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name: core-api-and-autograd description: "Use Jittor's core Var, Module, Function, gradient, execution, and serialization APIs for tensor and autograd tasks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Core API and autograd

Use this sub-skill when a task is about Jittor tensor creation, Var arithmetic, broadcasting, gradients, execution order, Module or Function behavior, synchronization, or basic serialization.

Do not use it for layer/optimizer training recipes, dataset/model zoo workflows, backend installation and debugging, or custom C++ operator authoring. Route those to the sibling sub-skills that own them.

Read or run map

• Read core API reference for the verified public signatures, construction helpers, Module/Function surface, and common shape or dtype expectations.
• Read autograd and execution for lazy execution, data/sync behavior, gradient updates, and safe no-grad scopes.
• Read troubleshooting when you see shape mismatches, dtype surprises, retained graphs, serialization problems, or confusing lazy-execution errors.
• Run core_api_smoke.py for a tiny deterministic CPU smoke that exercises Var, Module, gradients, and state transfer.

What this sub-skill owns

• jt.array, jt.float32, jt.zeros, jt.ones, jt.rand, jt.random, and similar core constructors.
• Var operations, broadcasting, data, numpy, sync, name, and simple reductions.
• jt.grad, jt.no_grad, jt.enable_grad, jt.flag_scope, lazy execution choices, and jt.sync_all.
• jt.Module, jt.Function, parameter/state layout, and checkpoint save/load basics.
• Minimal core debugging for graph liveness, accidental graph retention, and shape/dtype mistakes.

Quick start

1. Create or import a tiny Var and confirm the result synchronizes correctly.
2. If you need gradients, compute a scalar loss and call jt.grad on the target vars.
3. Use Module.execute, not forward.
4. Save and load only public state; keep private caches and generated values out of the model state.
5. If behavior looks wrong, read the autograd/execution reference before changing the model logic.

Baseline check

A normal CPU baseline for this sub-skill is the bundled smoke script:

python scripts/core_api_smoke.py --help
python scripts/core_api_smoke.py

The script should stay tiny and deterministic; it is meant to confirm the public API shape, not to benchmark or train a model.