torch-preflight

Docs | Rules | VRAM estimation | CLI

A static analyzer for PyTorch that understands autograd — it catches VRAM leaks and silent convergence bugs at commit time, and tells you whether your training run will OOM before you launch it.

$ torch-preflight check train.py

train.py
  7:19  error   TG001 (CRITICAL_OOM)
  `losses.append(...)` stores a tensor that is still attached to the autograd graph;
  every iteration's graph is retained in VRAM.
    7 │     losses.append(loss)
      │                   ^^^^
  help: Use `.item()` to keep just the scalar value, or `.detach()` to keep the tensor
        without its graph.
  fix:  add .detach() (run with --fix)

Found 1 error in 1 file(s).

One line, one wasted GPU hour. Caught in milliseconds, before it runs.

• 🔍 Six rules for bugs ruff and flake8 cannot see — retained autograd graphs, missing zero_grad(), evaluation without no_grad(), starved dataloaders, doubled softmax
• 🧮 Pre-flight VRAM estimation — projects peak memory from your script and says which change would make it fit
• 🛠️ Autofixes via concrete syntax tree rewrites, so formatting and comments survive untouched
• 📊 Measured, not guessed — every constant calibrated against real hardware, 5.0% mean error versus measured peaks
• 🤫 Quiet on real code — 5 findings across PyTorch's own 2,239 files, all deliberate
• ⚡ No GPU and no PyTorch required — pure static analysis over LibCST; a CI job asserts torch is never imported
• 🐍 Python 3.9–3.13, pyproject.toml config, pre-commit hook, GitHub Action, SARIF output

ruff and flake8 understand Python. They don't understand autograd graphs, gradient accumulation, or what num_workers=0 does to eight GPUs waiting on one CPU. torch-preflight is built for the bugs that only cost money once you're paying for a GPU.

Table of contents

• Getting started
• The line that costs you a GPU hour
• Will this fit on the GPU I'm about to rent?
• Why you can trust the numbers
• Integrations
• Documentation
• What stays free

Getting started

pip install torch-preflight

torch-preflight check ./src/                        # lint a tree
torch-preflight check ./src/ --fix                  # apply the safe fixes
torch-preflight estimate train.py --gpu a100-80gb   # will this run fit?
torch-preflight explain TG003                       # why a rule exists, and what it costs

The base install has no heavy dependencies. torch-preflight[hub] adds Hugging Face architecture lookup; torch-preflight[vram] adds exact meta-device profiling.

The line that costs you a GPU hour

losses = []
for batch, targets in loader:
    optimizer.zero_grad()
    loss = criterion(model(batch), targets)
    loss.backward()
    optimizer.step()
    losses.append(loss)          # ← keeps every step's graph alive in VRAM

You have written this. Everyone has. loss still carries its computational graph, so appending it retains every intermediate activation from that step — and the next, and the next. Memory climbs linearly until CUDA gives up, hours in.

Why this is hard: losses.append(x) is only a bug when x carries a graph. torch-preflight runs a dataflow pass to find out, tracing values across assignments, arithmetic, tensor methods and function scopes, and refusing to propagate through .detach(), .item() or argmax. So losses.append(loss.item()) stays silent, and so does anything inside torch.no_grad(). A linter that pattern-matched on .append( would be unusable.

See all six rules →

Will this fit on the GPU I'm about to rent?

$ torch-preflight estimate finetune.py --gpu a100-80gb

Model      llama-2-7b  (arch-snapshot)   6.74 B params
Config     amp · AdamW · batch 4 · seq 2048

  weights            25.10 GiB      autocast cache     12.55 GiB
  gradients          25.10 GiB      activations        66.44 GiB
  optimizer state    50.21 GiB      fragmentation      18.84 GiB
  ─────────────────────────────────────────────────────────────
  projected peak    198.37 GiB   (178.53 GiB – 218.21 GiB)

Target     NVIDIA A100 80GB (78.0 GiB usable)   →   254% of capacity   ✗ OOM

What would make it fit:
  ✗  − 66.30 GiB  →  132.07 GiB   gradient checkpointing
  ✗  − 41.61 GiB  →  156.76 GiB   8-bit AdamW (bitsandbytes)
  ✗  −112.02 GiB  →   86.35 GiB   all of the above + flash attention
                                  + halve micro-batch — still does not fit

A single 80GB A100 is the wrong tool for a full 7B fine-tune at sequence 2048. Better to learn that now than after the instance is running.

Model, batch size, sequence length, precision and sharding are read out of your script — nothing is imported or executed. 41 architectures ship built in, 23 GPUs and 34 cloud instances are known by name (--gpu p4de.24xlarge works), and anything else is measured exactly on PyTorch's meta device without allocating a byte.

Every other estimator stops at the number. The list of what to change is the part you actually wanted.

See VRAM estimation →

Why you can trust the numbers

Memory estimators are easy to write and easy to be quietly wrong about. So:

294 tests. PyTorch's entire source tree lints in ~50 seconds.

Integrations

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/highwaterlabs/torch-preflight
    rev: v0.1.0
    hooks:
      - id: torch-preflight

# .github/workflows/lint.yml
- uses: highwaterlabs/torch-preflight@v0
  with:
    paths: src/
    format: github      # inline PR annotations

SARIF output feeds GitHub code scanning; JSON feeds everything else. Set target_gpu in pyproject.toml and CI fails on a projected OOM before the job is ever submitted.

See CI integration →

Documentation

Design notes live in design/, including the RFC behind the estimator and the spike the cost model rests on.

What stays free

MIT licensed. These are commitments, not just current state:

• Every rule that has ever shipped free stays free.
• The estimator, the remediation solver and VRAMGuard stay complete — not a demo tier.
• The rule API stays open, so anyone can write and ship their own rules.
• The calibration method and data stay public and reproducible. Numbers are only worth trusting if you can check them.

A hosted service may come later for things that genuinely need a server or a team. Nothing above is part of that.

Contributing

Issues and pull requests are welcome. Adding a rule is one file plus a @register decorator — see development for the walkthrough and the test conventions.

License

MIT — see LICENSE.