g2n — a torch.compile backend that pays attention
Open-core PyTorch compiler: custom FX fusion passes, a Triton LayerNorm
kernel, and license-gated Inductor tuning. Plus g2n.quantum, a statevector
quantum circuit simulator (classical simulation — not quantum hardware).
pip install g2n torch
import torch, g2n
compiled = g2n.compile(model) # == torch.compile(model, backend="g2n")
y = compiled(x)
Free (Community) gives you the g2n fusion passes on stock Inductor and quantum circuits up to 24 qubits. A license key unlocks more — activation is one command and then fully offline:
export G2N_LICENSE_KEY=G2N-XXXX-XXXX-XXXX
g2n activate && g2n status
| Unlock | Tier |
|---|---|
| Persistent compile cache (warmup once per machine, not per run) | Pro |
| Enhanced planner: epilogue fusion + custom Triton kernels | Pro |
Serving platform (pip install g2n-enterprise): registry, HTTP node, quantization incl. weight-only int8, CUDA graphs |
Pro |
| Quantum: unlimited qubits + circuit fusion | Pro |
| max-autotune, dynamic batching, batched quantum sweeps, model zoo | Enterprise |
Quantum in 20 seconds
import g2n.quantum as qf
c = qf.Circuit(2).h(0).cnot(0, 1) # Bell state
c.measure_all(shots=1000) # {'00': ~500, '11': ~500}
c.expectation("ZZ") # tensor(1.)
Guarantees
- Never worse than eager: any compile failure returns your unmodified model with a one-line warning.
- Offline after activation: license tokens are Ed25519-verified locally; no phone-home during runs.
- Honest numbers: no benchmark claim without named hardware and a script to reproduce it — including on your own machine.
Docs, pricing, benchmarks: g2n.dev · full manual in
the repo's docs/.
1 maintainer
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