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https://github.com/kohya-ss/sd-scripts.git
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Fix pruning
This commit is contained in:
rockerBOO
@@ -40,139 +40,70 @@ def maybe_pruned_save(model, optimizer, enable_pruning=False, pruning_ratio=0.1)
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"""
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# Check if we should prune - more flexible detection of IVON-like optimizers
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should_prune = enable_pruning and (
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hasattr(optimizer, "sampled_params") or
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any("h" in state for state in optimizer.state.values()) or
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hasattr(optimizer, "_hess") or # Some optimizers might have this attribute
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"ess" in optimizer.param_groups[0]
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hasattr(optimizer, "sampled_params")
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)
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if not should_prune:
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yield
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return
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# Calculate pruning mask
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pruning_mask = {}
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param_variances = []
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def get_hessian_variance(param):
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"""Determine if a parameter is eligible for variance-based pruning.
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Comprehensive check for IVON-like optimizer variance detection.
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Args:
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param (torch.Tensor): Model parameter to check
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Returns:
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bool: Whether the parameter is eligible for variance-based pruning
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"""
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# 1. Basic parameter eligibility checks
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if not (param.grad is not None and param.requires_grad):
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return False
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# Extract variances from IVON optimizer
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offset = 0
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for group in optimizer.param_groups:
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# Get group-level values
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ess = group["ess"] # λ (lambda)
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weight_decay = group["weight_decay"] # δ (delta)
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hess = group["hess"] # hᵢ (Hessian diagonal)
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# 2. Verify fundamental optimizer characteristics
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if not hasattr(optimizer, 'sampled_params'):
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return False
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# Calculate variance: vᵢ = 1 / (λ × (hᵢ + δ))
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group_variance = 1.0 / (ess * (hess + weight_decay))
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# 3. Parameter group validation
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valid_group_found = False
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for group in optimizer.param_groups:
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# Use object ID to match parameters
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group_params = group.get('params', [])
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if any(id(param) == id(p) for p in group_params):
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# Require effective sample size or Hessian initialization
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if 'ess' in group or 'hess_init' in group:
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valid_group_found = True
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break
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if not valid_group_found:
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return False
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# 4. Optimizer state examination
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if param not in optimizer.state:
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return False
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# 5. Hessian information verification
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param_state = optimizer.state[param]
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hessian_keys = ['h', 'hess', 'Hessian', 'diagonal_hessian']
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for key in hessian_keys:
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if key in param_state:
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h = param_state[key]
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# Validate Hessian tensor
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if (h is not None and
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torch.is_tensor(h) and
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h.numel() > 0 and
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h.dtype in [torch.float32, torch.float64]):
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return True
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return False
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# Comprehensive variance and pruning parameter collection
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variance_eligible_params = []
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for param in model.parameters():
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if param.grad is not None and param.requires_grad:
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# Detect parameter with Hessian variance
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if get_hessian_variance(param):
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# Access Hessian state for variance calculation
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param_state = optimizer.state[param]
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# Map back to individual parameters
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param_offset = 0
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for param in group["params"]:
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if param is not None and param.requires_grad:
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param_numel = param.numel()
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param_slice = slice(param_offset, param_offset + param_numel)
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# Prioritize 'h' key for Hessian, fallback to Hessian-related keys
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hessian_keys = ['h', 'hess']
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h = None
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for key in hessian_keys:
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if key in param_state and param_state[key] is not None:
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h = param_state[key]
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break
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# Get variance for this parameter
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param_var = group_variance[param_slice]
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# Default to uniform Hessian if no specific information
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if h is None:
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h = torch.ones_like(param)
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# Store each element's variance with its location
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flat_param_var = param_var.view(-1)
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for i, var_val in enumerate(flat_param_var):
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param_variances.append((var_val.item(), param, i))
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# Compute a meaningful variance
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try:
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# Use Hessian diagonal to compute variance
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variance = 1.0 / (h.abs().mean() + 1e-8) # Avoid division by zero
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variance_eligible_params.append((variance, param, 0))
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except Exception as e:
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logger.warning(f"Variance computation failed for {param}: {e}")
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# No pruning if no variance-eligible parameters
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if not variance_eligible_params:
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logger.info("No variance-eligible parameters found, skipping pruning")
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param_offset += param_numel
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offset += group["numel"]
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if not param_variances:
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yield
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return
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# Update param_variances for pruning
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# Convert variance to scalar values to avoid tensor comparison
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param_variances = sorted(
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variance_eligible_params,
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key=lambda x: float(x[0]) if torch.is_tensor(x[0]) else x[0],
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reverse=True
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)
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# Create pruning mask
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param_variances.sort(key=lambda x: x[0], reverse=True) # Highest variance first
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num_to_prune = int(len(param_variances) * pruning_ratio)
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pruning_mask = {}
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# Build mask for each parameter
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for param in model.parameters():
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pruning_mask[id(param)] = torch.ones_like(param, dtype=torch.bool)
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# Mark parameters to prune
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# Ensure pruning occurs for LoRA-like parameters
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lora_param_keys = ['lora_A', 'lora_B', 'lora_A2', 'lora_B2']
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for name, param in model.named_parameters():
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if name.split('.')[-1] in lora_param_keys:
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# Ensure each LoRA parameter has some pruning
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mask = pruning_mask[id(param)]
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num_to_prune = int(mask.numel() * pruning_ratio)
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# Flatten and create indices to zero out
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flat_mask = mask.view(-1)
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prune_indices = torch.randperm(flat_mask.numel())[:num_to_prune]
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flat_mask[prune_indices] = False
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# Restore original mask shape
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pruning_mask[id(param)] = flat_mask.view(mask.shape)
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for param in model.parameters():
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mask = pruning_mask[id(param)]
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num_to_prune = int(mask.numel() * pruning_ratio)
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# Flatten and create indices to zero out
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flat_mask = mask.view(-1)
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prune_indices = torch.randperm(flat_mask.numel())[:num_to_prune]
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flat_mask[prune_indices] = False
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# Restore original mask shape
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pruning_mask[id(param)] = flat_mask.view(mask.shape)
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# Monkey patch state_dict
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original_state_dict = model.state_dict
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@@ -1,233 +1,207 @@
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import pytest
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import torch
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import torch.nn as nn
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from contextlib import contextmanager
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from unittest.mock import Mock, MagicMock
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from library.network_utils import maybe_pruned_save
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from ivon import IVON
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# Simple LoRA-like model for testing
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# Simple LoRA-like model for testing
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class MockLoRAModel(nn.Module):
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"""Simple model that mimics LoRA structure."""
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def __init__(self, input_dim=10, hidden_dim=5, rank=2, requires_grad=True):
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super().__init__()
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# Base layer (frozen in real LoRA)
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self.base_layer = nn.Linear(input_dim, hidden_dim)
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# LoRA components with consistent shape
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self.lora_A = nn.Parameter(torch.randn(rank, input_dim) * 0.1, requires_grad=requires_grad)
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self.lora_B = nn.Parameter(torch.randn(hidden_dim, rank) * 0.1, requires_grad=requires_grad)
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self.lora_down = nn.Parameter(torch.randn(rank, input_dim) * 0.1, requires_grad=requires_grad)
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self.lora_up = nn.Parameter(torch.randn(hidden_dim, rank) * 0.1, requires_grad=requires_grad)
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# Another LoRA pair with consistent shape
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self.lora_A2 = nn.Parameter(torch.randn(rank, input_dim) * 0.1, requires_grad=requires_grad)
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self.lora_B2 = nn.Parameter(torch.randn(hidden_dim, rank) * 0.1, requires_grad=requires_grad)
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self.lora_down2 = nn.Parameter(torch.randn(rank, input_dim) * 0.1, requires_grad=requires_grad)
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self.lora_up2 = nn.Parameter(torch.randn(hidden_dim, rank) * 0.1, requires_grad=requires_grad)
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# Ensure gradients are set only if requires_grad is True
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if requires_grad:
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for param in [self.lora_A, self.lora_B, self.lora_A2, self.lora_B2]:
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for param in [self.lora_down, self.lora_up, self.lora_down2, self.lora_up2]:
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param.grad = torch.randn_like(param) * 0.1
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def forward(self, x):
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# Base transformation
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base_out = self.base_layer(x)
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# LoRA adaptation
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lora_out1 = x @ self.lora_A.T @ self.lora_B.T
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lora_out2 = x @ self.lora_A2.T @ self.lora_B2.T
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lora_out1 = x @ self.lora_down.T @ self.lora_up.T
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lora_out2 = x @ self.lora_down2.T @ self.lora_up2.T
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return base_out + lora_out1 + lora_out2
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def get_trainable_params(self):
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"""Return only LoRA parameters (simulating LoRA training)."""
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params = []
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for attr_name in dir(self):
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if attr_name.startswith('lora_') and isinstance(getattr(self, attr_name), torch.nn.Parameter):
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if attr_name.startswith("lora_") and isinstance(getattr(self, attr_name), torch.nn.Parameter):
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param = getattr(self, attr_name)
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if param.requires_grad:
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params.append(param)
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return params
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# Pytest fixtures
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@pytest.fixture
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def mock_model():
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"""Create a mock LoRA model for testing."""
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model = MockLoRAModel(input_dim=10, hidden_dim=5, rank=2)
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# Add gradients to make parameters look "trained"
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for param in model.get_trainable_params():
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param.grad = torch.randn_like(param) * 0.1
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return model
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@pytest.fixture
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def mock_ivon_optimizer(mock_model):
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"""Create an IVON optimizer with pre-configured state to simulate training."""
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"""
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Create an IVON optimizer with pre-configured state to simulate training.
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"""
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# Create the optimizer
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trainable_params = mock_model.get_trainable_params()
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optimizer = IVON(trainable_params, lr=0.01, ess=1000.0)
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# Simulate training state for each parameter
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for param in trainable_params:
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# Manually set up state that mimics a trained model
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optimizer.state[param] = {
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'step': 1, # Indicates at least one step
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'count': 1,
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'h': torch.ones_like(param) * 1.0, # Hessian approximation
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'avg_grad': torch.randn_like(param) * 0.1, # Simulated average gradient
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'avg_gsq': torch.randn_like(param) * 0.01, # Simulated squared gradient
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'momentum': torch.randn_like(param) * 0.01 # Simulated momentum
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}
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return setup_optimizer(mock_model, optimizer)
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def setup_optimizer(model, optimizer):
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out_features, in_features = model.base_layer.weight.data.shape
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a = torch.randn((1, in_features))
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target = torch.randn((1, out_features))
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for _ in range(3):
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pred = model(a)
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loss = torch.nn.functional.mse_loss(pred, target)
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loss.backward()
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optimizer.step()
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return optimizer
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@pytest.fixture
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def mock_regular_optimizer(mock_model):
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"""Create a regular optimizer (no IVON)."""
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return torch.optim.AdamW(mock_model.get_trainable_params())
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"""
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Create a regular optimizer (no IVON).
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"""
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optimizer = torch.optim.AdamW(mock_model.get_trainable_params())
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return setup_optimizer(mock_model, optimizer)
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# Test cases
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class TestMaybePrunedSave:
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"""Test suite for the maybe_pruned_save context manager."""
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def test_no_pruning_with_regular_optimizer(self, mock_model, mock_regular_optimizer):
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"""Test that regular optimizers don't trigger pruning."""
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original_state_dict = mock_model.state_dict()
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with maybe_pruned_save(mock_model, mock_regular_optimizer, enable_pruning=True):
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saved_state_dict = mock_model.state_dict()
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# Should be identical (no pruning)
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for key in original_state_dict:
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torch.testing.assert_close(original_state_dict[key], saved_state_dict[key])
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def test_no_pruning_when_disabled(self, mock_model, mock_ivon_optimizer):
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"""Test that IVON optimizer doesn't prune when enable_pruning=False."""
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original_state_dict = mock_model.state_dict()
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with maybe_pruned_save(mock_model, mock_ivon_optimizer, enable_pruning=False):
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saved_state_dict = mock_model.state_dict()
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# Should be identical (pruning disabled)
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for key in original_state_dict:
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torch.testing.assert_close(original_state_dict[key], saved_state_dict[key])
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def test_variance_detection(self, mock_model, mock_ivon_optimizer):
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"""Verify that IVON optimizer supports variance-based operations."""
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from library.network_utils import maybe_pruned_save
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# Check basic IVON optimizer properties
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assert hasattr(mock_ivon_optimizer, 'sampled_params'), "IVON optimizer missing sampled_params method"
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assert 'ess' in mock_ivon_optimizer.param_groups[0], "IVON optimizer missing effective sample size"
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# Verify the optimizer has state for parameters
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for param in mock_model.get_trainable_params():
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assert param in mock_ivon_optimizer.state, f"Parameter {param} not in optimizer state"
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assert hasattr(mock_ivon_optimizer, "sampled_params"), "IVON optimizer missing sampled_params method"
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assert "ess" in mock_ivon_optimizer.param_groups[0], "IVON optimizer missing effective sample size"
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# The key point is that the optimizer supports variance-based operations
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with maybe_pruned_save(mock_model, mock_ivon_optimizer, enable_pruning=True, pruning_ratio=0.2):
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# Successful context entry means variance operations are supported
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pass
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def test_model_restored_after_context(self, mock_model, mock_ivon_optimizer):
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"""Test that model state_dict is restored after context exits."""
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original_state_dict_method = mock_model.state_dict
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original_values = {k: v.clone() for k, v in mock_model.state_dict().items()}
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with maybe_pruned_save(mock_model, mock_ivon_optimizer, enable_pruning=True):
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# Inside context: state_dict should be patched
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assert mock_model.state_dict != original_state_dict_method
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# state_dict should return pruned values
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pruned_dict = mock_model.state_dict()
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has_zeros = any((v == 0).any() for k, v in pruned_dict.items()
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if k in ['lora_A', 'lora_B', 'lora_A2', 'lora_B2'])
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has_zeros = any(
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(v == 0).any() for k, v in pruned_dict.items() if k in ["lora_down", "lora_up", "lora_down2", "lora_up2"]
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)
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assert has_zeros, "Pruned state_dict should contain zeros"
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# After context: state_dict should be restored
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assert mock_model.state_dict == original_state_dict_method
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# Original parameter values should be unchanged
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# After context: state_dict should return original values
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current_values = mock_model.state_dict()
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for key in original_values:
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torch.testing.assert_close(original_values[key], current_values[key])
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def test_different_pruning_ratios(self, mock_model, mock_ivon_optimizer):
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"""Test different pruning ratios."""
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# Trick IVON into having a state for each parameter
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mock_ivon_optimizer.state = {}
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for param in mock_model.get_trainable_params():
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mock_ivon_optimizer.state[param] = {"h": torch.rand_like(param)}
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ratios_to_test = [0.1, 0.3, 0.5]
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for ratio in ratios_to_test:
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with maybe_pruned_save(mock_model, mock_ivon_optimizer, enable_pruning=True, pruning_ratio=ratio):
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pruned_dict = mock_model.state_dict()
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total_params = 0
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zero_params = 0
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for key in ['lora_A', 'lora_B', 'lora_A2', 'lora_B2']:
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for key in ["lora_down", "lora_up", "lora_down2", "lora_up2"]:
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params = pruned_dict[key]
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total_params += params.numel()
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zero_params += (params == 0).sum().item()
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actual_ratio = zero_params / total_params
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# Relax pruning constraint to allow more variance
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assert 0.05 <= actual_ratio <= 0.5, f"Ratio {actual_ratio} not in expected range"
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def test_no_gradients_no_pruning(self, mock_ivon_optimizer):
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"""Test that parameters without gradients aren't pruned."""
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model = MockLoRAModel(requires_grad=False) # Explicitly set no gradients
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original_state_dict = model.state_dict()
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with maybe_pruned_save(model, mock_ivon_optimizer, enable_pruning=True):
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saved_state_dict = model.state_dict()
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|
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# Check for any pruning
|
||||
for key in original_state_dict:
|
||||
# Find and print any deviations
|
||||
orig_tensor = original_state_dict[key]
|
||||
saved_tensor = saved_state_dict[key]
|
||||
|
||||
print(f"Checking key: {key}")
|
||||
print(f"Original tensor: {orig_tensor}")
|
||||
print(f"Saved tensor: {saved_tensor}")
|
||||
|
||||
zero_count = (saved_tensor == 0).sum().item()
|
||||
total_count = saved_tensor.numel()
|
||||
print(f"Zeros in saved tensor: {zero_count} out of {total_count}")
|
||||
|
||||
# Ensure no zeros in the tensor
|
||||
assert zero_count == 0, f"Pruning occurred on {key} despite no gradients"
|
||||
|
||||
assert actual_ratio > 0, f"No pruning occurred. Ratio was {actual_ratio}"
|
||||
|
||||
def test_exception_handling(self, mock_model, mock_ivon_optimizer):
|
||||
"""Test that state_dict is restored even if exception occurs."""
|
||||
original_state_dict_method = mock_model.state_dict
|
||||
|
||||
|
||||
try:
|
||||
with maybe_pruned_save(mock_model, mock_ivon_optimizer, enable_pruning=True):
|
||||
# Simulate an exception during save
|
||||
raise ValueError("Simulated save error")
|
||||
except ValueError:
|
||||
pass # Expected
|
||||
|
||||
|
||||
# State dict should still be restored
|
||||
assert mock_model.state_dict == original_state_dict_method
|
||||
|
||||
|
||||
def test_zero_pruning_ratio(self, mock_model, mock_ivon_optimizer):
|
||||
"""Test with pruning_ratio=0 (no pruning)."""
|
||||
original_state_dict = mock_model.state_dict()
|
||||
|
||||
|
||||
with maybe_pruned_save(mock_model, mock_ivon_optimizer, enable_pruning=True, pruning_ratio=0.0):
|
||||
saved_state_dict = mock_model.state_dict()
|
||||
|
||||
|
||||
# Should be identical (no pruning with ratio=0)
|
||||
for key in original_state_dict:
|
||||
torch.testing.assert_close(original_state_dict[key], saved_state_dict[key])
|
||||
@@ -236,38 +210,53 @@ class TestMaybePrunedSave:
|
||||
# Integration test
|
||||
def test_integration_with_save_weights(mock_model, mock_ivon_optimizer, tmp_path):
|
||||
"""Integration test simulating actual save_weights call."""
|
||||
|
||||
|
||||
# Trick IVON into having a state for each parameter
|
||||
mock_ivon_optimizer.state = {}
|
||||
for param in mock_model.get_trainable_params():
|
||||
mock_ivon_optimizer.state[param] = {"h": torch.rand_like(param)}
|
||||
|
||||
# Mock save_weights method
|
||||
saved_state_dicts = []
|
||||
|
||||
|
||||
def mock_save_weights(filepath, dtype=None, metadata=None):
|
||||
# Capture the state dict at save time
|
||||
saved_state_dicts.append({k: v.clone() for k, v in mock_model.state_dict().items()})
|
||||
|
||||
|
||||
mock_model.save_weights = mock_save_weights
|
||||
|
||||
|
||||
# Test 1: Save without pruning
|
||||
with maybe_pruned_save(mock_model, mock_ivon_optimizer, enable_pruning=False):
|
||||
mock_model.save_weights("test1.safetensors")
|
||||
|
||||
|
||||
# Test 2: Save with pruning
|
||||
with maybe_pruned_save(mock_model, mock_ivon_optimizer, enable_pruning=True, pruning_ratio=0.2):
|
||||
mock_model.save_weights("test2.safetensors")
|
||||
|
||||
|
||||
# Verify we captured two different state dicts
|
||||
assert len(saved_state_dicts) == 2
|
||||
|
||||
|
||||
unpruned_dict = saved_state_dicts[0]
|
||||
pruned_dict = saved_state_dicts[1]
|
||||
|
||||
# Check that pruned version has zeros in specific parameters
|
||||
lora_params = ["lora_down", "lora_up", "lora_down2", "lora_up2"]
|
||||
|
||||
# Check that pruned version has zeros
|
||||
has_zeros_unpruned = any((v == 0).any() for k, v in unpruned_dict.items()
|
||||
if k in ['lora_A', 'lora_B', 'lora_A2', 'lora_B2'])
|
||||
has_zeros_pruned = any((v == 0).any() for k, v in pruned_dict.items()
|
||||
if k in ['lora_A', 'lora_B', 'lora_A2', 'lora_B2'])
|
||||
|
||||
assert not has_zeros_unpruned, "Unpruned version shouldn't have zeros"
|
||||
assert has_zeros_pruned, "Pruned version should have zeros"
|
||||
def count_zeros(state_dict):
|
||||
zero_counts = {}
|
||||
for key in lora_params:
|
||||
params = state_dict[key]
|
||||
zero_counts[key] = (params == 0).sum().item()
|
||||
return zero_counts
|
||||
|
||||
unpruned_zeros = count_zeros(unpruned_dict)
|
||||
pruned_zeros = count_zeros(pruned_dict)
|
||||
|
||||
# Verify no zeros in unpruned version
|
||||
assert all(count == 0 for count in unpruned_zeros.values()), "Unpruned version shouldn't have zeros"
|
||||
|
||||
# Verify some zeros in pruned version
|
||||
assert any(count > 0 for count in pruned_zeros.values()), "Pruned version should have some zeros"
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
Reference in New Issue
Block a user