Skip to content

v0.1.1

Choose a tag to compare

View all tags
@colin2328 colin2328 released this 17 May 18:12
v0.1.1
1d2a1a7

Changelog

pytorch.org Install

The recommended install location is now from download.pytorch.org. See README for details

Recmetrics

RecMetrics is a metrics library that collects common utilities and optimizations for Recommendation models.

  • A centralized metrics module that allows users to add new metrics
  • Commonly used metrics, including AUC, Calibration, CTR, MSE/RMSE, NE & Throughput
  • Optimization for metrics related operations to reduce the overhead of metric computation
  • Checkpointing

Torchrec inference

Larger models need GPU support for inference. Also, there is a difference between features used in common training stacks and inference stacks. The goal of this library is to make use of some features seen in training to make inference more unified and easier to use.

EmbeddingTower and EmbeddingTowerCollection

a new sharadable nn.Module called EmbeddingTower/EmbeddingTowerCollection. This module will give model authors the basic building block to establish a clear relationship between a set of embedding tables and post lookup modules.

Examples/tutorials

Inference example

documentation (installation and example), updated cmake build and gRPC server example

Bert4rec example

Reproduction of bert4rec paper showcasing EmbeddingCollection module (non pooling)

Sharding Tutorial

Overview of sharding in torchrec and the five types of sharding https://pytorch.org/tutorials/advanced/sharding.html

Improved Planner

  • Updated static estimates for perf
  • Models full model parallel path
  • Includes support for sequence embeddings, weighted features, and feature processors
  • Added grid search proposer
Assets 2
Loading