اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدDiverse Sample Generation: Pushing the Limit of Data-free Quantization
76
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Recently, generative data-free quantization emerges as a practical approach that compresses the neural network to low bit-width without access to real data. It generates data to quantize the network by utilizing the batch normalization (BN) statistics of its full-precision counterpart. However, our study shows that in practice, the synthetic data completely constrained by BN statistics suffers severe homogenization at distribution and sample level, which causes serious accuracy degradation of the quantized network. This paper presents a generic Diverse Sample Generation (DSG) scheme for the generative data-free post-training quantization and quantization-aware training, to mitigate the detrimental homogenization. In our DSG, we first slack the statistics alignment for features in the BN layer to relax the distribution constraint. Then we strengthen the loss impact of the specific BN layer for different samples and inhibit the correlation among samples in the generation process, to diversify samples from the statistical and spatial perspective, respectively. Extensive experiments show that for large-scale image classification tasks, our DSG can consistently outperform existing data-free quantization methods on various neural architectures, especially under ultra-low bit-width (e.g., 22% gain under W4A4 setting). Moreover, data diversifying caused by our DSG brings a general gain in various quantization methods, demonstrating diversity is an important property of high-quality synthetic data for data-free quantization.
قيم البحث
اقرأ أيضاً
Quantization has emerged as one of the most prevalent approaches to compress and accelerate neural networks. Recently, data-free quantization has been widely studied as a practical and promising solution. It synthesizes data for calibrating the quant
ized model according to the batch normalization (BN) statistics of FP32 ones and significantly relieves the heavy dependency on real training data in traditional quantization methods. Unfortunately, we find that in practice, the synthetic data identically constrained by BN statistics suffers serious homogenization at both distribution level and sample level and further causes a significant performance drop of the quantized model. We propose Diverse Sample Generation (DSG) scheme to mitigate the adverse effects caused by homogenization. Specifically, we slack the alignment of feature statistics in the BN layer to relax the constraint at the distribution level and design a layerwise enhancement to reinforce specific layers for different data samples. Our DSG scheme is versatile and even able to be applied to the state-of-the-art post-training quantization method like AdaRound. We evaluate the DSG scheme on the large-scale image classification task and consistently obtain significant improvements over various network architectures and quantization methods, especially when quantized to lower bits (e.g., up to 22% improvement on W4A4). Moreover, benefiting from the enhanced diversity, models calibrated by synthetic data perform close to those calibrated by real data and even outperform them on W4A4.
The rapid development of large pre-trained language models has greatly increased the demand for model compression techniques, among which quantization is a popular solution. In this paper, we propose BinaryBERT, which pushes BERT quantization to the
limit by weight binarization. We find that a binary BERT is hard to be trained directly than a ternary counterpart due to its complex and irregular loss landscape. Therefore, we propose ternary weight splitting, which initializes BinaryBERT by equivalently splitting from a half-sized ternary network. The binary model thus inherits the good performance of the ternary one, and can be further enhanced by fine-tuning the new architecture after splitting. Empirical results show that our BinaryBERT has only a slight performance drop compared with the full-precision model while being 24x smaller, achieving the state-of-the-art compression results on the GLUE and SQuAD benchmarks.
We study the challenging task of neural network quantization without end-to-end retraining, called Post-training Quantization (PTQ). PTQ usually requires a small subset of training data but produces less powerful quantized models than Quantization-Aw
are Training (QAT). In this work, we propose a novel PTQ framework, dubbed BRECQ, which pushes the limits of bitwidth in PTQ down to INT2 for the first time. BRECQ leverages the basic building blocks in neural networks and reconstructs them one-by-one. In a comprehensive theoretical study of the second-order error, we show that BRECQ achieves a good balance between cross-layer dependency and generalization error. To further employ the power of quantization, the mixed precision technique is incorporated in our framework by approximating the inter-layer and intra-layer sensitivity. Extensive experiments on various handcrafted and searched neural architectures are conducted for both image classification and object detection tasks. And for the first time we prove that, without bells and whistles, PTQ can attain 4-bit ResNet and MobileNetV2 comparable with QAT and enjoy 240 times faster production of quantized models. Codes are available at https://github.com/yhhhli/BRECQ.
Chemically Peculiar (CP) stars have been subject of systematic research since more than 50 years. With the discovery of pulsation of some of the cool CP stars, the availability of advanced spectropolarimetric instrumentation and high signal- to-noise
, high resolution spectroscopy, a new era of CP star research emerged about 20 years ago. Together with the success in ground-based observations, new space projects are developed that will greatly benefit for future investigations of these unique objects. In this contribution we will give an overview of some interesting results obtained recently from ground-based observations and discuss on future outstanding Gaia space mission and its impact on CP star research.
We introduce a simple but effective unsupervised method for generating realistic and diverse images. We train a class-conditional GAN model without using manually annotated class labels. Instead, our model is conditional on labels automatically deriv
ed from clustering in the discriminators feature space. Our clustering step automatically discovers diverse modes, and explicitly requires the generator to cover them. Experiments on standard mode collapse benchmarks show that our method outperforms several competing methods when addressing mode collapse. Our method also performs well on large-scale datasets such as ImageNet and Places365, improving both image diversity and standard quality metrics, compared to previous methods.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
