123 lines
3.7 KiB
Python
123 lines
3.7 KiB
Python
"""LoRA weight loading utilities."""
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import re
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from pathlib import Path
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from typing import Dict, List
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import mlx.core as mx
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from mlx_video.lora.types import LoRAConfig, LoRAWeights
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def load_lora_weights(lora_path: Path) -> Dict[str, LoRAWeights]:
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"""Load LoRA weights from a safetensors file.
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Supports both key conventions:
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- {module_name}.lora_A.weight / {module_name}.lora_B.weight
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- {module_name}.lora_down.weight / {module_name}.lora_up.weight
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Args:
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lora_path: Path to the LoRA safetensors file
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Returns:
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Dictionary mapping module names to LoRAWeights objects
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Raises:
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FileNotFoundError: If the LoRA file doesn't exist
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ValueError: If the LoRA file format is invalid
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"""
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if not lora_path.exists():
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raise FileNotFoundError(f"LoRA file not found: {lora_path}")
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all_weights = mx.load(str(lora_path))
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# Group weights by module name, handling both naming conventions
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lora_weights = {}
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module_names = set()
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for key in all_weights.keys():
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# Format 1: {module}.lora_A.weight / {module}.lora_B.weight
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match = re.match(r"(.+)\.lora_([AB])\.weight$", key)
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if match:
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module_names.add(match.group(1))
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continue
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# Format 2: {module}.lora_down.weight / {module}.lora_up.weight
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match = re.match(r"(.+)\.lora_(down|up)\.weight$", key)
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if match:
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module_names.add(match.group(1))
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for module_name in module_names:
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# Try both key conventions
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key_a = f"{module_name}.lora_A.weight"
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key_b = f"{module_name}.lora_B.weight"
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if key_a not in all_weights or key_b not in all_weights:
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key_a = f"{module_name}.lora_down.weight"
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key_b = f"{module_name}.lora_up.weight"
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if key_a not in all_weights or key_b not in all_weights:
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continue
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lora_a = all_weights[key_a]
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lora_b = all_weights[key_b]
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if lora_a.ndim != 2 or lora_b.ndim != 2:
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raise ValueError(
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f"Invalid LoRA shape for {module_name}: "
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f"lora_A={lora_a.shape}, lora_B={lora_b.shape}"
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)
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rank = lora_a.shape[0]
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if lora_b.shape[1] != rank:
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raise ValueError(
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f"LoRA rank mismatch for {module_name}: "
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f"lora_A rank={rank}, lora_B rank={lora_b.shape[1]}"
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)
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# Check for per-module alpha stored as a scalar tensor
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alpha_key = f"{module_name}.alpha"
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if alpha_key in all_weights:
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alpha = float(all_weights[alpha_key].item())
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else:
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alpha = float(rank)
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lora_weights[module_name] = LoRAWeights(
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lora_A=lora_a,
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lora_B=lora_b,
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rank=rank,
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alpha=alpha,
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module_name=module_name,
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)
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if not lora_weights:
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raise ValueError(f"No valid LoRA weights found in {lora_path}")
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return lora_weights
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def load_multiple_loras(
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configs: List[LoRAConfig],
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) -> Dict[str, List[tuple]]:
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"""Load multiple LoRA configurations.
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Args:
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configs: List of LoRAConfig objects
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Returns:
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Dictionary mapping module names to lists of (LoRAWeights, strength) tuples.
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"""
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module_to_loras: Dict[str, list] = {}
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for config in configs:
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lora_weights = load_lora_weights(config.path)
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for module_name, weights in lora_weights.items():
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if config.target_modules is not None:
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if module_name not in config.target_modules:
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continue
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if module_name not in module_to_loras:
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module_to_loras[module_name] = []
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module_to_loras[module_name].append((weights, config.strength))
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return module_to_loras
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