Monthly summary for 2026-03 focusing on embedding data pipelines in pytorch/FBGEMM. Delivered Raw Embedding Streaming (RES) support for embedding caches and memory-efficient, asynchronous data transfer for Tensor-Based Embedding. Key achievements include enabling RES to coexist with existing cache modes, exposing the raw embedding streamer to subclasses, and refining streaming execution; introduced safeguards to avoid double-streaming and ensure proper streaming callbacks. Tech debt addressed and bugs fixed to stabilize the integration across DRAM KV caches and SSD paths. Overall, these changes improve training data throughput, reduce memory pressure, and strengthen the embedding data pipeline.

February 2026 monthly summary for pytorch/FBGEMM: Focused on boosting embedding streaming performance while preserving CPU portability. Delivered targeted streaming enhancements in the embedding path and expanded raw embedding streaming capabilities through DRAM KV embedding cache plumbing, complemented by cleaning up CUDA dependencies to maintain CPU-only builds. These efforts improved throughput and reduced latency in streaming scenarios, enabling broader hardware support and more robust performance. Business value: higher embedding throughput, lower latency for streaming workloads, and greater portability across CPU/GPU environments, aligning with performance and maintainability goals.

Concise monthly summary for 2025-12 focusing on delivering robust embedding index handling in pytorch/FBGEMM and enabling cache/non-cache tables to be processed correctly within the same embedding spec. This work reduces index calculation errors and garbage updates, improving streaming weights and overall reliability for large-scale embedding workloads.

Concise monthly summary for 2025-09 focusing on feature delivery, codegen improvements, and business value for pytorch/torchrec.

Performance summary for 2025-08 for pytorch/FBGEMM. Key features delivered include Partial Rowwise Adam Optimizer support in fetch_from_l1_sp_w_row_ids and enhancements to the Raw Embedding Streaming Framework, including a standalone RawEmbeddingStreamer, identities support, and integration with SplitTableBatchedEmbeddingBagsCodegen. These efforts improve optimizer flexibility, streaming efficiency, and pre-cache update workflows, delivering business value through better training throughput, reduced memory footprint, and more robust embedding pipelines.

July 2025 performance summary focusing on SSDTBE data retrieval and backward-pass optimization across pytorch/FBGEMM and pytorch/torchrec. Key outcomes include on-demand retrieval of updated weights and optimizer states from L1 cache and secondary storage by row IDs, refactoring to ensure backward hooks execute before eviction, and encapsulation of fetch logic (fetch_from_l1_sp_w_row_ids) for maintainability. These efforts reduce memory footprint and latency, enabling training with larger models and faster backpropagation.

June 2025 (Month: 2025-06) Performance-focused delivery for embedding pipelines in pytorch/FBGEMM. This month’s work centers on streaming-based embeddings to accelerate training throughput and reduce latency for large embedding tables, enabling faster model iteration and cost efficiency in production workloads.

Month: 2025-05 Concise monthly summary focusing on feature delivery and technical execution across TorchRec and FBGEMM. The work centered on enabling and stabilizing raw embedding streaming for large embedding tables, with a focus on configurability, performance, and test coverage to support production-grade deployments. Key achievements: - TorchRec: Delivered configurable raw embedding streaming for SSD TBE, exposing new parameters and a KeyValueParams configuration option to control streaming; enables improved embedding throughput and flexibility in deployment scenarios. Commits: d6031f9ffb95ad1482a4a2bf14cb7f5ff955fa7e, cea9f0784ee07415c1fb53a73ea0f01875d6bdff. - FBGEMM: Implemented embedding streaming infrastructure with enable_raw_embedding_streaming support and asynchronous weight streaming to a parameter server via a background thread and thrift service, enabling scalable handling of large embedding tables. Commits: eb719e133e75335d5b5614e77edd42ddfb7a78cd, c5d19abb3ff8282d91cce0d373309061b961dcc8. - FBGEMM: Expanded test coverage with tensor_stream unit tests for SSD split embeddings cache, validating behavior across flags and indices to ensure reliability in streaming paths. Commit: e8284e2b77ec61807fd91340f25032dd9b1d325e. Overall impact and accomplishments: - Established configurable, scalable embedding streaming pipelines across TorchRec and FBGEMM, addressing throughput and memory challenges associated with large embedding tables. - Introduced/as maintained cross-repo streaming capabilities, setting the foundation for improved end-to-end performance in production workloads. - Strengthened reliability through dedicated unit tests for streaming components, reducing regression risk in future releases. Technologies and skills demonstrated: - Asynchronous processing, background streaming, and thrift-based data transfer. - Configuration-driven design with KeyValueParams integration. - Parameter server interaction patterns for embedding weights. - Unit testing strategy for streaming components and compatibility with feature flags. - Cross-repo collaboration between TorchRec and FBGEMM to deliver cohesive streaming capabilities.