v0.5.0

[Prototype] Zero Collision / Managed Collision Embedding Bags

A common constraint in Recommender Systems is the sparse id input range is larger than the number of embeddings the model can learn for a given parameter size. To resolve this issue, the conventional solution is to hash sparse ids into the same size range as the embedding table. This will ultimately lead to hash collisions, with multiple sparse ids sharing the same embedding space. We have developed a performant alternative algorithm that attempts to address this problem by tracking the N most common sparse ids and ensuring that they have a unique embedding representation. The module is defined here and an example can be found here.

[Prototype] UVM Caching - Prefetch Training Pipeline

For tables where on-device memory is insufficient to hold the entire embedding table, it is common to leverage a caching architecture where part of the embedding table is cached on device and the full embedding table is on host memory (typically DDR SDRAM). However, in practice, caching misses are common, and hurt performance due to relatively high latency of going to host memory. Building on TorchRec’s existing data pipelining, we developed a new Prefetch Training Pipeline to avoid these cache misses by prefetching the relevant embeddings for upcoming batch from host memory, effectively eliminating cache misses in the forward path.

v0.5.0-rc2

Install fbgemm via nova

v0.5.0-rc1

remove fbgemm-gpu-nightly instead

0.4.0

Train pipeline improvements

The train pipeline now allows the user to specify if they want all pipelined batches to be executed after exhausting the dataloader iterator. Normally when StopIteration is raised, the train pipeline will halt with the last 2 pipelined batches yet to be executed.
Core train pipeline logic has been refactored for better readability and maintainability.
The memcpy and data_dist streams have been set to high priority. We’ve seen kernel launches get delayed scheduling even with nothing on the GPU blocking the kernel. This will block the CPU unnecessarily, and we see perf gains after making this change.

FX + Script Inference Module

Sharded Quantized EmbeddingBagCollection and EmbeddingCollection are now torch.fx-able and torchscript-able ( via torch.script(torch.fx(module)) ), and can now be run with torchscript.

RecMetrics

Add include_logloss option to NE metric, to return the log of cross entropy loss on top of NE.
Add grouped AUC metric option. To use, toggle grouped_auc=True when instantiating AUC metric, and provide an additional grouping_keys tensor to specify the group_id for each element along the batch dimension in update method. The grouped AUC will then calculate AUCs per specified group, and return the averaged AUC.

Enable the grouped_auc during metric instantiation
auc = AUCMetric(world_size=4, my_rank=0, batch_size=64, tasks=["t1"], grouped_auc=True)
provide grouping keys during update
auc.update(predictions=predictions, labels=labels, weights=weights, grouping_keys=grouping_keys)

Full Changelog: https://github.com/pytorch/torchrec/commits/v0.4.0

v0.3.2

KeyedJaggedTensor

We observed performance regression due to a bottleneck in sparse data distribution for models that have multiple, large KJTs to redistribute.

To combat this we altered the comms pattern to transport the minimum data required in the initial collective to support the collective calls for the actual KJT tensor data. This data sent in the initial collective, ‘splits’ means more data is transmitted over the comms stream overall, but the CPU is blocked for significantly shorter amounts of time leading to better overall QPS.

Furthermore, we altered the TorchRec train pipeline to group the initial collective calls for the splits together before launching the more expensive KJT tensor collective calls. This pseudo ‘fusing’ minimizes the CPU blocked time as launching each subsequent input distribution is no longer dependent on the previous input distribution.

We no longer pass in variable batch size in the sharder

Planner

On the planner side, we introduced a new feature “early stopping” to GreedyProposer. This brings a 4X speedup to planner when there are many proposals (>1000) to propose with. To use the feature, simply add “threshold=10” to GreedyProposer (10 is the suggested number for it, which means GreedyProposer will stop proposing after seeing 10 consecutive bad proposals). Secondly, we refactored the “deepcopy” logic in the planner code, which bring a 8X speedup on the overall planning time. See PR #665 for the details.

Pinning requirements

We are also pinning requirements to add more stability to TorchRec users

v0.3.0

[ProtoType] Simplified Optimizer Fusion APIs

We’ve provided a simplified and more intuitive API for setting fused optimizer settings via apply_optimizer_in_backward. This new approach enables the ability to specify optimizer settings on a per-parameter basis and sharded modules will configure FBGEMM’s TableBatchedEmbedding modules accordingly. Additionally, this now let's TorchRec’s planner account for optimizer memory usage. This should alleviate reports of sharding jobs OOMing after using Adam using a plan generated from planner.

[ProtoType] Simplified Sharding APIs

We’re introducing the shard API, which now allows you to shard only the embedding modules within a model, and provides an alternative to the current main entry point - DistributedModelParallel. This lets you have a finer grained control over the rest of the model, which can be useful for customized parallelization logic, and inference use cases (which may not require any parallelization on the dense layers). We’re also introducing construct_module_sharding_plan, providing a simpler interface to the TorchRec sharder.

[Beta] Integration with FBGEMM's Quantized Comms Library

Applying quantization or mixed precision to tensors in a collective call during model parallel training greatly improves training efficiency, with little to no effect on model quality. TorchRec now integrates with the quantized comms library provided by FBGEMM GPU and provides an interface to construct encoders and decoders (codecs) that surround the all_to_all, and reduce_scatter collective calls in the output_dist of a sharded module. We also allow you to construct your own codecs to apply to your sharded module. The codces provided by FBGEMM allow FP16, BF16, FP8, and INT8 compressions, and you may use different quantizations for the forward path and backward pass.

Planner

• We removed several unnecessary copies inside of planner that drastically decreases the runtime.
• Cleaned up the Topology interface (no longer takes in unrelated information like batch size).

v0.2.0

Changelog

PyPi Installation

The recommended install location is now from pypy. Additionally, TorchRec's binary will not longer contain fbgemm_gpu. Instead fbgemm_gpu will be installed as a dependency. See README for details

Planner Improvements

We added some additional features and bug fixed some bugs
Variable batch size per feature to support request only features
Better calculations for quant UVM Caching
Bug fix for shard storage fitting on device

Single process Batched + Fused Embeddings

Previously TorchRec’s abstractions (EmbeddingBagCollection/EmbeddingCollection) over FBGEMM kernels, which provide benefits such as table batching, optimizer fusion, and UVM placement, could only be used in conjunction with DistributedModelParallel. We’ve decoupled these notions from sharding, and introduced the FusedEmbeddingBagCollection, which can be used as a standalone module, with all of the above features, and can also be sharded.

Sharder

We enabled embedding sharding support for variable batch sizes across GPUs.

Benchmarking and Examples

We introduce
A set of benchmarking tests, showing performance characteristics of TorchRec’s base modules and research models built out of TorchRec.
We provide an example demonstrating training a distributed TwoTower (i.e. User-Item) Retrieval model that is sharded using TorchRec. The projected item embeddings are added to an IVFPQ FAISS index for candidate generation. The retrieval model and KNN lookup are bundled in a Pytorch model for efficient end-to-end retrieval.
inference example with Torch Deploy for both single and multi GPU

Integrations

We demonstrate that TorchRec works out of the box with many components commonly used alongside PyTorch models in production like systems, such as

• Training a TorchRec model on Ray Clusters utilizing the Torchx Ray scheduler
• Preprocessing and DataLoading with NVTabular on DLRM
• Training a TorchRec model with on-the-fly preprocessing with TorchArrow showcasing RecSys domain UDFs.

Scriptable Unsharded Modules

The unsharded embedding modules (EmbeddingBagCollection/EmbeddingCollection and variants) are now torch scriptable.

EmbeddingCollection Column Wise Sharding

We now support column wise sharding for EmbeddingCollection, enabling sequence embeddings to be sharded column wise.

JaggedTensor

Boost performance of to_padded_dense function by implementing with FBGEMM.

Linting

Add lintrunner to allow contributors to lint and format their changes quickly, matching our internal formatter.

v0.1.1

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

Beta release

We are excited to announce TorchRec, a PyTorch domain library for Recommendation Systems. This new library provides common sparsity and parallelism primitives, enabling researchers to build state-of-the-art personalization models and deploy them in production.

Modeling primitives, such as embedding bags and jagged tensors, that enable easy authoring of large, performant multi-device/multi-node models using hybrid data-parallelism and model-parallelism.
Optimized RecSys kernels powered by FBGEMM , including support for sparse and quantized operations.
A sharder which can partition embedding tables with a variety of different strategies including data-parallel, table-wise, row-wise, table-wise-row-wise, and column-wise sharding.
A planner which can automatically generate optimized sharding plans for models.
Pipelining to overlap dataloading device transfer (copy to GPU), inter-device communications (input_dist), and computation (forward, backward) for increased performance.
GPU inference support.
Common modules for RecSys, such as models and public datasets (Criteo & Movielens).

See our announcement and docs