AMD-AGI/Primus
Jul 2025 – Dec 2025
5 Months active
Languages Used
PythonShellYAMLC++
Technical Skills
CI/CDConfiguration ManagementDistributed SystemsHigh-Performance ComputingModel TrainingPyTorch
llying-001
PROFILE
Llying-001
Liying Li contributed to the AMD-AGI/Primus repository by engineering distributed training features and backend enhancements for large-scale deep learning workflows. Over five months, Liying delivered transformer throughput optimizations, memory-aware layer recomputation, and robust unit and integration test suites, focusing on scalable model training and deployment reliability. The work involved integrating PyTorch and JAX with custom shell scripting and Docker-based CI/CD pipelines, enabling efficient configuration management and observability. By aligning model configurations with ROCm Megatron requirements and expanding support for MaxText and DeepSeek models, Liying improved training efficiency, test coverage, and maintainability, demonstrating depth in distributed systems and backend development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
14Total
Bugs
0
Commits
14
Features
10
Lines of code
8,649
Activity Months5
Your Network
1313 people
Work History
December 2025
5 Commits • 2 Features
Dec 1, 2025Month: 2025-12. This month focused on delivering core backend enhancements for the MaxText service and improving evaluation reliability for Megatron, with deployment readiness and observability improvements that enable faster iteration, safer deployments, and better model monitoring.
5 Commits • 2 Features
Dec 1, 2025Month: 2025-12. This month focused on delivering core backend enhancements for the MaxText service and improving evaluation reliability for Megatron, with deployment readiness and observability improvements that enable faster iteration, safer deployments, and better model monitoring.
December 2025
November 2025
3 Commits • 3 Features
Nov 1, 2025Month 2025-11: Key features and fixes delivered in AMD-AGI/Primus include memory-aware selective layer recomputation, MaxText backend support for large-scale training, and enhanced debugging/config tooling for DeepSeek V2 16B via XLA HLO dump switch and tokenizer path configuration. These deliver improved training efficiency, scalability, and development workflows.
November 2025
3 Commits • 3 Features
Nov 1, 2025Month 2025-11: Key features and fixes delivered in AMD-AGI/Primus include memory-aware selective layer recomputation, MaxText backend support for large-scale training, and enhanced debugging/config tooling for DeepSeek V2 16B via XLA HLO dump switch and tokenizer path configuration. These deliver improved training efficiency, scalability, and development workflows.
September 2025
3 Commits • 2 Features
Sep 1, 2025September 2025 – AMD-AGI/Primus: Delivered features that enhance transformer throughput and test reliability. Implemented Asynchronous Tensor Parallelism (async-tp) compatibility with the TE 2.x API and extended multi-stream GEMM overlap to enable concurrent GEMM and communication, improving Transformer performance. Expanded the Torchtitan Testing Framework with comprehensive unit and integration tests, plus new shell scripts and updated dependencies to boost reliability and robustness. These changes advance TE2 adoption, increase production confidence, and lay groundwork for further throughput optimizations. Technologies/skills demonstrated: TE 2.x API integration, async-tp optimization, multi-stream parallelism, comprehensive testing strategies (unit/integration), shell scripting, and dependency management.
3 Commits • 2 Features
Sep 1, 2025September 2025 – AMD-AGI/Primus: Delivered features that enhance transformer throughput and test reliability. Implemented Asynchronous Tensor Parallelism (async-tp) compatibility with the TE 2.x API and extended multi-stream GEMM overlap to enable concurrent GEMM and communication, improving Transformer performance. Expanded the Torchtitan Testing Framework with comprehensive unit and integration tests, plus new shell scripts and updated dependencies to boost reliability and robustness. These changes advance TE2 adoption, increase production confidence, and lay groundwork for further throughput optimizations. Technologies/skills demonstrated: TE 2.x API integration, async-tp optimization, multi-stream parallelism, comprehensive testing strategies (unit/integration), shell scripting, and dependency management.
September 2025
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 - AMD-AGI/Primus: Delivered a comprehensive unit test suite for Megatron's distributed checkpointing and model functionalities. This work included updating existing tests, introducing new patch files, and creating shell scripts to streamline test execution across multiple configurations. The updates were linked to commit 1434808c301ebcb616d8f1fac743ee50cb927a0d (#164) to support UT script additions. Result: improved test coverage, faster feedback on changes, and reduced risk of regressions in distributed training workflows.
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 - AMD-AGI/Primus: Delivered a comprehensive unit test suite for Megatron's distributed checkpointing and model functionalities. This work included updating existing tests, introducing new patch files, and creating shell scripts to streamline test execution across multiple configurations. The updates were linked to commit 1434808c301ebcb616d8f1fac743ee50cb927a0d (#164) to support UT script additions. Result: improved test coverage, faster feedback on changes, and reduced risk of regressions in distributed training workflows.
July 2025
2 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary: Delivered two core features that strengthen ROCm Megatron compatibility and distributed training capabilities for AMD-AGI/Primus, with clear business value in reduced configuration drift, expanded hardware support, and stronger CI validation. The work enhances training reliability and scalability on ROCm-enabled stacks, enabling faster experimentation and more robust deployment readiness.
2 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary: Delivered two core features that strengthen ROCm Megatron compatibility and distributed training capabilities for AMD-AGI/Primus, with clear business value in reduced configuration drift, expanded hardware support, and stronger CI validation. The work enhances training reliability and scalability on ROCm-enabled stacks, enabling faster experimentation and more robust deployment readiness.
July 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture84.2%
Performance77.2%
AI Usage42.8%
Skills & Technologies
Programming Languages
C++PythonShellYAML
Technical Skills
API DesignAPI IntegrationCI/CDCommunication OverlapConfiguration ManagementContinuous IntegrationData LoggingData ProcessingDeep LearningDeep Learning FrameworksDevOpsDistributed SystemsDockerGPU ProgrammingHigh-Performance Computing
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