intel/neural-compressor
Oct 2024 – Oct 2024
1 Month active
Languages Used
Python
Technical Skills
Deep LearningDeep Learning FrameworksMemory OptimizationPyTorchQuantizationSoftware Development
Dudi Lester
PROFILE
Dudi Lester
Worked on the intel/neural-compressor repository to enhance quantization workflows and memory efficiency for deep learning models. Delivered a feature that optimizes PCQ quantization by creating weight scales on demand, reducing memory usage during input quantization and enabling support for larger models. Addressed stability by refactoring ModuleInfo, simplifying its constructor and representation, and resolving a conversion bug. Improved FP8 quantization by updating get_scale_dtype to handle multi-element tensor scales, supporting more robust quantization scenarios. Utilized Python, PyTorch, and deep learning frameworks, applying memory optimization and software development skills to deliver more reliable and scalable quantization processes within the codebase.
Overall Statistics
Feature vs Bugs
33%Features
Repository Contributions
3Total
Bugs
2
Commits
3
Features
1
Lines of code
27
Activity Months1
Your Network
143 peopleSame Organization
@habana.ai106
Shared Repositories
37
Work History
October 2024
3 Commits • 1 Features
Oct 1, 2024Month: 2024-10 — intel/neural-compressor. Delivered three changes addressing ModuleInfo stability and FP8/PCQ quantization, with a focus on reducing memory footprint and improving reliability. Key features delivered: PCQ quantization memory optimization via on-demand weight scale creation (commit 98fe1bab53ef5033644ff3ae843891431aa71271). Major bugs fixed: ModuleInfo conversion bug fix/refactor (commit 95edb727a5d511dc9d50f4bd5e6c2763aa36bdb0) and FP8 quantization get_scale_dtype fix for multi-element tensor scales (commit fd16d3c6aefdfd1e56cf944ed4c2fd1214295794). Overall impact: stabilized ModuleInfo behavior, robust FP8/PCQ quantization workflow, and reduced in-memory scales during input quantization—enabling handling larger models and faster quantization cycles. Technologies demonstrated: Python refactoring, API stabilization, memory optimization techniques, and quantization workflow engineering.
3 Commits • 1 Features
Oct 1, 2024Month: 2024-10 — intel/neural-compressor. Delivered three changes addressing ModuleInfo stability and FP8/PCQ quantization, with a focus on reducing memory footprint and improving reliability. Key features delivered: PCQ quantization memory optimization via on-demand weight scale creation (commit 98fe1bab53ef5033644ff3ae843891431aa71271). Major bugs fixed: ModuleInfo conversion bug fix/refactor (commit 95edb727a5d511dc9d50f4bd5e6c2763aa36bdb0) and FP8 quantization get_scale_dtype fix for multi-element tensor scales (commit fd16d3c6aefdfd1e56cf944ed4c2fd1214295794). Overall impact: stabilized ModuleInfo behavior, robust FP8/PCQ quantization workflow, and reduced in-memory scales during input quantization—enabling handling larger models and faster quantization cycles. Technologies demonstrated: Python refactoring, API stabilization, memory optimization techniques, and quantization workflow engineering.
October 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability93.4%
Architecture80.0%
Performance73.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Deep LearningDeep Learning FrameworksMemory OptimizationPyTorchQuantizationSoftware Development
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