# SpecForge 整体架构
class SpecForgeTrainer:
    def __init__(self, config: EAGLE3Config):
        self.target_model = self.load_target_model(config.target_model)
        self.draft_model = self.initialize_draft_model(config.draft_config)
        self.training_strategy = self.setup_training_strategy(config.mode)
        self.feature_reuser = FeatureReuser(config.feature_layers)
        self.tree_attention = TreeAttentionModule() if config.tree_attention else None
        self.memory_optimizer = MemoryOptimizer()
        
    def train(self, dataset):
        if self.config.mode == "online":
            return self.online_training(dataset)
        else:
            return self.offline_training(dataset)
    
    def online_training(self, dataset):
        """在线训练模式：实时生成训练数据"""
        for batch in dataset:
            # 1. 使用目标模型生成参考输出
            with torch.no_grad():
                ref_outputs = self.target_model.generate(
                    batch.prompts, 
                    max_new_tokens=self.config.max_speculative_tokens,
                    temperature=self.config.temperature,
                    top_p=self.config.top_p
                )
            
            # 2. 获取目标模型的中间层特征
            target_features = self.target_model.get_intermediate_features(
                batch.prompts + ref_outputs[:-1],
                layers=self.config.feature_layers
            )
            
            # 3. 草稿模型预测下一个token（使用特征复用）
            draft_predictions = self.draft_model.predict(
                batch.prompts + ref_outputs[:-1],
                features=target_features
            )
            
            # 4. 计算损失并更新参数
            loss = self.compute_loss(draft_predictions, ref_outputs[1:])
            self.optimizer.step(loss)
            
            # 5. 内存清理
            self.memory_optimizer.cleanup()
    
    def offline_training(self, dataset):
        """离线训练模式：使用预生成数据集"""
        # 数据预处理：将目标模型的输出作为监督信号
        processed_dataset = self.preprocess_dataset(dataset)
        
        for epoch in range(self.config.num_epochs):
            for batch in processed_dataset:
                # 离线模式下直接使用预计算的特征
                loss = self.train_step(batch)
                self.update_draft_model(loss)
                
                # 定期验证
                if self.should_validate():
                    val_metrics = self.validate(val_dataset)
                    self.log_metrics(val_metrics)

from specforge import Trainer, EAGLE3Config
from specforge.optimizers import AdamWWithWarmup
from specforge.schedulers import CosineAnnealingLR

# 基础配置
config = EAGLE3Config(
    target_model="meta-llama/Llama-3.1-70B",
    draft_model_size="1B",  # 草稿模型大小仅为目标模型的1.4%
    training_mode="offline",  # 支持 "online" 或 "offline"
    
    # 训练参数
    num_epochs=3,
    batch_size=32,
    gradient_accumulation_steps=4,  # 梯度累积步数
    learning_rate=1e-4,
    weight_decay=0.01,
    
    # 模型架构参数
    feature_layers=[4, 8, 16],  # 复用目标模型的特定层
    tree_attention=True,  # 启用树状注意力机制
    num_speculative_tokens=5,  # 推测token数量
    
    # 采样参数
    temperature=0.7,      # 训练时的采样温度
    top_p=0.9,           # nucleus sampling 参数
    top_k=50,            # top-k sampling 参数
    
    # 正则化参数
    dropout=0.1,
    label_smoothing=0.1,
    
    # 内存优化
    mixed_precision="bf16",  # 混合精度训练
    gradient_checkpointing=True,  # 梯度检查点
    
    # 分布式训练
    world_size=8,        # GPU数量
    tensor_parallel_size=2,  # 张量并行度
    pipeline_parallel_size=4,  # 流水线并行度
    
    # 日志和监控
    log_interval=100,    # 日志间隔
    eval_interval=1000,  # 评估间隔
    save_interval=5000,  # 保存间隔
)

# 自定义优化器和学习率调度器
optimizer = AdamWWithWarmup(
    lr=config.learning_rate,
    weight_decay=config.weight_decay,
    warmup_steps=1000,
    total_steps=config.num_epochs * len(train_dataset) // config.batch_size
)

scheduler = CosineAnnealingLR(
    optimizer=optimizer,
    T_max=config.num_epochs * len(train_dataset) // config.batch_size,
    eta_min=1e-6
)

# 创建训练器
trainer = Trainer(
    config=config,
    optimizer=optimizer,
    scheduler=scheduler
)

# 开始训练
trainer.train(train_dataset, val_dataset)

# 保存最终模型
trainer.save_checkpoint("./eagle3_llama3_1b")

# 导出为SGLang格式
trainer.export_for_sglang("./eagle3_llama3_1b_sglang")

class EAGLE3Training:
    def on_policy_training_step(self, prompt_batch):
        """
        EAGLE-3 on-policy 训练步骤
        关键：草稿模型的预测会影响后续token的选择
        """
        # 初始化序列
        sequence = prompt_batch
        losses = []
        
        for step in range(self.max_decode_steps):
            # 1. 草稿模型预测多个候选token
            draft_candidates = self.draft_model(sequence, 
                                              num_candidates=self.num_speculative_tokens)
            
            # 2. 目标模型验证候选token
            with torch.no_grad():
                target_logits = self.target_model(sequence + draft_candidates)
            
            # 3. 计算草稿模型损失（基于目标模型的真实反馈）
            draft_loss = self.compute_on_policy_loss(
                draft_predictions=draft_candidates,
                target_feedback=target_logits
            )
            
            losses.append(draft_loss)
            
            # 4. 选择接受的token（基于树搜索策略）
            accepted_tokens = self.tree_acceptance(draft_candidates, target_logits)
            sequence = sequence + accepted_tokens
            
            # 5. 如果没有更多token被接受，结束当前序列
            if len(accepted_tokens) == 0:
                break
                
        return sum(losses) / len(losses)

class FeatureReuseDraftModel(nn.Module):
    def __init__(self, target_model, draft_config):
        super().__init__()
        self.target_model = target_model
        self.draft_layers = self.build_draft_layers(draft_config)
        
    def forward(self, input_ids, past_key_values=None):
        # 1. 获取目标模型的中间层特征
        with torch.no_grad():
            target_hidden_states = self.target_model.get_intermediate_features(
                input_ids, layers=[4, 8, 16]
            )
        
        # 2. 将目标模型特征作为草稿模型的输入
        draft_input = self.merge_features(input_ids, target_hidden_states)
        
        # 3. 草稿模型进行轻量级预测
        draft_output = self.draft_layers(draft_input)
        
        return draft_output

import sglang as sgl

# 高级配置示例
llm = sgl.LLM(
    model="meta-llama/Llama-3.1-70B",
    speculative_model="./eagle3_llama3_1b",
    speculative_method="EAGLE3",
    
    # 推测解码参数
    num_speculative_tokens=5,        # 每次推测的token数量
    tree_attention=True,             # 启用树状注意力
    draft_model_temperature=0.7,     # 草稿模型采样温度
    target_model_temperature=0.6,    # 目标模型采样温度
    
    # 内存和批处理参数
    max_num_batched_tokens=8192,     # 最大批处理tokens
    max_running_sequences=128,       # 最大并发序列数
    mem_fraction_static=0.85,        # 静态内存占比
    
    # 性能优化参数
    enable_chunked_prefill=True,     # 启用分块预填充
    chunked_prefill_size=8192,       # 分块大小
    enable_cuda_graph=True,          # 启用CUDA图优化
    
    # 监控和调试
    log_level="info",
    enable_tracing=True,
)

import sglang as sgl

# 加载目标模型和EAGLE-3草稿模型
llm = sgl.LLM(
    model="meta-llama/Llama-3.1-70B",                    # 目标模型
    speculative_model="./eagle3_llama3_1b",              # SpecForge训练的草稿模型
    speculative_method="EAGLE3",                         # 指定EAGLE-3方法
    num_speculative_tokens=5,                            # 每次推测5个token
    tree_attention=True,                                 # 启用树状注意力
    max_num_batched_tokens=8192,                         # 批处理优化
)

# 推理时会自动使用EAGLE-3加速
output = llm.generate("What is the capital of France?")

class SGLangEAGLE3Engine:
    def generate_with_speculation(self, prompts):
        results = []
        
        for prompt in prompts:
            # 1. 草稿模型快速生成候选token序列
            draft_tokens = self.draft_model.generate(
                prompt, 
                max_new_tokens=self.num_speculative_tokens
            )
            
            # 2. 目标模型验证候选序列（批量处理）
            full_sequence = prompt + draft_tokens
            target_logits = self.target_model.forward(full_sequence)
            
            # 3. 树状接受算法（Tree Acceptance）
            accepted_tokens = self.tree_acceptance_algorithm(
                draft_tokens, target_logits
            )
            
            # 4. 如果有未接受的token，继续生成
            remaining_tokens = len(draft_tokens) - len(accepted_tokens)
            if remaining_tokens > 0:
                final_output = self.target_model.generate(
                    prompt + accepted_tokens,
                    max_new_tokens=remaining_tokens
                )
            else:
                final_output = accepted_tokens
            
            results.append(final_output)
        
        return results
    
    def tree_acceptance_algorithm(self, draft_tokens, target_logits):
        """
        EAGLE-3 树状接受算法
        基于概率匹配的多token接受策略
        """
        accepted = []
        
        for i, draft_token in enumerate(draft_tokens):
            target_logit = target_logits[i + len(prompt)]
            
            # 计算接受概率
            target_prob = torch.softmax(target_logit, dim=-1)[draft_token]
            draft_prob = self.draft_model.get_probability(draft_token)
            
            # 使用随机采样决定是否接受
            acceptance_prob = min(1.0, target_prob.item() / draft_prob.item())
            if random.random() < acceptance_prob:
                accepted.append(draft_token)
            else:
                break  # 一旦拒绝某个token，停止接受后续token
        
        return accepted

import time
import numpy as np

def performance_benchmark():
    # 准备测试数据
    test_prompts = [
        "Explain quantum computing in simple terms.",
        "Write a poem about artificial intelligence.",
        "Summarize the theory of relativity.",
        # ... 更多测试样本
    ]
    
    # 基线测试（无推测解码）
    baseline_results = benchmark_single_model(
        model_path="meta-llama/Llama-3.1-70B",
        prompts=test_prompts,
        use_speculative=False
    )
    
    # EAGLE-3 测试
    eagle3_results = benchmark_single_model(
        model_path="meta-llama/Llama-3.1-70B",
        speculative_model_path="./eagle3_llama3_1b",
        speculative_method="EAGLE3",
        prompts=test_prompts,
        use_speculative=True
    )
    
    print(f"Baseline throughput: {baseline_results['tps']:.2f} tokens/sec")
    print(f"EAGLE-3 throughput: {eagle3_results['tps']:.2f} tokens/sec")
    print(f"Speedup: {eagle3_results['tps']/baseline_results['tps']:.2f}x")
    print(f"Acceptance rate: {eagle3_results['acceptance_rate']:.2f}%")

def benchmark_single_model(model_path, prompts, use_speculative=False, **kwargs):
    llm = sgl.LLM(
        model=model_path,
        **kwargs if use_speculative else {}
    )
    
    start_time = time.time()
    total_tokens = 0
    accepted_tokens = 0
    
    for prompt in prompts:
        output = llm.generate(prompt, max_new_tokens=256)
        total_tokens += len(output.split())
        
        if use_speculative and hasattr(output, 'speculative_stats'):
            accepted_tokens += output.speculative_stats.accepted_tokens
    
    elapsed_time = time.time() - start_time
    tps = total_tokens / elapsed_time
    
    return {
        'tps': tps,
        'total_time': elapsed_time,
        'acceptance_rate': accepted_tokens / total_tokens if use_speculative else 0
    }

class ReasoningModelSpeculation:
    def __init__(self, max_thinking_tokens=8192):
        self.max_thinking_tokens = max_thinking_tokens
        
    def generate_with_reasoning_budget(self, prompt):
        # 分离 thinking 和 final answer 阶段
        thinking_phase = True
        thinking_tokens = 0
        final_answer = []
        
        current_prompt = prompt
        
        while True:
            if thinking_phase and thinking_tokens < self.max_thinking_tokens:
                # 在 thinking 阶段限制推测长度
                draft_tokens = self.draft_model.generate(
                    current_prompt,
                    max_new_tokens=min(5, self.max_thinking_tokens - thinking_tokens)
                )
                
                # 验证并接受thinking tokens
                accepted = self.validate_and_accept(draft_tokens)
                thinking_tokens += len(accepted)
                
                if "[THINKING_END]" in accepted:
                    thinking_phase = False
                    
            else:
                # 在 final answer 阶段正常推测
                draft_tokens = self.draft_model.generate(
                    current_prompt, max_new_tokens=5
                )
                accepted = self.validate_and_accept(draft_tokens)
                
            current_prompt += accepted
            
            if self.is_completion(current_prompt):
                break
                
        return current_prompt

# SpecForge 训练推理模型专用的草稿模型
config = EAGLE3Config(
    target_model="deepseek-ai/DeepSeek-R1",
    draft_model_size="2B",
    training_mode="online",  # 推理模型更适合在线训练
    reasoning_aware=True,     # 启用推理感知训练
    max_thinking_tokens=8192,
    feature_layers=[2, 6, 12, 18],  # 更多层复用
)

# 训练时特别处理 thinking tokens
trainer = ReasoningAwareTrainer(config)
trainer.train_with_thinking_awareness(dataset)

# 分布式训练配置
from specforge.distributed import DistributedTrainer

dist_config = {
    "world_size": 8,          # 8张GPU
    "rank": int(os.environ.get("RANK", 0)),
    "master_addr": "localhost",
    "master_port": "12355",
    "backend": "nccl"
}

# 初始化分布式训练器
trainer = DistributedTrainer(
    config=eagle3_config,
    distributed_config=dist_config
)

# 启动训练
trainer.start_training(train_dataset, val_dataset)

class SpeculativeDecodingMonitor:
    def __init__(self):
        self.metrics = {
            'acceptance_rate': [],
            'throughput': [],
            'latency': [],
            'gpu_utilization': []
        }
    
    def collect_metrics(self, llm_engine):
        metrics = {
            'acceptance_rate': llm_engine.get_acceptance_rate(),
            'throughput': llm_engine.get_throughput(),
            'latency': llm_engine.get_avg_latency(),
            'gpu_memory_used': torch.cuda.memory_allocated(),
            'gpu_utilization': torch.cuda.utilization()
        }
        
        for key, value in metrics.items():
            self.metrics[key].append(value)
    
    def log_performance(self):
        if len(self.metrics['acceptance_rate']) >= 100:
            avg_acceptance = np.mean(self.metrics['acceptance_rate'][-100:])
            avg_throughput = np.mean(self.metrics['throughput'][-100:])
            
            print(f"Performance - Acceptance: {avg_acceptance:.2f}, "
                  f"Throughput: {avg_throughput:.2f} tps")

# 未来的API设计（概念性）
from specforge.ecosystem import ModelOptimizer, DeploymentManager

optimizer = ModelOptimizer()
optimized_models = optimizer.optimize_for_hardware(
    target_model="llama3.1-70b",
    hardware_specs={"gpu": "H200", "memory": "141GB"},
    latency_requirements={"p95": 0.5}  # 500ms P95延迟
)

deployment = DeploymentManager()
deployment.deploy_optimized_stack(
    optimized_models,
    scaling_policy="auto",
    monitoring_enabled=True
)