January 2026 performance summary focused on hardware-aware scalability and documentation across PaddlePaddle FastDeploy and PaddleCustomDevice for Intel hardware. Key work delivered MoE Expert Parallel (EP) enablement on Intel HPU with tensor-wise FP8 support, including intermediate-scale handling and loader_v1 ESP for tensor_wise FP8 TP/EP, plus activation_scale naming cleanup. Also produced targeted documentation for Tensorwise FP8 quantized model workflow on Intel Gaudi. Implemented Moe tensor alignment optimization for Intel HPU in PaddleCustomDevice, with unit-test updates to validate alignment. No major bugs fixed this month. These efforts improve model throughput, scalability, and maintainability on Intel platforms, and strengthen cross-repo collaboration around FP8 workflows and Moe optimizations.

Month 2025-11: Delivered FP8 tensor kernel enhancements and MoE tensor handling for PaddleCustomDevice. Implemented FP8 precision in tensor-wise kernels for fused block attention and Mixture of Experts (MoE), including FP8 embeddings, KV cache, and output projections; refactored MoE weight/scale handling from lists to tensors for better performance and PaddlePaddle compatibility, with improved quantization stability. This work aligns with Intel HPU optimization goals and lays the groundwork for scalable, quantized MoE deployments on PaddlePaddle. Business impact includes higher throughput for FP8 workloads, reduced memory overhead, and more reliable FP8 quantization across embedding and MoE paths.

September 2025 | PaddleCustomDevice MoE stack enhancements focused on enabling scalable, robust MoE deployments. Delivered vectorized weights and scales as tensors, refactoring the stack to support multiple expert configurations and scaling strategies. Updated kernel signatures and internal parameter handling to accommodate diverse configurations. Added alignment checks for tensor list offsets to improve robustness and error detection. This work lays groundwork for hardware-accelerated workflows and HPUs (as reflected in the related commit for MoE stack fallback).

Concise monthly summary for 2025-08 focusing on PaddlePaddle/PaddleCustomDevice developments. Delivered feature-rich Intel HPU MoE backend enhancements, a new stack kernel, and performance-oriented prefill/resource management optimizations, complemented by test-suite maintenance. These efforts improved inference throughput, memory efficiency, and reliability for Mixture-of-Experts workloads on Intel HPU, contributing to scalable, production-ready deployments. Tech stack involved C++ kernel development, Python unit tests, and environment-driven configurability for tuning performance in operational settings.

2025-07 PaddleCustomDevice monthly summary: Key back-end features were delivered for the Intel HPU backend, including 2D hidden-state representation across fused attention, MLP, and QKV; a transpose flag for QKV weights to support transposed/non-transposed formats; and a use_neox_style switch to toggle between blockwise and pairwise rotary embeddings for Neox-style models. A correctness-oriented fix was implemented to ensure RMS normalization runs before the linear transform in fused block attention by separating RMSNorm from the fused kernels. These changes enhance model fidelity, stability, and flexibility on Intel hardware and broaden support for Neox-style variants. Technologies demonstrated include kernel refactors for fused attention, 2D hidden states, QKV weight handling, RMSNorm sequencing, and rotary embedding strategies.

June 2025 monthly summary for PaddlePaddle/PaddleCustomDevice. Delivered QKV bias and Grouped Query Attention (GQA) support in fused block attention and RMS QKV Rope operations for Intel HPU, including refactoring to conditionally include bias terms and handle various head configurations required by GQA. Fixed a typo in fused_sdpa_proj_t.cc ('k_transpose' to 'v_transpose') and updated tests to align with the reference function and assertions. These work items improved attention flexibility, performance, correctness, and validation coverage on Intel HPU.

May 2025: PaddleCustomDevice delivered Intel HPU fused and optimized block attention for large language models, including refactoring metadata preparation and new fused kernels with RMS MLP/QKV support to boost inference efficiency on Intel hardware. The changes lay groundwork for higher throughput and lower latency for LLM inference on HPU devices.

April 2025 performance and feature focus centered on PaddleCustomDevice for Intel HPU, delivering a cohesive fused attention suite and related operators to boost throughput and data flow for attention-heavy workloads. The effort aligned with broader hardware acceleration goals and laid groundwork for scalable, high-performance inference and training on Intel HPU.

February 2025 (Month: 2025-02) - PaddleCustomDevice: Delivered consolidated Intel HPU backend optimizations for LLM inference with new kernels and performance improvements. Key features include fused RMS normalization and a fused Scaled Dot-Product Attention (SDPA) projection for decoder layers; enhanced Einsum and set_value kernels with a specialized float32 Einsum kernel and expanded broadcasting support; SwiGlu optimization for single-input scenarios with Silu dtype support, plus comprehensive test updates. These changes enhance throughput and model accuracy on Intel HPU-backed LLM workloads and improve maintainability of the HPU backend.

January 2025 summary for PaddleCustomDevice: Focused on Intel HPU backend improvements delivering performance and reliability gains for fused SDPA paths. Key deliverables include feature enhancements to fused SDPA projections and kernel optimizations that reduce latency and improve KV cache handling.

December 2024 performance summary focusing on accelerator-enabled feature delivery and API alignment across PaddlePaddle repos. Key efficiency gains were achieved by fusing critical kernels for Intel HPU in Llama inference and by aligning FSDPA custom kernel APIs with the latest SDPA changes, improving maintainability and throughput.

November 2024 monthly summary for PaddlePaddle/PaddleCustomDevice. Key outcomes: Enabled Intel HPU backend support for SDPA and CCL operations, updated kernels to use a corrected utility header, and added tests for CCL collectives (all-to-all, all-gather, all-reduce). Fixed a file-name typo in the utility header to ensure correct builds. These efforts expand HPU acceleration, improve build stability, and deliver business value by enabling scalable attention and faster inter-process communication for larger models.

October 2024 monthly summary highlighting business value and technical achievements for PaddleNLP. Delivered initial Intel HPU hardware support with Llama integration, enabling inference on Intel HPU devices and expanding hardware reach for PaddleNLP.