Pruning Scheduler

Pruning scheduler is new feature supported in pruning v2. It can bring more flexibility for pruning the model iteratively. All the built-in iterative pruners (e.g., AGPPruner, SimulatedAnnealingPruner) are based on three abstracted components: pruning scheduler, pruners and task generators. In addition to using the NNI built-in iterative pruners, users can directly use the pruning schedulers to customize their own iterative pruning logic.

Workflow of Pruning Scheduler

In iterative pruning, the final goal will be broken down into different small goals, and complete a small goal in each iteration. For example, each iteration increases a little sparsity ratio, and after several pruning iterations, the continuous pruned model reaches the final overall sparsity; fix the overall sparsity, try different ways to allocate sparsity between layers in each iteration, and find the best allocation way.

We define a small goal as Task, it usually includes states inherited from previous iterations (eg. pruned model and masks) and description of the current goal (eg. a config list that describes how to allocate sparsity). Details about Task can be found in this file.

Pruning scheduler handles two main components, a basic pruner, and a task generator. The logic of generating Task is encapsulated in the task generator. In an iteration (one pruning step), pruning scheduler parses the Task getting from the task generator, and reset the pruner by model, masks, config_list parsing from the Task. Then pruning scheduler generates the new masks by the pruner. During an iteration, the new masked model may also experience speed-up, finetuning, and evaluating. After one iteration is done, the pruning scheduler collects the compact model, new masks and evaluation score, packages them into TaskResult, and passes it to task generator. The iteration process will end until the task generator has no more Task.

How to Customized Iterative Pruning

Using AGP Pruning as an example to explain how to implement an iterative pruning by scheduler in NNI.

from nni.algorithms.compression.v2.pytorch.pruning import L1NormPruner, PruningScheduler
from import AGPTaskGenerator

pruner = L1NormPruner(model=None, config_list=None, mode='dependency_aware', dummy_input=torch.rand(10, 3, 224, 224).to(device))
task_generator = AGPTaskGenerator(total_iteration=10, origin_model=model, origin_config_list=config_list, log_dir='.', keep_intermediate_result=True)
scheduler = PruningScheduler(pruner, task_generator, finetuner=finetuner, speedup=True, dummy_input=dummy_input, evaluator=None, reset_weight=False)

_, model, masks, _, _ = scheduler.get_best_result()

The full script can be found here.

In this example, we use dependency-aware mode L1 Norm Pruner as a basic pruner during each iteration. Note we do not need to pass model and config_list to the pruner, because in each iteration the model and config_list used by the pruner are received from the task generator. Then we can use scheduler as an iterative pruner directly. In fact, this is the implementation of AGPPruner in NNI.

More about Task Generator

The task generator is used to give the model that needs to be pruned in each iteration and the corresponding config_list. For example, AGPTaskGenerator will give the model pruned in the previous iteration and compute the sparsity using in the current iteration. TaskGenerator put all these pruning information into Task and pruning scheduler will get the Task, then run it. The pruning result will return to the TaskGenerator at the end of each iteration and TaskGenerator will judge whether and how to generate the next Task.

The information included in the Task and TaskResult can be found here.

A clearer iterative pruning flow chart can be found here.

If you want to implement your own task generator, please following the TaskGenerator interface. Two main functions should be implemented, init_pending_tasks(self) -> List[Task] and generate_tasks(self, task_result: TaskResult) -> List[Task].

Why Use Pruning Scheduler

One of the benefits of using a scheduler to do iterative pruning is users can use more functions of NNI pruning components, because of simplicity of the interface and the restoration of the paper, NNI not fully exposing all the low-level interfaces to the upper layer. For example, resetting weight value to the original model in each iteration is a key point in lottery ticket pruning algorithm, and this is implemented in LotteryTicketPruner. To reduce the complexity of the interface, we only support this function in LotteryTicketPruner, not other pruners. If users want to reset weight during each iteration in AGP pruning, AGPPruner can not do this, but users can easily set reset_weight=True in PruningScheduler to implement this.

What’s more, for a customized pruner or task generator, using scheduler can easily enhance the algorithm. In addition, users can also customize the scheduling process to implement their own scheduler.