chunk
+            
+        true_norms = torch.cat(true_norms, dim=0)
+        true_mean_norm = true_norms.mean().item()
+        
+        # Compare with our estimate
+        estimated_norm = self.org_weight_norm_estimate.item()
+        
+        # Calculate error metrics
+        absolute_error = abs(true_mean_norm - estimated_norm)
+        relative_error = absolute_error / true_mean_norm * 100  # as percentage
+        
+        if verbose:
+            logger.info(f"True mean norm: {true_mean_norm:.6f}")
+            logger.info(f"Estimated norm: {estimated_norm:.6f}")
+            logger.info(f"Absolute error: {absolute_error:.6f}")
+            logger.info(f"Relative error: {relative_error:.2f}%")
+            
+        return {
+            'true_mean_norm': true_mean_norm,
+            'estimated_norm': estimated_norm,
+            'absolute_error': absolute_error,
+            'relative_error': relative_error
+        }
+
+
     @torch.no_grad()
     def update_norms(self):
         # Not running GGPO so not currently running update norms
@@ -228,8 +299,20 @@ class LoRAModule(torch.nn.Module):
             return
 
         # only update norms when we are training 
-        if self.lora_down.weight.requires_grad is not True:
+        if self.training is False:
-            print(f"skipping update_norms for {self.lora_name}")
+            return
+
+        module_weights = self.lora_up.weight @ self.lora_down.weight
+        module_weights.mul(self.scale)
+
+        self.weight_norms = torch.norm(module_weights, dim=1, keepdim=True)
+        self.combined_weight_norms = torch.sqrt((self.org_weight_norm_estimate**2) + 
+                                           torch.sum(module_weights**2, dim=1, keepdim=True))
+
+    @torch.no_grad()
+    def update_grad_norms(self):
+        if self.training is False:
+            print(f"skipping update_grad_norms for {self.lora_name}")
             return
 
         lora_down_grad = None
@@ -241,29 +324,12 @@ class LoRAModule(torch.nn.Module):
             elif name == "lora_up.weight":
                 lora_up_grad = param.grad
 
-        with torch.autocast(self.device.type):
-            module_weights = self.scale * (self.lora_up.weight @ self.lora_down.weight)
-            org_device = self._org_module_weight.device
-            org_dtype = self._org_module_weight.dtype
-            org_weight = self._org_module_weight.to(device=self.device, dtype=self.dtype)
-            combined_weight = org_weight + module_weights
-
-            self.combined_weight_norms = torch.norm(combined_weight, dim=1, keepdim=True)
-
-            self._org_module_weight.to(device=org_device, dtype=org_dtype)
-
-
         # Calculate gradient norms if we have both gradients
         if lora_down_grad is not None and lora_up_grad is not None:
             with torch.autocast(self.device.type):
                 approx_grad = self.scale * ((self.lora_up.weight @ lora_down_grad) + (lora_up_grad @ self.lora_down.weight))
                 self.grad_norms = torch.norm(approx_grad, dim=1, keepdim=True)
 
-        self.perturbation_scale = (self.ggpo_sigma * torch.sqrt(self.combined_weight_norms ** 2)) + (self.ggpo_beta * (self.grad_norms ** 2))
-        self.perturbation_scale_factor = (self.perturbation_scale * self.perturbation_norm_factor).to(self.device)
-
-        # LoRA Gradient-Guided Perturbation Optimization
-        self.perturbation_seed = torch.randint(0, 2**32 - 1, (1,)).detach().item()
 
     @property
     def device(self):
@@ -922,6 +988,32 @@ class LoRANetwork(torch.nn.Module):
         for lora in self.text_encoder_loras + self.unet_loras:
             lora.update_norms()
 
+    def update_grad_norms(self):
+        for lora in self.text_encoder_loras + self.unet_loras:
+            lora.update_grad_norms()
+
+    def grad_norms(self) -> Tensor:
+        grad_norms = []
+        for lora in self.text_encoder_loras + self.unet_loras:
+            if hasattr(lora, "grad_norms") and lora.grad_norms is not None:
+                grad_norms.append(lora.grad_norms.mean(dim=0))
+        return torch.stack(grad_norms) if len(grad_norms) > 0 else torch.tensor([])
+
+    def weight_norms(self) -> Tensor:
+        weight_norms = []
+        for lora in self.text_encoder_loras + self.unet_loras:
+            if hasattr(lora, "weight_norms") and lora.weight_norms is not None:
+                weight_norms.append(lora.weight_norms.mean(dim=0))
+        return torch.stack(weight_norms) if len(weight_norms) > 0 else torch.tensor([])
+
+    def combined_weight_norms(self) -> Tensor:
+        combined_weight_norms = []
+        for lora in self.text_encoder_loras + self.unet_loras:
+            if hasattr(lora, "combined_weight_norms") and lora.combined_weight_norms is not None:
+                combined_weight_norms.append(lora.combined_weight_norms.mean(dim=0))
+        return torch.stack(combined_weight_norms) if len(combined_weight_norms) > 0 else torch.tensor([])
+
+
     def load_weights(self, file):
         if os.path.splitext(file)[1] == ".safetensors":
             from safetensors.torch import load_file

train_network.py

@@ -69,13 +69,20 @@ class NetworkTrainer:
         keys_scaled=None,
         mean_norm=None,
         maximum_norm=None,
+        mean_grad_norm=None,
+        mean_combined_norm=None
     ):
         logs = {"loss/current": current_loss, "loss/average": avr_loss}
 
         if keys_scaled is not None:
             logs["max_norm/keys_scaled"] = keys_scaled
-            logs["max_norm/average_key_norm"] = mean_norm
             logs["max_norm/max_key_norm"] = maximum_norm
+        if mean_norm is not None:
+            logs["norm/avg_key_norm"] = mean_norm
+        if mean_grad_norm is not None:
+            logs["norm/avg_grad_norm"] = mean_grad_norm
+        if mean_combined_norm is not None:
+            logs["norm/avg_combined_norm"] = mean_combined_norm
 
         lrs = lr_scheduler.get_last_lr()
         for i, lr in enumerate(lrs):
@@ -1400,10 +1407,12 @@ class NetworkTrainer:
                             params_to_clip = accelerator.unwrap_model(network).get_trainable_params()
                             accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
 
-                    if global_step % 5 == 0:
+                        if hasattr(network, "update_grad_norms"):
+                            network.update_grad_norms()
                         if hasattr(network, "update_norms"):
                             network.update_norms()
 
+
                     optimizer.step()
                     lr_scheduler.step()
                     optimizer.zero_grad(set_to_none=True)
@@ -1412,9 +1421,23 @@ class NetworkTrainer:
                     keys_scaled, mean_norm, maximum_norm = accelerator.unwrap_model(network).apply_max_norm_regularization(
                         args.scale_weight_norms, accelerator.device
                     )
+                    mean_grad_norm = None
+                    mean_combined_norm = None
                     max_mean_logs = {"Keys Scaled": keys_scaled, "Average key norm": mean_norm}
                 else:
-                    keys_scaled, mean_norm, maximum_norm = None, None, None
+                    if hasattr(network, "weight_norms"):
+                        mean_norm = network.weight_norms().mean().item()
+                        mean_grad_norm = network.grad_norms().mean().item()
+                        mean_combined_norm = network.combined_weight_norms().mean().item()
+                        weight_norms = network.weight_norms()
+                        maximum_norm = weight_norms.max().item() if weight_norms.numel() > 0 else None
+                        keys_scaled = None
+                        max_mean_logs = {"avg weight norm": mean_norm, "avg grad norm": mean_grad_norm, "avg comb norm": mean_combined_norm}
+                    else:
+                        keys_scaled, mean_norm, maximum_norm = None, None, None
+                        mean_grad_norm = None
+                        mean_combined_norm = None
+                        max_mean_logs = {}
 
                 # Checks if the accelerator has performed an optimization step behind the scenes
                 if accelerator.sync_gradients:
@@ -1446,14 +1469,11 @@ class NetworkTrainer:
                 loss_recorder.add(epoch=epoch, step=step, loss=current_loss)
                 avr_loss: float = loss_recorder.moving_average
                 logs = {"avr_loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
-                progress_bar.set_postfix(**logs)
+                progress_bar.set_postfix(**{**max_mean_logs, **logs})
-
-                if args.scale_weight_norms:
-                    progress_bar.set_postfix(**{**max_mean_logs, **logs})
 
                 if is_tracking:
                     logs = self.generate_step_logs(
-                        args, current_loss, avr_loss, lr_scheduler, lr_descriptions, optimizer, keys_scaled, mean_norm, maximum_norm
+                        args, current_loss, avr_loss, lr_scheduler, lr_descriptions, optimizer, keys_scaled, mean_norm, maximum_norm, mean_grad_norm, mean_combined_norm
                     )
                     self.step_logging(accelerator, logs, global_step, epoch + 1)