Tencent/PocketFlow

Features

• Model Compression - Provides a comprehensive system for reducing neural network size and complexity through pruning, sparsification, and quantization.
• Distributed Training Accelerators - Splits training workloads across multiple GPUs to accelerate the fine-tuning and compression of large networks.
• Distributed Training - Features a multi-GPU acceleration layer for scaling the fine-tuning and compression of large neural networks.
• Knowledge Distillation - Uses knowledge distillation to train student models that mimic teacher models for accuracy recovery after compression.
• Model Compression Suites - Provides an integrated suite for reducing model size through pruning, quantization, and distillation.
• Weight Quantization - Lowers the precision of model weights and activations to reduce memory footprint and accelerate execution.
• Weight Pruning - Provides weight sparsification through a dynamic pruning schedule during training to reduce model size.
• Mobile Model Deployment - Optimizes and converts trained model checkpoints into lightweight formats for efficient on-device inference.
• Model Format Optimizers - Implements a pipeline to transform large checkpoints into optimized formats for mobile device deployment.
• Layer-Wise Optimization Strategies - Determines optimal pruning ratios or quantization widths for individual layers to maintain accuracy within resource budgets.
• Teacher-Student Distillation - Employs teacher-student distillation to train compact models that mimic the performance of larger teacher models.
• Convolutional Channel Pruning - Reduces input channels in convolutional layers to decrease model size while minimizing reconstruction loss.
• Pruning Ratio Optimization - Uses reinforcement learning to find optimal compression ratios that meet specific computation or FLOPs budgets.
• Model Sparsification - Implements a dynamic pruning schedule to reduce the number of non-zero weights and decrease inference cost.
• Non-Uniform Quantization - Optimizes the distribution of quantization points through back-propagation to approximate full-precision network behavior.
• Automated Bit Allocation - Employs reinforcement learning to automatically determine the most efficient bit distribution across model layers.
• Layer-Wise Bit Allocation - Assigns different quantization bit-widths to individual network layers to balance inference speed and model accuracy.
• Quantization-Aware Training - Fine-tunes models during the quantization process to recover accuracy lost during weight and activation compression.
• Hyperparameter Optimizers - Automatically searches for optimal compression ratios and bit-widths using reinforcement learning and statistical models.
• Non-Uniform Quantization - Implements back-propagation-based non-uniform quantization to approximate full-precision network behavior.
• Automated Hyperparameter Search - Uses a reinforcement learning agent to automatically determine optimal pruning ratios and bit-widths across model layers.
• RL-Based Parameter Tuning - Iteratively searches for the best configuration settings using reinforcement learning to maximize model performance.
• TFLite Exports - Transforms trained model checkpoints into the TFLite format for deployment on mobile devices.
• Mobile Model Format Converters - Transforms optimized checkpoints into a single file format compatible with mobile interpreters for deployment.
• Filter Pruning - AutoML for model compression and mobile acceleration.
• Inference Optimization - AutoML-based tool for model compression.

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