49. Constraint Decoding 在 vLLM 中的位置：解码约束与生成质量的平衡

class ConstraintManager:
    def __init__(self):
        self.constraints = {}
        self.constraint_cache = {}  # 缓存编译后的约束
    
    def add_constraint(self, constraint_id: str, constraint: dict):
        """添加约束条件"""
        self.constraints[constraint_id] = constraint
    
    def get_constraint(self, constraint_id: str) -> dict:
        """获取约束条件"""
        return self.constraints.get(constraint_id, {})
    
    def compile_constraint(self, constraint: dict) -> "CompiledConstraint":
        """编译约束条件"""
        # 检查缓存
        constraint_hash = self._hash_constraint(constraint)
        if constraint_hash in self.constraint_cache:
            return self.constraint_cache[constraint_hash]
        
        # 编译约束
        compiled_constraint = self._do_compile_constraint(constraint)
        
        # 缓存编译结果
        self.constraint_cache[constraint_hash] = compiled_constraint
        
        return compiled_constraint
    
    def _do_compile_constraint(self, constraint: dict) -> "CompiledConstraint":
        """执行约束编译"""
        constraint_type = constraint.get("type", "")
        
        if constraint_type == "json_schema":
            return self._compile_json_schema_constraint(constraint)
        elif constraint_type == "regex":
            return self._compile_regex_constraint(constraint)
        elif constraint_type == "custom":
            return self._compile_custom_constraint(constraint)
        else:
            raise ValueError(f"Unsupported constraint type: {constraint_type}")
    
    def _compile_json_schema_constraint(self, constraint: dict) -> "CompiledConstraint":
        """编译JSON Schema约束"""
        # 实现JSON Schema到有限状态机的转换
        pass
    
    def _compile_regex_constraint(self, constraint: dict) -> "CompiledConstraint":
        """编译正则表达式约束"""
        # 实现正则表达式到有限状态机的转换
        pass
    
    def _compile_custom_constraint(self, constraint: dict) -> "CompiledConstraint":
        """编译自定义约束"""
        # 实现自定义约束的编译
        pass
    
    def _hash_constraint(self, constraint: dict) -> str:
        """计算约束条件的哈希值"""
        import hashlib
        import json
        return hashlib.sha256(json.dumps(constraint, sort_keys=True).encode()).hexdigest()

class ConstraintDecoder:
    def __init__(self, constraint_manager: "ConstraintManager"):
        self.constraint_manager = constraint_manager
        self.current_constraints = {}
        self.constraint_states = {}
    
    def initialize_constraint(self, request_id: str, constraint: dict):
        """初始化约束条件"""
        # 编译约束
        compiled_constraint = self.constraint_manager.compile_constraint(constraint)
        
        # 保存约束信息
        self.current_constraints[request_id] = compiled_constraint
        
        # 初始化约束状态
        initial_state = compiled_constraint.get_initial_state()
        self.constraint_states[request_id] = initial_state
    
    def get_allowed_tokens(self, request_id: str, logits: torch.Tensor, generated_tokens: list) -> torch.Tensor:
        """获取允许的token掩码"""
        # 获取当前约束
        constraint = self.current_constraints.get(request_id)
        if not constraint:
            return torch.ones_like(logits)
        
        # 获取当前约束状态
        current_state = self.constraint_states.get(request_id)
        
        # 根据当前状态和生成历史，获取允许的token
        allowed_chars = constraint.get_allowed_chars(current_state, generated_tokens)
        
        # 将字符转换为token
        allowed_tokens = self._chars_to_tokens(allowed_chars)
        
        # 生成掩码
        mask = torch.zeros_like(logits)
        mask[:, allowed_tokens] = 1
        
        return mask
    
    def update_constraint_state(self, request_id: str, token: str):
        """更新约束状态"""
        # 获取当前约束
        constraint = self.current_constraints.get(request_id)
        if not constraint:
            return
        
        # 获取当前约束状态
        current_state = self.constraint_states.get(request_id)
        
        # 更新约束状态
        new_state = constraint.update_state(current_state, token)
        
        # 保存新状态
        self.constraint_states[request_id] = new_state
    
    def is_constraint_satisfied(self, request_id: str) -> bool:
        """检查约束是否满足"""
        # 获取当前约束
        constraint = self.current_constraints.get(request_id)
        if not constraint:
            return True
        
        # 获取当前约束状态
        current_state = self.constraint_states.get(request_id)
        
        # 检查当前状态是否为最终状态
        return constraint.is_final_state(current_state)
    
    def reset_constraint(self, request_id: str):
        """重置约束状态"""
        if request_id in self.current_constraints:
            del self.current_constraints[request_id]
        
        if request_id in self.constraint_states:
            del self.constraint_states[request_id]
    
    def _chars_to_tokens(self, chars: set) -> list:
        """将字符转换为token ID列表"""
        allowed_tokens = []
        for token_id in range(tokenizer.vocab_size):
            token = tokenizer.decode([token_id])
            if token in chars:
                allowed_tokens.append(token_id)
        
        return allowed_tokens

class ConstraintValidator:
    def __init__(self, constraint_manager: "ConstraintManager"):
        self.constraint_manager = constraint_manager
    
    def validate(self, content: str, constraint: dict) -> bool:
        """验证内容是否符合约束条件"""
        # 编译约束
        compiled_constraint = self.constraint_manager.compile_constraint(constraint)
        
        # 执行验证
        return compiled_constraint.validate(content)
    
    def validate_partial(self, content: str, constraint: dict) -> tuple[bool, bool]:
        """验证部分内容是否符合约束条件"""
        # 编译约束
        compiled_constraint = self.constraint_manager.compile_constraint(constraint)
        
        # 执行部分验证
        return compiled_constraint.validate_partial(content)
    
    def get_validation_error(self, content: str, constraint: dict) -> str:
        """获取验证错误信息"""
        # 编译约束
        compiled_constraint = self.constraint_manager.compile_constraint(constraint)
        
        # 获取验证错误
        return compiled_constraint.get_validation_error(content)

class FSM:
    def __init__(self):
        self.states = set()
        self.initial_state = None
        self.final_states = set()
        self.transitions = {}
    
    def add_state(self, state: str) -> str:
        """添加状态"""
        self.states.add(state)
        return state
    
    def set_initial_state(self, state: str):
        """设置初始状态"""
        self.initial_state = state
    
    def add_final_state(self, state: str):
        """添加最终状态"""
        self.final_states.add(state)
    
    def add_transition(self, from_state: str, char: str, to_state: str):
        """添加状态转移"""
        if from_state not in self.transitions:
            self.transitions[from_state] = {}
        
        self.transitions[from_state][char] = to_state
    
    def get_transitions(self, state: str) -> dict:
        """获取状态转移"""
        return self.transitions.get(state, {})
    
    def get_allowed_chars(self, state: str) -> set:
        """获取允许的字符"""
        transitions = self.get_transitions(state)
        return set(transitions.keys())
    
    def update_state(self, state: str, char: str) -> str:
        """更新状态"""
        transitions = self.get_transitions(state)
        if char in transitions:
            return transitions[char]
        else:
            return state  # 保持当前状态不变
    
    def is_final_state(self, state: str) -> bool:
        """检查是否为最终状态"""
        return state in self.final_states
    
    def validate(self, content: str) -> bool:
        """验证内容是否符合约束"""
        current_state = self.initial_state
        
        for char in content:
            current_state = self.update_state(current_state, char)
        
        return self.is_final_state(current_state)
    
    def validate_partial(self, content: str) -> tuple[bool, bool]:
        """验证部分内容是否符合约束"""
        current_state = self.initial_state
        
        for char in content:
            transitions = self.get_transitions(current_state)
            if char not in transitions:
                return False, False
            
            current_state = transitions[char]
        
        return True, self.is_final_state(current_state)

def generate_dynamic_mask(self, current_state: str, logits: torch.Tensor) -> torch.Tensor:
    """生成动态token掩码"""
    # 获取当前状态允许的字符
    allowed_chars = self.constraint.get_allowed_chars(current_state)
    
    # 将字符转换为token ID
    allowed_tokens = []
    for token_id in range(logits.shape[-1]):
        token = self.tokenizer.decode([token_id])
        if token in allowed_chars:
            allowed_tokens.append(token_id)
    
    # 生成掩码
    mask = torch.zeros_like(logits)
    mask[:, allowed_tokens] = 1
    
    # 应用温度缩放（可选）
    if self.temperature > 0:
        masked_logits = logits * mask
        masked_logits = masked_logits / self.temperature
        return masked_logits
    else:
        return mask

def parallel_validate(self, contents: list, constraint: dict) -> list:
    """并行验证多个内容"""
    from concurrent.futures import ThreadPoolExecutor
    
    results = []
    
    # 使用线程池并行验证
    with ThreadPoolExecutor(max_workers=self.num_threads) as executor:
        # 提交验证任务
        futures = [executor.submit(self.validate, content, constraint) for content in contents]
        
        # 获取验证结果
        for future in futures:
            results.append(future.result())
    
    return results

def generate_structured_output(self, prompt: str, schema: dict) -> str:
    """生成符合JSON Schema的结构化输出"""
    # 创建约束条件
    constraint = {
        "type": "json_schema",
        "schema": schema
    }
    
    # 设置采样参数
    sampling_params = SamplingParams(
        temperature=0.7,
        max_tokens=200,
        constraint=constraint
    )
    
    # 生成内容
    outputs = self.generate([prompt], sampling_params)
    
    return outputs[0].outputs[0].text

async def create_chat_completion(
    request: ChatCompletionRequest,
    raw_request: Request,
) -> Union[ChatCompletionResponse, StreamingResponse]:
    # 验证请求
    await validate_chat_completion_request(request)
    
    # 转换为vLLM请求
    vllm_req = convert_chat_completion_request_to_vllm_request(request)
    
    # 检查是否需要结构化输出
    if hasattr(request, "response_format") and request.response_format == "json":
        # 设置JSON约束
        vllm_req.constraint = {
            "type": "json_schema",
            "schema": {"type": "object"}
        }
    
    # 执行推理
    vllm_resp = await engine.generate(vllm_req)
    
    # 转换为响应格式
    response = convert_vllm_response_to_chat_completion_response(vllm_resp)
    
    return response

def optimize_constraint_compilation(self, constraint: dict) -> "CompiledConstraint":
    """优化约束编译"""
    # 1. 简化约束条件
    simplified_constraint = self._simplify_constraint(constraint)
    
    # 2. 编译为有限状态机
    fsm = self._compile_to_fsm(simplified_constraint)
    
    # 3. 最小化有限状态机
    minimized_fsm = self._minimize_fsm(fsm)
    
    # 4. 优化状态转移
    optimized_fsm = self._optimize_transitions(minimized_fsm)
    
    return optimized_fsm

def optimize_constraint_validation(self, constraint: "CompiledConstraint") -> "CompiledConstraint":
    """优化约束验证"""
    # 1. 预计算允许的token
    constraint.allowed_tokens = self._precompute_allowed_tokens(constraint)
    
    # 2. 优化状态转移表
    constraint.optimized_transitions = self._optimize_transition_table(constraint)
    
    # 3. 添加快速路径
    constraint.fast_path = self._add_fast_path(constraint)
    
    return constraint

# 安装vLLM
pip install vllm

# 安装其他依赖
pip install jsonschema regex

# 启动vLLM服务，启用约束解码功能
python -m vllm.entrypoints.api_server \
    --model meta-llama/Llama-2-7b-chat-hf \
    --port 8000 \
    --num-gpus 1

from vllm import LLM, SamplingParams

# 创建LLM实例
llm = LLM(model="meta-llama/Llama-2-7b-chat-hf")

# 设置约束条件
constraint = {
    "type": "json_schema",
    "schema": {
        "type": "object",
        "properties": {
            "name": {"type": "string"},
            "age": {"type": "integer"},
            "email": {"type": "string"}
        },
        "required": ["name", "age", "email"]
    }
}

# 设置采样参数
sampling_params = SamplingParams(
    temperature=0.7,
    max_tokens=200,
    constraint=constraint
)

# 生成内容
prompt = "Generate a user profile with name, age, and email"
outputs = llm.generate([prompt], sampling_params)

# 输出结果
print(f"Generated: {outputs[0].outputs[0].text}")

from vllm import LLM, SamplingParams

# 创建LLM实例
llm = LLM(model="meta-llama/Llama-2-7b-code-hf")

# 设置约束条件（Python函数）
constraint = {
    "type": "regex",
    "pattern": r"^def\s+\w+\s*\([^)]*\)\s*:\s*[\\n\\s]*[\\w\\W]*return\s+[^\\n]+$"
}

# 设置采样参数
sampling_params = SamplingParams(
    temperature=0.7,
    max_tokens=100,
    constraint=constraint
)

# 生成内容
prompt = "Generate a Python function to calculate the factorial of a number"
outputs = llm.generate([prompt], sampling_params)

# 输出结果
print(f"Generated: {outputs[0].outputs[0].text}")