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 NF4/FP4/FP8/INT8 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-0.1.10.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-0.1.10-py3-none-any.whl (3.3 kB view details)

Uploaded Python 3

File details

Details for the file mpsops-0.1.10.tar.gz.

File metadata

  • Download URL: mpsops-0.1.10.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-0.1.10.tar.gz
Algorithm Hash digest
SHA256 5df20ee3beb2ec1eaaba74584d95c5f63b3019907e29f7db9453ff38e76a064a
MD5 f3ddd60aff935e54b41f0959bedd1e3e
BLAKE2b-256 6f0db8cdd0d29ea2b76b6df8e47180525fce81b7fbc4c333c8b098e81c64fe10

See more details on using hashes here.

File details

Details for the file mpsops-0.1.10-py3-none-any.whl.

File metadata

  • Download URL: mpsops-0.1.10-py3-none-any.whl
  • Upload date:
  • Size: 3.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.0

File hashes

Hashes for mpsops-0.1.10-py3-none-any.whl
Algorithm Hash digest
SHA256 dadded66e027958da0c0daa74c407a55203bac5348cdaeaa5fdbcdb32e084bf6
MD5 fb28f8f3a5f44594dd2e8f69eadb68d7
BLAKE2b-256 0f422167607085867a842851185c404fdb04a740f8cee5898f8469681dd43779

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