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import os
import json
import torch
from safetensors.torch import load_file, save_file
import logging
import shutil
from typing import Dict, Any, Set
import re
logger = logging.getLogger("PeftMerger")
logger.setLevel(logging.INFO)
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.INFO)
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
def normalize_key(key: str) -> str:
"""Normalize key format to match base model"""
key = key.replace("transformer.double_blocks", "transformer_blocks")
key = key.replace("transformer.single_blocks", "single_transformer_blocks")
key = re.sub(r'\.+', '.', key) # Remove double dots
if key.endswith('.'):
key = key[:-1]
return key
def merge_lora_weights(base_weights: Dict[str, torch.Tensor],
lora_weights: Dict[str, torch.Tensor],
alpha: float = 1.0) -> Dict[str, torch.Tensor]:
"""Merge LoRA weights into base model weights"""
merged = base_weights.copy()
# Print first few keys for debugging
logger.info(f"Base model keys (first 5): {list(base_weights.keys())[:5]}")
logger.info(f"LoRA keys (first 5): {list(lora_weights.keys())[:5]}")
# Process LoRA keys
for key in lora_weights.keys():
if '.lora_A.weight' not in key:
continue
logger.info(f"Processing LoRA key: {key}")
base_key = key.replace('.lora_A.weight', '')
lora_a = lora_weights[key]
lora_b = lora_weights[base_key + '.lora_B.weight']
# Normalize after getting both A and B weights
normalized_key = normalize_key(base_key)
logger.info(f"Normalized key: {normalized_key}")
# Map double blocks
if 'img_attn_qkv' in base_key:
weights = torch.matmul(lora_b, lora_a)
q, k, v = torch.chunk(weights, 3, dim=0)
block_match = re.search(r'transformer_blocks\.(\d+)', normalized_key)
block_num = block_match.group(1)
q_key = f'transformer_blocks.{block_num}.attn.to_q.weight'
k_key = f'transformer_blocks.{block_num}.attn.to_k.weight'
v_key = f'transformer_blocks.{block_num}.attn.to_v.weight'
if all(k in merged for k in [q_key, k_key, v_key]):
merged[q_key] = merged[q_key] + alpha * q
merged[k_key] = merged[k_key] + alpha * k
merged[v_key] = merged[v_key] + alpha * v
logger.info(f"Updated keys: {q_key}, {k_key}, {v_key}")
else:
logger.warning(f"Missing some keys: {[k for k in [q_key, k_key, v_key] if k not in merged]}")
elif 'txt_attn_qkv' in base_key:
weights = torch.matmul(lora_b, lora_a)
q, k, v = torch.chunk(weights, 3, dim=0)
block_match = re.search(r'transformer_blocks\.(\d+)', normalized_key)
block_num = block_match.group(1)
q_key = f'transformer_blocks.{block_num}.attn.add_q_proj.weight'
k_key = f'transformer_blocks.{block_num}.attn.add_k_proj.weight'
v_key = f'transformer_blocks.{block_num}.attn.add_v_proj.weight'
if all(k in merged for k in [q_key, k_key, v_key]):
merged[q_key] = merged[q_key] + alpha * q
merged[k_key] = merged[k_key] + alpha * k
merged[v_key] = merged[v_key] + alpha * v
logger.info(f"Updated keys: {q_key}, {k_key}, {v_key}")
else:
logger.warning(f"Missing some keys: {[k for k in [q_key, k_key, v_key] if k not in merged]}")
elif 'img_attn_proj' in base_key:
block_match = re.search(r'transformer_blocks\.(\d+)', normalized_key)
block_num = block_match.group(1)
model_key = f'transformer_blocks.{block_num}.attn.to_out.0.weight'
if model_key in merged:
weights = torch.matmul(lora_b, lora_a)
merged[model_key] = merged[model_key] + alpha * weights
logger.info(f"Updated key: {model_key}")
else:
logger.warning(f"Missing key: {model_key}")
elif 'txt_attn_proj' in base_key:
block_match = re.search(r'transformer_blocks\.(\d+)', normalized_key)
block_num = block_match.group(1)
model_key = f'transformer_blocks.{block_num}.attn.to_add_out.weight'
if model_key in merged:
weights = torch.matmul(lora_b, lora_a)
merged[model_key] = merged[model_key] + alpha * weights
logger.info(f"Updated key: {model_key}")
else:
logger.warning(f"Missing key: {model_key}")
elif 'img_mlp.fc1' in base_key:
block_match = re.search(r'transformer_blocks\.(\d+)', normalized_key)
block_num = block_match.group(1)
model_key = f'transformer_blocks.{block_num}.ff.net.0.proj.weight'
if model_key in merged:
weights = torch.matmul(lora_b, lora_a)
merged[model_key] = merged[model_key] + alpha * weights
logger.info(f"Updated key: {model_key}")
else:
logger.warning(f"Missing key: {model_key}")
elif 'img_mlp.fc2' in base_key:
block_match = re.search(r'transformer_blocks\.(\d+)', normalized_key)
block_num = block_match.group(1)
model_key = f'transformer_blocks.{block_num}.ff.net.2.weight'
if model_key in merged:
weights = torch.matmul(lora_b, lora_a)
merged[model_key] = merged[model_key] + alpha * weights
logger.info(f"Updated key: {model_key}")
else:
logger.warning(f"Missing key: {model_key}")
elif 'txt_mlp.fc1' in base_key:
block_match = re.search(r'transformer_blocks\.(\d+)', normalized_key)
block_num = block_match.group(1)
model_key = f'transformer_blocks.{block_num}.ff_context.net.0.proj.weight'
if model_key in merged:
weights = torch.matmul(lora_b, lora_a)
merged[model_key] = merged[model_key] + alpha * weights
logger.info(f"Updated key: {model_key}")
else:
logger.warning(f"Missing key: {model_key}")
elif 'txt_mlp.fc2' in base_key:
block_match = re.search(r'transformer_blocks\.(\d+)', normalized_key)
block_num = block_match.group(1)
model_key = f'transformer_blocks.{block_num}.ff_context.net.2.weight'
if model_key in merged:
weights = torch.matmul(lora_b, lora_a)
merged[model_key] = merged[model_key] + alpha * weights
logger.info(f"Updated key: {model_key}")
else:
logger.warning(f"Missing key: {model_key}")
return merged
def save_sharded_model(weights: Dict[str, torch.Tensor],
index_data: dict,
output_dir: str,
base_model_path: str):
"""Save merged weights in same sharded format as original"""
os.makedirs(output_dir, exist_ok=True)
# Copy all non-safetensor files from original directory
index_dir = os.path.dirname(os.path.abspath(base_model_path))
for file in os.listdir(index_dir):
if not file.endswith('.safetensors'):
src = os.path.join(index_dir, file)
dst = os.path.join(output_dir, file)
if os.path.isfile(src):
shutil.copy2(src, dst)
elif os.path.isdir(src):
shutil.copytree(src, dst)
# Group weights by shard
weight_map = index_data['weight_map']
shard_weights = {}
for key, shard in weight_map.items():
if shard not in shard_weights:
shard_weights[shard] = {}
if key in weights:
shard_weights[shard][key] = weights[key]
# Save each shard
for shard, shard_dict in shard_weights.items():
if not shard_dict: # Skip empty shards
continue
shard_path = os.path.join(output_dir, shard)
logger.info(f"Saving shard {shard} with {len(shard_dict)} tensors")
save_file(shard_dict, shard_path)
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--base_model", type=str, required=True)
parser.add_argument("--adapter", type=str, required=True)
parser.add_argument("--output", type=str, required=True)
parser.add_argument("--alpha", type=float, default=1.0)
args = parser.parse_args()
# Load base model index
logger.info("Loading base model index...")
with open(args.base_model, 'r') as f:
index_data = json.load(f)
weight_map = index_data['weight_map']
# Load base weights
logger.info("Loading base model weights...")
base_dir = os.path.dirname(args.base_model)
base_weights = {}
for part_file in set(weight_map.values()):
part_path = os.path.join(base_dir, part_file)
logger.info(f"Loading from {part_path}")
weights = load_file(part_path)
base_weights.update(weights)
# Load LoRA
logger.info("Loading LoRA weights...")
lora_weights = load_file(args.adapter)
# Merge
logger.info(f"Merging with alpha={args.alpha}")
merged_weights = merge_lora_weights(base_weights, lora_weights, args.alpha)
# Save in sharded format
logger.info(f"Saving merged model to {args.output}")
save_sharded_model(merged_weights, index_data, args.output, args.base_model)
logger.info("Done!")
if __name__ == '__main__':
main() |