TurboQuant 深度解析：6x KV Cache 压缩的工程实践与全平台性能实测

import torch
import torch.nn.functional as F

def hadamard_transform(x: torch.Tensor) -> torch.Tensor:
    """快速 Hadamard 变换 (O(d log d))"""
    d = x.shape[-1]
    assert d & (d - 1) == 0, "维度必须是 2 的幂"
    h = 1
    while h < d:
        x_view = x.reshape(-1, 2, h, d // (2 * h))
        # 蝶形运算
        a = x_view[:, 0, :, :] + x_view[:, 1, :, :]
        b = x_view[:, 0, :, :] - x_view[:, 1, :, :]
        x = torch.stack([a, b], dim=1).reshape(x.shape)
        h *= 2
    return x / (d ** 0.5)

def turboquant_compress(
    kv: torch.Tensor,
    key_bits: float = 3.0,
    val_bits: float = 2.0,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    TurboQuant KV Cache 压缩
    :param kv: [batch, heads, seq_len, head_dim]
    :param key_bits: Key 量化比特数
    :param val_bits: Value 量化比特数
    :return: (quantized_keys, quantized_values)
    """
    keys, values = kv.chunk(2, dim=-1)  # 分离 K 和 V（简化示意）
    
    # Step 1: Hadamard 旋转
    keys_rotated = hadamard_transform(keys)
    
    # Step 2: Lloyd-Max 量化（Key 用 MSE 优化）
    key_levels = 2 ** key_bits  # e.g., 8 levels for 3-bit
    key_quantized = lloyd_max_quantize(keys_rotated, key_levels)
    
    # Step 3: 均匀量化（Value 用更低位数）
    val_levels = 2 ** val_bits  # e.g., 4 levels for 2-bit
    val_quantized = uniform_quantize(values, val_levels)
    
    return key_quantized, val_quantized

def lloyd_max_quantize(x: torch.Tensor, levels: int) -> torch.Tensor:
    """Lloyd-Max 最优标量量化器"""
    # 基于高斯分布假设的最优区间边界
    # TurboQuant 的关键：边界可以直接从 Hadamard 旋转后的统计量推导
    x_min, x_max = x.min(), x.max()
    boundaries = torch.linspace(x_min, x_max, levels + 1, device=x.device)
    centroids = (boundaries[:-1] + boundaries[1:]) / 2  # 重建值
    
    # 量化：找到每个值最近的 centroid
    indices = torch.bucketize(x, boundaries[1:-1])
    return centroids[indices]

# 基础用法：使用推荐的 turboquant_k8v4 预设
python -m vllm.entrypoints.openai.api_server \
    --model Qwen/Qwen3.5-27B-AWQ \
    --kv-cache-dtype turboquant_k8v4 \
    --max-model-len 131072 \
    --gpu-memory-utilization 0.90

# 高压缩模式：3.8x 压缩（适合长上下文 + 高并发）
python -m vllm.entrypoints.openai.api_server \
    --model Qwen/Qwen3.5-35B-A3B \
    --kv-cache-dtype turboquant_4bit_nc \
    --max-model-len 200000 \
    --tensor-parallel-size 8 \
    --gpu-memory-utilization 0.92

# 对比测试：bf16 baseline（不压缩）
python -m vllm.entrypoints.openai.api_server \
    --model Qwen/Qwen3.5-27B-AWQ \
    --kv-cache-dtype auto \
    --max-model-len 131072 \
    --gpu-memory-utilization 0.90

# 对比测试：FP8 KV（vLLM 原生 2x 压缩）
python -m vllm.entrypoints.openai.api_server \
    --model Qwen/Qwen3.5-27B-AWQ \
    --kv-cache-dtype fp8 \
    --max-model-len 131072 \
    --gpu-memory-utilization 0.90

"""TurboQuant vs bf16 vs FP8 KV Cache 性能对比"""
import openai
import time

client = openai.OpenAI(base_url="http://localhost:8000/v1", api_key="none")

def benchmark_throughput(context_len: int, num_requests: int = 5):
    """测量不同上下文长度下的吞吐量"""
    prompt = "请分析以下文本：" + "A" * (context_len * 4)  # 近似 token 数
    
    results = []
    for i in range(num_requests):
        start = time.time()
        response = client.completions.create(
            model="Qwen/Qwen3.5-27B-AWQ",
            prompt=prompt,
            max_tokens=128,
            temperature=0.0,
        )
        elapsed = time.time() - start
        tokens = response.usage.completion_tokens
        results.append({
            "context_len": context_len,
            "ttft": time.time() - start,  # 简化
            "decode_tps": tokens / elapsed,
            "total_tokens": response.usage.total_tokens,
        })
    return results

# 测试不同上下文长度
for ctx in [1000, 8000, 32000, 64000, 128000]:
    results = benchmark_throughput(ctx)
    avg_tps = sum(r["decode_tps"] for r in results) / len(results)
    print(f"Context {ctx:>7,} tokens → {avg_tps:.1f} tok/s (avg)")

# 1. 确保 ROCm 6.4+ 已安装
rocm-smi  # 验证 GPU 识别

# 2. 克隆 vLLM 的 TurboQuant 分支（AMD 适配版）
git clone https://github.com/vllm-project/vllm.git
cd vllm
git checkout feature/turboquant-hip-port-clean

# 3. 安装依赖
pip install -r requirements-rocm.txt

# 4. 启动服务（RX 7900 XTX 示例）
HIP_VISIBLE_DEVICES=0 python -m vllm.entrypoints.openai.api_server \
    --model Qwen/Qwen3.5-27B-AWQ \
    --kv-cache-dtype turboquant_k8v4 \
    --max-model-len 65536 \
    --gpu-memory-utilization 0.90

# 5. 如果有 iGPU 问题，先禁用
export HIP_VISIBLE_DEVICES=0  # 只用独显

# 6. Gemma 4 特殊处理：SWA 层保持 f16
# 需要在模型配置中设置：
# config.json → "turboquant_swa_layers": "none"
# 或环境变量：
export TURBOQUANT_SKIP_SWA=1

#!/bin/bash
# TurboQuant 生产部署脚本

# === Step 1: 环境检查 ===
echo "检查 vLLM 版本..."
python -c "import vllm; assert vllm.__version__ >= '0.18.0', '需要 vLLM >= 0.18.0'"

echo "检查 GPU..."
nvidia-smi --query-gpu=name,memory.total --format=csv,noheader

# === Step 2: 基准测试（bf16 baseline）===
echo "运行 bf16 baseline 测试..."
python -m vllm.entrypoints.openai.api_server \
    --model $MODEL_NAME \
    --kv-cache-dtype auto \
    --max-model-len $MAX_LEN \
    --gpu-memory-utilization 0.90 &
VLLM_PID=$!
sleep 30  # 等待服务启动

# 运行基准测试
python benchmark_script.py --output baseline_results.json
kill $VLLM_PID

# === Step 3: 启用 TurboQuant ===
echo "运行 TurboQuant 测试..."
python -m vllm.entrypoints.openai.api_server \
    --model $MODEL_NAME \
    --kv-cache-dtype turboquant_k8v4 \
    --max-model-len $MAX_LEN \
    --gpu-memory-utilization 0.90 &
VLLM_PID=$!
sleep 30

python benchmark_script.py --output turboquant_results.json
kill $VLLM_PID

# === Step 4: 对比结果 ===
python compare_results.py baseline_results.json turboquant_results.json

echo "如果结果满意，更新生产配置："
echo "  --kv-cache-dtype turboquant_k8v4"

apiVersion: apps/v1
kind: Deployment
metadata:
  name: vllm-turboquant
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: vllm
        image: vllm/vllm-openai:latest
        args:
        - --model=Qwen/Qwen3.5-27B-AWQ
        - --kv-cache-dtype=turboquant_k8v4    # TurboQuant 预设
        - --max-model-len=131072
        - --gpu-memory-utilization=0.90
        - --tensor-parallel-size=1
        - --max-num-seqs=16                    # 最大并发（TurboQuant 允许更高并发）
        resources:
          limits:
            nvidia.com/gpu: 1
        env:
        - name: VLLM_LOGGING_LEVEL
          value: "INFO"
        # 健康检查
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 120
          periodSeconds: 30
        readinessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 60
          periodSeconds: 10