January 2026 performance focused on Intel HPU backend enhancements across PaddleCustomDevice and FastDeploy. Delivered chunked prefill for encoder/decoder sequences, enabling segmental processing and mixed scheduling; added a fused RMSNorm kernel ensuring Paddle 3.2.2 compatibility; and improved resource management and execution to support chunked prefill. These changes increase throughput, stability, and scalability for production workloads on Intel HPU and lay groundwork for future sequence-length handling improvements.

December 2025 performance-focused sprint for PaddlePaddle/FastDeploy on Intel HPU. Delivered targeted improvements in benchmarking, quantization, caching, and stability to accelerate large-model inference and support robust mixed-precision workflows. Key outcomes include benchmark tooling, FP8 tensor-wise quantization with tests, KV cache scheduling v1, and fixes addressing memory fragmentation, MOE all_reduce, and MLP metadata handling, translating to higher throughput, lower latency, and more reliable HPU-backed inference.

2025-11 monthly summary focusing on Intel HPU integration across PaddleCustomDevice and FastDeploy. Delivered critical bug fixes, performance enhancements, and increased configurability to improve inference correctness, throughput, and robustness for Llama and larger models.

Month: 2025-10 — PaddleCustomDevice delivered a performance-focused enhancement for Llama inference on the Intel HPU backend by adding prefix caching. This work targets long-context attention bottlenecks, enabling faster responses and better hardware utilization for customers deploying Llama with Intel HPU. The feature introduces conditional inclusion of attention masks based on causality in fused_sdpa_proj_t.cc and adds a dedicated prefix caching workflow with sequence-length calculations and padding strategies in prepare_block_metadata.cc. The change is tracked under commits for #2086, including 7f594d0f99b69cac15f8b516d273aaa901f51641. Overall, this delivers tangible business value by reducing latency and increasing throughput in production inference pipelines.

September 2025 monthly summary for PaddlePaddle/FastDeploy: Delivered Intel Gaudi/HPC (HPU) hardware acceleration support across the FastDeploy stack, enabling model execution on Gaudi devices with improved performance. Implemented end-to-end integration across documentation, build scripts, custom operations, and inference logic. Achieved significant code quality and CI stability improvements, including pre-commit enforcement, formatting fixes, and import corrections. Completed naming and documentation cleanup (HPU references renamed to Gaudi; ForwardMeta_HPU renamed to HPUForwardMeta) to improve maintainability and onboarding.

August 2025: Delivered a robust recovery bug fix for the Intel HPU Step Paddle Function in PaddleCustomDevice, addressing edge cases and improving reliability. The changes removed an unused environment variable, updated total batch calculation to use encoder count directly, and tightened block-management logic with improved tie-breaking for maximum bid when used block numbers are equal. The work is tracked under commit 9cf922aab337af510db2c38780f800eb2265748c (#1901). Impact: higher stability for HPU-based training/inference, reduced risk of block-related failures, and clearer, traceable code changes.

July 2025 monthly summary: Focused on stabilizing the Intel HPU backend integration in PaddleCustomDevice. Implemented a bug fix to correct stop flag interpretation in post-processing by converting boolean stop flags to integer 0/1, addressing incorrect post-processing behavior. This change enhances reliability of stop conditions and reduces risk of erroneous termination in production workflows.

May 2025 monthly summary for PaddleCustomDevice: Key feature delivered is the HPU-Accelerated recover_block Operator, refactored into an Intel HPU-specific custom operator to optimize step generation by improving tensor slicing/insertions and data handling on HPU hardware. This delivers a user-facing performance improvement for HPU deployments. No major bugs fixed were documented this month in PaddleCustomDevice. Technologies demonstrated include Intel HPU integration, custom operator design, and performance-focused refactoring with clean separation of hardware-specific logic, enabling easier maintenance and future optimizations. Overall business value includes faster step generation throughput on HPU hardware, contributing to better end-user performance and deployment efficiency.

Concise monthly summary for 2025-04 focusing on business value and technical achievements delivered in PaddleCustomDevice for the Intel HPU backend.

March 2025 monthly summary for PaddlePaddle/PaddleCustomDevice focused on Intel HPU backend enhancements and reliability improvements. Delivered a new One-Hot operation kernel for Intel HPU with support for int32/int64 inputs, including kernel implementation, type registrations, and unit tests. Fixed reliability of reduce_prod and reduce_mean by refactoring ProdKernel to include a reduce_all parameter and updating tests, removing outdated skips and redundant test classes to improve stability. These efforts reduce integration risk and lay groundwork for broader HPU support and performance improvements.

January 2025 — PaddleCustomDevice (PaddlePaddle/PaddleCustomDevice): Implemented an end-to-end benchmarking script for Intel HPU with PaddlePaddle. The script automates testing across models and configurations, manages dependencies, pulls code, runs benchmark tests, and logs performance metrics to a CSV for reproducible analysis. Commit: 1d750cb0d3ebef1106fdcab20c523fd7cfd4d36f ([INTEL_HPU] add intel hpu e2e benchmark script (#1542)). No major bugs fixed this month. Impact: accelerates performance evaluation for Intel HPU integration, enabling data-driven optimization and faster hardware-specific decisions. Technologies demonstrated: PaddlePaddle, Intel HPU, automation scripting, CSV logging, parameterized benchmarking, dependency handling, and reproducible results.