depyf: PyTorch Compiler Decompiler

depyf is a tool designed to demystify the inner workings of the PyTorch compiler, introduced in PyTorch 2.x. The PyTorch compiler accelerates deep learning programs by operating at the Python bytecode level, which can be opaque to researchers. depyf decompiles the bytecode generated by PyTorch back into equivalent source code, establishing connections between code objects in memory and their counterparts in source code format on disk. This feature allows users to step through the source code line by line using debuggers, enhancing their understanding of the underlying processes. depyf is non-intrusive and user-friendly, relying on two convenient context managers for its core functionality. The project is openly available on GitHub and is recognized as a PyTorch ecosystem project.

Category: Artificial Intelligence
Subcategory: Deep Learning
Tags: PyTorchcompilerdecompilerdeep learning
AI Type: Deep Learning
Programming Languages: Python
Frameworks/Libraries: PyTorch
Application Areas: Machine learning researchdeep learning development
Manufacturer Company: THUML
Country: China
Algorithms Used

Bytecode decompilation

Model Architecture

N/A

Datasets Used

N/A

Performance Metrics

N/A

Deployment Options

Open-source tool

Cloud Based

No

On Premises

Yes

Features

Decompiler for PyTorch compiler, source code stepping

Enterprise

No

Hardware Requirements

Standard computational resources

Supported Platforms

Linux, Windows, macOS

Interoperability

Compatible with PyTorch

Security Features

None

Compliance Standards

None

Certifications

None

Open Source

Yes

Source Code URL

https://github.com/thuml/depyf

Documentation URL

https://pytorch.org/blog/introducing-depyf

Community Support

Active open-source community

Contributors

THUML research group

Training Data Size

N/A

Inference Latency

N/A

Energy Efficiency

N/A

Explainability Features

Source code stepping

Ethical Considerations

None

Known Limitations

Limited to PyTorch compiler

Industry Verticals

Machine learning research

Use Cases

Understanding PyTorch compiler internals, debugging

Customer Base

Machine learning researchers, developers

Integration Options

Integrates with PyTorch

Scalability

N/A

Support Options

Community support

SLA

None

User Interface

Command-line interface

Multi-Language Support

No

Localization

English

Pricing Model

Open-source

Trial Availability

Yes

Partner Ecosystem

PyTorch ecosystem

Patent Information

None

Regulatory Compliance

None

Version

1.0

Website URL

https://pytorch.org/blog/introducing-depyf

Service Type

Open-source tool

Has API

No

Business Model

Open-source

Price

0.00

Currency

N/A

License Type

Open-source license

Release Date

01/01/2023

Last Update Date

01/10/2023

Contact Email

contact@thuml.edu.cn

Contact Phone

+86-10-6278-1234

Social Media Links

http://None

Other Features

Enhances understanding of PyTorch compiler processes

Published

Yes