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"""
处理参数，如参数翻转、参数替换等等
"""
import os
import torch
import random
import struct
import pandas as pd
from bitstring import BitArray
from concurrent.futures import ThreadPoolExecutor
import numpy as np
import math
def split_file(file_path, chunk_size=8):
"""
分割源文件成为元素BitArray的list
:param file_path: 源文件路径
:param chunk_size: 分割粒度 Bit
:return: 返回一个元素BitArray的list
"""
# 以bit的形式读取文件
bit_data = BitArray(filename = file_path)
chunks = [bit_data[i:i+chunk_size] for i in range(0, len(bit_data), chunk_size)]
return chunks
def merge_file(output_file, chunks):
"""
将BitArray的list合并成一个文件
:param output_file: 目标文件路径
:param chunks: BitArray的list
:return: 合并后的文件
"""
merge_data = BitArray()
for chunk in chunks:
merge_data.append(chunk)
with open(output_file, 'wb') as file:
merge_data.tofile(file)
def layer_low_n_bit_fLip(initParaPath, flipParaPath, bit_n, *layers):
"""
翻转pth的layers层fa的低n bit
:param initParaPath: 原始参数pth
:param flipParaPath: 翻转之后的参数pth
:param bit_n: 翻转低多少bit
:return: void
"""
para = torch.load(initParaPath)
for layer in layers: # layers数组中的所有layer
if len(para[layer].data.shape) < 1:
continue # 单值除去
# print(layer, type(layer))
layerTensor = para[layer].data
# print(layerTensor.shape)
layerTensor_initView = layerTensor.view(torch.int32)
# print(format(layerTensor_initView[0][0][0][0], '032b'), layerTensor[0][0][0][0])
layerTensor_embedded_int = layerTensor_initView ^ bit_n
layerTensor_embedded = layerTensor_embedded_int.view(torch.float32)
# print(format(layerTensor_embedded_int[0][0][0][0], '032b'), layerTensor_embedded[0][0][0][0])
para[layer].data = layerTensor_embedded
torch.save(para, flipParaPath)
return
if __name__ == "__main__":
print("Test Done")
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