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# Copyright 2024-2025 qubitium@modelcloud.ai
# Contact: qubitium@modelcloud.ai, x.com/qubitium
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# adapted from @qwopqwop200 's [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa/tree/cuda), which itself is based on [gptq](https://github.com/IST-DASLab/gptq)
import torch
import torch.nn as nn
from ..quantization import QuantizeConfig
from ..utils.logger import setup_logger
log = setup_logger()
HF_OPTIMUM = "hf_optimum"
def quantize(x, scale, zero, maxq, requires_groupwise_processing: bool):
if maxq < 0:
return (x > scale / 2).float() * scale + (x < zero / 2).float() * zero
if requires_groupwise_processing:
q = torch.clamp(torch.round(x / scale), -maxq, maxq)
return scale * q
else:
q = torch.clamp(torch.round(x / scale) + zero, 0, maxq)
return scale * (q - zero)
class Quantizer(nn.Module):
def __init__(self, qcfg: QuantizeConfig, shape=1, name: str=None):
super(Quantizer, self).__init__()
self.qcfg = qcfg
self.register_buffer("maxq", torch.tensor(0))
self.register_buffer("scale", torch.zeros(shape))
self.register_buffer("zero", torch.zeros(shape))
self.name=name
def requires_groupwise_processing(self) -> bool:
return False
# FIXME, optimum shouldn't call this directly, it should call hf_configure
def configure(
self,
perchannel=False,
grid=100,
maxshrink=0.8,
trits=False,
bits:int=4, # for hf compat
sym:bool=False, # for hf compat
):
if self.name == HF_OPTIMUM:
self.qcfg.bits = bits
self.qcfg.sym = sym
if self.requires_groupwise_processing():
self.maxq = torch.tensor(2 ** (self.qcfg.bits - 1) - 1)
else:
self.maxq = torch.tensor(2 ** self.qcfg.bits - 1)
self.perchannel = perchannel
self.grid = grid
self.maxshrink = maxshrink
if trits:
self.maxq = torch.tensor(-1)
def find_params(self, x, weight=False):
dev = x.device
self.maxq = self.maxq.to(dev)
shape = x.shape
if self.perchannel:
if weight:
x = x.flatten(1)
else:
if len(shape) == 4:
x = x.permute([1, 0, 2, 3])
x = x.flatten(1)
if len(shape) == 3:
x = x.reshape((-1, shape[-1])).t()
if len(shape) == 2:
x = x.t()
else:
x = x.flatten().unsqueeze(0)
tmp = torch.zeros(x.shape[0], device=dev)
xmin = torch.minimum(x.min(1)[0], tmp)
xmax = torch.maximum(x.max(1)[0], tmp)
if self.qcfg.sym:
xmax = torch.maximum(torch.abs(xmin), xmax)
tmp = xmin < 0
if torch.any(tmp):
xmin[tmp] = -xmax[tmp]
tmp = (xmin == 0) & (xmax == 0)
xmin[tmp] = -1
xmax[tmp] = +1
if self.maxq < 0:
self.scale = xmax
self.zero = xmin
else:
if self.requires_groupwise_processing():
self.scale = xmax / self.maxq
self.zero = torch.zeros_like(self.scale)
else:
self.scale = (xmax - xmin) / self.maxq
if self.qcfg.sym:
self.zero = torch.full_like(self.scale, (self.maxq + 1) / 2)
else:
self.zero = torch.round(-xmin / self.scale)
if self.qcfg.mse > 0.0:
best = torch.full([x.shape[0]], float("inf"), device=dev)
for i in range(int(self.maxshrink * self.grid)):
p = 1 - i / self.grid
xmin1 = p * xmin
xmax1 = p * xmax
scale1 = (
xmax1 / self.maxq
if self.requires_groupwise_processing()
else (xmax1 - xmin1) / self.maxq
)
zero1 = torch.round(-xmin1 / scale1) if not self.qcfg.sym else self.zero
q = quantize(x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq, self.requires_groupwise_processing())
q -= x
q.abs_()
q.pow_(self.qcfg.mse)
err = torch.sum(q, 1)
tmp = err < best
if torch.any(tmp):
best[tmp] = err[tmp]
self.scale[tmp] = scale1[tmp]
self.zero[tmp] = zero1[tmp]
if not self.perchannel:
if weight:
tmp = shape[0]
else:
tmp = shape[1] if len(shape) != 3 else shape[2]
self.scale = self.scale.repeat(tmp)
self.zero = self.zero.repeat(tmp)
if weight:
shape = [-1] + [1] * (len(shape) - 1)
self.scale = self.scale.reshape(shape)
self.zero = self.zero.reshape(shape)
return
if len(shape) == 4:
self.scale = self.scale.reshape((1, -1, 1, 1))
self.zero = self.zero.reshape((1, -1, 1, 1))
if len(shape) == 3:
self.scale = self.scale.reshape((1, 1, -1))
self.zero = self.zero.reshape((1, 1, -1))
if len(shape) == 2:
self.scale = self.scale.unsqueeze(0)
self.zero = self.zero.unsqueeze(0)
def quantize(self, x):
return quantize(x, self.scale, self.zero, self.maxq, self.requires_groupwise_processing())
# def enabled(self):
# return self.maxq > 0
# def ready(self):
# return torch.all(self.scale != 0)
class QQQQuantizer(Quantizer):
def requires_groupwise_processing(self) -> bool:
return self.qcfg.group_size == -1 and self.qcfg.sym
__all__ = ["Quantizer"]
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