mps-conv3d 0.1.4

3D Convolution for Apple Silicon (MPS)

MPS Conv3D

3D Convolution for Apple Silicon (M1/M2/M3/M4).

Drop-in replacement for torch.nn.functional.conv3d on MPS.

Why?

3D convolutions are essential for video models:

• Synchformer: Audio-visual synchronization
• I3D: Video classification
• SlowFast: Action recognition
• C3D: Video feature extraction
• MMAudio: Audio generation from video

But PyTorch's MPS backend doesn't support 3D convolutions:

NotImplementedError: aten::slow_conv3d_forward is not implemented for MPS

This package provides a native Metal implementation.

Installation

pip install mps-conv3d

Or from source:

git clone https://github.com/mpsops/mps-conv3d
cd mps-conv3d
pip install -e .

Quick Start

Patch All Conv3D Operations (Recommended)

from mps_conv3d import patch_conv3d

# Patch at the start of your script
patch_conv3d()

# Now all conv3d operations use MPS!
import torch
import torch.nn.functional as F

x = torch.randn(1, 3, 16, 112, 112, device='mps')
w = torch.randn(64, 3, 3, 7, 7, device='mps')
out = F.conv3d(x, w, padding=(1, 3, 3))  # Uses MPS!

Direct Usage

import torch
from mps_conv3d import conv3d

x = torch.randn(1, 3, 16, 112, 112, device='mps')
w = torch.randn(64, 3, 3, 7, 7, device='mps')

out = conv3d(x, w, stride=1, padding=(1, 3, 3))

Conv3d Module

from mps_conv3d import Conv3d

conv = Conv3d(
    in_channels=3,
    out_channels=64,
    kernel_size=(3, 7, 7),
    stride=(1, 2, 2),
    padding=(1, 3, 3)
).to('mps')

x = torch.randn(1, 3, 16, 112, 112, device='mps')
out = conv(x)

API Reference

conv3d(input, weight, bias, stride, padding, dilation, groups)

Same signature as torch.nn.functional.conv3d.

Parameter	Type	Description
input	Tensor	Input tensor (N, C_in, D, H, W)
weight	Tensor	Weight tensor (C_out, C_in/groups, kD, kH, kW)
bias	Tensor	Optional bias (C_out,)
stride	int/tuple	Stride of convolution
padding	int/tuple	Padding added to input
dilation	int/tuple	Dilation of kernel
groups	int	Number of groups

patch_conv3d()

Monkey-patches torch.nn.functional.conv3d to use MPS implementation for MPS tensors.

unpatch_conv3d()

Restores original torch.nn.functional.conv3d.

Compatibility

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

Features

• Full forward and backward pass (training supported)
• fp32 and fp16 supported
• Groups and dilation supported
• Drop-in compatible with PyTorch API

License

MIT