
During March 2026, Zhu Qi developed and integrated support for the split_qkv_rmsnorm_mrope fusion operator in the vllm-project/vllm-ascend repository, targeting Qwen3.5’s attention mechanism on Ascend hardware. This work involved implementing a fused attention path to improve inference speed and reduce latency, with careful alignment to the vLLM mainline and validation across versions 0.16.0 and 0.17.0. Using Python and deep learning frameworks, Zhu focused on hardware-accelerated attention and cross-version compatibility, ensuring stable integration. The feature enhanced throughput and stability for Qwen3.5 on Ascend, demonstrating depth in operator fusion and repository-level performance optimization without reported bugs.
March 2026 monthly summary for vllm-ascend: Key feature delivered: Qwen3.5 attention fusion operator support (split_qkv_rmsnorm_mrope). This enables a fused attention path, improving performance and reducing latency on Ascend devices. The work included implementing the operator, aligning with vLLM mainline, and validating across multiple vLLM versions. No major bugs reported this month; focus was on feature delivery and integration stability. Overall impact: enhanced Qwen3.5 attention capability on Ascend, enabling faster inference and better model throughput for end users. Technologies/skills demonstrated: fusion operator integration, hardware-accelerated attention, cross-version compatibility testing, PR-driven development, and CI validation.
March 2026 monthly summary for vllm-ascend: Key feature delivered: Qwen3.5 attention fusion operator support (split_qkv_rmsnorm_mrope). This enables a fused attention path, improving performance and reducing latency on Ascend devices. The work included implementing the operator, aligning with vLLM mainline, and validating across multiple vLLM versions. No major bugs reported this month; focus was on feature delivery and integration stability. Overall impact: enhanced Qwen3.5 attention capability on Ascend, enabling faster inference and better model throughput for end users. Technologies/skills demonstrated: fusion operator integration, hardware-accelerated attention, cross-version compatibility testing, PR-driven development, and CI validation.

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