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https://github.com/pulp-platform/nemo
NEural Minimizer for pytOrch
https://github.com/pulp-platform/nemo
Last synced: 2 months ago
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NEural Minimizer for pytOrch
- Host: GitHub
- URL: https://github.com/pulp-platform/nemo
- Owner: pulp-platform
- License: apache-2.0
- Created: 2020-04-06T12:44:05.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-07-25T10:57:35.000Z (6 months ago)
- Last Synced: 2024-11-04T16:54:50.881Z (2 months ago)
- Language: Python
- Size: 111 KB
- Stars: 40
- Watchers: 11
- Forks: 14
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-approximate-dnn - NEMO - nn | PyTorch, ONNX | PTQ, QAT| (Tools / Approximations Frameworks)
README
# NEMO (NEural Minimizer for pytOrch)
**NEMO (NEural Minimizer for pytOrch)** is a small library for minimization of Deep Neural Networks developed in PyTorch, aimed at their deployment on ultra-low power, highly memory constrained platforms, in particular (but not exclusively) PULP-based microcontrollers.
NEMO features include:
- deployment-related transformations such as BatchNorm folding, bias removal, weight equalization
- collection of statistics on activations and weights
- post-training quantization
- quantization-aware fine-tuning, with partially automated precision relaxation
- mixed-precision quantization
- bit-accurate deployment model
- export to ONNX
NEMO operates on three different "levels" of quantization-aware DNN representations, all built upon `torch.nn.Module` and `torch.autograd.Function`:
- fake-quantized *FQ*: replaces regular activations (e.g., ReLU) with quantization-aware ones (PACT) and dynamically quantized weights (with linear PACT-like quantization), maintaining full trainability (similar to the native PyTorch support, but not based on it).
- quantized-deployable *QD*: replaces all function with deployment-equivalent versions, trading off trainability for a more accurate representation of numerical behavior on real hardware.
- integer-deployable *ID*: replaces all activation and weight tensors used along the network with integer-based ones. It aims at bit-accurate representation of actual hardware behavior.
All the quantized representations support mixed-precision weights (signed and asymmetric) and activations (unsigned). The current version of NEMO targets per-layer quantization; work on per-channel quantization is in progress.
NEMO is organized as a Python library that can be applied with relatively small changes to an existing PyTorch based script or training framework.
# Installation and requirements
The NEMO library currently supports PyTorch >= 1.3.1 and runs on Python >= 3.5.
To install it from PyPI, just run:
```
pip install pytorch-nemo
```
You can also install a development (and editable) version of NEMO by directly downloading this repo:
```
git clone https://github.com/pulp-platform/nemo
cd nemo
pip install -e .
```
Then, you can import it in your script using
```
import nemo
```
# Example
- MNIST post-training quantization: https://colab.research.google.com/drive/1AmcITfN2ELQe07WKQ9szaxq-WSu4hdQb
# Documentation
Full documentation for NEMO is under development (see `doc` folder). You can find a technical report covering the deployment-aware quantization methodology here: https://arxiv.org/abs/2004.05930
# License
NEMO is released under Apache 2.0, see the LICENSE file in the root of this repository for details.
# Acknowledgements
NEMO is an outcome of the European Commission [Horizon 2020 ALOHA Project](https://www.aloha-h2020.eu/), funded under the EU's Horizon 2020 Research and Innovation Programme, grant agreement no. 780788.