
Joshua Su contributed to the PyTorch ecosystem by developing and stabilizing core features across pytorch/torchrec, pytorch/pytorch, and pytorch/FBGEMM. He implemented feature order caching for QuantEBC inference, optimizing forward-pass efficiency and reliability using Python and PyTorch. Joshua addressed edge cases in embedding collections, fixed data structure compatibility in transform passes, and enhanced API safety by adding type checks for ScriptModule hook registration in C++. He restored CUDA memory allocation stability through targeted rollbacks and resolved tensor scaling issues in FB-GEMM inference using ARM NEON and SIMD. His work demonstrated careful regression testing and a focus on robust, maintainable code.

October 2025 monthly summary for the pytorch/FBGEMM repo focused on stabilization of prediction outputs through a targeted rollback. Restored correct tensor scaling and reliable inference across affected models by reverting a prior EmbeddingSpMDM8Bit_Sve change. Commit: 5beb3e6e0ef5ec830461ce163c012864677647a9 (Back out "Add EmbeddingSpMDM8Bit_Sve" (#4961)).
October 2025 monthly summary for the pytorch/FBGEMM repo focused on stabilization of prediction outputs through a targeted rollback. Restored correct tensor scaling and reliable inference across affected models by reverting a prior EmbeddingSpMDM8Bit_Sve change. Commit: 5beb3e6e0ef5ec830461ce163c012864677647a9 (Back out "Add EmbeddingSpMDM8Bit_Sve" (#4961)).
Monthly summary for 2025-08 (pytorch/pytorch): Restored stability in CUDA memory allocation configuration by reverting deprecated changes to CUDAAllocatorConfig, ensuring reliable behavior and compatibility with AcceleratorAllocatorConfig across CUDA builds and training workflows.
Monthly summary for 2025-08 (pytorch/pytorch): Restored stability in CUDA memory allocation configuration by reverting deprecated changes to CUDAAllocatorConfig, ensuring reliable behavior and compatibility with AcceleratorAllocatorConfig across CUDA builds and training workflows.
June 2025 (2025-06) – PyTorch: Delivered a safety-focused bug fix to ScriptModule hook registration, improving stability and developer experience. Implemented a type check to prevent forward hook registration on ScriptModule instances via register_forward_pre_hook, addressing an error encountered during hook setup. The change was implemented in pytorch/pytorch with commit 977abe786d907c1ff76528a550e3d53c9f3b1044. This fixes the error 'register_foward_pre_hook not supported on ScriptModule' (#156904). Benefits include reduced runtime failures during model construction and tooling, better API safety, and smoother user workflows.
June 2025 (2025-06) – PyTorch: Delivered a safety-focused bug fix to ScriptModule hook registration, improving stability and developer experience. Implemented a type check to prevent forward hook registration on ScriptModule instances via register_forward_pre_hook, addressing an error encountered during hook setup. The change was implemented in pytorch/pytorch with commit 977abe786d907c1ff76528a550e3d53c9f3b1044. This fixes the error 'register_foward_pre_hook not supported on ScriptModule' (#156904). Benefits include reduced runtime failures during model construction and tooling, better API safety, and smoother user workflows.
April 2025 (2025-04) monthly summary for repository pytorch/torchrec focused on robustness and compatibility in embedding collection. Delivered a bug fix to the DecoupleEmbeddingCollection Forward method: the method now returns the correct data structure, eliminating compatibility issues with subsequent transform passes. The change reduces downstream failures and stabilizes the embedding data flow across the training and inference pipeline.
April 2025 (2025-04) monthly summary for repository pytorch/torchrec focused on robustness and compatibility in embedding collection. Delivered a bug fix to the DecoupleEmbeddingCollection Forward method: the method now returns the correct data structure, eliminating compatibility issues with subsequent transform passes. The change reduces downstream failures and stabilizes the embedding data flow across the training and inference pipeline.
March 2025: Implemented QuantEBC Feature Order Caching for Inference to optimize the forward path by caching feature order and avoiding unnecessary indexing. Added robust edge-case handling for empty EmbeddingCollections/EmbeddingBagCollections, improving inference reliability. These changes reduce latency and prevent failures in edge cases, aligning with performance and robustness goals for pytorch/torchrec. Commits included: c5a4ff15a235c90c7df628764b549c91e4c1f03a; 055119ec2ebd53dbe38a98c7b2203bb75667660d.
March 2025: Implemented QuantEBC Feature Order Caching for Inference to optimize the forward path by caching feature order and avoiding unnecessary indexing. Added robust edge-case handling for empty EmbeddingCollections/EmbeddingBagCollections, improving inference reliability. These changes reduce latency and prevent failures in edge cases, aligning with performance and robustness goals for pytorch/torchrec. Commits included: c5a4ff15a235c90c7df628764b549c91e4c1f03a; 055119ec2ebd53dbe38a98c7b2203bb75667660d.
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