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merge.py
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merge.py
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import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn.functional as F
from safetensors.torch import safe_open, save_file
def merge_tensors(tensor1, tensor2, p):
# Calculate the delta of the weights
delta = tensor2 - tensor1
# Generate the mask m^t from Bernoulli distribution
m = torch.from_numpy(np.random.binomial(1, p, delta.shape)).to(tensor1.dtype)
# Apply the mask to the delta to get δ̃^t
delta_tilde = m * delta
# Scale the masked delta by the dropout rate to get δ̂^t
delta_hat = delta_tilde / (1 - p)
return delta_hat
def merge_safetensors(file_path1, file_path2, p, lambda_val):
merged_tensors = {}
with safe_open(file_path1, framework="pt", device="cpu") as f1, safe_open(file_path2, framework="pt", device="cpu") as f2:
keys1 = set(f1.keys())
keys2 = set(f2.keys())
common_keys = keys1.intersection(keys2)
for key in common_keys:
tensor1 = f1.get_tensor(key)
tensor2 = f2.get_tensor(key)
tensor1, tensor2 = resize_tensors(tensor1, tensor2)
merged_tensors[key] = tensor1 + lambda_val * merge_tensors(tensor1, tensor2, p)
print("merging", key)
return merged_tensors
class BinDataHandler():
def __init__(self, data):
self.data = data
def get_tensor(self, key):
return self.data[key]
def read_tensors(file_path, ext):
if ext == ".safetensors" and file_path.endswith(".safetensors"):
f = safe_open(file_path, framework="pt", device="cpu")
return f, set(f.keys())
if ext == ".bin" and file_path.endswith(".bin"):
data = torch.load(file_path, map_location=torch.device('cpu'))
f = BinDataHandler(data)
return f, set(data.keys())
return None, None
def resize_tensors(tensor1, tensor2):
if len(tensor1.shape) not in [1, 2]:
return tensor1, tensor2
# Pad along the last dimension (width)
if tensor1.shape[-1] < tensor2.shape[-1]:
padding_size = tensor2.shape[-1] - tensor1.shape[-1]
tensor1 = F.pad(tensor1, (0, padding_size, 0, 0))
elif tensor2.shape[-1] < tensor1.shape[-1]:
padding_size = tensor1.shape[-1] - tensor2.shape[-1]
tensor2 = F.pad(tensor2, (0, padding_size, 0, 0))
# Pad along the first dimension (height)
if tensor1.shape[0] < tensor2.shape[0]:
padding_size = tensor2.shape[0] - tensor1.shape[0]
tensor1 = F.pad(tensor1, (0, 0, 0, padding_size))
elif tensor2.shape[0] < tensor1.shape[0]:
padding_size = tensor1.shape[0] - tensor2.shape[0]
tensor2 = F.pad(tensor2, (0, 0, 0, padding_size))
return tensor1, tensor2
def merge_folder(tensor_map, directory_path, p, lambda_val):
keys1 = set(tensor_map.keys())
# Some repos have both bin and safetensors, choose safetensors if so
ext = None
for filename in os.listdir(directory_path):
# Default to safetensors
if filename.endswith(".safetensors"):
ext = ".safetensors"
if filename.endswith(".bin") and ext is None:
ext = ".bin"
if ext is None:
raise "Could not find model files"
for filename in os.listdir(directory_path):
file_path = os.path.join(directory_path, filename)
f, keys2 = read_tensors(file_path, ext)
if keys2:
common_keys = keys1.intersection(keys2)
for key in common_keys:
if "block_sparse_moe.gate" in key:
tensor1 = tensor_map[key]['tensor']
tensor2 = f.get_tensor(key)
tensor_map[key]['tensor'] = (tensor1 + tensor2) /2.0
continue
tensor1 = tensor_map[key]['tensor']
tensor2 = f.get_tensor(key)
tensor1, tensor2 = resize_tensors(tensor1, tensor2)
tensor_map[key]['tensor'] = tensor1 + lambda_val * merge_tensors(tensor1, tensor2, p)
return tensor_map
def map_tensors_to_files(directory_path):
tensor_map = {}
for filename in os.listdir(directory_path):
file_path = os.path.join(directory_path, filename)
f, keys = read_tensors(file_path, '.safetensors')
if keys:
for key in keys:
tensor = f.get_tensor(key)
tensor_map[key] = {'filename':filename, 'shape':tensor.shape, 'tensor': tensor}
return tensor_map
def copy_nontensor_files(from_path, to_path):
for filename in os.listdir(from_path):
file_path = os.path.join(from_path, filename)
if from_path != to_path and not filename.startswith(".") and not filename.startswith("README") and not filename.endswith(".bin") and not filename.endswith(".safetensors") and not filename.endswith(".pt") and not os.path.isdir(file_path):
print(f"Copying {file_path} to {to_path}")
shutil.copyfile(file_path, to_path+'/'+filename)
def save_tensor_map(tensor_map, output_folder):
metadata = {'format': 'pt'}
by_filename = {}
for key, value in tensor_map.items():
filename = value["filename"]
tensor = value["tensor"]
if filename not in by_filename:
by_filename[filename] = {}
by_filename[filename][key] = tensor
for filename in sorted(by_filename.keys()):
output_file = output_folder+'/'+filename
print("Saving:", output_file)
save_file(by_filename[filename], output_file, metadata=metadata)
def main():
# Parse command-line arguments
parser = argparse.ArgumentParser(description='Merge two safetensor model files.')
parser.add_argument('base_model', type=str, help='The base model safetensor file')
parser.add_argument('second_model', type=str, help='The second model safetensor file')
parser.add_argument('output_model', type=str, help='The output merged model safetensor file')
parser.add_argument('-p', type=float, default=0.5, help='Dropout probability')
parser.add_argument('-lambda', dest='lambda_val', type=float, default=1.0, help='Scaling factor for the weight delta')
args = parser.parse_args()
if os.path.isdir(args.base_model):
if not os.path.exists(args.output_model):
os.makedirs(args.output_model)
tensor_map = map_tensors_to_files(args.base_model)
tensor_map = merge_folder(tensor_map, args.second_model, args.p, args.lambda_val)
copy_nontensor_files(args.base_model, args.output_model)
save_tensor_map(tensor_map, args.output_model)
else:
merged = merge_safetensors(args.base_model, args.second_model, args.p, args.lambda_val)
save_file(merged, args.output_model)
if __name__ == '__main__':
main()