kimiyoung/transformer-xl
Transformer Xl
This project is an implementation of the Transformer-XL language model, a neural network architecture designed for long-context language modeling. It provides frameworks for training and deploying models that capture long-term dependencies and relationships in text sequences that extend beyond a fixed context window.
The implementation supports both PyTorch and TensorFlow, allowing for distributed training across multiple GPUs and host nodes. It employs a recurrent mechanism to maintain coherence in extended sequences, utilizing segment-level recurrence and state-based memory reuse.
The codebase includes capabilities for large-scale text preprocessing and record serialization to optimize data loading. It also provides tools for performance benchmarking and language model evaluation using standard metrics and benchmark datasets.
Features
- Segment-Level Recurrence - Implements segment-level recurrence to cache hidden states and maintain long-range dependencies across sequences.
- Distributed Training Frameworks - Provides a framework for scaling machine learning model training across multiple compute nodes and accelerators.
- Data-Parallel Training - Distributes model training workloads across multiple hardware units by synchronizing gradients and parameters.
- State-Based Memory Reuses - Passes computed representations from prior data blocks into the current processing window to maintain long-term dependencies.
- Long-Context Models - Engineers language models to maintain logical coherence across massive input sequences beyond fixed windows.
- Large-Scale Model Training - Uses specialized methodologies to train models that exceed the memory capacity of a single device.
- Recurrent Transformer Architectures - Implements a language model architecture that combines transformer attention with recurrent weight sharing.
- Distributed Training - Scales the training of language models across multiple compute nodes to accelerate processing.
- Large Language Model Training Frameworks - Implements a framework for training transformer-based models across multi-GPU environments to capture long-term dependencies.
- PyTorch Training Frameworks - Provides high-level structures and utilities to organize and execute the training of the model using PyTorch.
- TensorFlow Framework Implementations - Implements neural network operations and automatic differentiation using the TensorFlow library for distributed training.
- Relative - Employs relative positional encodings to represent token distances and improve generalization to unseen sequence lengths.
- Long-Term Dependency Modeling - Processes data sequences beyond fixed context windows to capture relationships between distant parts of a text.
- Text Preprocessing - Provides utilities for parsing and formatting raw text into optimized structures for model training.
- Large-Scale Data Preprocessing - Employs high-throughput utilities to clean and transform massive text datasets for efficient data loading.
- Tensor Record Serialization - Utilizes specialized binary serialization formats to stream large tensor datasets from disk for high-throughput training.
- Model Accuracy Evaluators - Measures model correctness and performance using benchmark datasets and standard metrics.
- Language Modeling - Attentive language models for long-context sequences.
- Natural Language Processing - Transformer-XL language model.
- Transformer Implementations - Official repository for the Transformer-XL research paper.
Star history
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