pytorch/FBGEMM
Nov 2024 – Apr 2026
10 Months active
Languages Used
C++CUDAJinjaPythonBashCMake
Technical Skills
CUDA programmingGPU ComputingKernel DevelopmentPerformance OptimizationC++CUDA
Andrey Bokovoy
PROFILE
Andrey Bokovoy
Over ten months, contributed to the pytorch/FBGEMM repository by developing and optimizing GPU kernels for embedding operations, with a focus on ROCm and CUDA environments. Delivered features such as forward-pass kernel optimizations, embedding inference enhancements, and performance improvements for dense and quantized workloads. Addressed stability and correctness by fixing memory management bugs, refining test coverage, and ensuring compatibility across diverse hardware. Leveraged C++, CUDA, and Python to implement runtime guards, vectorized operations, and robust test suites. The work emphasized maintainability and cross-platform support, resulting in more reliable, efficient, and flexible embedding workflows for deep learning production deployments.
Overall Statistics
Feature vs Bugs
43%Features
Repository Contributions
18Total
Bugs
8
Commits
18
Features
6
Lines of code
2,131
Activity Months10
Your Network
195 peopleShared Repositories
195
Work History
April 2026
3 Commits
Apr 1, 2026April 2026 monthly summary for pytorch/FBGEMM focusing on business value and technical achievements. Delivered stability, compatibility, and correctness improvements across CI tests, builds, and embedding kernels, enabling more reliable releases and broader hardware/compiler support.
3 Commits
Apr 1, 2026April 2026 monthly summary for pytorch/FBGEMM focusing on business value and technical achievements. Delivered stability, compatibility, and correctness improvements across CI tests, builds, and embedding kernels, enabling more reliable releases and broader hardware/compiler support.
April 2026
March 2026
3 Commits
Mar 1, 2026March 2026 monthly summary for pytorch/FBGEMM: Stabilized ROCm support and advanced test accuracy through targeted test and kernel fixes, delivering parity with CUDA and broader ROCm coverage. Focused efforts re-enabled ROCm tests for block_bucketize, corrected test_cache_int32_overflow handling, and fixed BF16 handling in backward_adagrad_large_dims to align with CUDA behavior and improve numerical precision. These changes improve CI reliability, reduce platform gaps, and enhance robustness for large-dim BF16 paths across ROCm GPUs.
March 2026
3 Commits
Mar 1, 2026March 2026 monthly summary for pytorch/FBGEMM: Stabilized ROCm support and advanced test accuracy through targeted test and kernel fixes, delivering parity with CUDA and broader ROCm coverage. Focused efforts re-enabled ROCm tests for block_bucketize, corrected test_cache_int32_overflow handling, and fixed BF16 handling in backward_adagrad_large_dims to align with CUDA behavior and improve numerical precision. These changes improve CI reliability, reduce platform gaps, and enhance robustness for large-dim BF16 paths across ROCm GPUs.
February 2026
2 Commits • 1 Features
Feb 1, 2026February 2026: Focused on correctness and performance enhancements for ROCm and group_index_select_or_add kernels in pytorch/FBGEMM. Delivered robust fix for ROCm mixed-precision path ensuring correct operation when embedding type differs from gradient type, with a runtime skip to avoid incorrect kernels; future mixed-precision support planned. Implemented cached search for member_id upper bound to reduce kernel latency in USE_VAR_COLS=true scenarios, delivering meaningful latency reductions across key kernels. Prepared comprehensive benchmarking and end-to-end analysis across a diverse hardware mix and merged/validated PRs across repositories. Business impact: improved correctness for production ROCm workloads and lower latency for critical GEMM kernels, enabling higher throughput and safer deployments.
2 Commits • 1 Features
Feb 1, 2026February 2026: Focused on correctness and performance enhancements for ROCm and group_index_select_or_add kernels in pytorch/FBGEMM. Delivered robust fix for ROCm mixed-precision path ensuring correct operation when embedding type differs from gradient type, with a runtime skip to avoid incorrect kernels; future mixed-precision support planned. Implemented cached search for member_id upper bound to reduce kernel latency in USE_VAR_COLS=true scenarios, delivering meaningful latency reductions across key kernels. Prepared comprehensive benchmarking and end-to-end analysis across a diverse hardware mix and merged/validated PRs across repositories. Business impact: improved correctness for production ROCm workloads and lower latency for critical GEMM kernels, enabling higher throughput and safer deployments.
February 2026
January 2026
1 Commits
Jan 1, 2026January 2026 monthly summary for pytorch/FBGEMM. Focused on robustness and flexibility of warp_per_row in the FBGEMM library by introducing a runtime fallback to the baseline kernel when weights are not located in device memory. This change addresses mixed-memory scenarios, ensuring correctness without sacrificing performance in all-device cases. Linked to PR #5357 and commit 0be45122ed6042927c00981e0c9f4bb0d16df24b, the work enhances resilience of the warp_per_row path and broadens deployment scenarios for production workloads.
January 2026
1 Commits
Jan 1, 2026January 2026 monthly summary for pytorch/FBGEMM. Focused on robustness and flexibility of warp_per_row in the FBGEMM library by introducing a runtime fallback to the baseline kernel when weights are not located in device memory. This change addresses mixed-memory scenarios, ensuring correctness without sacrificing performance in all-device cases. Linked to PR #5357 and commit 0be45122ed6042927c00981e0c9f4bb0d16df24b, the work enhances resilience of the warp_per_row path and broadens deployment scenarios for production workloads.
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 performance highlights for pytorch/FBGEMM. Delivered a focused optimization of the 2D kernel group_index_select_or_add_2d_kernel, increasing forward-pass efficiency for float embeddings with small dimensions. The work reduced synchronization overhead and improved thread management, contributing to higher CPU throughput for embedding-heavy workloads.
1 Commits • 1 Features
Nov 1, 2025November 2025 performance highlights for pytorch/FBGEMM. Delivered a focused optimization of the 2D kernel group_index_select_or_add_2d_kernel, increasing forward-pass efficiency for float embeddings with small dimensions. The work reduced synchronization overhead and improved thread management, contributing to higher CPU throughput for embedding-heavy workloads.
November 2025
May 2025
1 Commits
May 1, 2025May 2025 - pytorch/FBGEMM: Dense Embedding backward pass improvements and stability enhancements. Key achievements: - Fixed OOM, memory access violations, and assertion failures in backward dense tests; - Refactored tests to correctly handle gradient masking and zeroing per feature requirements; - Stabilized the backward path for dense embeddings, improving reliability and reducing flaky failures. Commit reference: a036ce7911f2a9c26fe28f4db5237c53de2c6cb6 (Fix backward_dense_test (#3702)). Impact: more reliable training workflows for models using dense embeddings and lower maintenance burden for test suites. Technologies/skills demonstrated: memory management and debugging, test engineering, gradient masking logic, and robust test refactoring in C++/CUDA environments.
May 2025
1 Commits
May 1, 2025May 2025 - pytorch/FBGEMM: Dense Embedding backward pass improvements and stability enhancements. Key achievements: - Fixed OOM, memory access violations, and assertion failures in backward dense tests; - Refactored tests to correctly handle gradient masking and zeroing per feature requirements; - Stabilized the backward path for dense embeddings, improving reliability and reducing flaky failures. Commit reference: a036ce7911f2a9c26fe28f4db5237c53de2c6cb6 (Fix backward_dense_test (#3702)). Impact: more reliable training workflows for models using dense embeddings and lower maintenance burden for test suites. Technologies/skills demonstrated: memory management and debugging, test engineering, gradient masking logic, and robust test refactoring in C++/CUDA environments.
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for pytorch/FBGEMM focusing on delivering performance and maintainability improvements for ROCm deployments through Inference PackedMode optimization. Work centers on feature delivery with traceable commits and clear kernel documentation; no major bugs fixed this period, paving the way for broader ROCm performance gains.
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for pytorch/FBGEMM focusing on delivering performance and maintainability improvements for ROCm deployments through Inference PackedMode optimization. Work centers on feature delivery with traceable commits and clear kernel documentation; no major bugs fixed this period, paving the way for broader ROCm performance gains.
March 2025
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for pytorch/FBGEMM: Focused on ROCm v2 forward kernel testing coverage and fixing ROCm-optimized forward pass embedding lookup bug. Delivered expanded validation coverage, reduced deployment risk, and improved maintainability. Demonstrates proficiency with ROCm, C++, and test configurations.
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for pytorch/FBGEMM: Focused on ROCm v2 forward kernel testing coverage and fixing ROCm-optimized forward pass embedding lookup bug. Delivered expanded validation coverage, reduced deployment risk, and improved maintainability. Demonstrates proficiency with ROCm, C++, and test configurations.
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for pytorch/FBGEMM focused on ROCm embedding inference performance and cross-arch compatibility. Key work delivered includes two ROCm-specific optimizations that enhance throughput and efficiency for quantized split-nbit embeddings: (1) manual loop unrolling to process multiple embedding rows per thread, enabling better utilization of ROCm compute resources; (2) Vec2 load/store capability for ROCm devices, with an updated embedding forward kernel to operate on two elements per step and ROCm-specific vector utilities to improve compatibility and throughput across ROCm hardware.
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for pytorch/FBGEMM focused on ROCm embedding inference performance and cross-arch compatibility. Key work delivered includes two ROCm-specific optimizations that enhance throughput and efficiency for quantized split-nbit embeddings: (1) manual loop unrolling to process multiple embedding rows per thread, enabling better utilization of ROCm compute resources; (2) Vec2 load/store capability for ROCm devices, with an updated embedding forward kernel to operate on two elements per step and ROCm-specific vector utilities to improve compatibility and throughput across ROCm hardware.
December 2024
November 2024
1 Commits • 1 Features
Nov 1, 2024Month 2024-11: Delivered ROCm forward-pass kernel optimization in FBGEMM, including manual loop unrolling, load/accumulate split, and runtime guards to ensure ROCm compatibility. Resulted in improved kernel throughput and ROCm device utilization while maintaining correctness across devices.
November 2024
1 Commits • 1 Features
Nov 1, 2024Month 2024-11: Delivered ROCm forward-pass kernel optimization in FBGEMM, including manual loop unrolling, load/accumulate split, and runtime guards to ensure ROCm compatibility. Resulted in improved kernel throughput and ROCm device utilization while maintaining correctness across devices.
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Quality Metrics
Correctness91.6%
Maintainability83.4%
Architecture81.2%
Performance85.6%
AI Usage22.2%
Skills & Technologies
Programming Languages
BashC++CMakeCUDAJinjaPython
Technical Skills
Autograd systemsBuild ConfigurationC++C++ developmentCI/CDCMakeCUDACUDA ProgrammingCUDA programmingCode GenerationCode documentationDebuggingDeep LearningGPU ComputingGPU Programming
Repositories Contributed To
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