def calculate_kv_cache_memory(seq_len, num_layers, num_heads, head_dim, dtype_bits):
    """计算KV Cache内存占用（GB）"""
    bytes_per_element = dtype_bits / 8
    total_bytes = 2 * seq_len * num_layers * num_heads * head_dim * bytes_per_element
    return total_bytes / (1024**3)

# DeepSeek V4配置
config = {
    "seq_len": 131072,  # 128K
    "num_layers": 61,
    "num_heads": 128,
    "head_dim": 128
}

fp16_memory = calculate_kv_cache_memory(**config, dtype_bits=16)
print(f"FP16 KV Cache: {fp16_memory:.2f} GB")  # 约1638.4 GB

import numpy as np
import matplotlib.pyplot as plt

def quantization_error_analysis():
    """量化误差对比分析"""
    # 生成模拟的KV Cache数据（长尾分布）
    np.random.seed(42)
    kv_data = np.concatenate([
        np.random.normal(0, 0.1, 8000),  # 大量小值
        np.random.normal(0, 1.0, 1500),  # 中等值
        np.random.normal(0, 5.0, 500)    # 少量大值
    ])
    
    # 不同量化格式的误差计算
    formats = ['INT4', 'FP4_E1M2', 'NF4', 'NVFP4']
    errors = {}
    
    for fmt in formats:
        if fmt == 'NVFP4':
            # NVFP4 E2M1格式
            quantized = nvfp4_quantize(kv_data)
        elif fmt == 'FP4_E1M2':
            quantized = fp4_e1m2_quantize(kv_data)
        elif fmt == 'INT4':
            quantized = int4_quantize(kv_data)
        else:  # NF4
            quantized = nf4_quantize(kv_data)
            
        mse = np.mean((kv_data - quantized) ** 2)
        mae = np.mean(np.abs(kv_data - quantized))
        errors[fmt] = {'MSE': mse, 'MAE': mae}
    
    return errors

def nvfp4_quantize(data):
    """简化的NVFP4量化实现"""
    # 实际实现会更复杂，这里仅作示意
    max_val = np.max(np.abs(data))
    scale = max_val / 7.5  # NVFP4最大表示值
    quantized = np.round(data / scale).clip(-8, 7) * scale
    return quantized

# 执行误差分析
errors = quantization_error_analysis()
print("量化误差对比:")
for fmt, err in errors.items():
    print(f"{fmt}: MSE={err['MSE']:.6f}, MAE={err['MAE']:.6f}")

class NVFP4Quantizer:
    def __init__(self):
        self.micro_block_size = 16  # 每个微块包含16个元素
        
    def quantize(self, tensor):
        """
        NVFP4量化过程
        """
        # 1. 按微块分组
        micro_blocks = tensor.view(-1, self.micro_block_size)
        
        # 2. 计算每个微块的最大绝对值
        block_max = micro_blocks.abs().max(dim=1, keepdim=True)[0]
        
        # 3. 计算FP8微块缩放因子
        fp8_scales = block_max / 7.5  # FP8_E4M3最大值约为7.5
        
        # 4. 计算FP32张量缩放因子
        tensor_scale = fp8_scales.max() / 255.0  # FP8最大值255
        
        # 5. 两阶段缩放
        scaled_tensor = tensor / tensor_scale / fp8_scales.unsqueeze(-1)
        
        # 6. 量化到NVFP4范围
        nvfp4_quantized = self._to_nvfp4(scaled_tensor.clamp(-8, 7))
        
        return nvfp4_quantized, fp8_scales, tensor_scale
    
    def _to_nvfp4(self, tensor):
        """转换为NVFP4格式"""
        # 实际硬件实现会使用专门的量化指令
        return tensor.round().clamp(-8, 7).to(torch.int8)

# Blackwell Tensor Core FP4性能示例
class BlackwellTensorCore:
    def __init__(self):
        self.fp4_ops_per_cycle = 1024  # 每周期FP4操作数
        self.fp16_ops_per_cycle = 256  # 每周期FP16操作数
        
    def performance_comparison(self):
        print(f"FP4吞吐量: {self.fp4_ops_per_cycle} ops/cycle")
        print(f"FP16吞吐量: {self.fp16_ops_per_cycle} ops/cycle")  
        print(f"FP4相对FP16性能提升: {self.fp4_ops_per_cycle/self.fp16_ops_per_cycle}x")

import flashinfer

def nvfp4_attention_example():
    # 配置NVFP4量化参数
    nvfp4_config = {
        "micro_block_size": 16,
        "scale_dtype": "fp8_e4m3",      # 微块缩放因子类型
        "tensor_scale_dtype": "fp32"    # 张量缩放因子类型
    }
    
    # 量化KV Cache
    k_cache_nvfp4 = flashinfer.quantize_nvfp4(k_cache, **nvfp4_config)
    v_cache_nvfp4 = flashinfer.quantize_nvfp4(v_cache, **nvfp4_config)
    
    # NVFP4注意力计算
    output = flashinfer.flash_attn_with_kv_cache(
        query, k_cache_nvfp4, v_cache_nvfp4,
        kv_layout="nvfp4",
        allow_fp16_qk_reduction=False  # 禁用FP16 QK归约以保持精度
    )
    
    return output

from vllm import LLM, SamplingParams

# 启用NVFP4 KV Cache的配置
llm = LLM(
    model="deepseek-ai/DeepSeek-V4",
    quantization="nvfp4",              # 模型量化格式
    kv_cache_dtype="nvfp4",           # KV Cache量化格式
    max_num_seqs=256,                 # 由于内存节省，可支持更大batch
    max_model_len=131072,             # 支持128K上下文
    gpu_memory_utilization=0.9,       # 更高的显存利用率
)

# 生成参数
sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=2048
)

# 批量推理
outputs = llm.generate(
    prompts=["Write a long essay about...", "Explain quantum computing..."],
    sampling_params=sampling_params
)

def benchmark_nvfp4_performance():
    """
    NVFP4性能对比基准测试
    """
    benchmarks = {
        "FP16": {"memory_gb": 1638.4, "latency_ms": 120, "throughput_tps": 85},
        "FP8":  {"memory_gb": 819.2,  "latency_ms": 95,  "throughput_tps": 110},
        "NVFP4":{"memory_gb": 409.6,  "latency_ms": 80,  "throughput_tps": 135}
    }
    
    print("性能对比 (DeepSeek V4, 128K context):")
    print("Format | Memory(GB) | Latency(ms) | Throughput(TPS)")
    print("-" * 50)
    
    for fmt, metrics in benchmarks.items():
        print(f"{fmt:6} | {metrics['memory_gb']:8.1f} | "
              f"{metrics['latency_ms']:10.1f} | {metrics['throughput_tps']:12.1f}")
    
    print("\nNVFP4相比FP16:")
    print(f"- 内存节省: {(1 - 409.6/1638.4)*100:.1f}%")
    print(f"- 延迟降低: {(1 - 80/120)*100:.1f}%")
    print(f"- 吞吐提升: {(135/85 - 1)*100:.1f}%")

benchmark_nvfp4_performance()

def validate_nvfp4_accuracy(model_name="deepseek-v4", test_dataset="humaneval"):
    """
    验证NVFP4量化的精度损失
    """
    import torch
    import torch.nn.functional as F
    
    # 加载FP16基准模型
    model_fp16 = load_model(model_name, dtype="fp16")
    
    # 加载NVFP4量化模型
    model_nvfp4 = load_model(model_name, kv_cache_dtype="nvfp4")
    
    # 在测试集上评估
    results_fp16 = evaluate(model_fp16, test_dataset)
    results_nvfp4 = evaluate(model_nvfp4, test_dataset)
    
    # 计算精度差异
    accuracy_drop = results_fp16["accuracy"] - results_nvfp4["accuracy"]
    
    print(f"FP16 Accuracy: {results_fp16['accuracy']:.4f}")
    print(f"NVFP4 Accuracy: {results_nvfp4['accuracy']:.4f}")
    print(f"Accuracy Drop: {accuracy_drop:.4f} ({accuracy_drop/results_fp16['accuracy']*100:.2f}%)")
    
    # 风险控制：精度损失应<1%
    assert accuracy_drop < 0.01, f"NVFP4精度损失超过1%: {accuracy_drop}"
    
    return accuracy_drop < 0.01

def adaptive_quantization_strategy(layer_importance):
    """
    自适应量化策略：对重要层使用更高精度
    """
    strategies = []
    for layer_idx, importance in enumerate(layer_importance):
        if importance > 0.8:  # 高重要性层
            strategy = "fp16"  # 使用FP16保持精度
        elif importance > 0.5:  # 中等重要性
            strategy = "fp8"   # 使用FP8平衡精度和性能
        else:  # 低重要性层
            strategy = "nvfp4" # 使用NVFP4最大化性能
            
        strategies.append(strategy)
    
    return strategies

def optimized_memory_config():
    """
    NVFP4生产环境内存配置最佳实践
    """
    config = {
        # KV Cache配置
        "kv_cache_dtype": "nvfp4",
        "block_size": 16,           # PageAttention块大小
        "swap_space": 16,           # CPU交换空间(GB)
        
        # 批处理优化
        "max_num_seqs": 256,        # 最大批处理大小
        "max_num_batched_tokens": 32768,  # 最大批处理token数
        
        # 显存分配
        "gpu_memory_utilization": 0.9,     # GPU显存利用率
        "swap_space": 32,                  # CPU交换空间
    }
    
    return config

def nvfp4_monitoring_metrics():
    """
    NVFP4量化监控关键指标
    """
    metrics = {
        # 性能指标
        "quantization_latency": "量化操作延迟",
        "cache_hit_rate": "KV Cache命中率",
        "memory_utilization": "显存利用率",
        
        # 精度指标  
        "perplexity_gap": "量化前后困惑度差异",
        "accuracy_drop": "任务准确率损失",
        "numerical_stability": "数值稳定性指标",
        
        # 系统指标
        "gpu_power_consumption": "GPU功耗",
        "temperature_profile": "温度分布",
        "bandwidth_utilization": "内存带宽利用率"
    }
    
    return metrics

import torch
import torch.nn.functional as F

class CustomNVFP4Quantizer:
    """
    自定义NVFP4量化器，适用于特殊需求场景
    """
    def __init__(self, micro_block_size=16, outlier_ratio=0.02):
        self.micro_block_size = micro_block_size
        self.outlier_ratio = outlier_ratio
        
    def quantize(self, tensor, return_metadata=False):
        """
        量化张量到NVFP4格式
        
        Args:
            tensor: 输入张量
            return_metadata: 是否返回元数据用于调试
            
        Returns:
            quantized_tensor: 量化后的张量
            scales: 缩放因子
            metadata: 可选的元数据
        """
        if tensor.numel() == 0:
            return tensor, torch.tensor(1.0, device=tensor.device)
            
        # 处理异常值
        if self.outlier_ratio > 0:
            k = int(tensor.numel() * self.outlier_ratio)
            if k > 0:
                topk_values, _ = torch.topk(tensor.abs().view(-1), k, largest=True)
                threshold = topk_values[-1]
                outlier_mask = tensor.abs() >= threshold
            else:
                outlier_mask = torch.zeros_like(tensor, dtype=torch.bool)
        else:
            outlier_mask = torch.zeros_like(tensor, dtype=torch.bool)
            
        # 分离正常值和异常值
        normal_values = tensor[~outlier_mask]
        outlier_values = tensor[outlier_mask]
        
        if normal_values.numel() > 0:
            # NVFP4量化正常值
            normal_quantized, normal_scales = self._quantize_normal(normal_values)
        else:
            normal_quantized = torch.empty(0, dtype=torch.int8, device=tensor.device)
            normal_scales = torch.empty(0, device=tensor.device)
            
        # 异常值保持原精度
        if outlier_values.numel() > 0:
            outlier_quantized = outlier_values.half()
        else:
            outlier_quantized = torch.empty(0, dtype=torch.float16, device=tensor.device)
            
        if return_metadata:
            metadata = {
                'outlier_count': outlier_values.numel(),
                'normal_count': normal_values.numel(),
                'compression_ratio': (normal_values.numel() * 0.5 + outlier_values.numel() * 2) / tensor.numel(),
                'outlier_indices': torch.nonzero(outlier_mask).squeeze()
            }
            return normal_quantized, normal_scales, outlier_quantized, metadata
            
        return normal_quantized, normal_scales, outlier_quantized
    
    def _quantize_normal(self, values):
        """量化正常值到NVFP4"""
        # 微块分组
        num_blocks = (values.numel() + self.micro_block_size - 1) // self.micro_block_size
        padded_size = num_blocks * self.micro_block_size
        
        if values.numel() < padded_size:
            padded_values = torch.zeros(padded_size, dtype=values.dtype, device=values.device)
            padded_values[:values.numel()] = values
        else:
            padded_values = values
            
        micro_blocks = padded_values.view(-1, self.micro_block_size)
        block_max = micro_blocks.abs().max(dim=1, keepdim=True)[0].clamp(min=1e-8)
        
        # 计算缩放因子
        fp8_scales = block_max / 7.5  # NVFP4 E2M1最大值约为7.5
        global_scale = fp8_scales.max() / 255.0  # FP8最大值为255
        
        # 两级缩放
        scaled_blocks = micro_blocks / global_scale / fp8_scales
        
        # 量化到NVFP4范围 [-8, 7]
        nvfp4_quantized = scaled_blocks.clamp(-8, 7).round().to(torch.int8)
        
        # 截断到原始大小
        result = nvfp4_quantized.view(-1)[:values.numel()]
        scales = {'fp8_scales': fp8_scales.squeeze(), 'global_scale': global_scale}
        
        return result, scales
    
    def dequantize(self, normal_quantized, normal_scales, outlier_quantized, outlier_indices, original_shape):
        """
        反量化NVFP4数据
        """
        # 反量化正常值
        if normal_quantized.numel() > 0:
            fp8_scales = normal_scales['fp8_scales']
            global_scale = normal_scales['global_scale']
            
            # 重建微块结构
            num_blocks = (normal_quantized.numel() + self.micro_block_size - 1) // self.micro_block_size
            padded_size = num_blocks * self.micro_block_size
            
            if normal_quantized.numel() < padded_size:
                padded_quantized = torch.zeros(padded_size, dtype=normal_quantized.dtype, device=normal_quantized.device)
                padded_quantized[:normal_quantized.numel()] = normal_quantized
            else:
                padded_quantized = normal_quantized
                
            micro_blocks = padded_quantized.view(-1, self.micro_block_size).float()
            fp8_scales_expanded = fp8_scales.unsqueeze(-1)
            
            # 反量化
            dequantized_blocks = micro_blocks * global_scale * fp8_scales_expanded
            normal_dequantized = dequantized_blocks.view(-1)[:normal_quantized.numel()]
        else:
            normal_dequantized = torch.empty(0, device=normal_quantized.device)
            
        # 重建完整张量
        total_elements = normal_dequantized.numel() + outlier_quantized.numel()
        result = torch.zeros(total_elements, dtype=torch.float16, device=normal_quantized.device)
        
        # 填充正常值
        normal_mask = torch.ones(total_elements, dtype=torch.bool, device=normal_quantized.device)
        if outlier_indices.numel() > 0:
            normal_mask[outlier_indices] = False
            result[~normal_mask] = outlier_quantized
            
        result[normal_mask] = normal_dequantized
        
        return result.view(original_shape)

# 使用示例
def example_usage():
    """NVFP4量化器使用示例"""
    # 创建测试数据
    kv_cache = torch.randn(128, 61, 128, 128, dtype=torch.float16, device='cuda')
    
    # 初始化量化器
    quantizer = CustomNVFP4Quantizer(micro_block_size=16, outlier_ratio=0.02)
    
    # 量化
    normal_q, scales, outlier_q, metadata = quantizer.quantize(
        kv_cache.view(-1), return_metadata=True
    )
    
    print(f"压缩比: {metadata['compression_ratio']:.2f}x")
    print(f"异常值数量: {metadata['outlier_count']}")
    
    # 反量化
    kv_cache_reconstructed = quantizer.dequantize(
        normal_q, scales, outlier_q, metadata['outlier_indices'], kv_cache.shape
    )
    
    # 计算重构误差
    mse = F.mse_loss(kv_cache.float(), kv_cache_reconstructed.float())
    print(f"重构MSE: {mse:.6f}")

class AdvancedNVFP4Quantizer:
    def __init__(self, outlier_threshold=3.0):
        self.micro_block_size = 16
        self.outlier_threshold = outlier_threshold
        
    def quantize_with_outlier_handling(self, tensor):
        """
        带异常值处理的NVFP4量化
        """
        # 1. 识别异常值
        tensor_abs = tensor.abs()
        median_val = torch.median(tensor_abs)
        mad = torch.median(torch.abs(tensor_abs - median_val))
        threshold = median_val + self.outlier_threshold * mad
        
        # 2. 分离正常值和异常值
        normal_mask = tensor_abs <= threshold
        outlier_mask = ~normal_mask
        
        # 3. 对正常值进行NVFP4量化
        normal_values = tensor[normal_mask]
        if normal_values.numel() > 0:
            normal_quantized, normal_scales = self._quantize_normal(normal_values)
        else:
            normal_quantized = torch.empty(0)
            normal_scales = torch.empty(0)
            
        # 4. 对异常值使用更高精度存储
        outlier_values = tensor[outlier_mask]
        if outlier_values.numel() > 0:
            outlier_quantized = outlier_values.half()  # 使用FP16存储异常值
        else:
            outlier_quantized = torch.empty(0)
            
        return {
            'normal_quantized': normal_quantized,
            'normal_scales': normal_scales,
            'outlier_quantized': outlier_quantized,
            'outlier_indices': torch.nonzero(outlier_mask).squeeze(),
            'normal_mask': normal_mask
        }
    
    def _quantize_normal(self, values):
        """对正常值进行NVFP4量化"""
        # 微块分组
        num_blocks = (values.numel() + self.micro_block_size - 1) // self.micro_block_size
        padded_size = num_blocks * self.micro_block_size
        padded_values = torch.zeros(padded_size, dtype=values.dtype, device=values.device)
        padded_values[:values.numel()] = values
        
        micro_blocks = padded_values.view(-1, self.micro_block_size)
        block_max = micro_blocks.abs().max(dim=1, keepdim=True)[0].clamp(min=1e-8)
        
        # FP8微块缩放因子
        fp8_scales = block_max / 7.5
        scaled_blocks = micro_blocks / fp8_scales
        
        # NVFP4量化
        nvfp4_quantized = scaled_blocks.clamp(-8, 7).round().to(torch.int8)
        
        return nvfp4_quantized.view(-1)[:values.numel()], fp8_scales.squeeze()