SAMP: A Toolkit for Model Inference with Self-Adaptive Mixed-Precision

The latest industrial inference engines, such as FasterTransformer1 and TurboTransformers, have verified that half-precision floating point (FP16) and 8-bit integer (INT8) quantization can greatly improve model inference speed. However, the existing FP16 or INT8 quantization methods are too complicated, and improper usage will lead to performance damage greatly. In this paper, we develop a toolkit for users to easily quantize their models for inference, in which a Self-Adaptive Mixed-Precision (SAMP) is proposed to automatically control quantization rate by a mixed-precision architecture to balance efficiency and performance. Experimental results show that our SAMP toolkit has a higher speedup than PyTorch and FasterTransformer while ensuring the required performance. In addition, SAMP is based on a modular design, decoupling the tokenizer, embedding, encoder and target layers, which allows users to handle various downstream tasks and can be seamlessly integrated into PyTorch.Comment: 6 page

TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities

Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pre-training models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-training model. The modular design enables users to efficiently reproduce existing pre-training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations

An On-Line System for High Temperature Dielectric Property Measurement of Microwave-Assisted Sintering Materials

Microwave-assisted sintering materials have been proven to deliver improvements in the mechanical and physicochemical properties of the materials, compared with conventional sintering methods. Accurate values of dielectric properties of materials under high temperatures are essential for microwave-assisted sintering. In view of this, this paper, proposes an on-line system to measure the high temperature dielectric properties of materials under microwave processing at a frequency of 2450 MHz. A custom-designed ridge waveguide is utilized, where samples are heated and measured simultaneously. An artificial neural network (ANN) trained with the corresponding simulation data is integrated into this system to reverse the permittivity of the measured materials. This whole system is tested at room temperature with different materials. Accuracies of measuring dielectric property with an error lower than 9% with respect to theoretical data have been achieved even for high loss media. The functionality of the dielectric measurement system has also been demonstrated by heating and measuring Macor and Duran ceramic glass samples up to 800 °C. All the preliminary experiments prove the feasibility of this system. It provides another method for dielectric property measurement and improves the understanding of the mechanism between microwave and media under high temperatures, which is helpful for optimizing the microwave-assisted sintering of materials

Simulation and Analysis of Oleic Acid Pretreatment for Microwave-Assisted Biodiesel Production

Oleic acid needs to be heated when it is utilized for biodiesel production, but, as a low-loss solution, oleic acid is difficult to heat by microwave. An efficient heating method for oleic acid is designed. A high loss material porous media is placed in a quartz tube, and a microwave directly heats the porous medium of the high loss material. The oleic acid flows through the pores of porous media so that the oleic acid exchanges heat during this process and rapid heating of oleic acid is achieved. A coupling model, based on the finite element method, is used to analyze the microwave heating process. The multiphysics model is based on a single mode cavity operating at 2450 MHz. An elaborate experimental system is developed to validate the multiphysics model through temperature measurements carried out for different flow velocities of oleic acid and different microwave power levels. The computational results are in good agreement with the experimental data. Based on the validated model, the effects of different sizes, porosities, and materials on microwave heating efficiency are analyzed