Skip to main content

faster-transformer

Self-attention Does Not Need $O(n^2)$ MemoryのPytorch実装

@misc{rabe2021selfattention,
    title={Self-attention Does Not Need $O(n^2)$ Memory}, 
    author={Markus N. Rabe and Charles Staats},
    year={2021},
    eprint={2112.05682},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

Metadata

Release files for memory-efficient-transformer-pytorch 0.2.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for memory-efficient-transformer-pytorch 0.2.0
File Size Uploaded Provenance
memory-efficient-transformer-pytorch-0.2.0.tar.gz 7.5 kB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for memory-efficient-transformer-pytorch 0.2.0
File Interpreter ABI Platform
memory_efficient_transformer_pytorch-0.2.0-py3-none-any.whl Python 3 none any Details

Total release size: 17.0 kB

Release files / memory-efficient-transformer-pytorch-0.2.0.tar.gz

Download URL memory-efficient-transformer-pytorch-0.2.0.tar.gz
Size 7.5 kB
Tags Source
SHA-256 checksum
How to use checksums
a3c4923295a5679decd9c3f338b606b7cdeb91a8beb286d4e29e19c2a99690f3
BLAKE2b-256 checksum
How to use checksums
07a6382c4db04af8409dbdc2df4b120c3d43b5f619cc69dccbade1cd4f3dad0e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via poetry/1.1.13 CPython/3.9.10 Darwin/20.6.0

Release files / memory_efficient_transformer_pytorch-0.2.0-py3-none-any.whl

Download URL memory_efficient_transformer_pytorch-0.2.0-py3-none-any.whl
Size 9.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
6569f3b6d73cc59e554ccfdc98cce32a12fa3517ae9122eede30f9adb62231ee
BLAKE2b-256 checksum
How to use checksums
32daa2ce9b4f4793ebe59ad86e867446c65d1d502a7eb17a47e365f58a22b268
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via poetry/1.1.13 CPython/3.9.10 Darwin/20.6.0

Security for memory-efficient-transformer-pytorch 0.2.0

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release

0.2.0 This release

2 release files No provenance

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page