Skip to main content

CUDA-only PyTorch operations for Apple Silicon - metapackage

Project description

MPS Ops

CUDA-only PyTorch operations for Apple Silicon.

This metapackage installs all mpsops packages - Metal implementations of operations that are typically CUDA-only.

Installation

Install everything:

pip install mpsops

Or install individual packages:

pip install mps-flash-attn    # Flash attention
pip install mps-bitsandbytes  # Quantized ops
pip install mps-deform-conv   # Deformable convolution
pip install mps-correlation   # Optical flow correlation
pip install mps-carafe        # Content-aware upsampling
pip install mps-conv3d        # 3D convolution for video

Packages

Package Description Use Case
mps-flash-attn Flash Attention Transformers, LLMs
mps-bitsandbytes 8-bit/4-bit quantization LLM inference, QLoRA
mps-deform-conv Deformable convolution Object detection (DETR, DCN)
mps-correlation Correlation layer Optical flow (RAFT, PWC-Net)
mps-carafe CARAFE upsampling Segmentation (Mask R-CNN)
mps-conv3d 3D Convolution Video models (I3D, SlowFast, MMAudio)

Quick Start

import mpsops

# Check what's installed
mpsops.print_status()

# Use the ops directly
from mpsops import flash_attn_func, deform_conv2d, correlation, carafe, conv3d, patch_conv3d

Compatibility

  • PyTorch: 2.0+
  • macOS: 12.0+ (Monterey)
  • Hardware: Apple Silicon (M1/M2/M3/M4)

Why?

Many state-of-the-art models use CUDA-only operations:

  • LLMs need flash attention and quantization
  • Object detection needs deformable convolution
  • Optical flow needs correlation layers
  • Segmentation needs specialized upsampling

On Mac, you get errors like:

NotImplementedError: flash_attn not implemented for MPS

MPS Ops provides native Metal implementations so these models run on Apple Silicon.

License

MIT

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mpsops-0.1.8.tar.gz (5.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

mpsops-0.1.8-py3-none-any.whl (3.2 kB view details)

Uploaded Python 3

File details

Details for the file mpsops-0.1.8.tar.gz.

File metadata

  • Download URL: mpsops-0.1.8.tar.gz
  • Upload date:
  • Size: 5.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.0

File hashes

Hashes for mpsops-0.1.8.tar.gz
Algorithm Hash digest
SHA256 d6dd5f2b94c684319a8f7d8d9651e4c31e503584340ee2456425193cf15d65f0
MD5 0af6342dc6245ba230d9b3900a558bbb
BLAKE2b-256 d48b0834a8ace9f5109885190a894183f0085c40e0ed93aa7e7cda37864d03e4

See more details on using hashes here.

File details

Details for the file mpsops-0.1.8-py3-none-any.whl.

File metadata

  • Download URL: mpsops-0.1.8-py3-none-any.whl
  • Upload date:
  • Size: 3.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.0

File hashes

Hashes for mpsops-0.1.8-py3-none-any.whl
Algorithm Hash digest
SHA256 35ccd852c78c293b4c6de2599cf44249a2dab40e078408fb76db62ea22434215
MD5 05bbd39909224606dc20bc68b2ff3f49
BLAKE2b-256 884e26247d5951f1e8712788112f19a897be5280d22edba12108f80f7aea30ca

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page