UofT-EcoSystem / Tempo
Memory footprint reduction for transformer models
☆11Updated 2 years ago
Alternatives and similar repositories for Tempo:
Users that are interested in Tempo are comparing it to the libraries listed below
- ☆41Updated 2 years ago
- PyTorch bindings for CUTLASS grouped GEMM.☆61Updated 2 months ago
- ☆70Updated 3 years ago
- pytorch-profiler☆50Updated last year
- Python package for rematerialization-aware gradient checkpointing☆24Updated last year
- Odysseus: Playground of LLM Sequence Parallelism☆64Updated 7 months ago
- ☆97Updated 5 months ago
- A sparse attention kernel supporting mix sparse patterns☆98Updated 3 months ago
- ☆134Updated 6 months ago
- TileFusion is a highly efficient kernel template library designed to elevate the level of abstraction in CUDA C for processing tiles.☆43Updated this week
- ☆48Updated 7 months ago
- (NeurIPS 2022) Automatically finding good model-parallel strategies, especially for complex models and clusters.☆37Updated 2 years ago
- nnScaler: Compiling DNN models for Parallel Training☆87Updated 3 weeks ago
- ☆72Updated 2 years ago
- ☆38Updated last year
- Memory Optimizations for Deep Learning (ICML 2023)☆62Updated 10 months ago
- extensible collectives library in triton☆77Updated 4 months ago
- ☆58Updated last week
- Official implementation of ICML 2024 paper "ExCP: Extreme LLM Checkpoint Compression via Weight-Momentum Joint Shrinking".☆44Updated 6 months ago
- Tritonbench is a collection of PyTorch custom operators with example inputs to measure their performance.☆86Updated this week
- PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models. ICML 2021☆55Updated 3 years ago
- [NeurIPS 2024] Efficient LLM Scheduling by Learning to Rank☆34Updated 2 months ago
- ☆64Updated 2 months ago
- ☆31Updated 6 months ago
- Summary of system papers/frameworks/codes/tools on training or serving large model☆56Updated last year
- PyTorch implementation of paper "Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline".☆81Updated last year
- ☆59Updated last month
- Curated collection of papers in MoE model inference☆41Updated last week
- ☆36Updated last month
- 16-fold memory access reduction with nearly no loss☆72Updated 2 months ago