Rockmate: an Efficient, Fast, Automatic and Generic Tool for
Re-materialization in PyTorch
- URL: http://arxiv.org/abs/2307.01236v1
- Date: Mon, 3 Jul 2023 11:42:14 GMT
- Title: Rockmate: an Efficient, Fast, Automatic and Generic Tool for
Re-materialization in PyTorch
- Abstract summary: We propose Rockmate to control the memory requirements when training PyTorch DNN models.
Rockmate is an automatic tool that starts from the model code and generates an equivalent model, using a predefined amount of memory for activations.
We show through experiments on many models that Rockmate is as fast as Rotor and as efficient as Checkmate, and that it allows in many cases to obtain a significantly lower memory consumption for activations.
- Score: 1.3816155029354618
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: We propose Rockmate to control the memory requirements when training PyTorch
DNN models. Rockmate is an automatic tool that starts from the model code and
generates an equivalent model, using a predefined amount of memory for
activations, at the cost of a few re-computations. Rockmate automatically
detects the structure of computational and data dependencies and rewrites the
initial model as a sequence of complex blocks. We show that such a structure is
widespread and can be found in many models in the literature (Transformer based
models, ResNet, RegNets,...). This structure allows us to solve the problem in
a fast and efficient way, using an adaptation of Checkmate (too slow on the
whole model but general) at the level of individual blocks and an adaptation of
Rotor (fast but limited to sequential models) at the level of the sequence
itself. We show through experiments on many models that Rockmate is as fast as
Rotor and as efficient as Checkmate, and that it allows in many cases to obtain
a significantly lower memory consumption for activations (by a factor of 2 to
5) for a rather negligible overhead (of the order of 10% to 20%). Rockmate is
open source and available at https://github.com/topal-team/rockmate.
Related papers
- Requential Coding: Pushing the Limits of Model Compression with Self-Generated Training Data [55.04392122319273]
We introduce requential coding, where a teacher model selects training samples drawn from the student's own distribution.<n>The resulting code length is independent of parameter count and data entropy, and often orders of magnitude shorter than the prequential counterpart.
arXiv Detail & Related papers (2026-07-13T17:58:50Z) - Next Tokens Denoising for Speech Synthesis [51.320443764269726]
Dragon-FM is a novel text-to-speech (TTS) design that unifies AR and flow-matching.<n>It processes 48 kHz audio tokens in chunks at a compact rate of 12.5 tokens per second.<n>Experiments on podcast datasets demonstrate its capability to efficiently generate high-quality zero-shot podcasts.
arXiv Detail & Related papers (2025-07-30T15:03:36Z) - A system identification approach to clustering vector autoregressive time series [50.66782357329375]
Clustering time series based on their underlying dynamics is keeping attracting researchers due to its impacts on assisting complex system modelling.<n>Most current time series clustering methods handle only scalar time series, treat them as white noise, or rely on domain knowledge for high-quality feature construction.<n>Instead of relying on feature/metric construction, the system identification approach allows treating vector time series clustering by explicitly considering their underlying autoregressive dynamics.
arXiv Detail & Related papers (2025-05-20T14:31:44Z) - The Persian Rug: solving toy models of superposition using large-scale symmetries [0.0]
We present a complete mechanistic description of the algorithm learned by a minimal non-linear sparse data autoencoder in the limit of large input dimension.
Our work contributes to neural network interpretability by introducing techniques for understanding the structure of autoencoders.
arXiv Detail & Related papers (2024-10-15T22:52:45Z) - RepCNN: Micro-sized, Mighty Models for Wakeword Detection [3.4888176891918654]
Always-on machine learning models require a very low memory and compute footprint.
We show that a small convolutional model can be better trained by first its computation into a larger multi-branched architecture.
We show that our always-on wake-word detector model, RepCNN, provides a good trade-off between latency and accuracy during inference.
arXiv Detail & Related papers (2024-06-04T16:14:19Z) - A Dynamical Model of Neural Scaling Laws [79.59705237659547]
We analyze a random feature model trained with gradient descent as a solvable model of network training and generalization.
Our theory shows how the gap between training and test loss can gradually build up over time due to repeated reuse of data.
arXiv Detail & Related papers (2024-02-02T01:41:38Z) - Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels [0.7499722271664144]
We introduce Sequential Feature Detachment (SFD) to identify and prune non-essential features in ROCKET-based models.
SFD can produce models with better test accuracy using only 10% of the original features.
We also present an end-to-end procedure for determining an optimal balance between the number of features and model accuracy.
arXiv Detail & Related papers (2023-09-25T20:24:36Z) - Back to Basics: A Sanity Check on Modern Time Series Classification
Algorithms [5.225544155289783]
In the current fast-paced development of new classifiers, taking a step back and performing simple baseline checks is essential.
These checks are often overlooked, as researchers are focused on establishing new state-of-the-art results, developing scalable algorithms, and making models explainable.
arXiv Detail & Related papers (2023-08-15T17:23:18Z) - Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model [89.8764435351222]
We propose a new family of unbiased estimators called WTA-CRS, for matrix production with reduced variance.
Our work provides both theoretical and experimental evidence that, in the context of tuning transformers, our proposed estimators exhibit lower variance compared to existing ones.
arXiv Detail & Related papers (2023-05-24T15:52:08Z) - RcTorch: a PyTorch Reservoir Computing Package with Automated
Hyper-Parameter Optimization [1.2891210250935146]
Reservoir computers (RCs) are among the fastest to train of all neural networks.
RC adoption has lagged because of the model's sensitivity to its hyper- parameters.
We introduce RcTorch, a PyTorch based RC neural network package with automated HP tuning.
arXiv Detail & Related papers (2022-07-12T22:24:36Z) - LCS: Learning Compressible Subspaces for Adaptive Network Compression at
Inference Time [57.52251547365967]
We propose a method for training a "compressible subspace" of neural networks that contains a fine-grained spectrum of models.
We present results for achieving arbitrarily fine-grained accuracy-efficiency trade-offs at inference time for structured and unstructured sparsity.
Our algorithm extends to quantization at variable bit widths, achieving accuracy on par with individually trained networks.
arXiv Detail & Related papers (2021-10-08T17:03:34Z) - Fast, Accurate, and Simple Models for Tabular Data via Augmented
Distillation [97.42894942391575]
We propose FAST-DAD to distill arbitrarily complex ensemble predictors into individual models like boosted trees, random forests, and deep networks.
Our individual distilled models are over 10x faster and more accurate than ensemble predictors produced by AutoML tools like H2O/AutoSklearn.
arXiv Detail & Related papers (2020-06-25T09:57:47Z) - Convolutional Tensor-Train LSTM for Spatio-temporal Learning [116.24172387469994]
We propose a higher-order LSTM model that can efficiently learn long-term correlations in the video sequence.
This is accomplished through a novel tensor train module that performs prediction by combining convolutional features across time.
Our results achieve state-of-the-art performance-art in a wide range of applications and datasets.
arXiv Detail & Related papers (2020-02-21T05:00:01Z) - Model Fusion via Optimal Transport [64.13185244219353]
We present a layer-wise model fusion algorithm for neural networks.
We show that this can successfully yield "one-shot" knowledge transfer between neural networks trained on heterogeneous non-i.i.d. data.
arXiv Detail & Related papers (2019-10-12T22:07:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.