tt-check 0.1.2

Small TTNN readiness check for Tenstorrent systems

tt-check

tt-check is a small command-line readiness test for a Tenstorrent system with tt-smi and the public ttnn Python package installed.

It performs the following checks:

1. Resets devices with tt-smi -r.
2. Collects system information from tt-smi -s --snapshot_no_tty.
3. Opens a TTNN device.
4. Runs a tensor-parallel three-weight gated MLP 100 times in both prefill and decode shapes:
   • BF16 activations
   • BFP8 weights
   • 1024 activation width per device shard
   • prefill rows: 1024
   • decode rows: 1
5. On multi-device systems, replicates activations, column-shards w1/w3, row-shards w2, and all-reduces the output activations across the mesh.
6. Warms each MLP shape once, captures one dynamic TTNN trace per shape, then executes each trace for the requested run count.
7. Compares each trace replay against a PyTorch reference with PCC >= 0.99 and requires TTNN output tensors to be identical across all replays.

Install

Run from PyPI with uv:

uvx --python 3.10 tt-check

Or install from a checkout:

uv tool install --python 3.10 .

Or run directly from a checkout:

uv run tt-check

uv uses the PyTorch CPU wheel index for this project; the check only needs Torch for reference math. The project pins a Python version compatible with the current public ttnn wheels.

Pip also works:

python3 -m pip install .

Run

tt-check

The command exits 0 if all checks pass. It exits 1 and writes the failure to stderr otherwise.

Example output:

tt-check: resetting device... ok
tt-check: 1x2 mesh (1x p300a | blackhole)
tt-check: prefill mlp: 100%|██████████| 100/100 [00:00<00:00, 206.20run/s]
tt-check: decode mlp: 100%|██████████| 100/100 [00:00<00:00, 2660.53run/s]
tt-check: passed in 44.9 seconds | prefill pcc 0.99981364 | decode pcc 0.99986366

Useful options:

tt-check --device-id 0 --runs 100 --pcc-threshold 0.99
tt-check --prefill-rows 1024 --decode-rows 1 --activation-width-per-device 1024