
Developed and integrated support for the split_qkv_rmsnorm_mrope fusion operator in Qwen3.5 within the vllm-project/vllm-ascend repository, focusing on enhancing the model’s attention mechanism for Ascend hardware. The work involved implementing a fused attention path to improve inference speed and reduce latency, aligning the new operator with the vLLM mainline, and validating compatibility across multiple vLLM versions. Emphasized stability and cross-version integration through continuous integration validation and pull request-driven development. Demonstrated expertise in deep learning, machine learning, and Python, delivering a targeted feature that improved attention throughput and end-to-end performance for users of Qwen3.5 on Ascend.
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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