pytorch/FBGEMM
Jun 2026 – Jun 2026
1 Month active
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Technical Skills
CUDAGPU ProgrammingPerformance OptimizationPythonTriton
Wenyao Gao
PROFILE
Wenyao Gao
Worked on restoring Tensor Memory Accelerator (TMA) support for grouped-GEMM workloads in the pytorch/FBGEMM repository, targeting Triton 3.x and NVIDIA H100 hardware. The approach involved migrating the load path to the modern tl.make_tensor_descriptor API, updating feature detection logic in Python utilities, and refactoring CUDA kernel load logic to construct descriptors device-side. Expanded test coverage for FP8 K-dimensions and validated performance with both fp8 and bf16 data types. Benchmarks demonstrated a reduction in latency from 1.587 ms to 1.147 ms with TMA enabled, reflecting a focus on GPU programming and performance optimization throughout the development process.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
117
Activity Months1
Your Network
210 peopleSame Organization
@u.rochester.edu4
Work History
June 2026
1 Commits • 1 Features
Jun 1, 2026Month: 2026-06 — Performance-forward delivery for FBGEMM: Restored Tensor Memory Accelerator (TMA) support for grouped-GEMM on Triton 3.x, enabling faster FP8 workloads on H100. Implemented load-path migration to tl.make_tensor_descriptor, updated utils.py detection logic, and refactored kernel load to construct descriptors device-side. Expanded FP8 K-dimension test coverage (64/128/256) and validated with fp8/bf16 on H100. Benchmarks show latency improvement from 1.587 ms to 1.147 ms (~1.38x) with TMA on. Ensured test suite passes (pytest fbgemm_gpu/experimental/gemm/test/grouped_gemm_test.py). Warp-specialized kernels remain on the host-built-descriptor path; full migration planned as a follow-up. PR 5937; Reviewed by jwfromm; DR: D109358672.
1 Commits • 1 Features
Jun 1, 2026Month: 2026-06 — Performance-forward delivery for FBGEMM: Restored Tensor Memory Accelerator (TMA) support for grouped-GEMM on Triton 3.x, enabling faster FP8 workloads on H100. Implemented load-path migration to tl.make_tensor_descriptor, updated utils.py detection logic, and refactored kernel load to construct descriptors device-side. Expanded FP8 K-dimension test coverage (64/128/256) and validated with fp8/bf16 on H100. Benchmarks show latency improvement from 1.587 ms to 1.147 ms (~1.38x) with TMA on. Ensured test suite passes (pytest fbgemm_gpu/experimental/gemm/test/grouped_gemm_test.py). Warp-specialized kernels remain on the host-built-descriptor path; full migration planned as a follow-up. PR 5937; Reviewed by jwfromm; DR: D109358672.
June 2026
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance100.0%
AI Usage80.0%
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Technical Skills
CUDAGPU ProgrammingPerformance OptimizationPythonTriton
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