File size: 4,380 Bytes
4968ea3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | """Memory LoRA_u module: create, load, save per-user LoRA adapters."""
import os
import re
from peft import LoraConfig, get_peft_model, PeftModel
from src.utils import load_yaml, setup_logger
logger = setup_logger(__name__)
def create_memory_lora(model, config_path: str = "configs/model/memory_lora.yaml"):
"""Create a new Memory LoRA adapter on the base model.
Targets early-to-mid layers (0-23 out of 36) for knowledge memory.
Args:
model: Base model (Qwen3ForCausalLM).
config_path: Path to memory LoRA config.
Returns:
PEFT model with LoRA adapter.
"""
config = load_yaml(config_path)
# Build layers_to_transform list (early-to-mid layers)
num_target_layers = config.get("num_target_layers", 24)
layers_to_transform = list(range(num_target_layers))
lora_config = LoraConfig(
r=config["lora_rank"],
lora_alpha=config["lora_alpha"],
lora_dropout=config["lora_dropout"],
target_modules=config["target_modules"],
layers_to_transform=layers_to_transform,
bias="none",
task_type="CAUSAL_LM",
)
peft_model = get_peft_model(model, lora_config)
trainable_params = sum(p.numel() for p in peft_model.parameters() if p.requires_grad)
total_params = sum(p.numel() for p in peft_model.parameters())
logger.info(
f"Memory LoRA created: {trainable_params:,} trainable / {total_params:,} total "
f"({100 * trainable_params / total_params:.2f}%)"
)
logger.info(f"Target layers: 0-{num_target_layers - 1}, modules: {config['target_modules']}")
return peft_model
def resolve_lora_path(user_dir: str, step: str = "latest") -> str:
"""Resolve the actual adapter path for a user.
Args:
user_dir: User's LoRA directory (e.g., checkpoints/memory_lora_longmem/user_001/).
step: Which checkpoint to load:
- "latest" or "final": root directory (final save after training)
- "max": highest step_N checkpoint
- integer string (e.g., "66"): specific step_66/ checkpoint
Returns:
Resolved path containing adapter_config.json.
Raises:
FileNotFoundError: if no valid adapter found.
"""
# Try root directory first for "latest"/"final"
root_adapter = os.path.join(user_dir, "adapter_config.json")
if step in ("latest", "final"):
if os.path.exists(root_adapter):
return user_dir
# Fallback: find max step checkpoint
step = "max"
# Find all step_N checkpoints
step_dirs = []
if os.path.isdir(user_dir):
for name in os.listdir(user_dir):
m = re.match(r"step_(\d+)$", name)
if m:
ckpt_path = os.path.join(user_dir, name)
if os.path.exists(os.path.join(ckpt_path, "adapter_config.json")):
step_dirs.append((int(m.group(1)), ckpt_path))
if not step_dirs:
if os.path.exists(root_adapter):
return user_dir
raise FileNotFoundError(f"No adapter found in {user_dir}")
step_dirs.sort(key=lambda x: x[0])
if step == "max":
return step_dirs[-1][1]
# Specific step number
target_step = int(step)
for s, path in step_dirs:
if s == target_step:
return path
raise FileNotFoundError(
f"step_{target_step} not found in {user_dir}. "
f"Available: {[s for s, _ in step_dirs]}"
)
def load_memory_lora(base_model, adapter_path: str, step: str = "latest"):
"""Load a pre-trained Memory LoRA adapter.
Args:
base_model: Base model without adapter.
adapter_path: User's LoRA directory (e.g., checkpoints/memory_lora_longmem/user_001/).
step: Which checkpoint to load ("latest", "max", or specific step number).
Returns:
Model with loaded adapter.
"""
resolved_path = resolve_lora_path(adapter_path, step=step)
logger.info(f"Loading Memory LoRA from {resolved_path}")
model = PeftModel.from_pretrained(base_model, resolved_path)
return model
def save_memory_lora(peft_model, save_path: str):
"""Save only the LoRA adapter weights.
Args:
peft_model: PEFT model with adapter.
save_path: Directory to save adapter.
"""
logger.info(f"Saving Memory LoRA to {save_path}")
peft_model.save_pretrained(save_path)
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