v0.8.0

New Features

In Training Embedding Pruning (ITEP) for more efficient RecSys training

Provides a representation of In-Training Embedding Pruning, which is used internally at Meta for more efficient RecSys training by decreasing memory footprint of embedding tables. Pull Request: #2074 introduces the modules into TorchRec, with tests showing how to use them.

Mean Pooling

Mean pooling enabled on embeddings for row-wise and table-row-wise sharding types in TorchRec. Mean pooling mode done through TBE (table-batched embedding) won’t be accurate for row-wise and table-row-wise sharding types, which modify the input due to sharding. This feature efficiently calculates the divisor using caching and overlapping in input dist to implement mean pooling, which had proved to be much more performant than out-of-library implementations. PR: #1772

Changelog

Torch.export (non-strict) compatibility with KJT/JT/KT, EBC/Quantized EBC, sharded variants #1815 #1816 #1788 #1850 #1976 and dynamic shapes #2058

torch.compile support with TorchRec #2045 #2018 #1979

TorchRec serialization with non-strict torch.export for regenerating eager sparse modules (EBC) from IR for sharding #1860 #1848 with meta functionalization when torch.exporting #1974

More benchmarking for TorchRec modules/data types #2094 #2033 #2001 #1855

More VBE support (data parallel sharding) #2093 (EmbeddingCollection) #2047 #1849

RegroupAsDict module for performance improvements with caching #2007

Train Pipeline improvements #1967 #1969 #1971

Bug Fixes and library improvements