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fb5b18e | 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 | # quantization/cfrn/quantlib/quant_utils.py
from __future__ import annotations
import math
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
# ---------------------------
# Helper: int4 pack / unpack
# ---------------------------
def pack_int4(q_int8: torch.Tensor) -> Tuple[torch.Tensor, torch.Size]:
"""
Pack int4 values stored in int8 tensor (expected range [-8,7]) into uint8.
Two int4 values per byte: low nibble is first, high nibble is second.
Returns: packed_uint8, original_shape
"""
if q_int8.dtype != torch.int8:
raise TypeError(f"pack_int4 expects int8, got {q_int8.dtype}")
shape = q_int8.shape
flat = q_int8.reshape(-1).to(torch.int16) # avoid overflow
# map [-8,7] -> [0,15]
flat = flat + 8
flat = torch.clamp(flat, 0, 15).to(torch.uint8)
if flat.numel() % 2 == 1:
flat = torch.cat([flat, torch.zeros(1, device=flat.device, dtype=flat.dtype)], dim=0)
lo = flat[0::2]
hi = flat[1::2]
packed = lo | (hi << 4)
return packed, shape
def unpack_int4(packed_uint8: torch.Tensor, shape: torch.Size) -> torch.Tensor:
"""
Unpack uint8 -> int8 int4 values in [-8,7], restoring given shape.
"""
if packed_uint8.dtype != torch.uint8:
raise TypeError(f"unpack_int4 expects uint8, got {packed_uint8.dtype}")
flat = packed_uint8.reshape(-1)
lo = flat & 0x0F
hi = (flat >> 4) & 0x0F
out = torch.empty((flat.numel() * 2,), device=flat.device, dtype=torch.int16)
out[0::2] = lo.to(torch.int16)
out[1::2] = hi.to(torch.int16)
out = out[: int(torch.tensor(shape).prod().item())]
out = out.to(torch.int16) - 8
return out.to(torch.int8).reshape(shape)
# ---------------------------
# Symmetric quant/dequant
# ---------------------------
@dataclass
class QParams:
scale: torch.Tensor # float tensor, can be scalar or per-channel/group
qmax: int
qmin: int
bits: int
per_channel: bool = False
ch_axis: int = 0
group_size: Optional[int] = None # for group-wise along last dim (Linear style)
def _signed_qrange(bits: int) -> Tuple[int, int]:
qmax = (1 << (bits - 1)) - 1
qmin = - (1 << (bits - 1))
return qmin, qmax
@torch.no_grad()
def calc_qparams_symmetric(
x: torch.Tensor,
bits: int,
per_channel: bool,
ch_axis: int,
clip_percentile: Optional[float] = None,
sample: Optional[torch.Tensor] = None,
eps: float = 1e-8,
) -> QParams:
"""
Compute symmetric scale such that x/scale is in [qmin,qmax].
If clip_percentile is provided, use percentile of |x| instead of max(|x|).
For percentile: pass a 1D sample of abs(x) values (optional); otherwise uses abs(x).flatten() which can be heavy.
"""
qmin, qmax = _signed_qrange(bits)
if per_channel:
# reduce all dims except ch_axis
dims = list(range(x.ndim))
dims.pop(ch_axis)
if clip_percentile is None:
max_abs = x.abs().amax(dim=dims)
else:
# approximate percentile per-channel by using max as fallback (cheap)
# For exact percentile per-channel, provide 'sample' externally.
max_abs = x.abs().amax(dim=dims)
scale = max_abs / float(qmax)
else:
if clip_percentile is None:
max_abs = x.abs().max()
else:
if sample is None:
# approximate: take a smaller random sample
flat = x.abs().flatten()
if flat.numel() > 4096:
idx = torch.randint(0, flat.numel(), (4096,), device=flat.device)
flat = flat[idx]
else:
flat = sample
k = max(0.0, min(1.0, float(clip_percentile)))
max_abs = torch.quantile(flat, k)
scale = max_abs / float(qmax)
scale = torch.clamp(scale, min=eps)
return QParams(scale=scale, qmax=qmax, qmin=qmin, bits=bits, per_channel=per_channel, ch_axis=ch_axis, group_size=None)
@torch.no_grad()
def quantize_symmetric(x: torch.Tensor, qparams: QParams) -> torch.Tensor:
"""
Return int8 tensor containing quantized values (still stored as int8 even for int4).
"""
scale = qparams.scale
if qparams.per_channel:
view_shape = [1] * x.ndim
view_shape[qparams.ch_axis] = -1
scale_b = scale.view(view_shape)
else:
scale_b = scale
q = torch.round(x / scale_b)
q = torch.clamp(q, qparams.qmin, qparams.qmax)
return q.to(torch.int8)
@torch.no_grad()
def dequantize_symmetric(q: torch.Tensor, qparams: QParams) -> torch.Tensor:
scale = qparams.scale
if qparams.per_channel:
view_shape = [1] * q.ndim
view_shape[qparams.ch_axis] = -1
scale_b = scale.view(view_shape)
else:
scale_b = scale
return q.to(torch.float32) * scale_b.to(torch.float32)
# ---------------------------
# Group-wise weight quant (LLM-style, along last dim of weight row)
# ---------------------------
@dataclass
class GroupQParams:
scale: torch.Tensor # [out, n_groups]
qmax: int
qmin: int
bits: int
group_size: int
in_features: int
padded_in: int
@torch.no_grad()
def calc_group_qparams_symmetric(W_2d: torch.Tensor, bits: int, group_size: int, eps: float = 1e-8) -> GroupQParams:
"""
W_2d shape [out, in]. Group along in dimension for each out row.
"""
if W_2d.ndim != 2:
raise ValueError("calc_group_qparams_symmetric expects 2D weight")
out, in_features = W_2d.shape
qmin, qmax = _signed_qrange(bits)
padded_in = int(math.ceil(in_features / group_size) * group_size)
if padded_in != in_features:
pad = padded_in - in_features
Wp = torch.cat([W_2d, torch.zeros((out, pad), device=W_2d.device, dtype=W_2d.dtype)], dim=1)
else:
Wp = W_2d
Wg = Wp.view(out, padded_in // group_size, group_size)
max_abs = Wg.abs().amax(dim=2) # [out, n_groups]
scale = torch.clamp(max_abs / float(qmax), min=eps)
return GroupQParams(scale=scale, qmax=qmax, qmin=qmin, bits=bits, group_size=group_size, in_features=in_features, padded_in=padded_in)
@torch.no_grad()
def quantize_group_symmetric(W_2d: torch.Tensor, qparams: GroupQParams) -> torch.Tensor:
out, in_features = W_2d.shape
if in_features != qparams.in_features:
raise ValueError("in_features mismatch")
padded_in = qparams.padded_in
if padded_in != in_features:
pad = padded_in - in_features
Wp = torch.cat([W_2d, torch.zeros((out, pad), device=W_2d.device, dtype=W_2d.dtype)], dim=1)
else:
Wp = W_2d
Wg = Wp.view(out, padded_in // qparams.group_size, qparams.group_size)
scale = qparams.scale.unsqueeze(-1) # [out, n_groups, 1]
q = torch.round(Wg / scale)
q = torch.clamp(q, qparams.qmin, qparams.qmax).to(torch.int8)
return q.view(out, padded_in)
@torch.no_grad()
def dequantize_group_symmetric(q_int8: torch.Tensor, qparams: GroupQParams) -> torch.Tensor:
out, padded_in = q_int8.shape
if padded_in != qparams.padded_in:
raise ValueError("padded_in mismatch")
qg = q_int8.view(out, padded_in // qparams.group_size, qparams.group_size).to(torch.float32)
scale = qparams.scale.unsqueeze(-1).to(torch.float32)
W = qg * scale
W = W.view(out, padded_in)[:, : qparams.in_features]
return W
# ---------------------------
# Prob/Softmax power-of-two quantization
# ---------------------------
@torch.no_grad()
def quantize_prob_power2(p: torch.Tensor, k: int) -> torch.Tensor:
"""
Quantize probabilities p in [0,1] to multiples of 1/2^k.
"""
k = int(k)
base = 1 << k
q = torch.round(p * base) / base
return torch.clamp(q, 0.0, 1.0)
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