import torch
import flashinfer

def demonstrate_jit_compilation():
    """
    演示 FlashInfer 的 JIT 编译能力
    """
    # 创建不同大小的输入张量
    batch_sizes = [1, 4, 8]
    seq_lens = [1024, 2048, 4096]
    
    for batch_size in batch_sizes:
        for seq_len in seq_lens:
            # 创建查询、键、值张量
            q = torch.randn(batch_size, 32, seq_len, 128).cuda()
            k = torch.randn(batch_size, 32, seq_len, 128).cuda() 
            v = torch.randn(batch_size, 32, seq_len, 128).cuda()
            
            # FlashInfer 会在首次调用时编译相应的 kernel
            # 后续相同参数的调用会复用已编译的 kernel
            output = flashinfer.single_prefill_with_kv_cache(
                q, k, v,
                causal=False,
                allow_fp16_qk_reduction=True
            )
            
            print(f"Batch: {batch_size}, SeqLen: {seq_len}, Output shape: {output.shape}")

class CustomALiBiTransform:
    """
    实现 ALiBi (Attention with Linear Biases) 位置编码的自定义变换
    """
    def __init__(self, num_heads, device='cuda'):
        # ALiBi 的斜率参数，每个头一个
        self.slopes = self._get_alibi_slopes(num_heads).to(device)
    
    def _get_alibi_slopes(self, n):
        """计算 ALiBi 的斜率参数"""
        x = torch.tensor([2**(-8/n) for _ in range(n)])
        return torch.pow(x, torch.arange(1, n+1)).float()
    
    def apply_logits_bias(self, logits, seq_len):
        """应用 ALiBi 偏置到注意力分数"""
        # 创建位置偏置矩阵
        positions = torch.arange(seq_len, device=logits.device)
        bias = positions.unsqueeze(0).unsqueeze(0) * self.slopes.view(-1, 1, 1)
        return logits + bias

# 使用自定义 ALiBi 变换
alibi_transform = CustomALiBiTransform(num_heads=32)

def alibi_attention_example():
    q = torch.randn(1, 32, 2048, 128).cuda()
    k = torch.randn(1, 32, 2048, 128).cuda()
    v = torch.randn(1, 32, 2048, 128).cuda()
    
    # 应用 ALiBi 偏置
    logits = torch.matmul(q, k.transpose(-2, -1)) / (128 ** 0.5)
    logits_with_bias = alibi_transform.apply_logits_bias(logits, 2048)
    
    # 计算最终输出
    attn_weights = torch.softmax(logits_with_bias, dim=-1)
    output = torch.matmul(attn_weights, v)
    
    return output

def standard_attention_examples():
    """
    演示 FlashInfer 支持的标准 Attention 场景
    """
    # Decode 场景：单个 token 解码
    q_decode = torch.randn(4, 32, 1, 128).cuda()  # batch_size=4, n_heads=32, seq_len=1
    kv_cache_k = torch.randn(4, 32, 1024, 128).cuda()  # 已缓存的 K
    kv_cache_v = torch.randn(4, 32, 1024, 128).cuda()  # 已缓存的 V
    
    decode_output = flashinfer.single_decode_with_kv_cache(
        q_decode, kv_cache_k, kv_cache_v
    )
    
    # Prefill 场景：批量预填充
    q_prefill = torch.randn(2, 32, 512, 128).cuda()  # batch_size=2, seq_len=512
    k_prefill = torch.randn(2, 32, 512, 128).cuda()
    v_prefill = torch.randn(2, 32, 512, 128).cuda()
    
    prefill_output = flashinfer.single_prefill_with_kv_cache(
        q_prefill, k_prefill, v_prefill,
        causal=True
    )
    
    print(f"Decode output shape: {decode_output.shape}")
    print(f"Prefill output shape: {prefill_output.shape}")

def mla_attention_example():
    """
    演示 MLA (Multi-Latent Attention) 的概念实现
    """
    # 压缩后的 KV，存储在低维空间
    compressed_kv = torch.randn(1, 32, 2048, 64).cuda()  # head_dim=64 (压缩后)
    q = torch.randn(1, 32, 2048, 128).cuda()  # 查询仍保持原始维度
    
    # MLA 的核心：在低维空间计算注意力，然后映射回原始维度
    # 这里简化为基本的线性投影过程
    expanded_k = torch.nn.Linear(64, 128).cuda()(compressed_kv)
    
    # 标准注意力计算
    logits = torch.matmul(q, expanded_k.transpose(-2, -1)) / (128 ** 0.5)
    attn_weights = torch.softmax(logits, dim=-1)
    
    # 假设 V 也经过类似的压缩-扩展过程
    v = torch.randn(1, 32, 2048, 128).cuda()
    output = torch.matmul(attn_weights, v)
    
    return output

def sparse_attention_example():
    """
    演示稀疏注意力的概念实现
    """
    # 创建稀疏块掩码
    block_size = 64
    seq_len = 2048
    num_blocks = seq_len // block_size
    
    # 模拟稀疏模式：只关注相邻块和对角块
    sparse_pattern = torch.zeros(num_blocks, num_blocks, dtype=torch.bool)
    for i in range(num_blocks):
        sparse_pattern[i, i] = True  # 对角块
        if i > 0:
            sparse_pattern[i, i-1] = True  # 前一个块
        if i < num_blocks - 1:
            sparse_pattern[i, i+1] = True  # 后一个块
    
    # 在 FlashInfer 中，可以通过自定义掩码实现
    q = torch.randn(1, 16, seq_len, 128).cuda()
    k = torch.randn(1, 16, seq_len, 128).cuda()
    v = torch.randn(1, 16, seq_len, 128).cuda()
    
    # 这里使用简化的稀疏掩码应用
    output = flashinfer.single_prefill_with_kv_cache(
        q, k, v,
        causal=True,
        # 实际中会使用更复杂的稀疏掩码
    )
    
    return output

import time
import numpy as np

def benchmark_attention_implementations():
    """
    比较不同 Attention 实现的性能
    """
    # 测试参数
    test_configs = [
        {'batch_size': 1, 'seq_len': 1024, 'n_heads': 32, 'head_dim': 128},
        {'batch_size': 4, 'seq_len': 2048, 'n_heads': 32, 'head_dim': 128},
        {'batch_size': 8, 'seq_len': 4096, 'n_heads': 32, 'head_dim': 128},
    ]
    
    results = {}
    
    for config in test_configs:
        print(f"\nTesting config: {config}")
        
        # 创建输入张量
        q = torch.randn(
            config['batch_size'], 
            config['n_heads'], 
            config['seq_len'], 
            config['head_dim']
        ).cuda()
        k = torch.randn_like(q)
        v = torch.randn_like(q)
        
        # PyTorch 原生实现
        torch_times = []
        for _ in range(10):  # 预热
            _ = torch.matmul(q, k.transpose(-2, -1)) / (config['head_dim'] ** 0.5)
        torch.cuda.synchronize()
        
        for _ in range(100):
            start = torch.cuda.Event(enable_timing=True)
            end = torch.cuda.Event(enable_timing=True)
            start.record()
            
            logits = torch.matmul(q, k.transpose(-2, -1)) / (config['head_dim'] ** 0.5)
            attn_weights = torch.softmax(logits, dim=-1)
            output_torch = torch.matmul(attn_weights, v)
            
            end.record()
            torch.cuda.synchronize()
            torch_times.append(start.elapsed_time(end))
        
        avg_torch_time = np.mean(torch_times[10:])  # 排除前10次预热
        
        # FlashInfer 实现
        flashinfer_times = []
        for _ in range(10):  # 预热
            _ = flashinfer.single_prefill_with_kv_cache(q, k, v, causal=True)
        torch.cuda.synchronize()
        
        for _ in range(100):
            start = torch.cuda.Event(enable_timing=True)
            end = torch.cuda.Event(enable_timing=True)
            start.record()
            
            output_flashinfer = flashinfer.single_prefill_with_kv_cache(
                q, k, v, causal=True
            )
            
            end.record()
            torch.cuda.synchronize()
            flashinfer_times.append(start.elapsed_time(end))
        
        avg_flashinfer_time = np.mean(flashinfer_times[10:])
        
        # 计算性能提升
        speedup = avg_torch_time / avg_flashinfer_time
        
        config_key = f"{config['batch_size']}_{config['seq_len']}"
        results[config_key] = {
            'pytorch_avg': avg_torch_time,
            'flashinfer_avg': avg_flashinfer_time,
            'speedup': speedup
        }
        
        print(f"PyTorch: {avg_torch_time:.3f}ms, "
              f"FlashInfer: {avg_flashinfer_time:.3f}ms, "
              f"Speedup: {speedup:.2f}x")
    
    return results

import sglang as sgl

@sgl.function
def multi_turn_conversation(s, question_1, question_2):
    s += sgl.system("You are a helpful AI assistant.")
    s += sgl.user(question_1)
    s += sgl.assistant(sgl.gen("answer_1", max_tokens=256))
    s += sgl.user(question_2) 
    s += sgl.assistant(sgl.gen("answer_2", max_tokens=256))

# 在 SGLang 中启用 FlashInfer 后端
state = multi_turn_conversation.run(
    question_1="Explain quantum computing",
    question_2="What are its applications?",
    backend=sgl.Runtime(
        model_path="meta-llama/Llama-3.1-70B-Instruct",
        attention_backend="flashinfer",  # 指定使用 FlashInfer
        tp_size=4
    )
)

class KernelGenerationStrategy:
    """
    AI 生成 Kernel 的策略抽象类
    """
    def generate_kernel(self, requirements):
        """
        根据性能要求和硬件约束生成 CUDA kernel
        """
        raise NotImplementedError
    
    def benchmark_kernel(self, kernel_code, test_cases):
        """
        对生成的 kernel 进行基准测试
        """
        raise NotImplementedError
    
    def optimize_kernel(self, kernel_code, profile_data):
        """
        基于性能分析数据优化 kernel
        """
        raise NotImplementedError

class FlashInferBench:
    """
    FlashInfer-Bench 框架的概念实现
    """
    def __init__(self):
        self.strategy = None  # 注入不同的生成策略
    
    def auto_optimize_attention(self, variant_spec):
        """
        自动优化指定的 Attention 变体
        """
        # 1. 根据规格生成初始 kernel
        initial_kernel = self.strategy.generate_kernel(variant_spec)
        
        # 2. 运行基准测试
        benchmark_results = self.strategy.benchmark_kernel(
            initial_kernel, 
            self.get_test_cases(variant_spec)
        )
        
        # 3. 基于结果进行优化
        optimized_kernel = self.strategy.optimize_kernel(
            initial_kernel, 
            benchmark_results.profile_data
        )
        
        # 4. 返回优化后的 kernel
        return optimized_kernel

def blackwell_sparse_attention_kernel_generator():
    """
    为 Blackwell 架构生成稀疏 Attention kernel 的概念示例
    """
    # Blackwell 特有的优化考虑
    blackwell_features = {
        'sm_version': '10.0',  # SM 10.0
        'tensor_core_support': ['fp4', 'fp6', 'fp8'],
        'shared_memory_per_sm': 228 * 1024,  # 228KB shared memory
        'max_threads_per_block': 2048,
    }
    
    # 稀疏模式定义
    sparse_config = {
        'pattern': 'variable_block_sparse',
        'block_size': 64,
        'sparsity_ratio': 0.9,  # 90% 稀疏
    }
    
    # 生成针对 Blackwell 优化的 kernel
    kernel_code = f"""
#include <cuda_runtime.h>
#include <cuda_fp8.h>

__global__ void blackwell_sparse_attention_kernel(
    const __nv_fp8_e4m3* __restrict__ q,
    const __nv_fp8_e4m3* __restrict__ k,
    const __nv_fp8_e4m3* __restrict__ v,
    float* __restrict__ output,
    int batch_size, 
    int seq_len,
    int num_heads,
    int head_dim
) 
"""
    
    return kernel_code

import flashinfer

def configure_flashinfer_performance():
    """
    配置 FlashInfer 性能参数
    """
    # 设置内存池大小
    flashinfer.set_memory_pool_size(1024 * 1024 * 1024)  # 1GB
    
    # 启用 FP16 约简以提高性能
    enable_fp16_reduction = True
    
    # 配置线程数
    import os
    os.environ['OMP_NUM_THREADS'] = str(os.cpu_count())
    
    # 选择最优的数据类型
    dtype = torch.float16  # 或 torch.bfloat16
    
    return dtype, enable_fp16_reduction

def monitor_flashinfer_performance():
    """
    监控 FlashInfer 的性能指标
    """
    import time
    import psutil
    
    class FlashInferMonitor:
        def __init__(self):
            self.kernel_compile_times = []
            self.execution_times = []
            self.memory_usage = []
        
        def measure_kernel_compilation(self, compile_func):
            """测量 kernel 编译时间"""
            start_time = time.time()
            kernel = compile_func()
            compile_time = time.time() - start_time
            self.kernel_compile_times.append(compile_time)
            return kernel
        
        def measure_execution(self, func, *args, **kwargs):
            """测量函数执行时间"""
            start_event = torch.cuda.Event(enable_timing=True)
            end_event = torch.cuda.Event(enable_timing=True)
            
            start_event.record()
            result = func(*args, **kwargs)
            end_event.record()
            
            torch.cuda.synchronize()
            execution_time = start_event.elapsed_time(end_event)
            self.execution_times.append(execution_time)
            
            # 记录显存使用
            memory_used = torch.cuda.memory_allocated()
            self.memory_usage.append(memory_used)
            
            return result
    
    return FlashInferMonitor()

def fp4_attention_example():
    """
    FP4 精度的 Attention 计算示例
    """
    try:
        # 尝试导入 FP4 支持
        from cuda_fp8 import fp4_tensor
        
        # 创建 FP4 精度的张量
        q_fp4 = fp4_tensor(torch.randn(1, 32, 2048, 128).cuda(), dtype=torch.fp4)
        k_fp4 = fp4_tensor(torch.randn(1, 32, 2048, 128).cuda(), dtype=torch.fp4)
        v_fp4 = fp4_tensor(torch.randn(1, 32, 2048, 128).cuda(), dtype=torch.fp4)
        
        # 使用 FP4 优化的 Attention 计算
        # （这里只是概念演示，实际实现会更复杂）
        output = flashinfer.fp4_attention(q_fp4, k_fp4, v_fp4)
        
        return output
    except ImportError:
        print("FP4 support not available, falling back to FP16")
        return None

def moe_attention_example():
    """
    MoE 场景下的 Attention 优化示例
    """
    class MoEAttentionOptimizer:
        def __init__(self, num_experts, top_k):
            self.num_experts = num_experts
            self.top_k = top_k
            
        def optimize_for_expert_routing(self, routing_scores):
            """
            为专家路由优化 Attention 计算
            """
            # 根据路由分数确定活跃的专家
            top_k_scores, top_k_indices = torch.topk(routing_scores, self.top_k, dim=-1)
            
            # 只对活跃专家计算 Attention
            # 这里简化为伪代码
            active_experts = top_k_indices.flatten().unique()
            
            # 为每个活跃专家生成专用的 Attention kernel
            expert_kernels = {}
            for expert_idx in active_experts:
                expert_kernels[expert_idx] = self._generate_expert_kernel(expert_idx)
            
            return expert_kernels
        
        def _generate_expert_kernel(self, expert_idx):
            """
            为特定专家生成优化的 Attention kernel
            """
            # 基于专家特征生成定制化 kernel
            pass
    
    return MoEAttentionOptimizer(num_experts=8, top_k=2)

# 生产环境推荐配置
flashinfer_config = {
    'kernel_cache_size': '2GB',  # 增大 kernel 缓存以减少重复编译
    'memory_pool_size': '4GB',   # 预分配内存池减少内存分配开销
    'enable_cudagraph': True,    # 启用 CUDA Graph 优化
    'fp16_reduction': True,      # 启用 FP16 约简
    'tensor_parallel_size': 8    # 8 路张量并行
}