optim-kit

name	optim-kit
description	Use OptimKit to build optimizers over trainable parameters and Transformers learning-rate schedulers in MVP-Engine recipes.

Optim Kit

Goal

Use OptimKit for standard optimizer and scheduler construction:

• build_optimizer(model, optimizer, lr, weight_decay, **kwargs) collects only parameters with requires_grad=True;
• build_lr_scheduler(optimizer, lr_scheduler, **kwargs) delegates scheduler construction to Transformers get_scheduler.

Required Inputs

• recipe optimizer config;
• model after freeze policy has been applied;
• total training steps and warmup policy;
• any optimizer-specific kwargs.

Workflow

1. Apply freeze policy before calling build_optimizer.
2. Pass the configured torch optimizer name, learning rate, and weight decay.
3. Build the scheduler after total steps are known.
4. Keep recipe-specific scheduler math, such as warmup-step calculation, in the recipe engine.

Validation

Soft Validation

• optimizer is built after freeze policy;
• at least one parameter remains trainable;
• scheduler step count matches the recipe loop config;
• optimizer-specific kwargs are intentional.

Hard Validation

Run recipe structure and smoke tests.

Output

• State optimizer, scheduler, warmup/step inputs, and validation status.

Read On Demand

• mvp_engine/kit/optim/__init__.py for the exact API.