---

name: methods description: "Use Composer speedup methods through algorithm classes, functional helpers, recipes, batch-key routing, and in-place model/optimizer mutations." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Composer Methods Router

Use this sub-skill when the task is to add, compose, debug, or translate MosaicML Composer speedup methods from composer.algorithms and composer.functional.

Route first

• For Trainer construction, dataloaders, ComposerModel, max duration, checkpointing, and optimizer basics, route to ../training/SKILL.md.
• For loggers, profiling, speed monitors, metric visualization, and throughput diagnosis, route to ../observability/SKILL.md.
• For distributed launch, DDP/FSDP setup, multi-node concerns, and backend placement, route to ../distributed/SKILL.md.
• For TorchScript/ONNX/export/inference packaging after method application, route to ../inference-export/SKILL.md.

Main decision: algorithm class or functional API

Prefer composer.algorithms classes in Trainer(algorithms=[...]) when the user is already using Composer Trainer or wants a recipe of multiple methods. The Trainer event engine places each algorithm at the relevant event, passes state.optimizers to model-surgery algorithms, handles paired before/after events, and lets batch algorithms use input_key / target_key without changing the dataloader.

Use composer.functional when the user owns a custom PyTorch loop, wants to test one method in isolation, or needs to mutate a model before creating the optimizer. With the functional API, the caller owns exact stage placement, target/loss interpolation, optimizer rebinding after surgery, and any schedule logic.

from composer import Trainer
from composer.algorithms import BlurPool, ChannelsLast, LabelSmoothing, MixUp

algorithms = [
    ChannelsLast(),
    BlurPool(replace_convs=True, replace_maxpools=True, blur_first=True, min_channels=16),
    MixUp(alpha=0.2, interpolate_loss=False, input_key="image", target_key="label"),
    LabelSmoothing(smoothing=0.1, target_key="label"),
]

trainer = Trainer(
    model=model,
    train_dataloader=train_dataloader,
    eval_dataloader=eval_dataloader,
    optimizers=optimizer,
    max_duration="10ep",
    algorithms=algorithms,
)

Placement rules

1. Image transforms such as RandAugment or AugMix belong in dataset transforms before batching.
2. Batch methods such as MixUp, CutMix, ColOut, CutOut, and LabelSmoothing run after a batch is available and before the forward/loss stage that consumes the modified tensors.
3. Model surgery such as BlurPool, SqueezeExcite, Factorize, StochasticDepth, Alibi, low-precision norm replacements, and ChannelsLast mutates the model in place. Apply it before optimizer construction, or pass existing optimizers to functional helpers that accept optimizers=. Trainer algorithms do this through state.optimizers.
4. Loop and optimizer methods such as LayerFreezing, ProgressiveResizing, SelectiveBackprop, SeqLengthWarmup, EMA, SWA, SAM, and GradientClipping depend on event timing or optimizer state. Use algorithm classes unless the user is explicitly maintaining those stages in a custom loop.

Batch key routing

Batch algorithms default to tuple batches: input_key=0, target_key=1. For dict batches, pass string keys instead of rewriting the dataloader:

from composer.algorithms import CutMix, MixUp, LabelSmoothing

algorithms = [
    MixUp(alpha=0.2, input_key="image", target_key="label"),
    CutMix(alpha=1.0, uniform_sampling=False, input_key="image", target_key="label"),
    LabelSmoothing(smoothing=0.1, target_key="label"),
]

For nested or custom batch objects, pass (getter, setter) callables. The getter receives the batch and returns the tensor; the setter receives (batch, value) and returns the updated batch.

References and bundled smoke

• references/functional-api.md: function families, stage contracts, batch-key semantics, and custom-loop recipes.
• references/algorithm-catalog.md: exported Composer method list grouped by taxonomy.
• references/troubleshooting.md: method-specific failure modes and fixes.
• scripts/functional_smoke.py: no-download random tensor smoke for a batch method plus functional model surgery.

Run the smoke from this sub-skill directory with:

python scripts/functional_smoke.py