jd-opensource/xllm
May 2026 – Jun 2026
2 Months active
Languages Used
C++
Technical Skills
C++Software DevelopmentTestingdeep learningmachine learningDistributed Systems
Joey Gao
PROFILE
Joey Gao
Worked on stabilizing and optimizing large language model inference pipelines across the vllm-project/vllm-ascend and jd-opensource/xllm repositories, focusing on distributed systems and deep learning reliability. Addressed device-mismatch errors in rotary multiplication for Qwen2.5-VL by ensuring tensor placement consistency on NPUs using PyTorch and C++. Improved throughput and decoding stability for Qwen3.5 by resolving causal convolution tiling issues and optimizing request handling logic for padded batches. Enhanced distributed runtime correctness by normalizing tensor view logic and aligning speculative verification with prefill paths, reducing runtime mismatches and increasing reliability for MTP graph inputs in complex machine learning workflows.
Overall Statistics
Feature vs Bugs
0%Features
Repository Contributions
4Total
Bugs
4
Commits
4
Features
0
Lines of code
251
Activity Months3
Your Network
329 peopleShared Repositories
329
Work History
June 2026
1 Commits
Jun 1, 2026June 2026 monthly summary for jd-opensource/xllm focusing on stability fixes and MTP input reliability in Qwen3.5 workflows. Highlights include a key bug fix for MTP graph inputs and improved distributed runtime correctness.
1 Commits
Jun 1, 2026June 2026 monthly summary for jd-opensource/xllm focusing on stability fixes and MTP input reliability in Qwen3.5 workflows. Highlights include a key bug fix for MTP graph inputs and improved distributed runtime correctness.
June 2026
May 2026
2 Commits
May 1, 2026May 2026 monthly summary for jd-opensource/xllm focused on reliability, performance, and throughput gains. Delivered two high-impact bug fixes that stabilize inference and improve request handling, enabling scalable, lower-latency decoding for padded batches.
May 2026
2 Commits
May 1, 2026May 2026 monthly summary for jd-opensource/xllm focused on reliability, performance, and throughput gains. Delivered two high-impact bug fixes that stabilize inference and improve request handling, enabling scalable, lower-latency decoding for padded batches.
July 2025
1 Commits
Jul 1, 2025July 2025 monthly summary: Delivered a critical robustness fix in the vllm-ascend path for the Qwen2.5-VL model without padding, resolving a device-mismatch runtime error during rotary multiplication. Implemented correct device placement by moving cos and sin tensors to the same NPU as q, ensuring stable inference in non-padding scenarios. The fix improves production reliability and aligns with existing padding logic, with minimal surface changes to reduce risk.
1 Commits
Jul 1, 2025July 2025 monthly summary: Delivered a critical robustness fix in the vllm-ascend path for the Qwen2.5-VL model without padding, resolving a device-mismatch runtime error during rotary multiplication. Implemented correct device placement by moving cos and sin tensors to the same NPU as q, ensuring stable inference in non-padding scenarios. The fix improves production reliability and aligns with existing padding logic, with minimal surface changes to reduce risk.
July 2025
Activity
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Quality Metrics
Correctness95.0%
Maintainability80.0%
Architecture80.0%
Performance85.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
BugfixC++Distributed SystemsLLM InferenceLLM IntegrationNPUPyTorchSoftware DevelopmentTestingdeep learningmachine learning
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
vllm-project/vllm-ascend
Jul 2025 – Jul 2025
1 Month active
Languages Used
Python
Technical Skills
BugfixLLM IntegrationNPUPyTorch