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6a0e18e | 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 | """Post-training quantization for KronLLM checkpoints.
KronLLM does not contain any ``nn.Linear`` layer: every weight matrix is a
Kronecker composition of small factors, so ``torch.ao.quantization`` cannot see
it. Instead we quantize the *stored* tensors -- factors, embeddings and the
tied head -- with symmetric per-channel scales and dequantize them when the
checkpoint is loaded. The forward pass is therefore bit-identical in structure
and only sees float weights; the win is a 4x (int8) or 8x (int4) smaller file,
which is what makes the model practical on a phone or a microcontroller.
Typical use::
from kronllm.quantization import quantize_checkpoint, load_any_checkpoint
report = quantize_checkpoint("runs/Q2/best.pt", "runs/Q2/model_int8.pt", bits=8)
cfg, state = load_any_checkpoint("runs/Q2/model_int8.pt")
"""
from __future__ import annotations
import time
from pathlib import Path
from typing import Any, Dict, Mapping, Tuple
import torch
# Tensors smaller than this are stored in float32: scales would cost more than
# the payload, and 1-D parameters (RMSNorm, tau, gate) must stay exact.
_MIN_NUMEL = 256
# Name fragments that must never be quantized (normalisation and scalars).
_SKIP_FRAGMENTS = ("norm", "tau", "gate", "theta", "anchor")
def _is_quantizable(name: str, tensor: torch.Tensor) -> bool:
"""Return True if ``tensor`` should be stored in low precision.
Args:
name: Parameter name inside the state dict.
tensor: Parameter tensor.
Returns:
True for 2-D (or higher) float tensors of at least ``_MIN_NUMEL``
elements whose name does not match a normalisation/scalar role.
"""
if not tensor.is_floating_point() or tensor.dim() < 2:
return False
if tensor.numel() < _MIN_NUMEL:
return False
return not any(fragment in name for fragment in _SKIP_FRAGMENTS)
def _pack_int4(codes: torch.Tensor) -> torch.Tensor:
"""Pack signed int4 codes (range ``[-8, 7]``) two per byte.
Args:
codes: Int8 tensor whose values are already clamped to ``[-8, 7]``.
Returns:
Flat uint8 tensor with the low nibble holding the even elements.
"""
flat = codes.reshape(-1).to(torch.int16)
if flat.numel() % 2:
flat = torch.cat([flat, torch.zeros(1, dtype=flat.dtype)])
shifted = flat + 8 # 0..15, safe in int16
packed = shifted[0::2] | (shifted[1::2] << 4)
return packed.to(torch.uint8)
def _unpack_int4(packed: torch.Tensor, numel: int) -> torch.Tensor:
"""Inverse of :func:`_pack_int4`.
Args:
packed: Flat uint8 tensor produced by :func:`_pack_int4`.
numel: Number of original elements.
Returns:
Int8 tensor of length ``numel`` in ``[-8, 7]``.
"""
low = (packed & 0x0F).to(torch.int16) - 8
high = ((packed >> 4) & 0x0F).to(torch.int16) - 8
out = torch.empty(packed.numel() * 2, dtype=torch.int16)
out[0::2] = low
out[1::2] = high
return out[:numel].to(torch.int8)
def _quantize_tensor(tensor: torch.Tensor, bits: int
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Symmetric per-channel quantization of one tensor.
Args:
tensor: Float tensor to quantize.
bits: Target bit width, either 8 or 4.
Returns:
A pair ``(codes, scale)``. ``codes`` is int8 (values clamped to the
signed range) and ``scale`` broadcasts against ``tensor``.
"""
qmax = float(2 ** (bits - 1) - 1)
reduce_dims = tuple(range(1, tensor.dim()))
amax = tensor.detach().abs().amax(dim=reduce_dims, keepdim=True)
scale = (amax / qmax).clamp_min(1e-8)
codes = torch.round(tensor.detach() / scale).clamp(-qmax, qmax).to(torch.int8)
return codes, scale
def quantize_state_dict(state: Mapping[str, torch.Tensor], bits: int = 8
) -> Dict[str, Any]:
"""Quantize every eligible tensor of a state dict.
Args:
state: Float state dict as stored in a training checkpoint.
bits: 8 or 4.
Returns:
A serialisable dict mapping parameter names to either a float tensor
(kept exact) or a ``{"q", "scale", "shape"}`` record.
"""
if bits not in (8, 4):
raise ValueError(f"bits must be 8 or 4, got {bits}")
out: Dict[str, Any] = {}
for name, tensor in state.items():
if not _is_quantizable(name, tensor):
out[name] = tensor.detach().clone()
continue
codes, scale = _quantize_tensor(tensor, bits)
record: Dict[str, Any] = {"shape": tuple(tensor.shape), "scale": scale}
if bits == 8:
record["q"] = codes.reshape(tensor.shape)
else:
record["q"] = _pack_int4(codes)
record["numel"] = tensor.numel()
out[name] = record
return out
def dequantize_state_dict(quantized: Mapping[str, Any], bits: int
) -> Dict[str, torch.Tensor]:
"""Rebuild a float state dict from :func:`quantize_state_dict` output.
Args:
quantized: Output of :func:`quantize_state_dict`.
bits: Bit width used during quantization.
Returns:
Float state dict that can be passed to ``model.load_state_dict``.
"""
state: Dict[str, torch.Tensor] = {}
for name, value in quantized.items():
if not isinstance(value, Mapping):
state[name] = value.float() if value.is_floating_point() else value
continue
scale = value["scale"].float()
if bits == 8:
codes = value["q"].float()
else:
codes = _unpack_int4(value["q"], int(value["numel"])).float()
codes = codes.reshape(value["shape"])
state[name] = codes * scale
return state
def quantize_checkpoint(src: str | Path, dst: str | Path, bits: int = 8
) -> Dict[str, Any]:
"""Quantize a training checkpoint and write a compact checkpoint.
Args:
src: Path to a checkpoint written by ``kronllm.train`` (``best.pt``).
dst: Destination path for the quantized checkpoint.
bits: 8 or 4.
Returns:
A report with the original and quantized sizes plus the parameter count.
Raises:
FileNotFoundError: If ``src`` does not exist.
"""
src, dst = Path(src), Path(dst)
if not src.exists():
raise FileNotFoundError(src)
checkpoint = torch.load(src, map_location="cpu", weights_only=False)
state = checkpoint.get("model", checkpoint)
quantized = quantize_state_dict(state, bits=bits)
payload = {
"quantized": True,
"bits": bits,
"cfg": checkpoint.get("cfg"),
"step": checkpoint.get("step"),
"val_ppl": checkpoint.get("val_ppl"),
"state": quantized,
}
dst.parent.mkdir(parents=True, exist_ok=True)
torch.save(payload, dst)
return {
"src": str(src),
"dst": str(dst),
"bits": bits,
"params": int(sum(v.numel() for v in state.values())),
"size_before_mb": round(src.stat().st_size / 1e6, 3),
"size_after_mb": round(dst.stat().st_size / 1e6, 3),
"compression": round(src.stat().st_size / dst.stat().st_size, 2),
"timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
}
def load_any_checkpoint(path: str | Path) -> Tuple[Dict[str, Any], Dict[str, torch.Tensor]]:
"""Load a float or a quantized checkpoint.
Args:
path: Checkpoint path.
Returns:
A pair ``(cfg, state_dict)`` where ``state_dict`` is always float and
can be loaded into :class:`kronllm.model.KronTransformer`.
Raises:
ValueError: If the checkpoint is quantized but carries no config.
"""
path = Path(path)
checkpoint = torch.load(path, map_location="cpu", weights_only=False)
if not checkpoint.get("quantized"):
cfg = checkpoint.get("cfg")
return cfg, checkpoint.get("model", checkpoint)
cfg = checkpoint.get("cfg")
if cfg is None:
raise ValueError(f"quantized checkpoint {path} has no config")
state = dequantize_state_dict(checkpoint["state"], int(checkpoint["bits"]))
return cfg, state
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