ANTLR4 Python解析大文件

我正在尝试为juniper / srx路由器访问控制列表编写解析器 . 以下是我使用的语法：

grammar SRXBackend;

acl:
    'security' '{' 'policies' '{' COMMENT* replaceStmt '{' policy* '}' '}' '}'
            applications
            addressBook
;

replaceStmt:
    'replace:' IDENT
|   'replace:' 'from-zone' IDENT 'to-zone' IDENT
;

policy:
    'policy' IDENT '{' 'match' '{' fromStmt* '}' 'then' (action | '{' action+ '}') '}'
;

fromStmt:
     'source-address' addrBlock                     # sourceAddrStmt
|    'destination-address' addrBlock                # destinationAddrStmt
|    'application' (srxName ';' | '[' srxName+ ']')  # applicationBlock
;

action:
    'permit' ';'
|   'deny' ';'
|   'log { session-close; }'
;

addrBlock:
    '[' srxName+ ']'
|   srxName ';'
;

applications:
    'applications' '{' application* '}'
|   'applications' '{' 'apply-groups' IDENT ';' '}' 'groups' '{' replaceStmt  '{' 'applications' '{' application* '}' '}' '}'
;

addressBook:
    'security' '{' 'address-book' '{' replaceStmt '{' addrEntry* '}' '}' '}'
|   'groups' '{' replaceStmt  '{' 'security' '{' 'address-book' '{' IDENT '{' addrEntry* '}' '}' '}' '}' '}' 'security' '{' 'apply-groups' IDENT ';' '}'
;

application:
    'replace:'? 'application' srxName '{' applicationStmt+ '}'
;

applicationStmt:
    'protocol' srxName ';'            #applicationProtocol
|   'source-port' portRange ';'       #applicationSrcPort
|   'destination-port' portRange ';'  #applicationDstPort
;

portRange:
    NUMBER             #portRangeOne
|   NUMBER '-' NUMBER  #portRangeMinMax
;

addrEntry:
    'address-set' IDENT '{' addrEntryStmt+ '}' #addrEntrySet
|   'address' srxName cidr ';'                 #addrEntrySingle
;

addrEntryStmt:
    ('address-set' | 'address') srxName ';'
;

cidr:
    NUMBER '.' NUMBER '.' NUMBER '.' NUMBER ('/' NUMBER)?
;

srxName:
    NUMBER
|   IDENT
|   cidr
;

COMMENT : '/*' .*? '*/' ;
NUMBER  : [0-9]+ ;
IDENT   : [a-zA-Z][a-zA-Z0-9,\-_:\./]* ;
WS      : [ \t\n]+ -> skip ;

当我尝试使用约80,000行的ACL时，生成解析树最多需要10分钟 . 我使用以下代码来创建解析树：

from antlr4 import *
from SRXBackendLexer import SRXBackendLexer
from SRXBackendParser import SRXBackendParser
import sys


    def main(argv):
        ipt = FileStream(argv[1])
        lexer = SRXBackendLexer(ipt)
        stream = CommonTokenStream(lexer)
        parser = SRXBackendParser(stream)
        parser.acl()

    if __name__ == '__main__':
        main(sys.argv)

我使用Python 2.7作为目标语言 . 我还运行了cProfile来确定哪些代码占用了大部分时间 . 以下是按时间排序的前几条记录：

ncalls  tottime  percall  cumtime  percall filename:lineno(function)
   608448   62.699    0.000  272.359    0.000 LexerATNSimulator.py:152(execATN)
  5007036   41.253    0.000   71.458    0.000 LexerATNSimulator.py:570(consume)
  5615722   32.048    0.000   70.416    0.000 DFAState.py:131(__eq__)
 11230968   24.709    0.000   24.709    0.000 InputStream.py:73(LA)
  5006814   21.881    0.000   31.058    0.000 LexerATNSimulator.py:486(captureSimState)
  5007274   20.497    0.000   29.349    0.000 ATNConfigSet.py:160(__eq__)
 10191162   18.313    0.000   18.313    0.000 {isinstance}
 10019610   16.588    0.000   16.588    0.000 {ord}
  5615484   13.331    0.000   13.331    0.000 LexerATNSimulator.py:221(getExistingTargetState)
  6832160   12.651    0.000   12.651    0.000 InputStream.py:52(index)
  5007036   10.593    0.000   10.593    0.000 InputStream.py:67(consume)
   449433    9.442    0.000  319.463    0.001 Lexer.py:125(nextToken)
        1    8.834    8.834   16.930   16.930 InputStream.py:47(_loadString)
   608448    8.220    0.000  285.163    0.000 LexerATNSimulator.py:108(match)
  1510237    6.841    0.000   10.895    0.000 CommonTokenStream.py:84(LT)
   449432    6.044    0.000  363.766    0.001 Parser.py:344(consume)
   449433    5.801    0.000    9.933    0.000 Token.py:105(__init__)

除了InputStream.LA需要大约半分钟，我才能真正理解它 . 我想这是因为整个文本字符串一次被缓冲/加载 . 是否有任何替代/更懒惰的方法来解析或加载Python目标的数据？我可以对语法进行任何改进以使解析更快吗？

谢谢