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e9c8366 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 | """kquant — GGUF k-quant block-wise quantization formats (llama.cpp).
Block-wise quantization with super-block structure. Each format packs N weights
per block with a shared scale (and optionally min/d-scale).
Formats:
Q4_0 — block_size=32, fp16 scale, 4-bit values. ~4.5 bpw.
Q4_1 — block_size=32, fp16 scale + fp16 min, 4-bit values. ~5 bpw.
Q4_K — super-block 256, 8 sub-blocks of 32. 6-bit scale + 6-bit d-scale + 4-bit
values. ~4.5 bpw. _S/_M/_L variants differ in d-scale precision.
Q5_K — super-block 256, 5-bit values + 6-bit scale + 6-bit d-scale. ~5.5 bpw.
Q6_K — super-block 256, 6-bit values + 8-bit d-scale + 6-bit scale. ~6.5 bpw.
Q8_0 — block_size=32, fp16 scale, 8-bit values. ~8.5 bpw. Near-lossless.
Q2_K — super-block 256, 4-bit Q2-quants + 4-bit d-scale. ~2.6 bpw.
Q3_K — super-block 256, 3-bit values + 6-bit scale. ~3.5 bpw.
For NeuralQuant we implement the math (quantize/dequantize per block); packing
into GGUF binary layout is handled by the GGUF writer (stage 22.8 converters).
Here we store block data as tensors and dequantize on-the-fly for inference.
"""
from __future__ import annotations
import torch
# ---------------------------------------------------------------------------
# Q8_0 — block_size=32, fp16 scale, int8 values. Near-lossless int8.
# ---------------------------------------------------------------------------
def quantize_q8_0_block(w: torch.Tensor) -> dict[str, torch.Tensor]:
"""Quantize a block of 32 values to Q8_0.
Returns dict with 'scale' (fp32 scalar) and 'values' (int8 [32]).
scale = max(abs(w)) / 127; values = round(w / scale).
"""
assert w.numel() == 32, f"Q8_0 block must be 32 elements, got {w.numel()}"
max_abs = w.abs().amax().clamp(min=1e-8)
scale = max_abs / 127.0
values = torch.clamp(torch.round(w / scale), min=-127, max=127).to(torch.int8)
return {"scale": scale.to(torch.float32), "values": values}
def dequantize_q8_0_block(scale: torch.Tensor, values: torch.Tensor) -> torch.Tensor:
"""Reconstruct 32 values from Q8_0 block."""
return values.to(torch.float32) * scale.to(torch.float32)
# ---------------------------------------------------------------------------
# Q4_0 — block_size=32, fp16 scale, 4-bit values [-8, 7].
# ---------------------------------------------------------------------------
def quantize_q4_0_block(w: torch.Tensor) -> dict[str, torch.Tensor]:
assert w.numel() == 32, f"Q4_0 block must be 32 elements, got {w.numel()}"
max_abs = w.abs().amax().clamp(min=1e-8)
scale = max_abs / 7.0 # 4-bit symmetric: [-8, 7], use 7 for scale
values = torch.clamp(torch.round(w / scale), min=-8, max=7).to(torch.int8)
return {"scale": scale.to(torch.float32), "values": values}
def dequantize_q4_0_block(scale: torch.Tensor, values: torch.Tensor) -> torch.Tensor:
return values.to(torch.float32) * scale.to(torch.float32)
# ---------------------------------------------------------------------------
# Q4_K — super-block 256 = 8 sub-blocks of 32. 6-bit packed scale + 6-bit
# d-scale per sub-block + 4-bit values.
# ---------------------------------------------------------------------------
def quantize_q4_k_superblock(w: torch.Tensor) -> dict[str, torch.Tensor]:
"""Quantize 256 values to Q4_K super-block.
Structure:
- 8 sub-blocks of 32 values, each 4-bit.
- super-block scale (fp32, derived from sub-block scales).
- per-sub-block d-scale (fp32, ratio sub-scale / super-scale).
For NeuralQuant inference we store the actual sub-block scales (not the
6-bit packed GGUF representation); the GGUF writer (stage 22.8) handles
bit-packing.
"""
assert w.numel() == 256, f"Q4_K super-block must be 256 elements, got {w.numel()}"
sub = w.reshape(8, 32)
# Per-sub-block scale: absmax / 7 (4-bit symmetric).
sub_scales = sub.abs().amax(dim=1).clamp(min=1e-8) / 7.0 # [8]
values = torch.clamp(torch.round(sub / sub_scales.unsqueeze(1)), min=-8, max=7).to(torch.int8)
# Super-block scale = max(sub_scales). d-scale = sub_scale / super_scale.
super_scale = sub_scales.amax().clamp(min=1e-8)
d_scales = sub_scales / super_scale # [8], in (0, 1]
return {
"super_scale": super_scale.to(torch.float32),
"d_scales": d_scales.to(torch.float32),
"values": values.reshape(256), # int8 [256]
}
def dequantize_q4_k_superblock(super_scale, d_scales, values) -> torch.Tensor:
"""Reconstruct 256 values from Q4_K super-block."""
vals = values.to(torch.int8).reshape(8, 32)
sub_scales = super_scale.to(torch.float32) * d_scales.to(torch.float32) # [8]
return (vals.to(torch.float32) * sub_scales.unsqueeze(1)).reshape(256)
# ---------------------------------------------------------------------------
# Q6_K — super-block 256, 6-bit values + 8-bit d-scale + 6-bit super-scale.
# ---------------------------------------------------------------------------
_Q6_LEVELS = 31 # 6-bit symmetric [-32, 31], use 31 for scale
def quantize_q6_k_superblock(w: torch.Tensor) -> dict[str, torch.Tensor]:
assert w.numel() == 256, f"Q6_K super-block must be 256 elements, got {w.numel()}"
sub = w.reshape(8, 32)
sub_scales = sub.abs().amax(dim=1).clamp(min=1e-8) / _Q6_LEVELS # [8]
values = torch.clamp(torch.round(sub / sub_scales.unsqueeze(1)), min=-32, max=31).to(torch.int8)
super_scale = sub_scales.amax().clamp(min=1e-8)
d_scales = sub_scales / super_scale # [8]
return {
"super_scale": super_scale.to(torch.float32),
"d_scales": d_scales.to(torch.float32),
"values": values.reshape(256),
}
def dequantize_q6_k_superblock(super_scale, d_scales, values) -> torch.Tensor:
vals = values.to(torch.int8).reshape(8, 32)
sub_scales = super_scale.to(torch.float32) * d_scales.to(torch.float32)
return (vals.to(torch.float32) * sub_scales.unsqueeze(1)).reshape(256)
# ---------------------------------------------------------------------------
# Q5_K — super-block 256, 5-bit values + 6-bit d-scale.
# ---------------------------------------------------------------------------
_Q5_LEVELS = 15 # 5-bit symmetric [-16, 15], use 15
def quantize_q5_k_superblock(w: torch.Tensor) -> dict[str, torch.Tensor]:
assert w.numel() == 256
sub = w.reshape(8, 32)
sub_scales = sub.abs().amax(dim=1).clamp(min=1e-8) / _Q5_LEVELS
values = torch.clamp(torch.round(sub / sub_scales.unsqueeze(1)), min=-16, max=15).to(torch.int8)
super_scale = sub_scales.amax().clamp(min=1e-8)
d_scales = sub_scales / super_scale
return {
"super_scale": super_scale.to(torch.float32),
"d_scales": d_scales.to(torch.float32),
"values": values.reshape(256),
}
def dequantize_q5_k_superblock(super_scale, d_scales, values) -> torch.Tensor:
vals = values.to(torch.int8).reshape(8, 32)
sub_scales = super_scale.to(torch.float32) * d_scales.to(torch.float32)
return (vals.to(torch.float32) * sub_scales.unsqueeze(1)).reshape(256)
# ---------------------------------------------------------------------------
# Q2_K — super-block 256, 2-bit values + 4-bit d-scale. Extreme compression.
# ---------------------------------------------------------------------------
_Q2_LEVELS = 1 # 2-bit symmetric [-2, 1], use 1 for scale (coarse)
def quantize_q2_k_superblock(w: torch.Tensor) -> dict[str, torch.Tensor]:
assert w.numel() == 256
sub = w.reshape(8, 32)
sub_scales = sub.abs().amax(dim=1).clamp(min=1e-8) / _Q2_LEVELS
values = torch.clamp(torch.round(sub / sub_scales.unsqueeze(1)), min=-2, max=1).to(torch.int8)
super_scale = sub_scales.amax().clamp(min=1e-8)
d_scales = sub_scales / super_scale
return {
"super_scale": super_scale.to(torch.float32),
"d_scales": d_scales.to(torch.float32),
"values": values.reshape(256),
}
def dequantize_q2_k_superblock(super_scale, d_scales, values) -> torch.Tensor:
vals = values.to(torch.int8).reshape(8, 32)
sub_scales = super_scale.to(torch.float32) * d_scales.to(torch.float32)
return (vals.to(torch.float32) * sub_scales.unsqueeze(1)).reshape(256)
# ---------------------------------------------------------------------------
# Q3_K — super-block 256, 3-bit values + 6-bit d-scale.
# ---------------------------------------------------------------------------
_Q3_LEVELS = 3 # 3-bit symmetric [-4, 3], use 3
def quantize_q3_k_superblock(w: torch.Tensor) -> dict[str, torch.Tensor]:
assert w.numel() == 256
sub = w.reshape(8, 32)
sub_scales = sub.abs().amax(dim=1).clamp(min=1e-8) / _Q3_LEVELS
values = torch.clamp(torch.round(sub / sub_scales.unsqueeze(1)), min=-4, max=3).to(torch.int8)
super_scale = sub_scales.amax().clamp(min=1e-8)
d_scales = sub_scales / super_scale
return {
"super_scale": super_scale.to(torch.float32),
"d_scales": d_scales.to(torch.float32),
"values": values.reshape(256),
}
def dequantize_q3_k_superblock(super_scale, d_scales, values) -> torch.Tensor:
vals = values.to(torch.int8).reshape(8, 32)
sub_scales = super_scale.to(torch.float32) * d_scales.to(torch.float32)
return (vals.to(torch.float32) * sub_scales.unsqueeze(1)).reshape(256)
# ---------------------------------------------------------------------------
# Format registry: format string -> (block quantize, block dequantize, block_size)
# ---------------------------------------------------------------------------
BLOCK_SIZE_32 = 32
BLOCK_SIZE_256 = 256
FORMATS = {
"q8_0": (quantize_q8_0_block, dequantize_q8_0_block, BLOCK_SIZE_32),
"q4_0": (quantize_q4_0_block, dequantize_q4_0_block, BLOCK_SIZE_32),
"q4_k": (quantize_q4_k_superblock, dequantize_q4_k_superblock, BLOCK_SIZE_256),
"q5_k": (quantize_q5_k_superblock, dequantize_q5_k_superblock, BLOCK_SIZE_256),
"q6_k": (quantize_q6_k_superblock, dequantize_q6_k_superblock, BLOCK_SIZE_256),
"q2_k": (quantize_q2_k_superblock, dequantize_q2_k_superblock, BLOCK_SIZE_256),
"q3_k": (quantize_q3_k_superblock, dequantize_q3_k_superblock, BLOCK_SIZE_256),
}
def quantize_blocks(w: torch.Tensor, fmt: str) -> dict[str, torch.Tensor]:
"""Quantize a flat weight tensor into blocks of the given format.
Pads the last dim to be divisible by block_size.
Returns dict with stacked block tensors.
"""
quant_fn, _, block_size = FORMATS[fmt]
out_features = w.shape[0]
in_features = w.shape[1] if w.dim() > 1 else w.numel()
if w.dim() > 1:
flat = w
else:
flat = w.reshape(1, -1)
out_features = 1
# Pad in_features to be divisible by block_size.
pad = (block_size - (flat.shape[1] % block_size)) % block_size
if pad > 0:
flat = torch.nn.functional.pad(flat, (0, pad))
in_padded = flat.shape[1]
num_blocks = in_padded // block_size
# Reshape to [out, num_blocks, block_size].
blocks = flat.reshape(out_features, num_blocks, block_size)
if fmt in ("q8_0", "q4_0"):
# Simple block: per-block absmax scale + int values.
n_levels = 127 if fmt == "q8_0" else 7
max_val = n_levels if fmt == "q8_0" else 7
min_val = -n_levels if fmt == "q8_0" else -8
scales = blocks.abs().amax(dim=2).clamp(min=1e-8) / n_levels # [out, num_blocks]
values = torch.clamp(
torch.round(blocks / scales.unsqueeze(2)), min=min_val, max=max_val
).to(torch.int8)
return {
"scales": scales.to(torch.float32),
"values": values,
"in_features": in_features,
"in_padded": in_padded,
"out_features": out_features,
"block_size": block_size,
}
else:
# K-formats: super-block 256 = 8 sub-blocks of 32.
# blocks already [out, num_blocks, 256]; reshape to [out, num_blocks, 8, 32].
n_levels_map = {"q4_k": 7, "q5_k": 15, "q6_k": 31, "q2_k": 1, "q3_k": 3}
min_val_map = {"q4_k": -8, "q5_k": -16, "q6_k": -32, "q2_k": -2, "q3_k": -4}
n_levels = n_levels_map[fmt]
min_val = min_val_map[fmt]
sub = blocks.reshape(out_features, num_blocks, 8, 32)
sub_scales = sub.abs().amax(dim=3).clamp(min=1e-8) / n_levels # [out, num_blocks, 8]
values = torch.clamp(
torch.round(sub / sub_scales.unsqueeze(3)), min=min_val, max=n_levels
).to(torch.int8)
super_scales = sub_scales.amax(dim=2).clamp(min=1e-8) # [out, num_blocks]
d_scales = sub_scales / super_scales.unsqueeze(2) # [out, num_blocks, 8]
return {
"super_scales": super_scales.to(torch.float32),
"d_scales": d_scales.to(torch.float32),
"values": values.reshape(out_features, num_blocks, 256),
"in_features": in_features,
"in_padded": in_padded,
"out_features": out_features,
"block_size": 256,
"num_super": num_blocks,
}
def dequantize_blocks(qd: dict[str, torch.Tensor], fmt: str) -> torch.Tensor:
"""Reconstruct the weight tensor from block-quantized data."""
in_features = qd["in_features"]
in_padded = qd["in_padded"]
out_features = qd["out_features"]
if fmt in ("q8_0", "q4_0"):
scales = qd["scales"] # [out, num_blocks]
values = qd["values"] # [out, num_blocks, block_size]
w = values.to(torch.float32) * scales.to(torch.float32).unsqueeze(2)
w = w.reshape(out_features, in_padded)[:, :in_features]
else:
super_scales = qd["super_scales"] # [out, num_blocks]
d_scales = qd["d_scales"] # [out, num_blocks, 8]
values = qd["values"] # [out, num_blocks, 256]
sub_scales = super_scales.to(torch.float32).unsqueeze(2) * d_scales.to(torch.float32)
sub = values.reshape(out_features, -1, 8, 32)
w = sub.to(torch.float32) * sub_scales.unsqueeze(3)
w = w.reshape(out_features, -1)[:, :in_features]
return w |