Running the Transformer with Tensor2Tensor

This tutorial shows you how to train the Transformer model (from Attention Is All You Need) with Tensor2Tensor on a Cloud TPU.

Model description

The Transformer model uses stacks of self-attention layers and feed-forward layers to process sequential input like text. It supports the following variants:

• transformer (encoder-decoder) for sequence to sequence modeling. Example use case: translation.
• transformer (decoder-only) for single sequence modeling. Example use case: language modeling.
• transformer_encoder (encoder-only) runs only the encoder for sequence to class modeling. Example use case: sentiment classification.

The Transformer is just one of the models in the Tensor2Tensor library. Tensor2Tensor (T2T) is a library of deep learning models and datasets as well as a set of scripts that allow you to train the models and to download and prepare the data.

Before you begin

Before starting this tutorial, follow the steps below to check that your Google Cloud Platform project is correctly set up.

1. Sign in to your Google Account.

   If you don't already have one, sign up for a new account.

2. Select or create a GCP project.

   Note: If you don't plan to keep the resources you create in this tutorial, create a new project instead of selecting an existing project. After you finish, you can delete the project, removing all resources associated with the project and tutorial.

   Go to the Manage resources page

3. Make sure that billing is enabled for your project.

   Learn how to enable billing

This walkthrough uses billable components of Google Cloud Platform. Check the Cloud TPU pricing page to estimate your costs. Be sure to clean up resources you create when you've finished with them to avoid unnecessary charges.

Set up your resources

This section provides information on setting up Cloud Storage storage, VM, and Cloud TPU resources for tutorials.

Important: Be sure to set up all resources in the same region/zone.

Create a Cloud Storage bucket

You need a Cloud Storage bucket to store the data you use to train your model and the training results. The ctpu up tool used in this tutorial sets up default permissions for the Cloud TPU service account. If you want finer-grain permissions, review the access level permissions.

The bucket you create must reside in the same region as your virtual machine (VM) and your Cloud TPU device or Cloud TPU slice (multiple TPU devices) do.

1. Go to the Cloud Storage page on the GCP Console.

   Go to the Cloud Storage page

2. Create a new bucket, specifying the following options:

   • A unique name of your choosing.
   • Default storage class: Regional
   • Location: If you want to use a Cloud TPU device, accept the default presented. If you want to use a Cloud TPU Pod slice, you must specify a region where Cloud TPU Pods are available.

Use the ctpu tool

This section demonstrates using the Cloud TPU provisioning tool (ctpu) for creating and managing Cloud TPU project resources. The resources are comprised of a virtual machine (VM) and a Cloud TPU resource that have the same name. These resources must reside in the same region/zone as the bucket you just created.

Run ctpu up to create resources

1. Open a Cloud Shell window.

   Open Cloud Shell

2. Run ctpu up and specify options for either a Cloud TPU device or Pod slice:

   You can use flags to change the following options:

   • --name - name of your Cloud TPU resource and your VM.
   • --zone - region and zone of the physical assets. The zone must be the same for the VM and Cloud TPU. The bucket must be in the same region.
   • --project name - name of an existing project.
   • --tpu_size - version and size of the Cloud TPU. The default is one device with 8 cores.
   • --disk-size-gb - disk size. Use only if your dataset requires more than the default 250GB.
   • --machine-type - virtual machine (VM) memory per CPU.
   • --preemptible - interruptable, but lower cost Cloud TPU.

3. Set up a Cloud TPU device:

   $ ctpu up
   

   Note: If you have more than one project, you must specify the project name.

   The following configuration message appears:

   ctpu will use the following configuration:
   
   Name: [your TPU's name]
   Zone: [your project's zone]
   GCP Project: [your project's name]
   TensorFlow Version: 1.12
   VM:
    Machine Type: [your machine type]
    Disk Size: [your disk size]
    Preemptible: [true or false]
   Cloud TPU:
    Size: [your TPU size]
    Preemptible: [true or false]
   
   OK to create your Cloud TPU resources with the above configuration? [Yn]:
   

   Press y to create your Cloud TPU resources.

Note: The first time you run ctpu up on a project it takes about 5 minutes to perform startup tasks such as SSH key propagation and API turnup.

The ctpu up command creates a virtual machine (VM) and Cloud TPU services.

From this point on, a prefix of (vm)$ means you should run the command on the Compute Engine VM instance.

Verify your Compute Engine VM

When the ctpu up command has finished executing, verify that your shell prompt has changed from username@project to username@tpuname. This change shows that you are now logged into your Compute Engine VM.

Add disk space to your VM

T2T conveniently packages data generation for many common open-source datasets in its t2t-datagen script. The script downloads the data, preprocess it, and makes it ready for training. To do so, it needs local disk space.

You can skip this step if you run t2t-datagen on your local machine (pip install tensor2tensor and then see the t2t-datagen command below).

• Follow the Compute Engine guide to add a disk to your Compute Engine VM.
• Set the disk size to 200GB (the recommended minimum size).
• Set When deleting instance to Delete disk to ensure that the disk is removed when you remove the VM.

Make a note of the path to your new disk. For example: /mnt/disks/mnt-dir.

Generate the training dataset

On your Compute Engine VM:

1. Create the following environment variables:

   (vm)$ STORAGE_BUCKET=gs://YOUR-BUCKET-NAME
   (vm)$ DATA_DIR=$STORAGE_BUCKET/data/
   (vm)$ TMP_DIR=/mnt/disks/mnt-dir/t2t_tmp

   where:

   • YOUR-BUCKET-NAME is the name of your Cloud Storage bucket.
   • DATA_DIR is a location on Cloud Storage.
   • TMP_DIR is a location on the disk that you added to your Compute Engine VM at the start of the tutorial.

2. Create a temporary directory on the disk that you added to your Compute Engine VM at the start of the tutorial:

   (vm)$ mkdir /mnt/disks/mnt-dir/t2t_tmp

3. Use the t2t-datagen script to generate the training and evaluation data on the Cloud Storage bucket, so that the Cloud TPU can access the data:

   (vm)$ t2t-datagen --problem=translate_ende_wmt32k_packed --data_dir=$DATA_DIR --tmp_dir=$TMP_DIR

You can view the data on Cloud Storage by going to the Google Cloud Platform Console and choosing Storage from the left-hand menu. Click the name of the bucket that you created for this tutorial. You should see sharded files named translate_ende_wmt32k_packed-train and translate_ende_wmt32k_packed-dev.

Train an English-German translation model

Run the following commands on your Compute Engine VM:

1. Set up an environment variable for the training directory, which must be a Cloud Storage location:

   (vm)$ OUT_DIR=$STORAGE_BUCKET/training/transformer_ende_1

2. Run t2t-trainer to train and evaluate the model:

   (vm)$ t2t-trainer \
     --model=transformer \
     --hparams_set=transformer_tpu \
     --problem=translate_ende_wmt32k_packed \
     --train_steps=10 \
     --eval_steps=3 \
     --data_dir=$DATA_DIR \
     --output_dir=$OUT_DIR \
     --use_tpu=True \
     --cloud_tpu_name=$TPU_NAME

   The above command runs 10 training steps, then 3 evaluation steps. You can (and should) increase the number of training steps by adjusting the --train_steps flag. Translations usually begin to be reasonable after ~40k steps. The model typically converges to its maximum quality after ~250k steps.

3. View the output in your Cloud Storage bucket by going to the Google Cloud Platform Console and choosing Storage from the left-hand menu. Click the name of the bucket that you created for this tutorial. Within the bucket, navigate to the training directory, for example, /training/transformer_ende_1, to see the model output.

4. To see training and evaluation metrics, launch TensorBoard and point it at the training directory in Cloud Storage.

Train a language model

You can use the transformer model for language modeling as well. Run the following commands to generate the training data and specify the output file:

(vm)$ t2t-datagen --problem=languagemodel_lm1b8k_packed --data_dir=$DATA_DIR --tmp_dir=$TMP_DIR
(vm)$ OUT_DIR=$STORAGE_BUCKET/training/transformer_lang_model

Run the following command to train and evaluate the model:

(vm)$ t2t-trainer \
  --model=transformer \
  --hparams_set=transformer_tpu \
  --problem=languagemodel_lm1b8k_packed \
  --train_steps=10 \
  --eval_steps=8 \
  --data_dir=$DATA_DIR \
  --output_dir=$OUT_DIR \
  --use_tpu=True \
  --cloud_tpu_name=$TPU_NAME

This model converges after approximately 250,000 steps.

Train a sentiment classifier

You can use the transformer_encoder model for sentiment classification. Run the following commands to generate the training data and specify the output file:

(vm)$ t2t-datagen --problem=sentiment_imdb --data_dir=$DATA_DIR --tmp_dir=$TMP_DIR
(vm)$ OUT_DIR=$STORAGE_BUCKET/training/transformer_sentiment_classifier

Run the following command to train and evaluate the model:

(vm)$ t2t-trainer \
  --model=transformer_encoder \
  --hparams_set=transformer_tiny_tpu \
  --problem=sentiment_imdb \
  --train_steps=10 \
  --eval_steps=1 \
  --data_dir=$DATA_DIR \
  --output_dir=$OUT_DIR \
  --use_tpu=True \
  --cloud_tpu_name=$TPU_NAME

This model achieves approximately 85% accuracy after approximately 2,000 steps.

Clean up

To avoid incurring charges to your GCP account for the resources used in this tutorial:

1. Disconnect from the Compute Engine VM:

   (vm)$ exit
   

   Your prompt should now be user@projectname, showing you are in the Cloud Shell.

2. In your Cloud Shell, run ctpu delete with the --zone flag you used when you set up the Cloud TPU to delete your Compute Engine VM and your Cloud TPU:

   $ ctpu delete [optional: --zone]
   

   The operation may take a few moments. A message like the one below indicates there are no more allocated instances:

   2018/04/28 16:16:23 WARNING: Setting zone to "us-central1-b"
   No instances currently exist.
           Compute Engine VM:     --
           Cloud TPU:             --
   

3. Run ctpu status with the --zone flag you used when you set up the Cloud TPU. This checks that your instance was deleted so you can avoid unnecessary charges for TPU usage.

4. Run gsutil as shown, replacing YOUR-BUCKET-NAME with the name of the Cloud Storage bucket you created for this tutorial:

   $ gsutil rm -r gs://YOUR-BUCKET-NAME
   

Note: For free storage limits and other pricing information, see the Cloud Storage pricing guide.

What's next

• Learn more about ctpu, including how to install it on a local machine.
• Explore more Tensor2Tensor models for TPU.
• Experiment with more TPU samples.
• Explore the TPU tools in TensorBoard.