mpsify 0.1.0

Run CUDA-hardcoded PyTorch repos on Apple Silicon (MPS) with zero source edits.

mpsify

Run CUDA-hardcoded PyTorch repos on Apple Silicon (MPS) — with zero source edits.

You inherit someone's training script. It's full of .cuda(), device='cuda', torch.cuda.amp, map_location='cuda'. PyTorch-MPS could run the actual math fine on your Mac — but the code dies before it gets the chance. mpsify patches torch at import time so all of that transparently retargets to MPS.

pip install mpsify

Usage

Point it at any script — no edits to their code:

python -m mpsify train.py --epochs 10 --lr 1e-3
# or, after install, the console script:
mpsify train.py --epochs 10 --lr 1e-3

Or drop one line at the top of your entry file:

import mpsify  # patches torch on import

The wrapper (python -m mpsify) is preferred: it sets PYTORCH_ENABLE_MPS_FALLBACK=1 before torch loads, which the import form can't always guarantee.

What it does

CUDA thing	Retargeted to
torch.cuda.is_available()	True
.cuda(), .to('cuda'), device='cuda'	mps
torch.device('cuda')	mps
torch.load(map_location='cuda')	mps
DataLoader(pin_memory=True)	pin_memory=False (no pinned memory on MPS)
torch.cuda.amp autocast / GradScaler	no-op, fp32 (see knobs)
nn.DataParallel	identity (single device)
nccl backend	gloo

Ops with no Metal kernel fall back to CPU automatically. mpsify catches those fallbacks, warns once per op live, and prints a summary at exit — so you can see exactly which ops are your latency hot spots.

Libraries with no Metal backend at all (bitsandbytes, apex, deepspeed, flash_attn, triton) are detected and reported loudly instead of crashing cryptically.

Diagnosing slow ops

python -m mpsify --profile train.py

Runs a dispatch-level profiler that counts calls and times ops. This adds per-op overhead — use it for a diagnostic pass, not production.

Knobs

Env var	Effect
MPSIFY_AMP=1	Re-enable AMP/autocast (default off = fp32, correct but slower). AMP on MPS is where correctness gets dicey.
MPSIFY_QUIET=1	Suppress live fallback warnings; keep the exit summary.

Scope

Handles pure-PyTorch repos (torchvision / timm models — ResNet, EfficientNet, ViT, etc.). It does not translate custom .cu/Triton kernels or make CUDA-only libraries (flash-attention, DeepSpeed, apex) actually work — those are detected and reported, not fixed.

License

MIT