Role: Software Engineer Intern on the PyTorch Compiler team at Meta Superintelligence Lab.
Distributed diagnostics: Shipped torch.compile and torch.distributed tooling to detect synchronization issues across multi-GPU jobs, built with Rust and Python.
Compiler replay: Extended torch.compile support for dynamic-shape and higher-order graph replay with per-graph microbenchmarks, plus AOT diagnostics that capture failures and minimal repros.
Compile-time guards: Engineered checks across TorchDynamo JIT and AOT-Autograd pipelines, diffing graph fingerprints, NVRTC kernel cache hashes, collective schedules, and execution-order drift across GPU ranks to reduce desyncs and distributed deadlocks by 40%.
Kernel provenance: Built Inductor provenance to map Triton kernels back to source PyTorch ops and log per-kernel stack traces for debugging.
Rank-level anomalies: Fused GPU runtime counters with FX graph topology to identify NCCL all-reduce imbalance and deliver an 8% distributed training throughput gain.
tlparse: Released a PyTorch tlparse update with plugin/registry support for custom ops and multi-rank aggregation, unifying internal and open-source code paths.