File size: 7,203 Bytes
4a28d4d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 | """Common test utilities for TurboQuant (quant_policy=QuantPolicy.TURBO_QUANT)
kernel tests.
This module contains shared helper functions for testing TurboQuant quantization,
which is used by quant_policy=QuantPolicy.TURBO_QUANT (K=QJL4, V=2bit mixed precision).
TurboQuant is a quantization method that:
- Uses Lloyd-Max algorithm for optimal quantization
- Applies Hadamard rotation for better distribution
- Stores only L2 norms (not scales/zeros) for dequantization
"""
import math
import torch
from lmdeploy.pytorch.kernels.cuda.turbo_quant import (
get_lloyd_max_codebook,
hadamard_rotate,
hadamard_rotate_inv,
)
def _div_up(a, b):
"""Integer division with rounding up."""
return (a + b - 1) // b
def _unpack_indices(packed: torch.Tensor, nbits: int, original_dim: int) -> torch.Tensor:
"""Unpack bit-packed indices back to integer tensor."""
if nbits == 2:
i0 = (packed & 0x03)
i1 = ((packed >> 2) & 0x03)
i2 = ((packed >> 4) & 0x03)
i3 = ((packed >> 6) & 0x03)
indices = torch.cat([i0, i1, i2, i3], dim=-1)
elif nbits == 4:
# Unpack 2 nibbles per byte: low nibble and high nibble
i0 = (packed & 0x0F)
i1 = ((packed >> 4) & 0x0F)
indices = torch.cat([i0, i1], dim=-1)
else:
indices = packed
# Trim to original dimension
return indices[..., :original_dim].long()
def _unpack_qjl4_nibbles(packed: torch.Tensor, original_dim: int):
"""Unpack 4bit qjl nibbles into:
- idx3: [0, 7]
- bit1: [0, 1]
"""
nib = _unpack_indices(packed, 4, original_dim)
idx3 = nib & 0x7
bit1 = (nib >> 3) & 0x1
return idx3.long(), bit1.long()
def quant_turboquant_mse(kv: torch.Tensor, nbits: int):
"""TurboQuant MSE quantization (without QJL).
Args:
kv: input tensor of shape (..., head_dim)
nbits: number of bits (only 2 supported)
Returns:
q_kv: bit-packed indices (uint8)
norms: L2 norms for dequantization, shape (...,)
"""
head_dim = kv.shape[-1]
device = str(kv.device)
# Get Lloyd-Max codebook
_, boundaries = get_lloyd_max_codebook(head_dim, nbits, device=device)
# Compute L2 norms
norms = kv.float().norm(dim=-1, keepdim=True)
# Normalize to unit sphere
kv_unit = kv.float() / (norms + 1e-10)
y = hadamard_rotate(kv_unit)
# Quantize: find nearest centroid via searchsorted
indices = torch.searchsorted(boundaries, y.contiguous())
indices = indices.clamp(0, 2 ** nbits - 1)
# Bit-pack indices (2-bit: 4 values per byte)
if nbits == 2:
q_kv1, q_kv2, q_kv3, q_kv4 = indices.split(indices.shape[-1] // 4, -1)
q_kv = q_kv1 + q_kv2 * 4 + q_kv3 * 16 + q_kv4 * 64
else:
q_kv = indices
return q_kv.to(torch.uint8), norms.squeeze(-1)
def quant_turboquant_qjl4(kv: torch.Tensor):
"""TurboQuant QJL4 quantization for K: 3bit MSE + 1bit QJL.
Returns:
q_kv: packed uint8 tensor, shape (..., head_dim // 2)
meta: (..., 2)
meta[..., 0] = mse_norm
meta[..., 1] = qjl_norm
"""
head_dim = kv.shape[-1]
device = str(kv.device)
# Get Lloyd-Max codebook (3-bit)
centroids, boundaries = get_lloyd_max_codebook(head_dim, 3, device=device)
# Compute MSE norm
mse_norm = kv.float().norm(dim=-1, keepdim=True)
kv_unit = kv.float() / (mse_norm + 1e-10)
# Apply hadamard rotation
y = hadamard_rotate(kv_unit)
# Quantize: find nearest centroid
idx3 = torch.searchsorted(boundaries, y.contiguous()).clamp(0, 7).long()
c = centroids[idx3]
# Compute QJL residual
residual = y - c
qjl_bit = (residual >= 0).long()
qjl_norm = residual.norm(dim=-1, keepdim=True) / math.sqrt(head_dim)
# Pack nibble: low 3 bits = MSE index, high 1 bit = QJL sign
nibble = idx3 | (qjl_bit << 3)
q1, q2 = nibble.split(nibble.shape[-1] // 2, dim=-1)
q_kv = (q1 + (q2 << 4)).to(torch.uint8)
meta = torch.cat([mse_norm, qjl_norm], dim=-1)
return q_kv, meta
def dequantize_turboquant_mse(q_kv: torch.Tensor, norms: torch.Tensor, nbits: int):
"""TurboQuant MSE dequantization (without QJL).
Args:
q_kv: bit-packed indices (uint8)
norms: L2 norms for rescaling, shape (...,)
nbits: number of bits (only 2 supported)
Returns:
reconstructed kv tensor in original domain
"""
# First dequantize to rotate domain
y_hat = dequantize_turboquant_mse_rot(q_kv, norms, nbits)
# Then inverse rotate to original domain
x_hat = hadamard_rotate_inv(y_hat)
return x_hat
def dequantize_turboquant_mse_rot(q_kv: torch.Tensor, norms: torch.Tensor, nbits: int):
"""TurboQuant MSE dequantization to ROTATE domain (no inverse rotation).
Args:
q_kv: bit-packed indices (uint8)
norms: L2 norms for rescaling, shape (...,)
nbits: number of bits (only 2 supported)
Returns:
reconstructed kv tensor in rotate domain
"""
# Infer head_dim from packed shape
if nbits == 2:
head_dim = q_kv.shape[-1] * 4
else:
head_dim = q_kv.shape[-1]
device = str(q_kv.device)
# Get Lloyd-Max codebook
centroids, _ = get_lloyd_max_codebook(head_dim, nbits, device=device)
# Unpack indices
indices = _unpack_indices(q_kv, nbits, head_dim)
# Look up centroids
y_hat = centroids[indices]
# Rescale by norms (in rotate domain, no inverse rotation)
y_hat = y_hat * norms.unsqueeze(-1)
return y_hat
def dequantize_turboquant_qjl4(q_kv: torch.Tensor, meta: torch.Tensor):
"""Dequantize TurboQuant QJL4 to original domain."""
# First dequantize to rotate domain
y_hat = dequantize_turboquant_qjl4_rot(q_kv, meta)
# Then inverse rotate to original domain
x_hat = hadamard_rotate_inv(y_hat)
return x_hat
def dequantize_turboquant_qjl4_rot(q_kv: torch.Tensor, meta: torch.Tensor):
"""Dequantize TurboQuant QJL4 to ROTATE domain (no inverse rotation)."""
head_dim = q_kv.shape[-1] * 2
device = str(q_kv.device)
# Get Lloyd-Max codebook (3-bit)
centroids, _ = get_lloyd_max_codebook(head_dim, 3, device=device)
# Unpack nibbles
idx3, bit1 = _unpack_qjl4_nibbles(q_kv, head_dim)
sign = bit1.float() * 2.0 - 1.0
# Get meta values
mse_norm = meta[..., 0]
qjl_norm = meta[..., 1]
# Reconstruct in rotate domain (no inverse rotation)
y_hat = centroids[idx3] + qjl_norm.unsqueeze(-1) * sign
y_hat = y_hat * mse_norm.unsqueeze(-1)
return y_hat
def compute_metrics(a: torch.Tensor, b: torch.Tensor):
"""Compute similarity metrics between two tensors.
Args:
a, b: tensors to compare
Returns:
dict with 'cosine', 'nmse', 'snr_db' keys
"""
import math
a_flat = a.flatten()
b_flat = b.flatten()
cosine = torch.cosine_similarity(a_flat, b_flat, dim=0).item()
mse = ((a - b) ** 2).mean().item()
nmse = mse / (b ** 2).mean().item()
signal = (b ** 2).mean().item()
noise = ((a - b) ** 2).mean().item()
snr_db = 10 * math.log10(signal / (noise + 1e-10))
return {'cosine': cosine, 'nmse': nmse, 'snr_db': snr_db}
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