I had previously discussed how one can implement a programming language using big step semantics. In this post, I want to so something similar. Here, we implement a meta-circular interpreter over Python. The code is available here.

A meta-circular interpreter is an interpreter for a language that is written in that language itself. The MCI can implement a subset or superset of the host language.

## Uses of a Meta Circular Interpreter

Why should one want to write a meta-circular interpreter? Writing such an interpreter gives you a large amount of control over how the code is executed. Further, it also gives you a way to track the execution as it proceeds. Using the same machinery as the meta-circular interpreter, one can:

• Write a concolic interpreter that tracks the concrete execution
• Extract coverage
• Extract the control flow graph, and execution path through the graph
• Extract the call graph of execution
• Write a symbolic execution engine
• Write a taint tracker that will not be confused by indirect control flow
• Extend the host language with new features
• Reduce the capabilities exposed by the host language (e.g. no system calls).

I will be showing how to do these things in the upcoming posts.

## The Implementation (tested in Python 3.6.8)

First, we import everything we need.

import string
import ast
import astunparse
import sys
import json
import builtins
from functools import reduce
import importlib


The basic idea is to make use of the Python infrastructure as much as possible. That is, we do not want to implement things that are not related to the actual interpretation. Hence, we use the Python parsing infrastructure exposed by the ast module that parses Python source files, and returns back the AST. AST (Abstract Syntax Tree) as the name indicates is a data structure in the format of a tree.

Once we have the AST, we simply walk the tree, and interpret the statements as we find them.

### The meta-circular-interpreter class

The walk() method is at the heart of our interpreter. Given the AST, It iterates through the statements, and evaluates each by invoking the corresponding method. If the method is not implemented, it raises a SynErr which is derived from SyntaxError.

class SynErr(SyntaxError): pass

class PyMCInterpreter:
def walk(self, node):
if node is None: return
res = "on_%s" % node.__class__.__name__.lower()
if hasattr(self, res):
return getattr(self,res)(node)
raise SynErr('walk: Not Implemented in %s' % type(node))


We provide eval() which converts a given string to its AST, and calls walk(). It is possible to write a parser of our own, as I have shown before which can get us the AST. However, as I mentioned earlier, we use the Python infrastructure where possible.

class PyMCInterpreter(PyMCInterpreter):
def eval(self, src):
return self.walk(ast.parse(src))


#### The Pythonic data structures.

We need to define data. For the primitive data types, we only implement string and number for now. These are trivial as it is a direct translation of the AST values.

##### Str(string s)
class PyMCInterpreter(PyMCInterpreter):
def on_str(self, node):
return node.s

##### Number(object n)
class PyMCInterpreter(PyMCInterpreter):
def on_num(self, node):
return node.n


#### Containers

Essentially, we want to be able to make use of all pythonic container data structures such as lists, tuples, sets and dictionaries. For demonstration, however, we have implemented only list and tuple.

Unlike the primitives, the containers may be defined such that the values inside them are result of evaluating some expression. Hence, we first walk() the elements, and add the results.

##### List(elts)
class PyMCInterpreter(PyMCInterpreter):
def on_list(self, node):
res = []
for p in node.elts:
v = self.walk(p)
res.append(v)
return res

##### Tuple(elts)
class PyMCInterpreter(PyMCInterpreter):
def on_tuple(self, node):
res = []
for p in node.elts:
v = self.walk(p)
res.append(v)
return res


Containers provide the ability to access their contained items via Subscript.

##### Subscript(expr value, slice slice, expr_context ctx)

The tuple and list provide a means to access its elements via subscript. The subscript requires a special Index value as input, which is also defined below.

class PyMCInterpreter(PyMCInterpreter):
def on_subscript(self, node):
value = self.walk(node.value)
slic = self.walk(node.slice)
return value[slic]

def on_index(self, node):
return self.walk(node.value)

##### Attribute(expr value, identifier attr, expr_context ctx)

Similar to subscript for arrays, objects provide attribute access.

class PyMCInterpreter(PyMCInterpreter):
def on_attribute(self, node):
obj = self.walk(node.value)
attr = node.attr
return getattr(obj, attr)


#### Simple control flow statements

The return, break and continue are implemented as exceptions.

class Return(Exception):
def __init__(self, val): self.__dict__.update(locals())

class Break(Exception):
def __init__(self): pass

class Continue(Exception):
def __init__(self): pass


#### Implementation

class PyMCInterpreter(PyMCInterpreter):
def on_return(self, node):
raise Return(self.walk(node.value))

def on_break(self, node):
raise Break(self.walk(node.value))

def on_continue(self, node):
raise Continue(self.walk(node.value))

def on_pass(self, node):
pass


The difference between break and continue is in how they are handled in the loop statemens as in While below. The return is handled in the Call part.

#### Major control flow statements

Only basic loops and conditionals – while() and if() are implemented.

##### While(expr test, stmt* body, stmt* orelse)

Implementing the While loop is fairly straight forward. The while.body is a list of statements that need to be interpreted if the while.test is True. The break and continue statements provide a way to either stop the execution or to restart it.

As can be seen, these are statements rather than expressions, which means that their return value is not important. Hence, we do not return anything.

class PyMCInterpreter(PyMCInterpreter):
def on_while(self, node):
while self.walk(node.test):
try:
for b in node.body:
self.walk(b)
except Break:
break
except Continue:
continue

##### If(expr test, stmt* body, stmt* orelse)

The If statement is similar to While. We check if.test and if True, execute the if.body. If False, we execute the if.orelse.

class PyMCInterpreter(PyMCInterpreter):

def on_if(self, node):
v = self.walk(node.test)
body = node.body if v else node.orelse
if body:
res = None
for b in body:
res = self.walk(b)


#### The scope and symbol table

Now we come to a slightly more complex part. We want to define a symbol table. The reason this is complicated is that the symbol table interacts with the scope, which is a nested data structrue, and we need to provide a way to look up symbols in enclosing scopes. We have a choice to make here. Essentially, what variables do the calling program have access to? Historically, the most common conventions are lexical and dynamic scoping. The most intuitive is the lexical scoping convention. Hence, we implement lexical scoping, but with a restriction: If we modify a variable in parent scopes, then the new variable is created in current scope.

class Scope:
def __init__(self, parent=None, table=None):
self.table = table
self.children = []
self.parent = parent

def new_child(table):
return Scope(parent=self, table=table)

def __setitem__(self, i, v):
# choice here. We can check and set then named variable (if any)
# in parent scopes. See nonlocal in Python
self.table[i] = v

def __getitem__(self, i):
if i in self.table: return self.table[i]
if self.parent is None: return None
return self.parent[i]


#### Hooking up the symbol table

We allow the user to load a pre-defined symbol table. We have a choice to make here. Should we allow access to the Python default symbol table? and if we do, what should form the root? The Python symbol table or what the user supplied?

Here, we assume that the default Python symbol table is the root.

We will discuss the OP statements later.

class PyMCInterpreter(PyMCInterpreter):
def __init__(self, symtable, args):
self.unaryop = UnaryOP
self.binop = BinOP
self.cmpop = CmpOP
self.boolop = BoolOP

self.symtable = Scope(parent=None, table=builtins.__dict__)
self.symtable['sys'] = ast.Module(ast.Pass())
setattr(self.symtable['sys'], 'argv', args)

self.symtable = Scope(parent=self.symtable, table=symtable)


#### The following statements use symbol table.

##### Name(identifier id, expr_context ctx)

Retrieving a referenced symbol is simple enough.

class PyMCInterpreter(PyMCInterpreter):
def on_name(self, node):
return self.symtable[node.id]

##### Assign(expr* targets, expr value)

Python allows multi-target assignments. The problem is that, the type of the value received may be different based on whether the statement is multi-target or single-target. Hence, we split both kinds.

class PyMCInterpreter(PyMCInterpreter):
def on_assign(self, node):
value = self.walk(node.value)
tgts = [t.id for t in node.targets]
if len(tgts) == 1:
self.symtable[tgts[0]] = value
else:
for t,v in zip(tgts, value):
self.symtable[t] = v

##### Call(expr func, expr* args, keyword* keywords)

During function calls, we need to make sure that the functions that are implemented in C are proxied directly.

For others, we want to correctly bind the arguments and create a new scope. The big question is how should the scopes be nested. We use lexical scopes. So, we recover the symbol table used at the time of definition, use it for the call, and reset it back to the current one after the call.

Note that we handle the return exception here.

class PyMCInterpreter(PyMCInterpreter):
def on_call(self, node):
func = self.walk(node.func)
args = [self.walk(a) for a in node.args]
if str(type(func)) == "<class 'builtin_function_or_method'>":
return func(*args)
elif str(type(func)) == "<class 'type'>":
return func(*args)
else:
[fname, argument, returns, fbody, symtable] = func
argnames = [a.arg for a in argument.args]
defs= dict(zip(argnames, args))
oldsyms = self.symtable
self.symtable = Scope(parent=symtable, table=defs)
try:
for i in fbody:
res = self.walk(i)
return res
except Return as e:
return e.val
finally:
self.symtable = oldsyms

##### FunctionDef(identifier name, arguments args, stmt* body, expr* decorator_list, expr? returns, string? type_comment)

The function definition itself is quite simple. We simply update the symbol table with the given values. Note that because we implement lexical scoping, we have to maintain the scoping references during creation.

class PyMCInterpreter(PyMCInterpreter):
def on_functiondef(self, node):
fname = node.name
args = node.args
returns = node.returns
self.symtable[fname] = [fname, args, returns, node.body, self.symtable]

##### Import(alias* names)

Import is similar to a definition except that we want to update the symbol table with predefined values.

class PyMCInterpreter(PyMCInterpreter):
def on_import(self, node):
for im in node.names:
if im.name == 'sys': continue
v = importlib.import_module(im.name)
self.symtable[im.name] = v


#### Arithmetic Expressions

The arithmetic expressions are proxied directly to corresponding Python operators.

##### Expr(expr value)
UnaryOP = {
ast.Invert: lambda a: ~a,
ast.Not: lambda a: not a,
ast.USub: lambda a: -a
}

BinOP = {
ast.Add: lambda a, b: a + b,
ast.Sub: lambda a, b: a - b,
ast.Mult:  lambda a, b: a * b,
ast.MatMult:  lambda a, b: a @ b,
ast.Div: lambda a, b: a / b,
ast.Mod: lambda a, b: a % b,
ast.Pow: lambda a, b: a ** b,
ast.LShift:  lambda a, b: a << b,
ast.RShift: lambda a, b: a >> b,
ast.BitOr: lambda a, b: a | b,
ast.BitXor: lambda a, b: a ^ b,
ast.BitAnd: lambda a, b: a & b,
ast.FloorDiv: lambda a, b: a // b
}

CmpOP = {
ast.Eq: lambda a, b: a == b,
ast.NotEq: lambda a, b: a != b,
ast.Lt: lambda a, b: a < b,
ast.LtE: lambda a, b: a <= b,
ast.Gt: lambda a, b: a > b,
ast.GtE: lambda a, b: a >= b,
ast.Is: lambda a, b: a is b,
ast.IsNot: lambda a, b: a is not b,
ast.In: lambda a, b: a in b,
ast.NotIn: lambda a, b: a not in b
}

BoolOP = {
ast.And: lambda a, b: a and b,
ast.Or: lambda a, b: a or b
}

class PyMCInterpreter(PyMCInterpreter):
def on_expr(self, node):
return self.walk(node.value)

def on_compare(self, node):
hd = self.walk(node.left)
op = node.ops[0]
tl = self.walk(node.comparators[0])
return self.cmpop[type(op)](hd, tl)

def on_unaryop(self, node):
return self.unaryop[type(node.op)](self.walk(node.operand))

def on_boolop(self, node):
return reduce(self.boolop[type(node.op)], [self.walk(n) for n in node.values])

def on_binop(self, node):
return self.binop[type(node.op)](self.walk(node.left), self.walk(node.right))


#### Modules

##### Module(stmt* body)

The complete AST is wrapped in a Module statement.

class PyMCInterpreter(PyMCInterpreter):
def on_module(self, node):
# return value of module is the last statement
res = None
for p in node.body:
res = self.walk(p)
return res


### The driver

if __name__ == '__main__':
print(v)


### An example

import sys
def triangle(a, b, c):
if a == b:
if b == c:
return 'Equilateral'
else:
return 'Isosceless'
else:
if b == c:
return "Isosceles"
else:
if a == c:
return "Isosceles"
else:
return "Scalene"
def main(arg):
v = arg.split(' ')
v = triangle(int(v[0]), int(v[1]), int(v[2]))
print(v)

if __name__ == '__main__':
main(sys.argv[1])
pass


### Usage

\$ python3 interp.py triangle.py '1 2 3'