v0.7.0-rc1

Pre release for v0.7.0

v0.6.0

VBE

TorchRec now natively supports VBE (variable batched embeddings) within the EmbeddingBagCollection module. This allows variable batch size per feature, unlocking sparse input data deduplication, which can greatly speed up embedding lookup and all-to-all time. To enable, simply initialize KeyedJaggedTensor with stride_per_key_per_rank and inverse_indices fields, which specify batch size per feature and inverse indices to reindex the embedding output respectively.

Embedding offloading

Embedding offloading is UVM caching (i.e. storing embedding tables on host memory with cache on HBM memory) plus prefetching and optimal sizing of cache. Embedding offloading would allow running a larger model with fewer GPUs, while maintaining competitive performance. To use, one needs to use the prefetching pipeline (PrefetchTrainPipelineSparseDist) and pass in per table cache load factor and the prefetch_pipeline flag through constraints in the planner.

Trec.shard/shard_modules

These APIs replace embedding submodules with its sharded variant. The shard API applies to an individual embedding module while the shard_modules API replaces all embedding modules and won’t touch other non-embedding submodules.
Embedding sharding follows similar behavior to the prior TorchRec DistributedModuleParallel behavior, except the ShardedModules have been made composable, meaning the modules are backed by TableBatchedEmbeddingSlices which are views into the underlying TBE (including .grad). This means that fused parameters are now returned with named_parameters(), including in DistributedModuleParallel.

v0.6.0-rc2

v0.6.0-rc2

v0.6.0-rc1

This should support python 3.8 - 3.11 and 3.12 (experimental)

pip install torchrec --index-url https://download.pytorch.org/whl/test/cpu
pip install torchrec --index-url https://download.pytorch.org/whl/test/cu118
pip install torchrec --index-url https://download.pytorch.org/whl/test/cu121

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).