Lyken17/pytorch-OpCounter
Pytorch OpCounter
pytorch-OpCounter is a profiling utility for PyTorch neural networks designed to quantify model efficiency by calculating floating point operations and multiply-accumulate counts. It functions as a complexity analyzer to measure the computational cost and theoretical workload of different model architectures.
The tool allows for the definition of custom operation counting rules to support third-party modules not covered by default. It uses forward hooks to intercept module calls and recursive traversal of the module tree to aggregate operations across child sub-modules.
The project provides capabilities for performance auditing and complexity analysis, transforming raw operation counts into human-readable scaled metric formats.
Features
- Model Complexity Analysis - Quantifies the theoretical workload of a model to estimate its efficiency and resource requirements before deployment.
- Model Complexity Calculators - Calculates multiply-accumulate and floating point operations to quantify neural network computational cost.
- Deep Learning Optimization - Evaluates the operational overhead of different model architectures to optimize for speed and hardware constraints.
- Computational Cost Profilers - Measures the computational cost of PyTorch neural networks by calculating floating point operations and multiply-accumulate counts.
- FLOPs Counters - Measures floating point operations to evaluate the efficiency and performance of PyTorch model architectures.
- MACs and FLOPs Analyzers - Calculates the number of MACs and FLOPs to quantify the computational cost of PyTorch neural networks.
- Recursive Module Operations - Walks through the hierarchical structure of a neural network to aggregate total operations across all child sub-modules.
- Model Execution Hooks - Intercepts PyTorch module calls using forward hooks to record and accumulate operation counts during a single pass.
- Operation Count Profilers - Provides a profiling tool for counting multiply-accumulate operations and defining custom counting rules for PyTorch modules.
- Custom Operation Counting Rules - Defining specific operation counting logic for specialized PyTorch modules not supported by default profiling tools.
- Operational State Accumulators - Implements a global state to sum multiply-accumulate and floating point operations across disparate layers of the model.
- Complexity Counting Rules - Provides a mechanism to specify how to count operations for third-party modules not supported by default.
- Computational Complexity Mappings - Maps specific module types or function names to predefined mathematical formulas for calculating computational complexity.
- Developer Tools - FLOPs counting utility.
- More to explore - Tool for counting MACs and FLOPs.
- PyTorch Utilities - Listed in the “PyTorch Utilities” section of the The Incredible Pytorch awesome list.
Star history
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