July 2026 — Delivered a memory-efficient upgrade to Ascend fused neighborhood attention in linkedin/Liger-Kernel, migrating from a dense S×S implementation to a sparse [S, kernel_size] band representation. This reduced memory footprint and compute on masked positions, enabling larger sequences and reducing risk of UB/OOM. Implemented memory-aware chunking and dual backward paths with autograd result caching to speed repeated gradient calculations. Added hardware-aware tiling strategies and seq_len-adaptive block sizes to respect Ascend device constraints, while validating correctness with a full test suite on Atlas 800T A3 hardware.

June 2026 focused on optimizing Ascend NPU kernel performance and reliability for Liger-Kernel, delivering measurable throughput improvements and stable long-running training for wide-embedding workloads. Work spanned kernel-level optimizations, hardware-aware tiling, and robust test infra across LayerNorm, embedding, fused MoE, and Attention Residual, with targeted fixes addressing grid/UB constraints and reproducibility.

Summary for 2026-05: Delivered performance-focused kernel refactors and stability improvements in LinkedIn/Liger-Kernel with a strong emphasis on Ascend hardware. The work includes (1) refactoring cross_entropy into separate forward/backward Triton kernels with device-side reduction stats and a fast no-weight backward path; (2) moving gradient computation out of the forward pass in fused_linear_cross_entropy, adding a plain CE fast path; (3) fixes to benchmarks for fused_linear_jsd and dyt to avoid torch.compile issues and grid-size limits; (4) validation via make test, checkstyle, and convergence tests to ensure numerical parity and convergence. Overall, improved training throughput, reduced synchronization overhead, and more reliable benchmark results on Ascend hardware, enabling faster iterations and better production confidence.

April 2026: Delivered performance optimization for ASCEND NPU Group Normalization in linkedin/Liger-Kernel. Implemented new block size selection functions and refined kernel configurations to maximize hardware utilization and throughput. Validated changes with unit tests, style checks, and convergence tests; no major bugs fixed this month, all changes pass CI. Impact: faster normalization path enabling improved training/inference performance and lower latency. Skills demonstrated: performance optimization, kernel tuning, hardware-aware development, and rigorous code quality practices.

July 2025 performance month focused on stability, consistency, and training correctness for volcengine/verl. Delivered memory-stability improvements for large Qwen models on ASCEND NPUs, standardized device configuration across modules, and fixed training pipeline issues affecting PPO/DP. These changes reduce runtime failures, improve reproducibility, and enable scalable experimentation with larger model sizes across hardware.

June 2025 – Volcengine Verl focused on stabilizing ASCEND NPU training and expanding model support to broaden hardware compatibility and accelerate value delivery. Key deliverables include a bug fix for distributed training loss calculation on ASCEND NPUs and the introduction of Qwen2.5 VL model support on ASCEND NPU, accompanied by CI workflow updates, documentation, and new training/testing scripts. A transformers library patch was applied to optimize performance on NPU hardware, further improving training throughput and reliability. These efforts resulted in more accurate training outcomes, reduced operational risk, and greater flexibility in deploying VL models on ASCEND-based pipelines.