May 2026 Monthly Summary for PaddlePaddle/FastDeploy. Delivered a critical stability fix for Reinforcement Learning workflows by addressing a memory safety bug in CUDA Graph recapture. The fix ensures buffer sizes align with initial allocations, preventing illegal memory access during recapture and avoiding CUDA error 700. The change was implemented as a targeted bug fix in a single commit and significantly improves RL reliability in CUDA graph execution.

April 2026 monthly summary for PaddlePaddle/FastDeploy focusing on delivering performance-oriented features, stabilizing speculative decoding, and tightening resource scheduling. Key outcomes include mtp overlap in pd-split mode with improved async data handling across CUDA and non-CUDA paths, default-enabled CUDA graph for speculative decoding with improved server logging, and robust fixes to token budgeting and decoding stability. These changes improve throughput, predictability, and observability, enabling more reliable deployments and cost-effective scaling.

March 2026 (2026-03) focused on performance optimization and deployment flexibility for PaddlePaddle FastDeploy. Key work centered on a major overhaul of speculative decoding with a unified path for spec and non-spec modes, plus MTP integration, to boost throughput, robustness, and end-to-end reliability. Launched deployment modalities support in the serving engine to enable text-only and multimodal workloads with configurable processing and resource management. These efforts improved inference efficiency, reduced risk in production deployments, and strengthened the platform’s ability to support diverse customer workflows.

January 2026 — PaddlePaddle/FastDeploy: Delivered performance, reliability, and governance enhancements across inference and generation. Implemented CUDA-accelerated multi-step draft-model execution via cudagraphs to boost throughput; expanded attention mechanism test coverage for robustness in speculative decoding and masking; added a reasoning-phase token enforcement kernel to tighten control over generated outputs; hardened token_penalty kernel with XPU compatibility and comprehensive unit tests. These changes directly improve runtime efficiency, output quality, and production reliability, enabling safer and faster deployments.

December 2025 for PaddlePaddle/FastDeploy: Key advances in speculative decoding stability, diversified inference seeds, and CUDA-graph-based multi-step inference. Fixed critical bugs in attention handling and qknorm cache, added seeds and padding sampling improvements with updated unit tests, and hardened multi-step training/prediction in splitwise-prefill scenarios. These changes improved decoding stability, inference throughput, and GPU utilization, enhancing production readiness and RL-related workloads. Demonstrated skills include CUDA graphs, speculative decoding optimizations, seeds-based inference, and rigorous unit testing.

Monthly summary for 2025-11 focused on PaddlePaddle/FastDeploy. Delivered substantial MTP (Multi-Task Processing) enhancements with decoding optimizations and memory efficiency improvements across the month. Implemented MTP support in splitwise and scheduler_v1 modes, including speculative decoding improvements, multi-stop sequences, improved attention mask handling, and quantization work, partnered with tooling to improve memory and performance. Strengthened CI/tests and tooling, and fixed critical correctness issues, enabling higher throughput and more robust production deployments.

October 2025 monthly summary for PaddlePaddle/FastDeploy focused on advancing decoding performance and reliability in speculative decoding with Multi-Turn Processing (MTP) integration. Delivered feature enhancements, fixed key bugs, and reinforced testing to support scalable inference workloads and robust verification workflows.

Monthly performance summary for 2025-09 focusing on delivering key features in PaddlePaddle/FastDeploy, with an emphasis on speculative decoding, MTP integration, and RoPE enhancements. The month delivered production-ready improvements enabling better draft token coverage, scalable resharding, and advanced attention through rope_3d support. These workstreams jointly improve throughput, decoding quality, and model scale in production environments.

Month: 2025-08 — Delivered critical MTPSampler bug fix, enhanced speculative decoding, and updated documentation for broader model support. Key achievements include a correct input args fix for MTPSampler._sample in MTP, improvements to multi-draft-token strategy, introduction of hybrid MTP with n-gram, tree-attention support in speculative decoding, and updated MTP compatibility tables. Impact: more reliable sampling, faster decoding, and wider model coverage across FastDeploy deployments. Demonstrated skills in Python, kernel-level attention modifications, performance optimization, and cross-team collaboration.

July 2025 - PaddlePaddle/FastDeploy: Accelerated MTP-based inference, refined parallelism, and streamlined build/docs to improve deployment speed, throughput, and reliability. Delivered feature-rich MTP updates along with targeted bug fixes to ensure correctness in production.

Monthly work summary for 2025-03 (PaddlePaddle/PaddleNLP). Focused on delivering business value through performance optimization, reliability improvements, and deployment guidance. Key outcomes include: 1) MTP/MLA performance optimization to boost throughput and reduce latency; 2) Speculative decoding improvements with comprehensive deployment guidance and documentation; 3) Serving allocation bug fix to ensure correct block allocation during inference. Overall impact: faster, more reliable model serving with clearer deployment paths. Technologies demonstrated: GPU kernel tuning, precision optimization, serving architecture, and documentation practices.

February 2025 PaddleNLP monthly summary focusing on business value and technical achievements for the PaddleNLP repo. Key features delivered include MTP inference and serving for Deepseek-v3, with refactored kernels and preprocessing to enable efficient speculative decoding and production-grade serving. Major bugs fixed include improvements to dynamic forward pass and multi-device behavior for Llama-Eagle, enhancing stability across multi-GPU deployments. Overall impact includes higher inference throughput, lower latency in multi-GPU setups, and stronger readiness for production workloads. Technologies demonstrated span inference optimization, kernel refactors, model preprocessing, serving integration, and tensor-parallel configuration tuning.

Concise monthly summary for PaddleNLP (2025-01): - Delivered Eagle inference method support for Llama models with speculative decoding, expanding high-performance options for advanced text generation. - Implemented new CUDA kernels for preprocessing, postprocessing, and hidden state updates to enable faster, more efficient inference pipelines. - Established Python integration to support Eagle proposer, enabling easier adoption and end-to-end workflow within PaddleNLP. - Verified integration with the repository and committed work under a focused update to ensure maintainability and traceability. Business value: unlocks higher throughput and lower latency for Llama-based generation tasks, enabling customers to scale inference workloads and reduce compute costs per token. Also lays groundwork for broader model support and future inference optimizations. Notes: This month includes a single feature delivery with the commit bb103a32da2e98579a13e0bd2eb4272543e47665 ([Inference] Support eagle for llama (#9812)).