Merge 15136ca505 into fa53f71ec0

networks/lora_flux.py

@@ -48,6 +48,8 @@ class LoRAModule(torch.nn.Module):
         split_dims: Optional[List[int]] = None,
         ggpo_beta: Optional[float] = None,
         ggpo_sigma: Optional[float] = None,
+        mgpo_rho: float | None = None,
+        mgpo_beta: float | None = None,
     ):
         """
         if alpha == 0 or None, alpha is rank (no scaling).
@@ -117,6 +119,25 @@ class LoRAModule(torch.nn.Module):
             self.initialize_norm_cache(org_module.weight)
             self.org_module_shape: tuple[int] = org_module.weight.shape
 
+        self.ggpo_sigma = ggpo_sigma
+        self.ggpo_beta = ggpo_beta
+
+        self.mgpo_rho = mgpo_rho
+        self.mgpo_beta = mgpo_beta
+
+        # EMA of gradient magnitudes for adaptive normalization
+        self.register_buffer('_grad_magnitude_ema_down', torch.tensor(1.0), persistent=False)
+        self.register_buffer('_grad_magnitude_ema_up', torch.tensor(1.0), persistent=False)
+
+        self.optimizer: torch.optim.Optimizer | None = None
+
+        if self.ggpo_beta is not None and self.ggpo_sigma is not None:
+            self.combined_weight_norms = None
+            self.grad_norms = None
+            self.perturbation_norm_factor = 1.0 / math.sqrt(org_module.weight.shape[0])
+            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
         self.org_module.forward = self.forward
@@ -158,6 +179,18 @@ class LoRAModule(torch.nn.Module):
 
             lx = self.lora_up(lx)
 
+            # LoRA Momentum-Guided Perturbation Optimization (MGPO)
+            if (
+                self.training
+                and hasattr(self, "mgpo_rho")
+                and self.mgpo_rho is not None
+                and hasattr(self, "optimizer")
+                and self.optimizer is not None
+            ):
+                mgpo_perturbation_output = self.get_mgpo_output_perturbation(x)
+                if mgpo_perturbation_output is not None:
+                    return org_forwarded + (self.multiplier * scale * lx) + mgpo_perturbation_output
+
             # LoRA Gradient-Guided Perturbation Optimization
             if (
                 self.training
@@ -304,6 +337,97 @@ class LoRAModule(torch.nn.Module):
                 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)
 
+    def update_gradient_ema(self):
+        """
+        Update EMA of gradient magnitudes for adaptive perturbation normalization
+        Formula: ḡₗ⁽ᵗ⁾ = β * ḡₗ⁽ᵗ⁻¹⁾ + (1 - β) * ||∇ΔWₗL||₂
+        """
+        if self.mgpo_beta is None:
+            return
+        
+        # Update EMA for lora_down gradient magnitude
+        if self.lora_down.weight.grad is not None:
+            current_grad_norm = torch.norm(self.lora_down.weight.grad, p=2)
+            self._grad_magnitude_ema_down.mul_(self.mgpo_beta).add_(
+                current_grad_norm, alpha=(1 - self.mgpo_beta)
+            )
+        
+        # Update EMA for lora_up gradient magnitude
+        if self.lora_up.weight.grad is not None:
+            current_grad_norm = torch.norm(self.lora_up.weight.grad, p=2)
+            self._grad_magnitude_ema_up.mul_(self.mgpo_beta).add_(
+                current_grad_norm, alpha=(1 - self.mgpo_beta)
+            )
+
+    def get_mgpo_output_perturbation(self, x: Tensor) -> Tensor | None:
+        """
+        Generate MGPO perturbation using both momentum direction and gradient magnitude normalization
+
+        Full MGPO Formula: ε = -ρ · (vₜ / ||vₜ||₂) · (ḡₗ⁽ᵗ⁾)⁻¹
+        Where:
+        - ε = perturbation vector
+        - ρ = perturbation radius (mgpo_rho)
+        - vₜ = momentum vector from optimizer (exp_avg) - provides DIRECTION
+        - ||vₜ||₂ = L2 norm of momentum for unit direction
+        - ḡₗ⁽ᵗ⁾ = EMA of gradient magnitude - provides ADAPTIVE SCALING
+
+        Two separate EMAs:
+        1. Momentum EMA (from Adam): vₜ = β₁ * vₜ₋₁ + (1 - β₁) * ∇L(Wₜ)
+        2. Gradient Magnitude EMA: ḡₗ⁽ᵗ⁾ = β * ḡₗ⁽ᵗ⁻¹⁾ + (1 - β) * ||∇L(Wₜ)||₂
+        """
+        if self.optimizer is None or self.mgpo_rho is None or self.mgpo_beta is None:
+            return None
+
+        total_perturbation_scale = 0.0
+        valid_params = 0
+
+        # Handle both single and split dims cases
+        if self.split_dims is None:
+            params_and_emas = [
+                (self.lora_down.weight, self._grad_magnitude_ema_down),
+                (self.lora_up.weight, self._grad_magnitude_ema_up),
+            ]
+        else:
+            # For split dims, use average EMA (or extend to per-param EMAs)
+            avg_ema = (self._grad_magnitude_ema_down + self._grad_magnitude_ema_up) / 2
+            params_and_emas = []
+            for lora_down in self.lora_down:
+                params_and_emas.append((lora_down.weight, avg_ema))
+            for lora_up in self.lora_up:
+                params_and_emas.append((lora_up.weight, avg_ema))
+
+        for param, grad_ema in params_and_emas:
+            if param in self.optimizer.state and "exp_avg" in self.optimizer.state[param]:
+                # Get momentum direction: vₜ / ||vₜ||₂
+                momentum = self.optimizer.state[param]["exp_avg"]
+                momentum_norm = torch.norm(momentum, p=2)
+
+                if momentum_norm > 1e-8 and grad_ema > 1e-8:
+                    # Apply full MGPO formula: ρ · (momentum_direction) · (1/grad_magnitude_ema)
+                    direction_component = momentum_norm  # We'll use this for scaling
+                    adaptive_scale = 1.0 / grad_ema  # Adaptive normalization
+
+                    perturbation_scale = self.mgpo_rho * direction_component * adaptive_scale
+                    total_perturbation_scale += perturbation_scale.item()
+                    valid_params += 1
+
+        if valid_params == 0:
+            return None
+
+        # Average perturbation scale across all valid parameters
+        avg_perturbation_scale = total_perturbation_scale / valid_params
+
+        with torch.no_grad():
+            # Generate random perturbation scaled by MGPO formula
+            perturbation = torch.randn(self.org_module_shape, dtype=self.dtype, device=self.device)
+            perturbation.mul_(avg_perturbation_scale)
+            perturbation_output = x @ perturbation.T  # Result: (batch × n)
+
+        return perturbation_output
+
+    def register_optimizer(self, optimizer):
+        self.optimizer = optimizer
+
     @property
     def device(self):
         return next(self.parameters()).device
@@ -574,6 +698,15 @@ def create_network(
     if ggpo_sigma is not None:
         ggpo_sigma = float(ggpo_sigma)
 
+    mgpo_beta = kwargs.get("mgpo_beta", None)
+    mgpo_rho = kwargs.get("mgpo_rho", None)
+
+    if mgpo_beta is not None:
+        mgpo_beta = float(mgpo_beta)
+
+    if mgpo_rho is not None:
+        mgpo_rho = float(mgpo_rho)
+
     # train T5XXL
     train_t5xxl = kwargs.get("train_t5xxl", False)
     if train_t5xxl is not None:
@@ -642,6 +775,8 @@ def create_network(
         reg_dims=reg_dims,
         ggpo_beta=ggpo_beta,
         ggpo_sigma=ggpo_sigma,
+        mgpo_rho=mgpo_rho,
+        mgpo_beta=mgpo_beta,
         reg_lrs=reg_lrs,
         verbose=verbose,
     )
@@ -745,6 +880,8 @@ class LoRANetwork(torch.nn.Module):
         reg_dims: Optional[Dict[str, int]] = None,
         ggpo_beta: Optional[float] = None,
         ggpo_sigma: Optional[float] = None,
+        mgpo_rho: Optional[float] = None,
+        mgpo_beta: Optional[float] = None,
         reg_lrs: Optional[Dict[str, float]] = None,
         verbose: Optional[bool] = False,
     ) -> None:
@@ -790,6 +927,8 @@ class LoRANetwork(torch.nn.Module):
         if ggpo_beta is not None and ggpo_sigma is not None:
             logger.info(f"LoRA-GGPO training sigma: {ggpo_sigma} beta: {ggpo_beta}")
 
+        if mgpo_beta is not None and mgpo_rho is not None:
+            logger.info(f"LoRA-MGPO training rho: {mgpo_rho} beta: {mgpo_beta}")
         if self.split_qkv:
             logger.info(f"split qkv for LoRA")
         if self.train_blocks is not None:
@@ -924,6 +1063,8 @@ class LoRANetwork(torch.nn.Module):
                                 split_dims=split_dims,
                                 ggpo_beta=ggpo_beta,
                                 ggpo_sigma=ggpo_sigma,
+                                mgpo_rho=mgpo_rho,
+                                mgpo_beta=mgpo_beta,
                             )
                             loras.append(lora)
 

tests/networks/test_lora_flux_mgpo.py

@@ -0,0 +1,119 @@
+import pytest
+import torch
+import math
+from networks.lora_flux import LoRAModule
+
+
+class MockLinear(torch.nn.Module):
+    def __init__(self, in_features, out_features):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.randn(out_features, in_features))
+        self.in_features = in_features
+        self.out_features = out_features
+
+    def forward(self, x):
+        return torch.matmul(x, self.weight.t())
+
+    def state_dict(self):
+        return {"weight": self.weight}
+
+
+class MockOptimizer:
+    def __init__(self, param):
+        self.state = {param: {"exp_avg": torch.randn_like(param)}}
+
+
+@pytest.fixture
+def lora_module():
+    org_module = MockLinear(10, 20)
+    lora_module = LoRAModule(org_module, org_module, multiplier=1.0, lora_dim=4, alpha=1.0, mgpo_rho=0.1, mgpo_beta=0.9)
+    # Manually set org_module_shape to match the original module's weight
+    lora_module.org_module_shape = org_module.weight.shape
+    return lora_module
+
+
+def test_mgpo_parameter_initialization(lora_module):
+    """Test MGPO-specific parameter initialization."""
+    # Check MGPO-specific attributes
+    assert hasattr(lora_module, "mgpo_rho")
+    assert hasattr(lora_module, "mgpo_beta")
+    assert lora_module.mgpo_rho == 0.1
+    assert lora_module.mgpo_beta == 0.9
+
+    # Check EMA parameters initialization
+    assert hasattr(lora_module, "_grad_magnitude_ema_down")
+    assert hasattr(lora_module, "_grad_magnitude_ema_up")
+    assert isinstance(lora_module._grad_magnitude_ema_down, torch.nn.Parameter)
+    assert isinstance(lora_module._grad_magnitude_ema_up, torch.nn.Parameter)
+    assert lora_module._grad_magnitude_ema_down.requires_grad == False
+    assert lora_module._grad_magnitude_ema_up.requires_grad == False
+    assert lora_module._grad_magnitude_ema_down.item() == 1.0
+    assert lora_module._grad_magnitude_ema_up.item() == 1.0
+
+
+def test_update_gradient_ema(lora_module):
+    """Test gradient EMA update method."""
+    # Ensure method works when mgpo_beta is set
+    lora_module.lora_down.weight.grad = torch.randn_like(lora_module.lora_down.weight)
+    lora_module.lora_up.weight.grad = torch.randn_like(lora_module.lora_up.weight)
+
+    # Store initial EMA values
+    initial_down_ema = lora_module._grad_magnitude_ema_down.clone()
+    initial_up_ema = lora_module._grad_magnitude_ema_up.clone()
+
+    # Update gradient EMA
+    lora_module.update_gradient_ema()
+
+    # Check EMA update logic
+    down_grad_norm = torch.norm(lora_module.lora_down.weight.grad, p=2)
+    up_grad_norm = torch.norm(lora_module.lora_up.weight.grad, p=2)
+
+    # Verify EMA calculation
+    expected_down_ema = lora_module.mgpo_beta * initial_down_ema + (1 - lora_module.mgpo_beta) * down_grad_norm
+    expected_up_ema = lora_module.mgpo_beta * initial_up_ema + (1 - lora_module.mgpo_beta) * up_grad_norm
+
+    assert torch.allclose(lora_module._grad_magnitude_ema_down, expected_down_ema, rtol=1e-5)
+    assert torch.allclose(lora_module._grad_magnitude_ema_up, expected_up_ema, rtol=1e-5)
+
+    # Test when mgpo_beta is None
+    lora_module.mgpo_beta = None
+    lora_module.update_gradient_ema()  # Should not raise an exception
+
+
+def test_get_mgpo_output_perturbation(lora_module):
+    """Test MGPO perturbation generation."""
+    # Create a mock optimizer
+    mock_optimizer = MockOptimizer(lora_module.lora_down.weight)
+    lora_module.register_optimizer(mock_optimizer)
+
+    # Prepare input
+    x = torch.randn(5, 10)  # batch × input_dim
+
+    # Ensure method works with valid conditions
+    perturbation = lora_module.get_mgpo_output_perturbation(x)
+
+    # Verify perturbation characteristics
+    assert perturbation is not None
+    assert isinstance(perturbation, torch.Tensor)
+    assert perturbation.shape == (x.shape[0], lora_module.org_module.out_features)
+
+    # Test when conditions are not met
+    lora_module.optimizer = None
+    lora_module.mgpo_rho = None
+    lora_module.mgpo_beta = None
+
+    no_perturbation = lora_module.get_mgpo_output_perturbation(x)
+    assert no_perturbation is None
+
+
+def test_register_optimizer(lora_module):
+    """Test optimizer registration method."""
+    # Create a mock optimizer
+    mock_optimizer = MockOptimizer(lora_module.lora_down.weight)
+
+    # Register optimizer
+    lora_module.register_optimizer(mock_optimizer)
+
+    # Verify optimizer is correctly registered
+    assert hasattr(lora_module, "optimizer")
+    assert lora_module.optimizer == mock_optimizer

train_network.py

@@ -750,6 +750,9 @@ class NetworkTrainer:
         optimizer_name, optimizer_args, optimizer = train_util.get_optimizer(args, trainable_params)
         optimizer_train_fn, optimizer_eval_fn = train_util.get_optimizer_train_eval_fn(optimizer, args)
 
+        if hasattr(network, "register_optimizer"):
+            network.register_optimizer(optimizer)
+
         # prepare dataloader
         # strategies are set here because they cannot be referenced in another process. Copy them with the dataset
         # some strategies can be None
@@ -1438,6 +1441,8 @@ class NetworkTrainer:
                             network.update_grad_norms()
                         if hasattr(network, "update_norms"):
                             network.update_norms()
+                        if hasattr(network, "update_gradient_ema"):
+                            network.update_gradient_ema()
 
                     optimizer.step()
                     lr_scheduler.step()