Port PyTorch model to Cerebras#

Option 1 (Easy): Modify reference models in Cerebras Model Zoo GIT repository#

The Cerebras Model Zoo repository contains reference implementations in PyTorch of popular neural networks such as BERT, GPT-2, T5, and UNet. These implementations have been modularized to separate data preprocessing, model implementation, and additional functions for execution.

If your primary goal is to use one of these models, even with some model or data preprocessing changes, we recommend start from the Cerebras Model Zoo and add the changes you need.

Example 1: Changing the data loader#

For this example, we work with the PyTorch implementation of FC_MNIST in the Cerebras Model Zoo. We create a synthetic dataloader to evaluate performance of the network with respect to different input sizes and number of classes.

In data.py, we create a function called get_random_dataloader that creates random images and labels. We instrument the function to specify in the params.yaml file the number of examples, the batch size the seed, the image_size and the number of classes of this dataset.

import torch
import numpy as np

def get_random_dataloader(input_params,shuffle,num_classes):
    num_examples = input_params.get("num_examples")
    batch_size = input_params.get("batch_size")
    seed = input_params.get("seed",1)
    image_size = input_params.get("image_size",[1,28,28])
    # Note: please cast the tensor to be of dtype `np.int32` when running on CS-2 systems and to `np.int64` when running on cpus/gpus.
    np.random.seed(seed)
    image = np.random.random(size = [num_examples,]+image_size).astype(np.float32)
    label = np.random.randint(low =0, high = num_classes, size = num_examples).astype(np.int32)

    dataset = torch.utils.data.TensorDataset(
        torch.from_numpy(image),
        torch.from_numpy(label)
    )

    return torch.utils.data.DataLoader(
        dataset,
        batch_size=batch_size,
        shuffle=shuffle,
        num_workers=input_params.get("num_workers", 0),
    )

def get_train_dataloader(params):
    return get_random_dataloader(
        params["train_input"],
        params["train_input"].get("shuffle"),
        params["model"].get("num_classes")
    )

def get_eval_dataloader(params):
    return get_random_dataloader(
        params["eval_input"],
        False,
        params["model"].get("num_classes")
    )

In model.py, we change the fix number of classes to a parameter in the params.yaml file.

class MNIST(nn.Module):
    def __init__(self, model_params):
        super().__init__()
        self.loss_fn = nn.NLLLoss()
        self.fc_layers = []
        input_size = model_params.get("input_size",784)
        num_classes = model_params.get("num_classes",10)
        ...
        self.last_layer = nn.Linear(input_size, num_classes)
        ...

In configs/params.yaml, we add the additional fields used in the dataloader and model definition.

train_input:
    batch_size: 128
    drop_last_batch: True
    num_examples: 1000
    seed: 123
    image_size: [1,28,28]
    shuffle: True

eval_input:
    data_dir: "./data/mnist/val"
    batch_size: 128
    num_examples: 1000
    drop_last_batch: True
    seed: 1234
    image_size: [1,28,28]

model:
    name: "fc_mnist"
    mixed_precision: True
    input_size: 784 #1*28*28
    num_classes: 10
    ...

Option 2 (Moderate): Create new models leveraging Cerebras run function available in Cerebras Model Zoo#

All PyTorch implementations in the Cerebras Model Zoo use a common harness to manage execution on CS system and other hardware. This harness implements the necessary code changes to compile a model for a Cerebras cluster, run a compiled model on a Cerebras cluster, or run the model on CPU/GPU. Therefore, it provides a training/evaluation interface in which models and data preprocessing scripts can be plugged into, without worrying about line-by-line modifications to have Cerebras-friendly code.

If your primary goal is to develop new model and data preprocessing scripts, we suggest to start by leveraging the common backbone in Cerebras Model Zoo, the run function.

As an example, let’s port a PyTorch implementation of a fully connected dense neural network for the MNIST dataset.

Prerequisites#

To use the run function, you must have the Cerebras Model Zoo repository compatible with the release installed in the target Cerebras cluster. The run function can be imported as

from modelzoo.common.pytorch.run_utils import run

All the code related with run function lives inside the Cerebras Model Zoo and can be found in the common/pytorch folder.

How to use the run function#

The run function modularizes the model implementation, the data loaders, the hyperparameters and the execution. To use the run function you need:

  1. Params YAML file where optimizers and runtime configuration is specified.

  2. Implementation that includes:

    1. Model definition

    2. Data loaders for training and evaluation

Step 1: Define Model#

To define the model architecture, the run function requires a callable (either class or function) that takes as input a dictionary of params and returns a torch.nn.Module whose forward implementation returns a loss tensor.

As an example, let’s implement FC_MNIST parametrized by the depth and the hidden size of the network. We assume that the input size is 784 and the last output dimension is 10. We use ReLU as non linearity, and a negative log likelihood loss.

In model.py:

import torch
import torch.nn as nn
import torch.nn.functional as F

class MNISTModel(nn.Module):
    def __init__(self, model_params):
        super().__init__()
        self.fc_layers = []
        input_size = 784

        # Depth is len(hidden_sizes)
        model_params["hidden_sizes"] = [
            model_params["hidden_size"]
        ] * model_params["depth"]

        for hidden_size in model_params["hidden_sizes"]:
            fc_layer = nn.Linear(input_size, hidden_size)
            self.fc_layers.append(fc_layer)
            input_size = hidden_size
        self.fc_layers = nn.ModuleList(self.fc_layers)
        self.last_layer = nn.Linear(input_size, 10)

        self.nonlin = nn.ReLU()

        self.dropout = nn.Dropout(model_params["dropout"])

        self.loss_fn = nn.NLLLoss()

    def forward(self, batch):
        inputs, targets = batch

        x = torch.flatten(inputs, 1)
        for fc_layer in self.fc_layers:
            x = fc_layer(x)
            x = self.nonlin(x)
            x = self.dropout(x)

        pred_logits = self.last_layer(x)

        outputs = F.log_softmax(pred_logits, dim=1)

        loss = self.loss_fn(outputs, targets)

        return loss

Note

The input to a torch.nn.Module object defined in the run function includes both the inputs and the labels to compute the loss. It is up to the model to extract the inputs and labels from the batch before using them

Note

The output of the model is expected to be the loss of that forward pass

Step 2: Define dataloaders#

To define the data loaders, the run function requires a callable (either class or function) that takes as input a dictionary of params, and returns a torch.utils.data.DataLoader. When running training, the train_data_fn must be provided. When running evaluation, the eval_data_fn must be provided.

As an example, to implement FCMNIST, we create two different functions for training and evaluation. We use torchvision.datasets functionality to download MNIST dataset. Each of these functions returns a torch.utils.data.DataLoader.

In data.py:

import torch
from torchvision import datasets, transforms

def get_train_dataloader(params):
    input_params = params["train_input"]

    batch_size = input_params.get("batch_size")
    dtype = torch.float16 if input_params["to_float16"] else torch.float32
    shuffle = input_params["shuffle"]

    train_dataset = datasets.MNIST(
        input_params["data_dir"],
        train=True,
        download=True,
        transform=transforms.Compose(
            [
                transforms.ToTensor(),
                transforms.Normalize((0.1307,), (0.3081,)),
                transforms.Lambda(
                    lambda x: torch.as_tensor(x, dtype=dtype)
                ),
            ]
        ),
        target_transform=transforms.Lambda(
            lambda x: torch.as_tensor(x, dtype=torch.int32)
        ),
    )

    train_loader = torch.utils.data.DataLoader(
        train_dataset,
        batch_size=batch_size,
        drop_last=input_params["drop_last_batch"],
        shuffle=shuffle,
        num_workers=input_params.get("num_workers", 0),
    )
    return train_loader

def get_eval_dataloader(params):
    input_params = params["eval_input"]

    batch_size = input_params.get("batch_size")
    dtype = torch.float16 if input_params["to_float16"] else torch.float32

    eval_dataset = datasets.MNIST(
        input_params["data_dir"],
        train=False,
        download=True,
        transform=transforms.Compose(
            [
                transforms.ToTensor(),
                transforms.Normalize((0.1307,), (0.3081,)),
                transforms.Lambda(
                    lambda x: torch.as_tensor(x, dtype=dtype)
                ),
            ]
        ),
        target_transform=transforms.Lambda(
            lambda x: torch.as_tensor(x, dtype=torch.int32)
        ),
    )

    eval_loader = torch.utils.data.DataLoader(
        eval_dataset,
        batch_size=batch_size,
        drop_last=input_params["drop_last_batch"],
        shuffle=False,
        num_workers=input_params.get("num_workers", 0),
    )
    return eval_loader

Step 3: Set up the run function#

The run function must be imported from run_utils.py. Always remember to append the parent directory of the Cerebras Model Zoo repository in your local setup.

All the input parameters of run function are callables that take as input a dictionary, called params. params is a dictionary containing all of the model and data parameters specified by the params YAML file of the model.

Parameter

Type

Notes

model_fn

Callable[[dict], torch.nn.Module]

Required. A callable that takes in a dictionary of parameters. Returns a torch.nn.Module.

train_data_fn

Callable[[dict], torch.utils.data.DataLoader]

Required during training run.

eval_data_fn

Callable[[dict], torch.utils.data.DataLoader]

Required during evaluation run.

default_params_fn

Callable[[dict], Optional[dict]]

Optional. A callable that takes in a dictionary of parameters. Sets default parameters.

For the FCMNIST example, with all of the elements in place, now we import the run function from modelzoo.common.pytorch.run_utils. We append the parent directory of Cerebras Model Zoo.

In run.py:

import os
import sys

#Append path to parent directory of Cerebras ModelZoo Repository
sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
from modelzoo.common.pytorch.run_utils import run

from data import (
    get_train_dataloader,
    get_eval_dataloader,
)
from model import MNISTModel

def main():
    run(MNISTModel, get_train_dataloader, get_eval_dataloader)

if __name__ == '__main__':
    main()

Manage common params for multiple experiments#

To avoid params replication between multiple similar experiments, the run function has an optional input parameter called default_params_fn. This parameter modifies the dictionary of the params YAML file, adding default values of unspecified params.

Setting up a default_params_fn could be beneficial if the user is planning multiple experiments in which only a small subset of the params YAML file changes. The default_params_fn sets up the values shared in all of the experiments. The user can create different configuration YAML files to only address the changes between experiments.

The default_params_fn should be a callable that takes in the params dictionary and returns a new dictionary. If the default_params_fn is omitted, the params dictionary will be used as is.

Step 4: Create params YAML file#

At runtime, the run function requires a separate params YAML. This file is specified during execution with the flag --params in the command line.

For example, this is the params.yaml file for the FCMNIST implementation. We customize the fields in train_input, eval_input, model, to be used inside get_train_dataloader, get_eval_dataloader, MNISTModel. We also specify the required optimizer and runconfig params.

train_input:
    data_dir: "./data/mnist/train"
    batch_size: 128
    drop_last_batch: True
    shuffle: True
    to_float16: True

eval_input:
    data_dir: "./data/mnist/val"
    batch_size: 128
    drop_last_batch: True
    to_float16: True

model:
    name: "fc_mnist"
    mixed_precision: True
    depth: 10
    hidden_size: 50
    dropout: 0.0
    activation_fn: "relu"

optimizer:
    optimizer_type: "SGD"
    learning_rate: 0.001
    momentum: 0.9
    loss_scaling_factor: 1.0

runconfig:
    max_steps: 10000
    checkpoint_steps: 2000
    log_steps: 50
    seed: 1

The params YAML file has the following sections:

Section

Required

Notes

runconfig

Yes

Used by run to set up logging and execution. It expects fields: max_steps, checkpoint_steps, log_steps.

optimizer

Yes

Used by PyTorchBaseModel to set up optimizer. It expects fields: optimizer_type, learning_rate, loss_scaling_factor.

model

No

By convention, it is used to customize the model architecture in nn.Module. Fields are tailored to needs inside the model.

train_input

No

By convention, it is used to customize train_data_fn. Fields are tailored to needs inside train_data_fn.

eval_input

No

By convention, it is used to customize eval_data_fn. Fields are tailored to needs inside eval_data_fn.

Optimizer#

There are a number of optimizer parameters that can be used to configure the optimizer for the run.

Currently, the only supported optimizers are SGD and AdamW. The optimizer type can be specified via the optimizer_type sub parameter. Below are the required and optional params that can be used to configure them

optimizer_type

Parameters

Descriptions

SGD

learning_rate

See “Learning Rate Scheduling” subsection.

momentum

The momentum factor.

weight_decay_rate

Optional. weight decay. (L2 penalty) (Default: 0.0)

AdamW

learning_rate

See “Learning Rate Scheduling” subsection.

beta1

Optional. Adam’s first beta parameter. (Default: 0.9)

beta2

Optional. Adam’s second beta parameter. (Default: 0.999)

correct_bias

Optional. Whether or not to correct bias in Adam. (Default: False)

exclude_from_weight_decay

Parameters to exclude from weight decay.

All above parameters being sub parameters to the optimizer top-level parameter. Refer to the Cerebras Model Zoo git repository for examples of how to configure the optimizer.

Learning Rate Scheduler#

We also support various learning rate schedulers. They are configurable using the learning_rate sub parameter. Valid configurations include the following:

learning_rate

Parameters

Descriptions

Constant

A floating point number specifying the learning rate to be used throughout .

PieceWiseConstant

values

The constant values to use.

boundaries

The steps on which to change the learning rate values.

Linear

initial_learning_rate

The starting learning rate value.

end_learning_rate

The final learning rate value.

steps

The number of steps over which to transition from the starting learning rate to the final learning rate.

Exponential

initial_learning_rate

The starting learning rate value.

decay_steps

The number of steps to decay the learning rate.

decay_rate

The rate at which to decay the learning rate.

Loss scaling#

We support static and dynamic loss scaling which are configurable through the optimizer’s subparameters:

loss_scaling_factor

A constant scalar value means configure for static loss scaling. Passing in the string "dynamic" configures it for dynamic loss scaling. (Default: 1. Don’t configure any loss scaling.)

initial_loss_scale

The initial loss scale value if loss_scale == "dynamic". (Default: 2e15)

steps_per_increase

The number of steps after which to increase the loss scaling condition. (Default: 2000)

min_loss_scale

The minimum loss scale value that can be chosen by dynamic loss scaling. (Default: 2e-14)

max_loss_scale

The maximum loss scale value that can be chosen by dynamic loss scaling. (Default: 2e15)

Global Gradient Clipping#

We support global gradient clipping by value or by the normalized value. They are configurable through the optimizer’s subparameters:

max_gradient_norm

max norm of the gradients

max_gradient_value

max value of the gradients

Note

The above subparameters are mutually exclusive. They cannot both be specified at the same time.

Step 5: Execute script with run function#

All models in Cerebras Model Zoo use the run function inside the script run.py for both PyTorch and TensorFlow implementations. Therefore, once you have ported your model to use the run function, you can follow the steps in Launch your job section to launch your training or evaluation job.

Additional functionality#

Logging#

By default, the run function logs training information to the console and to TensorBoard, as explained in Measure throughput of your model. You can also define your your own scalar and tensor summaries

Evaluation metrics#

The Cerebras Model Zoo git repository uses a base class to compute evaluation metrics called CBMetric. Metrics already defined in the Model Zoo git repository can be imported as:

from modelzoo.common.pytorch.metrics import (
    AccuracyMetric,
    DiceCoefficientMetric,
    MeanIOUMetric,
    PerplexityMetric,
)

As an example, the GPT2 implementation in PyTorch uses some of these metrics.

How to use evaluation metrics#

  1. Registration: All metrics must be registered with the corresponding torch.nn.Module class. This is automatically done when the CBMetric object is constructed. That is, to register a metric to a torch.nn.Module class, construct the metric object in the torch.nn.Module class’ constructor.

  2. Update: The metrics are stateful. This means that every call to the metric object with the appropriate arguments automatically the latest metric value and save it in the metric’s internal state.

  3. Logging: At the very end of the run, the final metrics values will be computed and then logged both to the console and to the TensorBoard SummaryWriter.

More on evaluation metrics#

Implementation of CBMetric class can be found here. The CBMetric class is a base class for creating metrics on CS devices. Subclasses must override methods to provide the full functionality of the metric. These methods are meant to split the computation graph into two portions:

  1. update_on_device: Compiles and runs on the device (i.e., CS system).

  2. update_on_host: Runs on the host (i.e., CPU).

These metrics also support running on CPU and GPU.

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