179 lines
6.7 KiB
Python
179 lines
6.7 KiB
Python
"""
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LoRA Merging Methods for EasyLoRAMerger - Version 1.2.0
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Includes Adaptive Shape Safety for 4B/9B Architecture Mixing
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"""
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import torch
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import numpy as np
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# ==================== SHAPE ADAPTATION UTILITIES ====================
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def ensure_shape_match(a, b):
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"""
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Adjusts tensors a and b to match the larger shape.
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Pads the smaller tensor with zeros to prevent math errors.
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"""
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if a.shape == b.shape:
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return a, b
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# Decide which shape is the 'target' based on number of elements
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if a.numel() >= b.numel():
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target_shape = a.shape
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# Create a zero-filled version of B in the shape of A
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new_b = torch.zeros(target_shape, device=b.device, dtype=b.dtype)
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# Calculate the intersection (min size for each dimension) to copy data
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slices = tuple(slice(0, min(i, j)) for i, j in zip(b.shape, target_shape))
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new_b[slices] = b[slices]
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return a, new_b
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else:
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target_shape = b.shape
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# Create a zero-filled version of A in the shape of B
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new_a = torch.zeros(target_shape, device=a.device, dtype=a.dtype)
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slices = tuple(slice(0, min(i, j)) for i, j in zip(a.shape, target_shape))
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new_a[slices] = a[slices]
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return new_a, b
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def universal_merge_executor(method_fn, a, b, wa, wb, **kwargs):
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"""
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Safety wrapper that ensures shapes match before executing any merge method.
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Prevents "RuntimeError: size mismatch" between different model architectures.
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"""
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a_safe, b_safe = ensure_shape_match(a, b)
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return method_fn(a_safe, b_safe, wa, wb, **kwargs)
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# ==================== CORE METHODS ====================
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def merge_linear(a, b, wa, wb, **kwargs):
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"""Simple weighted average: a*wa + b*wb"""
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return a * wa + b * wb
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# ==================== TIES METHODS ====================
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def merge_ties_strict(a, b, wa, wb, **kwargs):
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"""TIES merging: only keep weights where signs agree."""
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a_weighted = a * wa
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b_weighted = b * wb
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sign_agreement = torch.sign(a_weighted) == torch.sign(b_weighted)
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return (a_weighted + b_weighted) * sign_agreement
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def merge_ties_gentle(a, b, wa, wb, agreement_threshold=0.3, **kwargs):
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"""Gentle TIES: Only apply when strong disagreement."""
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a_weighted = a * wa
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b_weighted = b * wb
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a_flat = a_weighted.flatten().float()
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b_flat = b_weighted.flatten().float()
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norm_prod = (torch.norm(a_flat) * torch.norm(b_flat) + 1e-8)
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cos_sim = torch.dot(a_flat, b_flat) / norm_prod
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if cos_sim < -agreement_threshold:
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sign_agreement = torch.sign(a_weighted) == torch.sign(b_weighted)
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return (a_weighted + b_weighted) * sign_agreement
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return a_weighted + b_weighted
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# ==================== DARE METHODS ====================
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def merge_dare_lite(a, b, wa, wb, drop_rate=0.1, **kwargs):
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"""DARE: Random dropout without rescaling."""
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merged = a * wa + b * wb
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mask = (torch.rand_like(merged) > drop_rate).float()
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return merged * mask
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def merge_dare_rescale(a, b, wa, wb, drop_rate=0.1, **kwargs):
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"""DARE with proper rescaling."""
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drop_rate = max(0.0, min(0.9, drop_rate))
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mask_a = (torch.rand_like(a) > drop_rate).float()
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mask_b = (torch.rand_like(b) > drop_rate).float()
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rescale = 1.0 / (1.0 - drop_rate) if drop_rate < 1.0 else 1.0
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return (a * mask_a * rescale) * wa + (b * mask_b * rescale) * wb
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# ==================== NEW INTUITIVE METHODS ====================
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def merge_subtract(a, b, wa, wb, threshold=0.0, **kwargs):
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"""Subtract B from A - useful for removing unwanted styles."""
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a_weighted = a * wa
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b_weighted = b * wb
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if threshold > 0:
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a_magnitude = a_weighted.abs()
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b_magnitude = b_weighted.abs()
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b_significant = b_magnitude > (threshold * a_magnitude + 1e-8)
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b_to_subtract = b_weighted * b_significant.float()
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return a_weighted - b_to_subtract
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else:
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return a_weighted - b_weighted
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def merge_magnitude(a, b, wa, wb, blend=0.5, **kwargs):
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"""Keep larger magnitude from either LoRA."""
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a_w = a * wa
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b_w = b * wb
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a_larger = a_w.abs() > b_w.abs()
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selected = torch.where(a_larger, a_w, b_w)
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averaged = (a_w + b_w) / 2
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return selected * blend + averaged * (1 - blend)
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def merge_feature_mix(a, b, wa, wb, uniqueness=0.7, **kwargs):
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"""Preserve features unique to each LoRA."""
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a_w = a * wa
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b_w = b * wb
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ratio = a_w.abs() / (b_w.abs() + 1e-8)
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a_unique = (ratio > (1/uniqueness)).float()
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b_unique = (ratio < uniqueness).float()
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shared = torch.clamp(1 - a_unique - b_unique, 0, 1)
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result = a_w * a_unique + b_w * b_unique
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result += ((a_w + b_w) / 2) * shared
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return result
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# ==================== ADVANCED METHODS ====================
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def merge_svd_preserve(a, b, wa, wb, preserve_ratio=0.8, **kwargs):
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"""SVD-based merge with rank reduction."""
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if a.numel() < 100:
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return a * wa + b * wb
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combined = a.float() * wa + b.float() * wb
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if min(combined.shape) > 1:
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try:
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U, S, Vh = torch.linalg.svd(combined, full_matrices=False)
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total = torch.sum(S**2)
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cumulative = torch.cumsum(S**2, dim=0)
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k = torch.searchsorted(cumulative, total * preserve_ratio).item() + 1
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k = min(k, len(S))
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result = U[:, :k] @ torch.diag(S[:k]) @ Vh[:k, :]
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except:
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result = combined
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else:
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result = combined
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return result.to(a.dtype)
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def merge_noise_aware(a, b, wa, wb, noise_threshold=0.01, **kwargs):
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"""Reduce noise before merging."""
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threshold_a = noise_threshold * a.abs().max()
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threshold_b = noise_threshold * b.abs().max()
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a_clean = torch.where(a.abs() < threshold_a, a * 0.1, a)
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b_clean = torch.where(b.abs() < threshold_b, b * 0.1, b)
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return a_clean * wa + b_clean * wb
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def merge_gradient_alignment(a, b, wa, wb, **kwargs):
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"""Merge based on directional alignment."""
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a_flat, b_flat = a.flatten().float(), b.flatten().float()
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norm_prod = (torch.norm(a_flat) * torch.norm(b_flat) + 1e-8)
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cos_sim = torch.dot(a_flat, b_flat) / norm_prod
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alignment = (cos_sim + 1) / 2
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return a * wa * alignment + b * wb * alignment
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# ==================== UTILITY FUNCTIONS ====================
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def apply_density(tensor, density):
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"""Keep top 'density' percentage of weights."""
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if density >= 1:
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return tensor
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flat = tensor.abs().flatten()
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k = max(1, int(flat.numel() * density))
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threshold = torch.topk(flat, k).values.min()
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mask = tensor.abs() >= threshold
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return tensor * mask |