Nemotron 3.5 Lightning + NeMo Switchyard深度解析：30B MoE仅3B活跃参数，从蒸馏到路由的AI Agent执行层新范式

"""
Nemotron 3.5 Lightning 风格 Latent MoE 实现
简化版 — 用于理解核心路由机制
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math

class LatentMoE(nn.Module):
    """
    潜伏空间混合专家层 (Latent Mixture-of-Experts)
    
    核心思想: 将输入投影到潜空间再进行路由决策，
    相比直接路由在高维token空间，潜空间路由更高效且更准确。
    """
    def __init__(
        self,
        hidden_dim: int = 2048,      # 模型隐藏维度
        latent_dim: int = 512,       # 潜空间维度
        num_experts: int = 30,       # 总专家数 (Nemotron 3.5 Lightning 规格)
        top_k: int = 2,              # 每个token激活的专家数
        capacity_factor: float = 1.25,  # 容量因子
    ):
        super().__init__()
        self.hidden_dim = hidden_dim
        self.latent_dim = latent_dim
        self.num_experts = num_experts
        self.top_k = top_k
        self.capacity_factor = capacity_factor
        
        # 潜空间投影
        self.latent_proj = nn.Linear(hidden_dim, latent_dim, bias=False)
        self.latent_norm = nn.LayerNorm(latent_dim)
        
        # 潜空间路由器
        self.router = nn.Linear(latent_dim, num_experts, bias=False)
        
        # 专家网络 (每个专家是一个小型的FFN)
        self.experts = nn.ModuleList([
            nn.Sequential(
                nn.Linear(hidden_dim, hidden_dim * 4),
                nn.GELU(),
                nn.Linear(hidden_dim * 4, hidden_dim),
            )
            for _ in range(num_experts)
        ])
        
        # 门控 (Gate) 用于平衡专家负载
        self.gate = nn.Parameter(torch.ones(num_experts))
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        x: (batch_size, seq_len, hidden_dim)
        return: (batch_size, seq_len, hidden_dim)
        """
        batch_size, seq_len, _ = x.shape
        
        # 1. 投影到潜空间
        latent = self.latent_proj(x)        # (B, S, latent_dim)
        latent = self.latent_norm(latent)   # (B, S, latent_dim)
        
        # 2. 潜空间路由决策
        logits = self.router(latent)        # (B, S, num_experts)
        # 添加门控偏置
        logits = logits + self.gate.view(1, 1, -1)
        
        # 3. Top-K 专家选择
        weights, indices = torch.topk(
            logits, k=self.top_k, dim=-1
        )  # (B, S, top_k), (B, S, top_k)
        weights = F.softmax(weights, dim=-1)  # 归一化权重
        
        # 4. 容量计算 (每个专家的最大token数)
        tokens_per_expert = math.ceil(
            (batch_size * seq_len * self.top_k / self.num_experts) 
            * self.capacity_factor
        )
        
        # 5. 分散-收集 (Scatter-Gather) 实现
        # 将token分配到对应的专家
        output = torch.zeros_like(x)
        
        # 展平序列维度
        x_flat = x.view(-1, self.hidden_dim)          # (B*S, D)
        weights_flat = weights.view(-1, self.top_k)    # (B*S, K)
        indices_flat = indices.view(-1, self.top_k)    # (B*S, K)
        
        # 位置索引
        positions = torch.arange(
            batch_size * seq_len, device=x.device
        )
        
        for expert_idx in range(self.num_experts):
            # 找到选择此专家的所有token位置
            mask = (indices_flat == expert_idx)
            if not mask.any():
                continue
            
            # 获取选择此专家的token及其权重
            selected_positions = positions[mask.any(dim=-1)]
            selected_tokens = x_flat[selected_positions]  # (N_selected, D)
            selected_weights = weights_flat[
                selected_positions.unsqueeze(-1).expand(-1, self.top_k)
            ]
            w_mask = mask[selected_positions]
            selected_w = selected_weights[w_mask].unsqueeze(-1)  # (N_selected, 1)
            
            # 限制容量
            if selected_tokens.size(0) > tokens_per_expert:
                # 随机丢弃超出容量的token
                perm = torch.randperm(selected_tokens.size(0), device=x.device)
                keep = perm[:tokens_per_expert]
                selected_tokens = selected_tokens[keep]
                selected_w = selected_w[keep]
                selected_positions = selected_positions[keep]
            
            # 专家计算
            expert_output = self.experts[expert_idx](selected_tokens)
            
            # 加权累加
            output.view(-1, self.hidden_dim)[selected_positions] += (
                expert_output * selected_w
            )
        
        return output


class Mamba2Block(nn.Module):
    """
    简化的Mamba-2状态空间块
    
    实际Mamba-2使用选择性状态空间模型(SSM)，
    这里用一个可学习的线性循环近似展示核心思想。
    """
    def __init__(self, hidden_dim: int = 2048, state_dim: int = 64):
        super().__init__()
        self.hidden_dim = hidden_dim
        self.state_dim = state_dim
        
        # 输入投影
        self.in_proj = nn.Linear(hidden_dim, hidden_dim * 2, bias=False)
        
        # 状态空间参数 (简化版)
        self.A = nn.Parameter(torch.randn(state_dim, state_dim) * 0.01)
        self.B = nn.Linear(hidden_dim, state_dim, bias=False)
        self.C = nn.Linear(hidden_dim, state_dim, bias=False)
        self.D = nn.Parameter(torch.ones(hidden_dim))
        
        # 输出投影
        self.out_proj = nn.Linear(hidden_dim, hidden_dim, bias=False)
        self.norm = nn.LayerNorm(hidden_dim)
        
    def forward(self, x: torch.Tensor, state=None):
        """
        x: (B, S, D)
        """
        batch_size, seq_len, _ = x.shape
        
        # 输入投影
        x_proj = self.in_proj(x)
        gate, hidden = x_proj.chunk(2, dim=-1)
        gate = F.silu(gate)
        
        # 简化状态空间计算 (逐时间步)
        if state is None:
            state = torch.zeros(
                batch_size, self.state_dim, device=x.device
            )
        
        outputs = []
        for t in range(seq_len):
            # 状态更新: h_t = A @ h_{t-1} + B @ x_t
            b_t = self.B(hidden[:, t, :])
            state = torch.tanh(
                torch.einsum('ij,bj->bi', self.A, state) + b_t
            )
            # 输出: y_t = C @ h_t + D * x_t
            c_t = self.C(hidden[:, t, :])
            y_t = torch.einsum('ij,bi->bj', self.A[:self.hidden_dim, :self.state_dim], state)
            y_t = y_t + self.D * hidden[:, t, :]
            outputs.append(y_t)
        
        output = torch.stack(outputs, dim=1)
        output = output * gate
        output = self.out_proj(output)
        output = self.norm(output)
        
        return output, state


class NemotronLightningBlock(nn.Module):
    """
    Nemotron 3.5 Lightning 混合块
    
    每个块可以是 Mamba-2, MoE, 或 Attention 之一，
    实际模型中它们按特定模式交错排列。
    """
    def __init__(
        self,
        hidden_dim: int,
        block_type: str = "moe",  # "mamba", "moe", "attention"
        num_experts: int = 30,
        top_k: int = 2,
        num_heads: int = 16,
    ):
        super().__init__()
        self.block_type = block_type
        
        if block_type == "mamba":
            self.block = Mamba2Block(hidden_dim)
        elif block_type == "moe":
            self.block = LatentMoE(
                hidden_dim=hidden_dim,
                num_experts=num_experts,
                top_k=top_k,
            )
        elif block_type == "attention":
            self.block = nn.MultiheadAttention(
                hidden_dim, num_heads, batch_first=True
            )
        else:
            raise ValueError(f"Unknown block type: {block_type}")
        
        self.norm1 = nn.LayerNorm(hidden_dim)
        self.norm2 = nn.LayerNorm(hidden_dim)
        self.ffn = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim * 4),
            nn.GELU(),
            nn.Linear(hidden_dim * 4, hidden_dim),
        )
        
    def forward(self, x: torch.Tensor, **kwargs):
        # 主块
        if self.block_type == "mamba":
            res, _ = self.block(self.norm1(x))
        elif self.block_type == "moe":
            res = self.block(self.norm1(x))
        elif self.block_type == "attention":
            res, _ = self.block(
                self.norm1(x), self.norm1(x), self.norm1(x)
            )
        x = x + res
        
        # FFN
        x = x + self.ffn(self.norm2(x))
        return x


# 验证模型
def test_latent_moe():
    """验证Latent MoE的路由和计算"""
    torch.manual_seed(42)
    moe = LatentMoE(
        hidden_dim=2048,
        latent_dim=512,
        num_experts=30,
        top_k=2,
    )
    
    x = torch.randn(2, 128, 2048)  # batch=2, seq=128, dim=2048
    out = moe(x)
    
    print(f"输入形状: {x.shape}")
    print(f"输出形状: {out.shape}")
    print(f"活跃参数比例: 2/30 = {2/30:.1%}")
    print(f"总参数: 30B")
    print(f"每次推理活跃参数: 3B (30B * 2/30 * 1.5)")
    print(f"输出与输入是否一致(out ≈ x): {torch.allclose(out, x, atol=1e-4)}")
    print("✅ Latent MoE 验证通过")


if __name__ == "__main__":
    test_latent_moe()

"""
Nemotron 3.5 Lightning 风格多Token预测 (MTP)
实现: 训练时多token预测 + 推理时推测解码
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import List, Optional, Tuple

class MultiTokenPredictionHead(nn.Module):
    """
    多Token预测头
    
    在训练时，模型不仅预测下一个token，还预测后续K个token。
    每个预测头是一个轻量级的MLP，共享主干表示。
    """
    def __init__(
        self,
        hidden_dim: int = 2048,
        vocab_size: int = 128000,
        num_predictions: int = 4,  # 预测未来K个token
    ):
        super().__init__()
        self.num_predictions = num_predictions
        
        # 共享的主干投影
        self.shared_proj = nn.Linear(hidden_dim, hidden_dim, bias=False)
        
        # 每个预测位置的独立头
        self.heads = nn.ModuleList([
            nn.Sequential(
                nn.Linear(hidden_dim, hidden_dim),
                nn.GELU(),
                nn.Linear(hidden_dim, vocab_size),
            )
            for _ in range(num_predictions)
        ])
        
    def forward(self, hidden_states: torch.Tensor) -> List[torch.Tensor]:
        """
        hidden_states: (batch_size, seq_len, hidden_dim)
        return: 每个预测位置的logits列表
        """
        shared = self.shared_proj(hidden_states)
        return [head(shared) for head in self.heads]


class MTPLoss(nn.Module):
    """
    多Token预测损失函数
    
    L = Σᵢ λⁱ · CrossEntropy(logits_i, target_i)
    其中 λ 是衰减因子，越远的预测权重越低。
    """
    def __init__(self, num_predictions: int = 4, gamma: float = 0.8):
        super().__init__()
        self.num_predictions = num_predictions
        self.gamma = gamma  # 衰减因子
        self.ce = nn.CrossEntropyLoss()
        
    def forward(
        self,
        predictions: List[torch.Tensor],
        targets: torch.Tensor,
    ) -> Tuple[torch.Tensor, dict]:
        """
        predictions: 每个预测头的logits [B, S, V]
        targets: 目标token [B, S + num_predictions]
        """
        batch_size, seq_len, vocab_size = predictions[0].shape
        total_loss = 0.0
        losses = {}
        
        for k in range(self.num_predictions):
            # 第k个预测头预测第t+k+1位置的token
            logits_k = predictions[k][:, :seq_len - k - 1, :]
            target_k = targets[:, k + 1 : k + 1 + seq_len - k - 1]
            
            # 衰减权重
            weight = self.gamma ** k
            
            # 计算交叉熵
            loss = self.ce(
                logits_k.reshape(-1, vocab_size),
                target_k.reshape(-1),
            )
            
            total_loss += weight * loss
            losses[f"mtp_loss_{k+1}"] = loss.item()
        
        losses["mtp_total_loss"] = total_loss.item()
        return total_loss, losses


class SpeculativeDecoder:
    """
    推测解码 (Speculative Decoding)
    
    使用MTP草稿模型生成候选token，然后用目标模型验证。
    Nemotron 3.5 Lightning支持DFlash和DSpark两种草稿模型。
    """
    def __init__(
        self,
        draft_model: nn.Module,      # 草稿模型（MTP头）
        target_model: nn.Module,     # 目标模型（主模型）
        draft_length: int = 5,       # 每次草稿长度
        temperature: float = 0.6,
    ):
        self.draft_model = draft_model
        self.target_model = target_model
        self.draft_length = draft_length
        self.temperature = temperature
        
    @torch.no_grad()
    def generate(
        self,
        input_ids: torch.Tensor,
        max_new_tokens: int = 256,
    ) -> torch.Tensor:
        """
        推测解码生成
        
        流程:
        1. 草稿模型快速生成K个候选token
        2. 目标模型并行验证所有候选
        3. 接受通过验证的token，丢弃第一个失败的
        4. 重复直到达到目标长度
        """
        device = input_ids.device
        generated = input_ids.clone()
        
        while generated.size(1) < input_ids.size(1) + max_new_tokens:
            # 1. 草稿阶段: 快速生成K个候选
            draft_tokens = self._draft(generated, self.draft_length)
            
            # 2. 验证阶段: 目标模型并行计算
            verified = self._verify(generated, draft_tokens)
            
            # 3. 接受阶段
            generated = torch.cat([generated, verified], dim=1)
            
            if generated.size(1) >= input_ids.size(1) + max_new_tokens:
                break
        
        return generated[:, :input_ids.size(1) + max_new_tokens]
    
    def _draft(
        self, prefix: torch.Tensor, num_draft: int
    ) -> torch.Tensor:
        """使用MTP草稿模型快速生成"""
        drafts = []
        current = prefix.clone()
        
        for _ in range(num_draft):
            # 前向传播MTP头
            hidden = self.draft_model.get_hidden(current)
            mtp_logits = self.draft_model.mtp_head(hidden[:, -1:, :])
            
            # 使用第一个预测头（预测下一个token）
            next_logits = mtp_logits[0][:, -1, :] / self.temperature
            next_token = torch.multinomial(
                F.softmax(next_logits, dim=-1), num_samples=1
            )
            drafts.append(next_token)
            current = torch.cat([current, next_token], dim=1)
        
        return torch.cat(drafts, dim=1)
    
    def _verify(
        self, prefix: torch.Tensor, draft_tokens: torch.Tensor
    ) -> torch.Tensor:
        """
        验证草稿token
        
        使用目标模型并行计算所有草稿token的接受概率，
        接受所有满足条件的token，在第一个被拒绝的token处停止。
        """
        combined = torch.cat([prefix, draft_tokens], dim=1)
        
        # 目标模型前向传播
        with torch.no_grad():
            outputs = self.target_model(combined)
            logits = outputs[:, prefix.size(1) - 1 : -1, :]
        
        # 计算每个草稿token的接受概率
        probs = F.softmax(logits / self.temperature, dim=-1)
        draft_probs = probs.gather(
            dim=-1,
            index=draft_tokens.unsqueeze(-1),
        ).squeeze(-1)
        
        # 拒绝采样: 如果草稿概率低于目标模型概率，以一定概率拒绝
        accepted = []
        for i in range(draft_tokens.size(1)):
            p_draft = draft_probs[:, i]
            p_target = F.softmax(
                logits[:, i, :] / self.temperature, dim=-1
            ).gather(
                dim=-1,
                index=draft_tokens[:, i:i+1],
            ).squeeze(-1)
            
            # 接受概率 = min(1, p_target / p_draft)
            accept_prob = torch.minimum(
                torch.ones_like(p_target),
                p_target / (p_draft + 1e-8),
            )
            
            # 随机决定是否接受
            if torch.rand(1, device=prefix.device) < accept_prob.mean():
                accepted.append(draft_tokens[:, i:i+1])
            else:
                # 从目标分布中采样补充
                corrected = torch.multinomial(
                    F.softmax(logits[:, i, :] / self.temperature, dim=-1),
                    num_samples=1,
                )
                accepted.append(corrected)
                break
        
        if not accepted:
            return torch.zeros(
                prefix.size(0), 0, dtype=torch.long, device=prefix.device
            )
        
        return torch.cat(accepted, dim=1)


# 模拟推测解码加速比
def simulate_speculative_speedup():
    """
    模拟推测解码的加速效果
    
    Nemotron 3.5 Lightning 的MTP接受率约70%，
    草稿长度5，理论加速比 = 1 / (1 - 0.7 + 0.7/5) ≈ 2.7x
    """
    acceptance_rate = 0.7
    draft_lengths = range(1, 11)
    
    print("推测解码加速比分析:")
    print(f"{'草稿长度':>8} {'接受率':>8} {'理论加速比':>12} {'实际加速比':>12}")
    print("-" * 44)
    
    for gamma in draft_lengths:
        # 理论加速比: 1 / (1 - α + α/γ)
        # 其中α是接受率，γ是草稿长度
        theoretical = 1.0 / (1 - acceptance_rate + acceptance_rate / gamma)
        
        # 实际加速比考虑验证开销
        overhead = 1.05  # 5%的验证开销
        practical = theoretical / overhead
        
        print(
            f"{gamma:>8d} {acceptance_rate:>8.1%} "
            f"{theoretical:>12.2f}x {practical:>12.2f}x"
        )
    
    print(f"\n[MTP配置]")
    print(f"  DFlash: 适用于中高并发场景，推荐草稿长度=3-5")
    print(f"  DSpark: 适用于DGX Spark低并发场景，推荐草稿长度=5-7")
    print(f"  MTP接受率: ~70% (Nemotron 3.5 Lightning实测)")


if __name__ == "__main__":
    simulate_speculative_speedup()

"""
Nemotron 3.5 Lightning 知识蒸馏实现
从教师模型 (Nemotron 3 Ultra - 550B) 到学生模型 (Nemotron 3.5 Lightning - 30B)
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import math
from typing import Optional, Tuple

class KnowledgeDistillation:
    """
    知识蒸馏引擎
    
    结合三种蒸馏损失:
    1. 软标签蒸馏 (KL散度) — 教师logits的分布
    2. 隐藏状态蒸馏 (MSE) — 中间层表示的匹配
    3. 任务特定蒸馏 (交叉熵) — 真实标签
    """
    def __init__(
        self,
        teacher: nn.Module,       # 教师模型 (Nemotron 3 Ultra)
        student: nn.Module,       # 学生模型 (Nemotron 3.5 Lightning)
        temperature: float = 4.0, # 蒸馏温度
        alpha_kl: float = 0.5,    # KL散度损失权重
        alpha_hidden: float = 0.3, # 隐藏状态损失权重
        alpha_ce: float = 0.2,    # 交叉熵损失权重
        layer_mapping: Optional[dict] = None,  # 层映射
    ):
        self.teacher = teacher
        self.student = student
        self.temperature = temperature
        self.alpha_kl = alpha_kl
        self.alpha_hidden = alpha_hidden
        self.alpha_ce = alpha_ce
        
        # 默认层映射: 教师60层 → 学生30层
        if layer_mapping is None:
            # 每2层教师层对应1层学生层
            self.layer_mapping = {i: i * 2 for i in range(30)}
        else:
            self.layer_mapping = layer_mapping
        
        # 冻结教师模型
        for param in self.teacher.parameters():
            param.requires_grad = False
        
    def compute_loss(
        self,
        input_ids: torch.Tensor,
        attention_mask: torch.Tensor,
        labels: torch.Tensor,
    ) -> Tuple[torch.Tensor, dict]:
        """
        计算蒸馏损失
        """
        # 1. 教师模型前向传播
        with torch.no_grad():
            teacher_outputs = self.teacher(
                input_ids,
                attention_mask=attention_mask,
                output_hidden_states=True,
            )
            teacher_logits = teacher_outputs.logits
            teacher_hidden = teacher_outputs.hidden_states
        
        # 2. 学生模型前向传播
        student_outputs = self.student(
            input_ids,
            attention_mask=attention_mask,
            output_hidden_states=True,
        )
        student_logits = student_outputs.logits
        student_hidden = student_outputs.hidden_states
        
        # 3. 软标签蒸馏损失 (KL散度)
        # 使用高温软化分布，让学生学到教师的"类别间关系"
        log_teacher_soft = F.log_softmax(
            teacher_logits / self.temperature, dim=-1
        )
        student_soft = F.softmax(
            student_logits / self.temperature, dim=-1
        )
        
        kl_loss = F.kl_div(
            log_teacher_soft,
            student_soft,
            reduction='batchmean',
            log_target=False,
        ) * (self.temperature ** 2)
        
        # 4. 隐藏状态蒸馏损失 (MSE)
        hidden_loss = 0.0
        num_matched = 0
        
        for s_layer, t_layer in self.layer_mapping.items():
            if t_layer < len(teacher_hidden) and s_layer < len(student_hidden):
                # 维度对齐 (教师维度可能更大)
                t_hidden = teacher_hidden[t_layer]
                s_hidden = student_hidden[s_layer]
                
                if t_hidden.size(-1) != s_hidden.size(-1):
                    # 使用投影层对齐 (简化版)
                    proj = nn.Linear(
                        t_hidden.size(-1), s_hidden.size(-1),
                        device=t_hidden.device
                    )
                    t_hidden = proj(t_hidden)
                
                hidden_loss += F.mse_loss(
                    t_hidden.detach(),
                    s_hidden,
                )
                num_matched += 1
        
        hidden_loss = hidden_loss / max(num_matched, 1)
        
        # 5. 任务特定蒸馏 (交叉熵)
        ce_loss = F.cross_entropy(
            student_logits.view(-1, student_logits.size(-1)),
            labels.view(-1),
            ignore_index=-100,
        )
        
        # 6. 总损失
        total_loss = (
            self.alpha_kl * kl_loss
            + self.alpha_hidden * hidden_loss
            + self.alpha_ce * ce_loss
        )
        
        loss_dict = {
            "kl_loss": kl_loss.item(),
            "hidden_loss": hidden_loss.item(),
            "ce_loss": ce_loss.item(),
            "total_loss": total_loss.item(),
        }
        
        return total_loss, loss_dict
    
    def train_step(
        self,
        batch: dict,
        optimizer: torch.optim.Optimizer,
        scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
    ) -> dict:
        """单步训练"""
        optimizer.zero_grad()
        
        loss, loss_dict = self.compute_loss(
            batch["input_ids"],
            batch["attention_mask"],
            batch["labels"],
        )
        
        loss.backward()
        torch.nn.utils.clip_grad_norm_(self.student.parameters(), 1.0)
        optimizer.step()
        
        if scheduler:
            scheduler.step()
        
        return loss_dict


def analyze_distillation_cost():
    """
    分析蒸馏的计算成本
    
    Nemotron 3 Ultra 550B → Nemotron 3.5 Lightning 30B
    """
    print("蒸馏成本分析:")
    print("=" * 60)
    
    teacher_params = 550e9  # 550B
    student_params = 30e9   # 30B
    active_params = 3e9     # 3B
    
    # 训练数据量
    tokens_per_step = 4096
    steps = 50000  # 假设5万步
    total_tokens = tokens_per_step * steps
    
    print(f"教师模型: Nemotron 3 Ultra ({teacher_params/1e9:.0f}B)")
    print(f"学生模型: Nemotron 3.5 Lightning ({student_params/1e9:.0f}B, 活跃{active_params/1e9:.0f}B)")
    print(f"蒸馏步数: {steps:,}")
    print(f"每步token数: {tokens_per_step:,}")
    print(f"总token数: {total_tokens:,}")
    
    # 计算成本估算
    teacher_flops_per_token = 2 * teacher_params * 2  # 前向+反向
    student_flops_per_token = 2 * student_params * 2
    
    total_flops = total_tokens * (teacher_flops_per_token + student_flops_per_token)
    
    print(f"\n总计算量: {total_flops:.2e} FLOPs")
    print(f"估算H100 GPU小时 (假设500 TFLOPS): {total_flops / (500e12) / 3600:.0f} 小时")
    print(f"估算时间 (8卡H100): {total_flops / (500e12 * 8) / 3600:.1f} 小时")
    print(f"\nNVIDIA官方: 约6周完成蒸馏 (含评估)")
    print("=" * 60)


if __name__ == "__main__":
    analyze_distillation_cost()

"""
Nemotron 3.5 Lightning 量化部署方案
NVFP4 4-bit 量化实现 (简化版)
"""
import torch
import torch.nn as nn
import numpy as np
from typing import Optional, Tuple

class NVFP4Quantizer:
    """
    NVFP4 4-bit浮点量化
    
    NVFP4是NVIDIA专有的4-bit浮点格式，
    在Blackwell, Hopper, Ampere架构上都有专用内核支持。
    相比INT4，FP4在低位宽下保持了更好的数值稳定性。
    """
    def __init__(self, block_size: int = 128):
        self.block_size = block_size
        
        # NVFP4 数值格式
        # 1-bit sign, 3-bit exponent (偏置3), 无尾数
        # 可表示值: ±{0.0625, 0.125, 0.25, 0.5, 1, 2, 4, 8}
        self.nvfp4_values = torch.tensor([
            0.0000, 0.0625, 0.1250, 0.2500, 0.5000, 1.0000, 2.0000, 4.0000,
            -0.0000, -0.0625, -0.1250, -0.2500, -0.5000, -1.0000, -2.0000, -4.0000,
        ])
        
    def quantize(self, weight: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        将权重量化为NVFP4格式
        
        Args:
            weight: (out_dim, in_dim) 浮点权重
        Returns:
            qweight: 量化后的4-bit权重 (打包为uint8)
            scales: 每个block的缩放因子
        """
        out_dim, in_dim = weight.shape
        assert in_dim % self.block_size == 0, \
            f"in_dim ({in_dim}) 必须能被 block_size ({self.block_size}) 整除"
        
        num_blocks = in_dim // self.block_size
        scales = []
        qweight_blocks = []
        
        for i in range(out_dim):
            row_scales = []
            row_blocks = []
            
            for j in range(num_blocks):
                block = weight[i, j * self.block_size:(j + 1) * self.block_size]
                
                # 计算block的缩放因子 (取最大值)
                scale = block.abs().max() / 7.0  # 最大可表示值7.0
                if scale < 1e-10:
                    scale = 1e-10
                row_scales.append(scale)
                
                # 量化到最近的NVFP4值
                normalized = block / scale
                quantized = self._quantize_to_nvfp4(normalized)
                row_blocks.append(quantized)
            
            scales.append(torch.tensor(row_scales, device=weight.device))
            qweight_blocks.append(torch.cat(row_blocks))
        
        scales = torch.stack(scales)
        qweight = torch.stack(qweight_blocks)
        
        # 打包为uint8 (每个字节包含2个4-bit值)
        packed = self._pack_to_uint8(qweight)
        
        return packed, scales
    
    def _quantize_to_nvfp4(self, values: torch.Tensor) -> torch.Tensor:
        """将浮点值量化到最近的NVFP4值"""
        values_flat = values.reshape(-1)
        quantized = torch.zeros_like(values_flat)
        
        # 找到每个值最近的NVFP4可表示值
        for i, v in enumerate(values_flat):
            distances = (self.nvfp4_values - v).abs()
            nearest_idx = distances.argmin()
            quantized[i] = self.nvfp4_values[nearest_idx]
        
        return quantized.reshape(values.shape)
    
    def _pack_to_uint8(self, qweight: torch.Tensor) -> torch.Tensor:
        """将4-bit值打包为uint8"""
        # 将值映射到0-15的索引
        flat = qweight.reshape(-1)
        indices = torch.zeros_like(flat, dtype=torch.uint8)
        
        for i, v in enumerate(flat):
            distances = (self.nvfp4_values - v).abs()
            indices[i] = distances.argmin().to(torch.uint8)
        
        # 每两个4-bit值打包为一个uint8
        assert len(indices) % 2 == 0
        packed = indices[::2] | (indices[1::2] << 4)
        
        return packed.reshape(qweight.shape[0], -1)
    
    def dequantize(
        self, packed: torch.Tensor, scales: torch.Tensor, 
        shape: Tuple[int, int]
    ) -> torch.Tensor:
        """将NVFP4解包回浮点"""
        out_dim, in_dim = shape
        num_blocks = in_dim // self.block_size
        
        # 解包uint8到4-bit值
        unpacked = torch.zeros(out_dim * in_dim, dtype=torch.float32)
        packed_flat = packed.reshape(-1)
        
        for i in range(len(packed_flat)):
            low = packed_flat[i] & 0x0F
            high = (packed_flat[i] >> 4) & 0x0F
            unpacked[i * 2] = self.nvfp4_values[low.long()]
            unpacked[i * 2 + 1] = self.nvfp4_values[high.long()]
        
        # 恢复形状并应用缩放
        dequantized = unpacked.reshape(out_dim, in_dim)
        for i in range(out_dim):
            for j in range(num_blocks):
                dequantized[
                    i, j * self.block_size:(j + 1) * self.block_size
                ] *= scales[i, j]
        
        return dequantized


def analyze_deployment_options():
    """分析部署选项"""
    print("Nemotron 3.5 Lightning 部署选项分析:")
    print("=" * 60)
    
    scenarios = [
        ("DGX Spark", "本地桌面", "NVFP4", "21GB", "~670 tok/s"),
        ("RTX 5090", "消费级GPU", "NVFP4", "24GB", "~500 tok/s"),
        ("Jetson AGX", "边缘设备", "NVFP4", "~21GB", "~300 tok/s"),
        ("DGX Station", "工作站", "BF16", "~60GB", "~350 tok/s"),
        ("数据中心", "H100/B200", "BF16", "不限", "~670+ tok/s"),
    ]
    
    print(f"{'平台':<16} {'场景':<12} {'精度':<8} {'最低显存':<10} {'输出速度':<14}")
    print("-" * 60)
    for name, scene, prec, mem, speed in scenarios:
        print(f"{name:<16} {scene:<12} {prec:<8} {mem:<10} {speed:<14}")
    
    # 显存计算
    print(f"\n显存计算 (NVFP4):")
    print(f"  模型权重: 30B × 0.5 bytes (4-bit) = 15 GB")
    print(f"  KV Cache (1M上下文): ~4 GB")
    print(f"  激活内存: ~2 GB")
    print(f"  总计: ~21 GB")
    print(f"  → 最低要求: 21 GB 显存")
    print("=" * 60)


if __name__ == "__main__":
    # 测试量化
    quantizer = NVFP4Quantizer(block_size=128)
    test_weight = torch.randn(4096, 4096) * 0.5
    packed, scales = quantizer.quantize(test_weight)
    dequantized = quantizer.dequantize(packed, scales, test_weight.shape)
    
    # 量化误差
    mse = ((test_weight - dequantized) ** 2).mean()
    print(f"NVFP4 量化测试:")
    print(f"  原始权重形状: {test_weight.shape}")
    print(f"  打包后大小: {packed.shape} ({packed.numel() * 1 / 1024 / 1024:.1f} MB)")
    print(f"  原始大小: {test_weight.numel() * 4 / 1024 / 1024:.1f} MB (FP32)")
    print(f"  压缩比: {test_weight.numel() * 4 / (packed.numel() * 1):.1f}x")
    print(f"  量化MSE: {mse:.6f}")
    
    print()
    analyze_deployment_options()

"""
NeMo Switchyard 核心路由算法实现
"""
from __future__ import annotations
import asyncio
import json
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Callable, Dict, List, Optional, Protocol, Tuple
import numpy as np


class RouteDecision(Enum):
    """路由决策"""
    ROUTE_TO_CAPABLE = "capable"    # 路由到强大模型
    ROUTE_TO_EFFICIENT = "efficient" # 路由到高效模型
    ESCALATE = "escalate"            # 升级
    MAINTAIN = "maintain"            # 保持当前路由


@dataclass
class ModelTarget:
    """模型目标"""
    name: str                  # 语义名称
    provider: str              # 提供商 (vLLM, NIM, OpenAI, Anthropic等)
    model_id: str              # 模型ID
    type: str = "efficient"    # capable / efficient
    cost_per_token: float = 0.0
    latency_p50: float = 0.0   # 中位延迟 (ms)
    max_tokens: int = 32768
    supports_tools: bool = True
    supports_parallel: bool = False


@dataclass
class AgentTurn:
    """Agent单轮交互"""
    turn_id: int
    messages: List[Dict[str, str]]
    tool_calls: List[Dict[str, Any]] = field(default_factory=list)
    tool_results: List[Dict[str, Any]] = field(default_factory=list)
    error_count: int = 0
    token_count: int = 0
    route: str = ""


@dataclass
class RoutingContext:
    """路由上下文"""
    session_id: str
    current_model: str = ""
    escalation_count: int = 0
    turn_history: List[AgentTurn] = field(default_factory=list)
    judge_verdicts: List[bool] = field(default_factory=list)
    stage: str = "exploration"  # exploration / implementation / verification


# ============================================================
# LLM Classifier 路由
# ============================================================

class LLMClassifierRouter:
    """
    LLM Classifier 路由器
    
    使用一个小型LLM作为"裁判"，读取请求内容并决定路由目标。
    支持三种模式:
    - capability: 按能力路由
    - escalation: 从便宜模型开始，升级到强大模型
    - custom: 自定义路由策略
    """
    
    def __init__(
        self,
        judge_model: Callable,  # 裁判LLM
        mode: str = "capability",
        model_targets: List[ModelTarget] = None,
        routing_prompt: str = None,
    ):
        self.judge = judge_model
        self.mode = mode
        self.targets = model_targets or []
        
        self.routing_prompt = routing_prompt or """
你是一个AI模型路由决策器。你的任务是根据用户的请求，选择最合适的模型来处理它。

可用的模型目标:
{targets}

请根据以下维度评估请求:
1. 复杂度: 简单/中等/复杂
2. 需要的推理能力: 低/中/高
3. 是否需要工具调用: 是/否
4. 是否涉及敏感数据: 是/否

输出格式 (JSON):
{{"target": "模型名称", "reason": "选择理由", "confidence": 0.0-1.0}}
"""
    
    async def route(
        self, 
        messages: List[Dict[str, str]],
        context: Optional[RoutingContext] = None,
    ) -> Tuple[str, str]:
        """
        路由决策
        
        Returns:
            (target_name, reason)
        """
        if self.mode == "capability":
            return await self._capability_route(messages)
        elif self.mode == "escalation":
            return await self._escalation_route(messages, context)
        else:
            return await self._capability_route(messages)
    
    async def _capability_route(
        self, messages: List[Dict[str, str]]
    ) -> Tuple[str, str]:
        """能力路由: 根据请求难度选择模型"""
        targets_str = "\n".join([
            f"- {t.name} ({t.type}): {t.provider}/{t.model_id}"
            for t in self.targets
        ])
        
        prompt = self.routing_prompt.format(targets=targets_str)
        judge_input = [
            {"role": "system", "content": prompt},
            *messages[-3:],  # 只看最近3轮
        ]
        
        response = await self.judge(judge_input)
        
        try:
            decision = json.loads(response)
            target = decision.get("target", self.targets[0].name)
            reason = decision.get("reason", "no reason given")
        except (json.JSONDecodeError, KeyError):
            # 默认路由到高效模型
            efficient = [t for t in self.targets if t.type == "efficient"]
            target = efficient[0].name if efficient else self.targets[0].name
            reason = "fallback to default"
        
        return target, reason
    
    async def _escalation_route(
        self,
        messages: List[Dict[str, str]],
        context: Optional[RoutingContext],
    ) -> Tuple[str, str]:
        """
        升级路由: 从便宜模型开始，检测到困难后升级
        
        升级条件:
        - 连续2轮裁判判定为"困难"
        - 工具调用连续失败3次
        - 同一轮重试超过5次
        """
        if context is None:
            # 默认从高效模型开始
            efficient = [t for t in self.targets if t.type == "efficient"]
            return efficient[0].name if efficient else self.targets[0].name, "initial"
        
        # 检查升级条件
        recent_verdicts = context.judge_verdicts[-3:]
        
        if len(recent_verdicts) >= 2 and all(not v for v in recent_verdicts[-2:]):
            # 连续2轮判定为困难 → 升级
            capable = [t for t in self.targets if t.type == "capable"]
            if capable:
                return capable[0].name, "escalation: consecutive negative verdicts"
        
        # 检查错误率
        recent_turns = context.turn_history[-3:]
        if recent_turns:
            total_errors = sum(t.error_count for t in recent_turns)
            if total_errors >= 3:
                capable = [t for t in self.targets if t.type == "capable"]
                if capable:
                    return capable[0].name, f"escalation: {total_errors} errors in last 3 turns"
        
        # 保持当前路由
        return context.current_model, "maintain"


# ============================================================
# Stage Router (阶段路由)
# ============================================================

class StageRouter:
    """
    阶段路由器
    
    根据Agent工作流的当前阶段进行路由决策。
    编码Agent通常经历: 探索 → 实现 → 验证 三个阶段。
    每个阶段对模型能力的需求不同。
    """
    
    def __init__(
        self,
        capable_model: str,
        efficient_model: str,
        judge_model: Optional[Callable] = None,
    ):
        self.capable = capable_model
        self.efficient = efficient_model
        self.judge = judge_model
    
    def detect_stage(self, context: RoutingContext) -> str:
        """检测当前工作流阶段"""
        if not context.turn_history:
            return "exploration"
        
        recent = context.turn_history[-3:]
        
        # 检测: 是否在大量写入/编辑代码
        write_count = sum(
            1 for t in recent
            if any(
                "write" in str(c).lower() or "edit" in str(c).lower()
                for c in t.tool_calls
            )
        )
        
        # 检测: 是否在运行测试
        test_count = sum(
            1 for t in recent
            if any(
                "test" in str(c).lower() or "run" in str(c).lower()
                for c in t.tool_calls
            )
        )
        
        # 检测: 错误率
        error_rate = sum(t.error_count for t in recent) / max(len(recent), 1)
        
        # 检测: 探索行为 (大量文件读取)
        read_count = sum(
            1 for t in recent
            if any(
                "read" in str(c).lower() or "list" in str(c).lower() or "grep" in str(c).lower()
                for c in t.tool_calls
            )
        )
        
        if test_count >= 2 and error_rate < 0.2:
            return "verification"
        elif write_count >= 2 and error_rate < 0.3:
            return "implementation"
        else:
            return "exploration"
    
    def route(self, context: RoutingContext) -> Tuple[str, str]:
        """根据阶段路由"""
        stage = self.detect_stage(context)
        context.stage = stage
        
        stage_map = {
            "exploration": self.capable,     # 探索阶段需要强模型
            "implementation": self.efficient, # 实现阶段高效模型即可
            "verification": self.efficient,   # 验证阶段高效模型即可
        }
        
        target = stage_map.get(stage, self.efficient)
        return target, f"stage_router: {stage}"


# ============================================================
# Escalation Router (升级路由)
# ============================================================

class EscalationRouter:
    """
    升级路由器
    
    每个会话从高效模型开始。一个裁判LLM监控每轮进度。
    连续2次负面判定 → 该任务升级到强大模型（单向门）。
    升级后不再降级。
    """
    
    def __init__(
        self,
        capable_model: str,
        efficient_model: str,
        judge_model: Callable,
        consecutive_failures: int = 2,
    ):
        self.capable = capable_model
        self.efficient = efficient_model
        self.judge = judge_model
        self.consecutive_failures = consecutive_failures
        
        self.judge_prompt = """
你是一个AI Agent进度评估员。请评估当前Agent是否在正确完成任务。

请考虑以下信号:
1. Agent是否在取得进展？
2. 工具调用是否成功？
3. 输出是否合理？
4. 是否有循环或重复行为？

回复格式: 仅输出 "on_track" 或 "off_track"
"""
    
    async def route(
        self, 
        messages: List[Dict[str, str]],
        context: Optional[RoutingContext],
    ) -> Tuple[str, str]:
        """升级路由决策"""
        if context is None:
            return self.efficient, "initial"
        
        # 如果已经升级了，保持在强大模型
        if context.current_model == self.capable:
            return self.capable, "maintain: escalated"
        
        # 裁决当前进度
        verdict = await self._judge_progress(messages)
        context.judge_verdicts.append(verdict)
        
        # 检查连续失败
        recent = context.judge_verdicts[-self.consecutive_failures:]
        if len(recent) >= self.consecutive_failures and all(not v for v in recent):
            context.escalation_count += 1
            return self.capable, f"escalation: {self.consecutive_failures} consecutive off_track"
        
        return self.efficient, "maintain: on_track"
    
    async def _judge_progress(
        self, messages: List[Dict[str, str]]
    ) -> bool:
        """裁决Agent是否在正确轨道上"""
        judge_input = [
            {"role": "system", "content": self.judge_prompt},
            *messages[-2:],
        ]
        
        response = await self.judge(judge_input)
        return "on_track" in response.lower()


# ============================================================
# Prefill Router (可训练路由器)
# ============================================================

class PrefillRouter(nn.Module):
    """
    预填充路由器
    
    在推理时，从模型的残差流中提取预填充阶段的信号，
    预测每个候选模型的成功概率，然后结合成本和延迟进行路由。
    
    这是NeMo Switchyard中唯一需要训练的路由器。
    """
    
    def __init__(
        self,
        hidden_dim: int = 2048,
        num_models: int = 4,
        num_layers: int = 3,
    ):
        super().__init__()
        self.hidden_dim = hidden_dim
        self.num_models = num_models
        
        # 共享主干MLP
        self.shared_trunk = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.GELU(),
            nn.Linear(hidden_dim // 2, hidden_dim // 4),
            nn.GELU(),
            *[
                nn.Sequential(
                    nn.Linear(hidden_dim // 4, hidden_dim // 4),
                    nn.GELU(),
                )
                for _ in range(num_layers - 2)
            ],
        )
        
        # 每个候选模型的预测头
        self.model_heads = nn.ModuleList([
            nn.Sequential(
                nn.Linear(hidden_dim // 4, 64),
                nn.GELU(),
                nn.Linear(64, 1),  # 输出成功概率
                nn.Sigmoid(),
            )
            for _ in range(num_models)
        ])
        
        # 成本权重
        self.cost_weights = nn.Parameter(torch.ones(num_models))
        
    def forward(
        self, 
        residual_states: torch.Tensor,
        model_costs: Optional[torch.Tensor] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Args:
            residual_states: (batch, hidden_dim) 预填充阶段的残差流
            model_costs: (num_models,) 每个模型的成本
        Returns:
            scores: (batch, num_models) 每个模型的得分
            probs: (batch, num_models) 每个模型的成功概率
        """
        # 共享主干
        shared = self.shared_trunk(residual_states)  # (B, D//4)
        
        # 每个模型的预测
        probs = torch.stack([
            head(shared) for head in self.model_heads
        ], dim=-1)  # (B, num_models, 1) → (B, num_models)
        
        # 结合成本和概率
        if model_costs is not None:
            # 得分 = 概率 / (成本 ^ 成本权重)
            normalized_costs = model_costs / model_costs.sum()
            cost_penalty = normalized_costs ** self.cost_weights
            scores = probs / (cost_penalty + 1e-8)
        else:
            scores = probs
        
        return scores, probs


# ============================================================
# Switchyard 主引擎
# ============================================================

class NeMoSwitchyard:
    """
    NeMo Switchyard 主引擎
    
    协调多个路由策略，管理会话状态，处理API格式转换。
    """
    
    def __init__(
        self,
        targets: List[ModelTarget],
        router: Any,
        router_type: str = "llm_classifier",
    ):
        self.targets = {t.name: t for t in targets}
        self.router = router
        self.router_type = router_type
        self.sessions: Dict[str, RoutingContext] = {}
    
    async def handle_request(
        self,
        messages: List[Dict[str, str]],
        session_id: str = "",
        tools: Optional[List[Dict]] = None,
    ) -> Dict[str, Any]:
        """处理一次Agent请求"""
        if session_id not in self.sessions:
            self.sessions[session_id] = RoutingContext(
                session_id=session_id
            )
        
        context = self.sessions[session_id]
        turn = AgentTurn(
            turn_id=len(context.turn_history),
            messages=messages,
        )
        
        # 路由决策
        start_time = time.time()
        target_name, reason = await self.router.route(messages, context)
        routing_time = time.time() - start_time
        
        # 更新上下文
        context.current_model = target_name
        turn.route = target_name
        context.turn_history.append(turn)
        
        target = self.targets[target_name]
        
        return {
            "target": target_name,
            "provider": target.provider,
            "model_id": target.model_id,
            "reason": reason,
            "routing_time_ms": routing_time * 1000,
            "session_id": session_id,
        }


# 演示: LangChain风格路由配置
async def demo_switchyard_routing():
    """
    演示NeMo Switchyard的路由配置
    
    模拟LangChain的测试场景:
    - 7%的调用送到前沿模型 (Opus 4.8)
    - 93%由Nemotron 3.5 Lightning处理
    - 成本降低74%，准确率仅损失6个点
    """
    # 定义模型池
    targets = [
        ModelTarget(
            name="nemotron-lightning",
            provider="local",
            model_id="nvidia/Nemotron-3.5-Lightning-30B-A3B-NVFP4",
            type="efficient",
            cost_per_token=0.00001,  # $0.01/1K tokens
            latency_p50=50,  # 50ms
        ),
        ModelTarget(
            name="opus-4.8",
            provider="anthropic",
            model_id="claude-opus-4.8",
            type="capable",
            cost_per_token=0.00015,  # $0.15/1K tokens (15x更贵)
            latency_p50=500,  # 500ms
        ),
    ]
    
    # 模拟裁判模型
    async def mock_judge(messages):
        """模拟裁判LLM"""
        return json.dumps({
            "target": "nemotron-lightning",
            "reason": "simple task, no complex reasoning needed",
            "confidence": 0.85,
        })
    
    # 创建LLM Classifier路由器
    classifier = LLMClassifierRouter(
        judge_model=mock_judge,
        mode="capability",
        model_targets=targets,
    )
    
    # 创建Switchyard引擎
    switchyard = NeMoSwitchyard(
        targets=targets,
        router=classifier,
        router_type="llm_classifier",
    )
    
    # 模拟请求
    test_requests = [
        {"role": "user", "content": "读取这个文件并总结"},
        {"role": "user", "content": "分析这个复杂的算法复杂度"},
        {"role": "user", "content": "将这个JSON格式化为表格"},
        {"role": "user", "content": "修复这个代码中的安全漏洞"},
    ]
    
    print("NeMo Switchyard 路由演示:")
    print("=" * 60)
    
    capable_count = 0
    efficient_count = 0
    total_cost = 0.0
    
    for i, req in enumerate(test_requests):
        result = await switchyard.handle_request(
            messages=[req],
            session_id=f"demo-session-{i}",
        )
        
        target = switchyard.targets[result["target"]]
        cost = target.cost_per_token * 100  # 假设每轮100 tokens
        
        if target.type == "capable":
            capable_count += 1
        else:
            efficient_count += 1
        
        total_cost += cost
        
        print(f"请求: {req['content'][:30]}...")
        print(f"  路由到: {result['target']} ({result['reason']})")
        print(f"  成本: ${cost:.5f}")
        print()
    
    total_requests = len(test_requests)
    print(f"\n汇总:")
    print(f"  总请求: {total_requests}")
    print(f"  前沿模型占比: {capable_count}/{total_requests} = {capable_count/total_requests:.1%}")
    print(f"  高效模型占比: {efficient_count}/{total_requests} = {efficient_count/total_requests:.1%}")
    print(f"  总成本: ${total_cost:.5f}")
    print(f"  (对比全用前沿模型: ${total_requests * 0.00015 * 100:.5f})")
    print(f"  成本节约: {(1 - total_cost / (total_requests * 0.00015 * 100)):.1%}")
    print("=" * 60)


if __name__ == "__main__":
    import asyncio
    asyncio.run(demo_switchyard_routing())

"""
CodeRabbit 案例: 使用NeMo AutoModel微调Nemotron 3.5 Lightning
"""
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from typing import Dict, List, Optional

class CodeReviewRoutingDataset(Dataset):
    """
    CodeRabbit代码审查路由数据集
    
    每个样本: 代码审查请求 → 应该路由到哪个模型
    """
    def __init__(
        self,
        samples: List[Dict],
        tokenizer: Any,
        max_length: int = 4096,
    ):
        self.samples = samples
        self.tokenizer = tokenizer
        self.max_length = max_length
    
    def __len__(self):
        return len(self.samples)
    
    def __getitem__(self, idx) -> Dict:
        sample = self.samples[idx]
        
        # 格式化输入
        prompt = f"""代码审查请求:
仓库: {sample.get('repo', 'unknown')}
文件: {sample.get('file', 'unknown')}
变更: {sample.get('diff', '')}
上下文: {sample.get('context', '')}

问题: 这个代码变更应该路由到哪个模型？
选项:
A) 轻量模型 (简单格式化/风格检查)
B) 标准模型 (常见逻辑错误)
C) 强大模型 (复杂安全漏洞/架构问题)

回答: {sample.get('label', 'A')}"""
        
        encoding = self.tokenizer(
            prompt,
            max_length=self.max_length,
            padding='max_length',
            truncation=True,
            return_tensors='pt',
        )
        
        return {
            "input_ids": encoding["input_ids"].squeeze(0),
            "attention_mask": encoding["attention_mask"].squeeze(0),
            "labels": encoding["input_ids"].squeeze(0),
        }


class LoRALayer(nn.Module):
    """
    LoRA (Low-Rank Adaptation) 微调层
    
    在冻结的原始权重上添加低秩适配矩阵。
    训练时只更新LoRA参数，不更新原始权重。
    """
    def __init__(
        self,
        original_layer: nn.Linear,
        rank: int = 8,
        alpha: float = 16,
    ):
        super().__init__()
        self.original_layer = original_layer
        self.rank = rank
        self.alpha = alpha
        
        # 冻结原始层
        for param in self.original_layer.parameters():
            param.requires_grad = False
        
        in_dim = original_layer.in_features
        out_dim = original_layer.out_features
        
        # LoRA低秩矩阵
        self.lora_A = nn.Parameter(
            torch.randn(in_dim, rank) * 0.01
        )
        self.lora_B = nn.Parameter(
            torch.zeros(rank, out_dim)
        )
        self.scaling = alpha / rank
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # 原始路径 + LoRA路径
        original_out = self.original_layer(x)
        lora_out = (x @ self.lora_A @ self.lora_B) * self.scaling
        return original_out + lora_out


def train_with_neMo_automodel():
    """
    模拟NeMo AutoModel的SFT+RL训练流程
    
    CodeRabbit结果:
    - 路由准确率: 75.8% → 80.4% (+4.6%)
    - 输出token减少: 63.4%
    - 成本: 减半
    """
    print("CodeRabbit SFT + RL 微调流程:")
    print("=" * 60)
    
    # 阶段1: SFT (监督微调)
    print("\n阶段1: SFT (Supervised Fine-Tuning)")
    print("  - 使用路由标注数据")
    print("  - LoRA rank=8, alpha=16")
    print("  - 学习率: 2e-4")
    print("  - 批次大小: 32")
    print("  - 训练步数: 500")
    print("  - 结果: 路由准确率 75.8%")
    
    # 阶段2: RL (强化学习)
    print("\n阶段2: RL (Reinforcement Learning)")
    print("  - 奖励函数: 路由准确率 + 成本惩罚")
    print("  - PPO算法, KL散度约束")
    print("  - 奖励 = 0.7 * accuracy - 0.3 * cost_ratio")
    print("  - 训练步数: 300")
    print("  - 结果: 路由准确率 80.4% (+4.6%)")
    
    # Token优化分析
    print("\nToken优化分析:")
    print("=" * 60)
    
    before = {
        "avg_input_tokens": 2048,
        "avg_output_tokens": 512,
        "cost_per_call": 0.00015,  # $0.15/1K tokens
    }
    
    after = {
        "avg_input_tokens": 1024,  # 更短的路由推理
        "avg_output_tokens": 187,  # 63.4% less
        "cost_per_call": 0.00001,  # Lightning
    }
    
    print(f"微调前 (Opus 4.8):")
    print(f"  平均输入token: {before['avg_input_tokens']}")
    print(f"  平均输出token: {before['avg_output_tokens']}")
    print(f"  单次调用成本: ${before['cost_per_call'] * before['avg_output_tokens'] / 1000:.5f}")
    
    print(f"\n微调后 (Nemotron 3.5 Lightning):")
    print(f"  平均输入token: {after['avg_input_tokens']}")
    print(f"  平均输出token: {after['avg_output_tokens']}")
    print(f"  单次调用成本: ${after['cost_per_call'] * after['avg_output_tokens'] / 1000:.6f}")
    
    cost_reduction = 1 - (
        after['cost_per_call'] * after['avg_output_tokens']
    ) / (
        before['cost_per_call'] * before['avg_output_tokens']
    )
    
    print(f"\n成本降低: {cost_reduction:.1%}")
    print(f"输出token减少: {1 - after['avg_output_tokens'] / before['avg_output_tokens']:.1%}")
    print("=" * 60)


if __name__ == "__main__":
    train_with_neMo_automodel()

# 安装Ollama后，一条命令即可运行
ollama run nemotron-3.5-lightning

"""
使用vLLM部署Nemotron 3.5 Lightning
"""
# 安装: pip install vllm

from vllm import LLM, SamplingParams

# 加载模型 (NVFP4量化版本)
llm = LLM(
    model="nvidia/Nemotron-3.5-Lightning-30B-A3B-NVFP4",
    tensor_parallel_size=1,  # 单卡即可
    max_model_len=131072,    # 128K上下文
    dtype="auto",
    quantization="nvfp4",    # 使用NVFP4量化
)

# 推理参数
sampling_params = SamplingParams(
    temperature=0.6,
    top_p=0.95,
    max_tokens=4096,
)

# 执行推理
outputs = llm.generate(
    ["请解释这段代码的功能: def fibonacci(n): return n if n <= 1 else fibonacci(n-1) + fibonacci(n-2)"],
    sampling_params,
)

for output in outputs:
    print(output.outputs[0].text)

# switchyard_config.yaml
# NeMo Switchyard 路由配置示例

profiles:
  agent_execution:
    router_type: escalation
    judge_model:
      provider: local
      model: nvidia/Nemotron-3.5-Lightning-30B-A3B-NVFP4
    targets:
      - name: lightning
        type: efficient
        provider: local
        model: nvidia/Nemotron-3.5-Lightning-30B-A3B-NVFP4
        cost_per_token: 0.00001
      - name: opus
        type: capable
        provider: anthropic
        model: claude-opus-4.8
        cost_per_token: 0.00015
    escalation:
      consecutive_failures: 2
      max_judge_calls: 10

  coding_agent:
    router_type: stage
    stages:
      exploration:
        target: opus
      implementation:
        target: lightning
      verification:
        target: lightning