Package diffq

This package implements different quantization strategies:

Also, do check BaseQuantizer for the common methods of all Quantizers.

Expand source code 
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

# flake8: noqa
"""
This package implements different quantization strategies:

- `diffq.uniform.UniformQuantizer`: classic uniform quantization over n bits.
- `diffq.diffq.DiffQuantizer`: differentiable quantizer based on scaled noise injection.
- `diffq.lsq.LSQ`: Learnt Step size Quantizer based on [Esser et al. 2019] https://arxiv.org/abs/1902.08153
- `diffq.bitpack`: efficient CPU bit-packing for returning quantized states.
- `diffq.torch_pack`: torch based bit-packing compatible with torchscript.
- `diffq.ts_export`: support exporting DiffQ based models to torchscript.


Also, do check `diffq.base.BaseQuantizer` for the common methods of all Quantizers.
"""

from .uniform import UniformQuantizer
from .diffq import DiffQuantizer
from .lsq import LSQ
from .base import restore_quantized_state
from . import ts_export


__version__ = "0.2.4"

Sub-modules

diffq.base

Base class for all quantizers.

diffq.bitpack
diffq.diffq

Differentiable quantizer based on scaled noise injection.

diffq.lsq

Learnt-Stepsize quantizer from [Esser et al. 2019] https://arxiv.org/abs/1902.08153.

diffq.tests
diffq.torch_pack

Bit packing in pure PyTorch. Slower than bitpack.pyx but compatible with torchscript.

diffq.ts_export

TorchScript export support. We have to do a lot of black magic for TorchScript to be happy because we cannot dynamically allocate new weights when …

diffq.uniform

Classic uniform quantization over n bits.

diffq.utils