pytorch/FBGEMM
Dec 2025 – Jun 2026
4 Months active
Languages Used
C++PythonCUDA
Technical Skills
CUDADeep learningGPU programmingUnit testingCUDA DevelopmentGPU Programming
Aryaman Gupta
PROFILE
Aryaman Gupta
Worked on the pytorch/FBGEMM repository to enhance GPU embedding workflows and optimize inference performance on ROCm. Delivered targeted bug fixes in CUDA and HIP kernels, such as correcting pointer arithmetic for half-precision data and aligning momentum buffer typing with PyTorch’s Python implementation. Developed performance optimizations for embedding kernels, including a fast path for INT-weight nobag inference and improved efficiency for small embedding dimensions by processing multiple rows per warp. Leveraged C++, CUDA, and Python to implement and validate these changes, focusing on correctness, stability, and throughput improvements for deep learning and quantized inference workloads in production environments.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
4Total
Bugs
2
Commits
4
Features
2
Lines of code
474
Activity Months4
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1939 peopleSame Organization
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Work History
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for pytorch/FBGEMM focused on ROCm performance optimizations in the INT-weight nobag inference path. Delivered a ROCm-specific fast path for the TBE inference kernel that skips unnecessary iterations in the store loop when D is a multiple of kWarpSize * kOutputsPerThread, significantly reducing tail-work and improving throughput for common configurations (e.g., D=256, INT8, wave-64). Implemented codegen-time gating and runtime predicates to avoid per-iteration branch overhead, and hardened mode separation to prevent unintended tile shape changes between nobag and bag modes. Preserved bit-for-bit compatibility for CUDA, FP8/FP16/FP32 weight paths, bag path, and weighted path. The patch is contained to ROCm builds for INT-weight nobag and does not affect other paths. PRs: 5833 (commit 4fe9193…), 5778; Differential Revision: D106106843; Test Plan: validated with the provided fbcode test and plan.
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for pytorch/FBGEMM focused on ROCm performance optimizations in the INT-weight nobag inference path. Delivered a ROCm-specific fast path for the TBE inference kernel that skips unnecessary iterations in the store loop when D is a multiple of kWarpSize * kOutputsPerThread, significantly reducing tail-work and improving throughput for common configurations (e.g., D=256, INT8, wave-64). Implemented codegen-time gating and runtime predicates to avoid per-iteration branch overhead, and hardened mode separation to prevent unintended tile shape changes between nobag and bag modes. Preserved bit-for-bit compatibility for CUDA, FP8/FP16/FP32 weight paths, bag path, and weighted path. The patch is contained to ROCm builds for INT-weight nobag and does not affect other paths. PRs: 5833 (commit 4fe9193…), 5778; Differential Revision: D106106843; Test Plan: validated with the provided fbcode test and plan.
June 2026
March 2026
1 Commits
Mar 1, 2026March 2026 monthly summary focusing on stabilizing kernel-level momentum handling in FBGEMM's HIP TBE path and aligning it with PyTorch's acc_type and Python implementation. Delivered a targeted bug fix for p_momentum typing when cache_t is half, improving correctness, stability, and consistency across HIP kernels and Python/CUDA codepaths. The work reduced risk of incorrect momentum computations during training on ROCm and aligns with CUDA behavior for momentum buffers.
March 2026
1 Commits
Mar 1, 2026March 2026 monthly summary focusing on stabilizing kernel-level momentum handling in FBGEMM's HIP TBE path and aligning it with PyTorch's acc_type and Python implementation. Delivered a targeted bug fix for p_momentum typing when cache_t is half, improving correctness, stability, and consistency across HIP kernels and Python/CUDA codepaths. The work reduced risk of incorrect momentum computations during training on ROCm and aligns with CUDA behavior for momentum buffers.
January 2026
1 Commits • 1 Features
Jan 1, 2026Concise monthly summary for 2026-01 focusing on pytorch/FBGEMM. Delivered ROCm Embedding Kernel Performance Optimization for group_index_select_or_add_2d_kernel to improve efficiency for small embedding dimensions by processing multiple rows within a warp. This included host-side adjustments to warp_offsets and kernel-side changes in sparse_group_index.cu to support a separate code path for small embedding dims. The work enhances ROCm throughput for small embedding tables, reducing latency in common embedding lookups and enabling better scalability for production workloads. PRs and commits: Commit b34ced630bcb8153c50b2c6b1885700751e08d6a; message: "Optimize group_index_select_or_add_2d_kernel on ROCm by adding a separate codepath for small embedding dimensions (#5233)"; Pull Request: https://github.com/pytorch/FBGEMM/pull/5233; Differential Revision: D89316371; Reviewed by: echen4096.
1 Commits • 1 Features
Jan 1, 2026Concise monthly summary for 2026-01 focusing on pytorch/FBGEMM. Delivered ROCm Embedding Kernel Performance Optimization for group_index_select_or_add_2d_kernel to improve efficiency for small embedding dimensions by processing multiple rows within a warp. This included host-side adjustments to warp_offsets and kernel-side changes in sparse_group_index.cu to support a separate code path for small embedding dims. The work enhances ROCm throughput for small embedding tables, reducing latency in common embedding lookups and enabling better scalability for production workloads. PRs and commits: Commit b34ced630bcb8153c50b2c6b1885700751e08d6a; message: "Optimize group_index_select_or_add_2d_kernel on ROCm by adding a separate codepath for small embedding dimensions (#5233)"; Pull Request: https://github.com/pytorch/FBGEMM/pull/5233; Differential Revision: D89316371; Reviewed by: echen4096.
January 2026
December 2025
1 Commits
Dec 1, 2025Month: 2025-12 — The focus this month was on correctness and stability of GPU embedding workflows in FBGEMM. Delivered a targeted fix to the half-precision embedding data path, ensuring reliable data loading in the load_row_per_warp kernel for emb_t = half and embedding_dim = 320 on ROCm/HIP. Key details: fixed a bug in pointer arithmetic within a specialized HIP instantiation of the warp-per-row kernel, addressed via a minimal one-line change in fbgemm_gpu/rocm/split_embeddings_common.h. The change landed in the commit 6b44875471b9586ac4c277ef2834892ce7ab30f7 and was pushed through PR #5214, with additional context from related PRs referenced in the commit message. Validation: used the provided test plan to exercise the FP16/Warp-Per-Row path on HIP, confirming correctness for the impacted configuration. Tests were run as HIP_VISIBLE_DEVICES=1 buck2 ... (specific tests cited in the commit message). Business impact: prevents subtle data-loading errors from affecting FP16 embedding workloads, increasing reliability of GPU-based inference/training pipelines and reducing risk in production deployments. This work enhances trust in FP16 optimizations and supports stable performance for embedding-heavy workloads. Technologies/skills demonstrated: HIP/Rocm, GPU kernel development, pointer arithmetic in CUDA-like code, half-precision (FP16) data paths, code review and cross-repo collaboration, targeted debugging of specialized kernel instantiations.
December 2025
1 Commits
Dec 1, 2025Month: 2025-12 — The focus this month was on correctness and stability of GPU embedding workflows in FBGEMM. Delivered a targeted fix to the half-precision embedding data path, ensuring reliable data loading in the load_row_per_warp kernel for emb_t = half and embedding_dim = 320 on ROCm/HIP. Key details: fixed a bug in pointer arithmetic within a specialized HIP instantiation of the warp-per-row kernel, addressed via a minimal one-line change in fbgemm_gpu/rocm/split_embeddings_common.h. The change landed in the commit 6b44875471b9586ac4c277ef2834892ce7ab30f7 and was pushed through PR #5214, with additional context from related PRs referenced in the commit message. Validation: used the provided test plan to exercise the FP16/Warp-Per-Row path on HIP, confirming correctness for the impacted configuration. Tests were run as HIP_VISIBLE_DEVICES=1 buck2 ... (specific tests cited in the commit message). Business impact: prevents subtle data-loading errors from affecting FP16 embedding workloads, increasing reliability of GPU-based inference/training pipelines and reducing risk in production deployments. This work enhances trust in FP16 optimizations and supports stable performance for embedding-heavy workloads. Technologies/skills demonstrated: HIP/Rocm, GPU kernel development, pointer arithmetic in CUDA-like code, half-precision (FP16) data paths, code review and cross-repo collaboration, targeted debugging of specialized kernel instantiations.
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture90.0%
Performance90.0%
AI Usage25.0%
Skills & Technologies
Programming Languages
C++CUDAPython
Technical Skills
CUDACUDA DevelopmentDeep LearningDeep learningGPU ProgrammingGPU programmingMachine LearningPerformance OptimizationPerformance optimizationQuantizationUnit testing
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