util.py

import json
import pandas as pd
import math
from typing import Union
TB=1024**4
GB=1024**3
MB=1024**2
KB=1024
T=1000**4
G=1000**3
M=1000**2
K=1000
def load_config(input_path):
    # 读取json配置文件
    with open(input_path, 'r') as file:
        # 从文件中加载 JSON 数据
        config = json.load(file)
    return config
def save_file(data, file_path):
    # 转换为list+dict类型
    df = pd.DataFrame(data)
    # 将DataFrame保存到Excel中，index参数用于指定是否包含行索引
    df.to_excel(file_path, index=False)
def MBytes(list0,bytes=2):
    if list0==None or list0==0:
        return 0
    #List 维度乘积
    res=1 
    for i in list0:
        res*=i
    return res/1024/1024*bytes

def min_multiple_of(num,factor=16):
    return math.ceil(num/factor)*factor
def dim_norm(dims,tile_num=16):
    #将维度规则到tile_num的倍数
    newdims=[]
    for dim in dims:
        newdims.append(min_multiple_of(dim,tile_num))
    return newdims
def dim_analysis(optype,dims,para_dims):
    #将gemm矩阵维度按并行切分度重新计算数据shape
    if optype=='GEMM':#[b,m,k,n]
        reduce=True if para_dims[2]>1 else False
        newdims=[math.ceil(dims[0]/para_dims[0]),math.ceil(dims[1]/para_dims[1]),math.ceil(dims[2]/para_dims[2]),math.ceil(dims[3]/para_dims[3])]
        i_shape=[math.ceil(newdims[0]),math.ceil(newdims[1]),math.ceil(newdims[2])]
        w_shape=[math.ceil(newdims[2]),math.ceil(newdims[3])]
        o_shape=[math.ceil(newdims[0]),math.ceil(newdims[1]),math.ceil(newdims[3])]
        return newdims,i_shape,o_shape,w_shape,reduce

def block_range(dim,min_block=1,max_block=None):
    #遍历dim可以因式分解的所有公因子，满足大于等于min_block，且为min_block的倍数，且小于等于max_block
    if max_block==None:
        max_block=dim
    factors = []
    sqrt_n = int(math.sqrt(dim))
    for i in range(1, sqrt_n + 1):
        if dim % i == 0 :
            if i % min_block ==0 and i <= max_block:
                factors.append(i)
            if i != dim // i:
                if dim // i % min_block ==0 and dim // i <= max_block:
                    factors.append(dim // i)
    return factors

if __name__ == "__main__":
    new_dims=dim_norm([16,4096,5,511],factor=16)
    print(new_dims)
    for dim in new_dims:
        print(block_range(dim,min_block=16))
    print(dim_analysis('GEMM',new_dims,[1,128,64,256]))