项目结构
multi_value_compiler/
├── ai_agent/
│ ├── __init__.py
│ ├── code_generator.py
│ ├── optimizer.py
│ └── tester.py
├── compiler/
│ ├── __init__.py
│ ├── lexer.py
│ ├── parser.py
│ ├── semantic_analyzer.py
│ └── code_generator.py
├── hardware/
│ ├── __init__.py
│ ├── triple_machine.py
│ └── simulator.py
├── examples/
│ └── demo.tri
├── main.py
└── requirements.txt
完整代码实现
1. 项目配置文件
requirements.txt
numpy
transformers
torch
tabulate
2. AI Agent 核心模块
ai_agent/init.py
class AIAgent:
def __init__(self):
self.knowledge_base = {
"trit_operations": {
"AND": {
(0,0):0, (0,1):0, (0,'X'):0,
(1,0):0, (1,1):1, (1,'X'):'X',
('X',0):0, ('X',1):'X', ('X','X'):'X'
},
"OR": {
(0,0):0, (0,1):1, (0,'X'):'X',
(1,0):1, (1,1):1, (1,'X'):1,
('X',0):'X', ('X',1):1, ('X','X'):'X'
},
"NOT": {0:1, 1:0, 'X':'X'}
},
"optimization_rules": [
"eliminate_dead_code",
"constant_propagation",
"strength_reduction"
]
}
def query(self, question):
"""简单的问答系统"""
if "三值逻辑" in question or "trit" in question:
return "三值逻辑使用0,1,X三个值，其中X代表未知或不确定状态"
elif "优化" in question:
return f"可用的优化规则: {', '.join(self.knowledge_base['optimization_rules'])}"
else:
return "我了解多值计算机编译器的相关知识，请提问"
ai_agent/code_generator.py
import random
class AICodeGenerator:
def __init__(self):
self.templates = {
"function": """
func {name}({params}) -> {return_type} {{
{body}
}}
""",
"if_else": """
if ({condition}) {{
{then_block}
}} else {{
{else_block}
}}
"""
}
def generate_test_case(self, complexity=1):
"""生成测试用例"""
test_cases = [
"func main() -> trit { return 1; }",
"func and_gate(a: trit, b: trit) -> trit { return a & b; }",
"func complex_logic(a: trit, b: trit) -> trit { return (a | b) & ~a; }"
]
return random.choice(test_cases[:complexity+1])
def suggest_optimization(self, code):
"""提供代码优化建议"""
suggestions = []
if "return 1" in code and "main" in code:
suggestions.append("简单常量返回可以进一步优化")
if "&" in code and "|" in code:
suggestions.append("复杂逻辑表达式可以应用布尔代数简化")
return suggestions
ai_agent/optimizer.py
class AIOptimizer:
def __init__(self):
self.optimization_patterns = {
"dead_code": [
("if (0) { ... }", "// 删除死代码块"),
("if (1) { ... }", "... // 简化条件")
],
"constant_propagation": [
("a = 1; b = a & x;", "b = 1 & x; // 常量传播")
]
}
def optimize_ir(self, ir_code):
"""优化中间表示"""
optimized = ir_code
# 应用简单的优化规则
if "MOV 1, result" in optimized and "RET result" in optimized:
optimized = optimized.replace("MOV 1, result\nRET result", "RET 1")
return optimized
def suggest_instruction_selection(self, operation):
"""建议指令选择"""
instruction_map = {
"&": "TAND",
"|": "TOR",
"~": "TNOT"
}
return instruction_map.get(operation, "UNKNOWN")
ai_agent/tester.py
import random
class AITester:
def generate_test_cases(self, function_signature, num_cases=5):
"""为函数生成测试用例"""
test_cases = []
trit_values = [0, 1, 'X']
for _ in range(num_cases):
if "a: trit, b: trit" in function_signature:
a = random.choice(trit_values)
b = random.choice(trit_values)
test_cases.append(f"({a}, {b})")
else:
test_cases.append("()")
return test_cases
def differential_test(self, source_code, reference_impl):
"""差分测试建议"""
return f"""
// 建议的参考实现（C语言）
#include <stdio.h>
// {reference_impl}
// 在三值模拟器上运行编译后的代码进行比较
"""
3. 编译器核心模块
compiler/lexer.py
class Lexer:
def __init__(self):
self.keywords = {'func', 'return', 'if', 'else', 'trit'}
self.operators = {'&', '|', '~', '=', '(', ')', '{', '}', ':', '->'}
def tokenize(self, source_code):
tokens = []
lines = source_code.split('\n')
for line_num, line in enumerate(lines, 1):
chars = list(line)
i = 0
while i < len(chars):
if chars[i].isspace():
i += 1
continue
# 标识符和关键字
if chars[i].isalpha():
identifier = []
while i < len(chars) and (chars[i].isalnum() or chars[i] == '_'):
identifier.append(chars[i])
i += 1
identifier = ''.join(identifier)
token_type = 'KEYWORD' if identifier in self.keywords else 'IDENTIFIER'
tokens.append((token_type, identifier, line_num))
# 运算符
elif chars[i] in self.operators:
tokens.append(('OPERATOR', chars[i], line_num))
i += 1
# 数字字面量
elif chars[i].isdigit():
number = []
while i < len(chars) and chars[i].isdigit():
number.append(chars[i])
i += 1
tokens.append(('NUMBER', ''.join(number), line_num))
# 注释
elif chars[i] == '/':
i += 1
break # 简化处理，跳过注释
else:
i += 1
return tokens
compiler/parser.py
class Parser:
def __init__(self, tokens):
self.tokens = tokens
self.pos = 0
def current_token(self):
return self.tokens[self.pos] if self.pos < len(self.tokens) else None
def eat(self, expected_type=None, expected_value=None):
token = self.current_token()
if not token:
return None
if expected_type and token[0] != expected_type:
raise SyntaxError(f"Expected {expected_type}, got {token[0]}")
if expected_value and token[1] != expected_value:
raise SyntaxError(f"Expected {expected_value}, got {token[1]}")
self.pos += 1
return token
def parse(self):
return self.parse_function()
def parse_function(self):
"""解析函数定义"""
self.eat('KEYWORD', 'func')
func_name = self.eat('IDENTIFIER')[1]
self.eat('OPERATOR', '(')
params = self.parse_parameters()
self.eat('OPERATOR', ')')
self.eat('OPERATOR', '->')
return_type = self.eat('IDENTIFIER')[1]
body = self.parse_block()
return {
'type': 'function',
'name': func_name,
'parameters': params,
'return_type': return_type,
'body': body
}
def parse_parameters(self):
params = []
while self.current_token() and self.current_token()[1] != ')':
if self.current_token()[0] == 'IDENTIFIER':
param_name = self.eat('IDENTIFIER')[1]
self.eat('OPERATOR', ':')
param_type = self.eat('IDENTIFIER')[1]
params.append({'name': param_name, 'type': param_type})
if self.current_token() and self.current_token()[1] == ',':
self.eat('OPERATOR', ',')
return params
def parse_block(self):
self.eat('OPERATOR', '{')
statements = []
while self.current_token() and self.current_token()[1] != '}':
if self.current_token()[1] == 'return':
statements.append(self.parse_return())
else:
# 简化处理，跳过其他语句
self.pos += 1
self.eat('OPERATOR', '}')
return statements
def parse_return(self):
self.eat('KEYWORD', 'return')
expr = self.parse_expression()
self.eat('OPERATOR', ';')
return {'type': 'return', 'expression': expr}
def parse_expression(self):
# 简化处理，只解析简单表达式
token = self.current_token()
if token[0] in ('IDENTIFIER', 'NUMBER'):
return self.eat(token[0])[1]
return None
compiler/semantic_analyzer.py
class SemanticAnalyzer:
def __init__(self):
self.symbol_table = {}
def analyze(self, ast):
errors = []
if ast['type'] == 'function':
# 检查返回类型
if ast['return_type'] != 'trit':
errors.append(f"函数 {ast['name']} 的返回类型必须是 trit")
# 检查函数体
for stmt in ast['body']:
if stmt['type'] == 'return':
errors.extend(self.check_expression(stmt['expression']))
return errors
def check_expression(self, expr):
# 简化处理，实际中需要更复杂的类型检查
if expr in ['0', '1', 'X']:
return []
return [f"表达式 {expr} 可能需要更严格的类型检查"]
compiler/code_generator.py
class CodeGenerator:
def __init__(self):
self.instruction_set = {
'TAND': '三值与操作',
'TOR': '三值或操作',
'TNOT': '三值非操作',
'MOV': '数据传输',
'RET': '函数返回'
}
def generate(self, ast):
assembly = []
if ast['type'] == 'function':
assembly.append(f"; 函数: {ast['name']}")
for stmt in ast['body']:
if stmt['type'] == 'return':
if stmt['expression'] in ['0', '1']:
assembly.append(f"MOV {stmt['expression']}, result")
else:
# 复杂表达式需要更多指令
assembly.extend(self.generate_expression(stmt['expression']))
assembly.append("RET result")
return '\n'.join(assembly)
def generate_expression(self, expr):
# 简化处理
return [f"; 表达式: {expr}", "MOV X, result ; 需要实现表达式编译"]
4. 硬件模拟器
hardware/triple_machine.py
class TripleMachine:
"""三值逻辑计算机模拟器"""
def __init__(self):
self.memory = {}
self.registers = {'result': 0}
self.operations = {
'TAND': lambda a, b: self.trit_and(a, b),
'TOR': lambda a, b: self.trit_or(a, b),
'TNOT': lambda a: self.trit_not(a),
'MOV': self.move,
'RET': self.return_value
}
def trit_and(self, a, b):
truth_table = {
(0,0):0, (0,1):0, (0,'X'):0,
(1,0):0, (1,1):1, (1,'X'):'X',
('X',0):0, ('X',1):'X', ('X','X'):'X'
}
return truth_table.get((a, b), 'X')
def trit_or(self, a, b):
truth_table = {
(0,0):0, (0,1):1, (0,'X'):'X',
(1,0):1, (1,1):1, (1,'X'):1,
('X',0):'X', ('X',1):1, ('X','X'):'X'
}
return truth_table.get((a, b), 'X')
def trit_not(self, a):
truth_table = {0:1, 1:0, 'X':'X'}
return truth_table.get(a, 'X')
def move(self, value, target):
if value in ['0', '1', 'X']:
self.registers[target] = int(value) if value in ['0', '1'] else value
def return_value(self, value):
return self.registers.get(value, value)
def execute(self, assembly_code):
lines = assembly_code.split('\n')
result = None
for line in lines:
line = line.split(';')[0].strip() # 移除注释
if not line:
continue
parts = line.split()
if not parts:
continue
instruction = parts[0]
if instruction == 'MOV':
value = parts[1].rstrip(',')
target = parts[2]
self.move(value, target)
elif instruction == 'RET':
result = self.return_value(parts[1])
break
return result
hardware/simulator.py
class Simulator:
def __init__(self):
self.machine = TripleMachine()
def simulate(self, assembly_code, inputs=None):
"""模拟执行汇编代码"""
print("=== 三值计算机模拟执行 ===")
print("汇编代码:")
print(assembly_code)
print("\n执行结果:")
result = self.machine.execute(assembly_code)
print(f"返回值: {result}")
return result
5. 主程序
main.py
import os
import sys
from ai_agent import AIAgent, AICodeGenerator, AIOptimizer, AITester
from compiler.lexer import Lexer
from compiler.parser import Parser
from compiler.semantic_analyzer import SemanticAnalyzer
from compiler.code_generator import CodeGenerator
from hardware.simulator import Simulator
class MultiValueCompilerDemo:
def __init__(self):
self.ai_agent = AIAgent()
self.code_gen_ai = AICodeGenerator()
self.optimizer_ai = AIOptimizer()
self.tester_ai = AITester()
self.lexer = Lexer()
self.semantic_analyzer = SemanticAnalyzer()
self.code_generator = CodeGenerator()
self.simulator = Simulator()
def demonstrate_workflow(self, source_code):
print("=" * 60)
print("多值计算机编译器 AI Agent Demo")
print("=" * 60)
# 1. AI 辅助代码生成和建议
print("\n1. AI 代码生成与建议")
print("-" * 30)
test_code = self.code_gen_ai.generate_test_case(complexity=2)
print(f"AI 生成的测试代码: {test_code}")
suggestions = self.code_gen_ai.suggest_optimization(source_code)
print(f"AI 优化建议: {suggestions}")
# 2. 词法分析
print("\n2. 词法分析")
print("-" * 30)
tokens = self.lexer.tokenize(source_code)
for token in tokens:
print(f"行 {token[2]}: {token[0]}({token[1]})")
# 3. 语法分析
print("\n3. 语法分析")
print("-" * 30)
try:
parser = Parser(tokens)
ast = parser.parse()
print(f"AST: {ast}")
except Exception as e:
print(f"语法分析错误: {e}")
ast = None
# 4. 语义分析
print("\n4. 语义分析")
print("-" * 30)
if ast:
errors = self.semantic_analyzer.analyze(ast)
if errors:
print("语义错误:")
for error in errors:
print(f" - {error}")
else:
print("语义分析通过")
# 5. 代码生成
print("\n5. 代码生成")
print("-" * 30)
if ast:
assembly = self.code_generator.generate(ast)
print("生成的汇编代码:")
print(assembly)
# 6. AI 优化
print("\n6. AI 优化")
print("-" * 30)
optimized_asm = self.optimizer_ai.optimize_ir(assembly)
print("优化后的汇编代码:")
print(optimized_asm)
# 7. 模拟执行
print("\n7. 模拟执行")
print("-" * 30)
result = self.simulator.simulate(optimized_asm)
# 8. AI 测试建议
print("\n8. AI 测试建议")
print("-" * 30)
test_cases = self.tester_ai.generate_test_cases(
"func main() -> trit", 3
)
print(f"AI 生成的测试用例: {test_cases}")
# 9. AI 问答演示
print("\n9. AI 问答系统")
print("-" * 30)
questions = [
"什么是三值逻辑？",
"编译器有哪些优化方法？",
"如何实现三值逻辑的AND操作？"
]
for question in questions:
answer = self.ai_agent.query(question)
print(f"Q: {question}")
print(f"A: {answer}\n")
def main():
# 示例源代码
source_code = """
func main() -> trit {
return 1;
}
"""
demo = MultiValueCompilerDemo()
demo.demonstrate_workflow(source_code)
# 演示更复杂的例子
print("\n" + "="*60)
print("复杂示例演示")
print("="*60)
complex_source = """
func and_gate(a: trit, b: trit) -> trit {
return a & b;
}
"""
demo.demonstrate_workflow(complex_source)
if __name__ == "__main__":
main()
6. 示例代码
examples/demo.tri
// 三值逻辑示例程序
// 简单的三值与门
func and_gate(a: trit, b: trit) -> trit {
return a & b;
}
// 三值或门
func or_gate(a: trit, b: trit) -> trit {
return a | b;
}
// 复杂逻辑函数
func complex_logic(a: trit, b: trit, c: trit) -> trit {
return (a & b) | (~c);
}
// 主函数
func main() -> trit {
return and_gate(1, 0);
}
Demo 功能特点
-
完整的编译器工作流：从词法分析到代码生成和模拟执行
-
AI 辅助开发：
-
代码生成和建议
-
优化建议
-
测试用例生成
-
智能问答系统
-
-
三值逻辑支持：实现了三值逻辑的基本操作
-
硬件模拟：包含三值计算机模拟器
-
模块化设计：易于扩展和维护
这个Demo展示了AI Agent如何在整个编译器开发流程中提供帮助，从代码生成到优化建议，再到测试和调试支持。虽然这是一个简化版本，但它清晰地展示了核心概念和实现方法。
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