.rst .pdf

On This Page

PyTorch Runners

The run function that was described in Porting PyTorch Model to CS exists as a wrapper around the PyTorch runners. The run function’s true purpose is to act as an interface between the user and the PyTorchBaseRunner.

The PyTorchBaseRunner is, as the name suggests, the base runner class. It contains all of the common elements required to train or evaluate any PyTorch model. There are a number of PyTorchBaseRunner subclasses that customize the behavior of the run to fit the needs of the specific run being conducted.

• PyTorchRunner: The PyTorchBaseRunner subclass that facilitates CPU/GPU runs

• PyTorchCSCompiler: The PyTorchBaseRunner subclass that facilitates compiling a PyTorch model for the Cerebras system

• PyTorchCSRunner: The PyTorchBaseRunner subclass that facilitates executing a training/evaluation run of a PyTorch model on a Cerebras system

The run function’s job is to parse the command line arguments, configure the appropriate runner, and start the actual training or evaluation run. However, the bulk of the training/evaluation loop code lives inside the PyTorchBaseRunner.

Creating Custom PyTorch Runners (Advanced)

We highly recommend that the run function be used as it is provided. However, if it is simply insufficient, there is a way to customize the existing PyTorch runners to add custom behaviors such as custom logging and performance profiling.

At the end of the day, all custom runners should subclass either the PyTorchBaseRunner or one of its subclasses. We recommend doing the latter, as they are already custom tailored to facilitate their use case.

There are a number of hooks that are provided that can be overridden in the respective subclass to customize the runs:

on_train_start	A method that is run at the very beginning of training, right before the training loop is entered.
on_train_end	A method that is run at the very end of training, right after the training loop is finished, (but only if the training loop finished successfully and without exceptions).
on_eval_start	A method that is run at the very beginning of evaluation, right before the evaluation loop is entered.
on_eval_end	A method that is run at the very end of evaluation, right after the evaluation loop is finished, (but only if the evaluation loop finished successfully and without exceptions).
on_train_epoch_start	A method that is run at the very beginning of each training epoch, right before the training epoch is started.
on_train_epoch_end	A method that is run at the very end of each training epoch, right after the epoch ends, (but only if the epoch finished successfully and without exceptions).
on_eval_epoch_start	A method that is run at the very beginning of each evaluation epoch, right before the evaluation epoch is started.
on_eval_epoch_end	A method that is run at the very end of each evaluation epoch, right after the epoch ends, (but only if the epoch finished successfully and without exceptions).
on_train_batch_start	A method that is run at the very beginning of each training step, right before the forward/backwards pass and optimizer step is performed.
on_train_batch_end	A method that is run at the very end of each training step, right after the optimizer step is performed.
on_train_batch_start	A method that is run at the very beginning of each evaluation step, right before the forward pass.
on_train_batch_end	A method that is run at the very end of each evaluation step, right after the forward pass.

Note

Many of these hooks already have overrides defined in the PyTorchBaseRunner, PyTorchRunner, PyTorchCSCompiler, and PyTorchCSRunner. We highly recommended to call the super class’s implementation of the hook to prevent any unexpected failures.

Warning

We do not recommended overriding any other method from PyTorchBaseRunner besides these hooks. There is a good chance that not overriding them correctly could lead to failures in either the compile or execution.

Using Custom Runner

To use a custom PyTorch runner, you must modify the PyTorchBaseRunner’s create static method in cerebras_reference_implementations/common/pytorch/pytorch_base_runner.py to make use of your custom runner instead of the predefined ones.

Note

It is important that only the runner initialization is modified. There are other components in the create function that are integral to configuring and ensuring the run on the Cerebras system works.

Future Work

Note

Note that the plan is to eventually deprecate the PyTorch runners in favor of using PyTorch Lightning’s LightningModule and Trainer interfaces.

This is one of the reasons why we do not recommend creating any custom PyTorch runners unless absolutely necessary.

previous

Porting PyTorch Model to CS

next

PyTorch Variable Tensor Shape