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
(decoder-only) for single sequence modeling. Example use case: language modeling.transformer
(encoder-decoder) for sequence to sequence modeling. Example use case: translation.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.
Objectives
- Generate the training dataset
- Train a language model on a single Cloud TPU or a Cloud TPU Pod
- Train an English-German translation model on a single Cloud TPU
- Train a sentiment classifier on a single Cloud TPU
- Clean up Cloud TPU resources
Costs
This tutorial uses billable components of Google Cloud, including:- Compute Engine
- Cloud TPU
- Cloud Storage
Use the pricing calculator to generate a cost estimate based on your projected usage. New Google Cloud users might be eligible for a free trial.
Before you begin
If you plan to train on a TPU Pod slice, please make sure you read Training on TPU Pods that explains the special considerations when training on a Pod slice.
Before starting this tutorial, follow the steps below to check that your Google Cloud project is correctly set up.
This section provides information on setting up Cloud Storage bucket and a Compute Engine VM.
Open a Cloud Shell window.
Create a variable for your project's ID.
export PROJECT_ID=project-id
Configure the
gcloud
command-line tool to use the project where you want to create Cloud TPU.gcloud config set project ${PROJECT_ID}
The first time you run this command in a new Cloud Shell VM, an
Authorize Cloud Shell
page is displayed. ClickAuthorize
at the bottom of the page to allowgcloud
to make GCP API calls with your credentials.Create a Service Account for the Cloud TPU project.
gcloud beta services identity create --service tpu.googleapis.com --project $PROJECT_ID
The command returns a Cloud TPU Service Account with following format:
service-PROJECT_NUMBER@cloud-tpu.iam.gserviceaccount.com
Create a Cloud Storage bucket using the following command:
gsutil mb -p ${PROJECT_ID} -c standard -l europe-west4 -b on gs://bucket-name
This Cloud Storage bucket stores the data you use to train your model and the training results. The
ctpu up
tool used in this tutorial sets up default permissions for the Cloud TPU Service Account. If you want finer-grain permissions, review the access level permissions.The bucket location must be in the same region as your virtual machine (VM) and your TPU node. VMs and TPU nodes are located in specific zones, which are subdivisions within a region.
Launch a Compute Engine VM using the
ctpu up
command.ctpu up --project=${PROJECT_ID} \ --zone=europe-west4-a \ --vm-only \ --disk-size-gb=300 \ --machine-type=n1-standard-8 \ --tf-version=1.15.5 \ --name=transformer-tutorial
Command flag descriptions
project
- Your GCP project ID
zone
- The zone where you plan to create your Cloud TPU.
vm-only
- Create a VM only. By default the
ctpu up
command creates a VM and a Cloud TPU. disk-size-gb
- The size of the disk for the VM in GB.
machine_type
- The machine type
of the VM the
ctpu up
command creates. tf-version
- The version of Tensorflow
ctpu
installs on the VM. name
- The name of the Cloud TPU to create.
The configuration you specified appears. Enter y to approve or n to cancel.
When the
ctpu up
command has finished executing, verify that your shell prompt has changed fromusername@projectname
tousername@vm-name
. This change shows that you are now logged into your Compute Engine VM.gcloud compute ssh transformer-tutorial --zone=europe-west4-a
As you continue these instructions, run each command that begins with
(vm)$
in your VM session window.
On your Compute Engine VM:
Create the following environment variables:
(vm)$ export STORAGE_BUCKET=gs://bucket-name
(vm)$ export MODEL_DIR=${STORAGE_BUCKET}/transformer (vm)$ export DATA_DIR=${STORAGE_BUCKET}/data (vm)$ export TMP_DIR=${HOME}/t2t_tmp
Create a directory to store temporary files:
(vm)$ mkdir ${TMP_DIR}
Add the path to the
tensor2tensor
scripts used to process the model data:(vm)$ export PATH=.local/bin:${PATH}
Train a language model on a single Cloud TPU
Generate the training dataset for the language model.
(vm)$ t2t-datagen --problem=languagemodel_lm1b32k_packed \ --data_dir=${DATA_DIR} \ --tmp_dir=${TMP_DIR}
Run the following command to create your Cloud TPU resource.
(vm)$ ctpu up --project=${PROJECT_ID} \ --tpu-only \ --tf-version=1.15.5 \ --name=transformer-tutorial
Set an environment variable for the TPU name.
(vm)$ export TPU_NAME=transformer-tutorial
Run the training script.
(vm)$ t2t-trainer \ --model=transformer \ --hparams_set=transformer_tpu \ --problem=languagemodel_lm1b32k_packed \ --eval_steps=3 \ --data_dir=${DATA_DIR} \ --output_dir=${MODEL_DIR}/language_lm1b32k \ --use_tpu=True \ --cloud_tpu_name=${TPU_NAME} \ --train_steps=10
The above command runs 10 training steps, then 3 evaluation steps. It runs in approximately 5 minutes on a v3-8 TPU node. To make this model more accurate, you need to increase the number of training steps by adjusting the
--train_steps
flag. It is recommended that you train the model using at least 40k steps. The model typically converges to its maximum quality after ~250k steps.Delete the Cloud TPU resource you created.
(vm)$ ctpu delete --project=${PROJECT_ID} \ --tpu-only \ --zone=europe-west4-a \ --name=transformer-tutorial
Train a language model on a Cloud TPU Pod
Run the
ctpu up
command, using thetpu-size
parameter to specify the Pod slice you want to use. For example, the following command uses a v2-32 Pod slice.(vm)$ ctpu up --project=${PROJECT_ID} \ --tpu-only \ --tpu-size=v2-32 \ --zone=europe-west4-a \ --tf-version=1.15.5 \ --name=transformer-tutorial-pod
Set an environment variable for the new TPU name.
(vm)$ export TPU_NAME=transformer-tutorial-pod
Run the training script.
(vm)$ t2t-trainer \ --model=transformer \ --hparams_set=transformer_tpu \ --problem=languagemodel_lm1b32k_packed \ --eval_steps=3 \ --data_dir=${DATA_DIR} \ --output_dir=${MODEL_DIR}/language_lm1b32k_pod \ --use_tpu=True \ --cloud_tpu_name=${TPU_NAME} \ --tpu_num_shards=32 \ --schedule=train \ --train_steps=25000
The above command runs 25,000 training steps, and then runs three evaluation steps. It takes approximately 30 minutes to complete this training on a Cloud TPU v2-32.
It is recommended that you train the model using at least 40k steps. The model typically converges to its maximum quality after ~250k steps.
Delete the Cloud TPU resource you created for training the model on a single device.
(vm)$ ctpu delete --project=${PROJECT_ID} \ --tpu-only \ --zone=europe-west4-a \ --name=transformer-tutorial-pod
Train an English-German translation model on a single Cloud TPU
Use the
t2t-datagen
script to generate the training and evaluation data for the translation model on the Cloud Storage bucket:(vm)$ t2t-datagen \ --problem=translate_ende_wmt32k_packed \ --data_dir=${DATA_DIR} \ --tmp_dir=${TMP_DIR}
Run the following command to create your Cloud TPU resource.
(vm)$ ctpu up --project=${PROJECT_ID} \ --tpu-only \ --tf-version=1.15.5 \ --name=transformer-tutorial
Set an environment variable for the new TPU name.
(vm)$ export TPU_NAME=transformer-tutorial
Run
t2t-trainer
to train and evaluate the model:(vm)$ t2t-trainer \ --model=transformer \ --hparams_set=transformer_tpu \ --problem=translate_ende_wmt32k_packed \ --eval_steps=3 \ --data_dir=${DATA_DIR} \ --output_dir=${MODEL_DIR}/translate_ende \ --use_tpu=True \ --cloud_tpu_name=${TPU_NAME} \ --train_steps=10
The above command runs 10 training steps, then 3 evaluation steps. It runs in approximately 5 minutes on a v3-8 TPU node. You can (and should) increase the number of training steps by adjusting the
--train_steps
flag. Translations usually begin to be reasonable after ~40k steps. The model typically converges to its maximum quality after ~250k steps.Delete the Cloud TPU resource you created for training the model on a single device.
(vm)$ ctpu delete --project=${PROJECT_ID} \ --tpu-only \ --zone=europe-west4-a \ --name=transformer-tutorial
Train a sentiment classifier model on a single Cloud TPU
Generate the dataset for the sentiment classifier model.
(vm)$ t2t-datagen --problem=sentiment_imdb \ --data_dir=${DATA_DIR} \ --tmp_dir=${TMP_DIR}
Run the following command to create your Cloud TPU resource.
(vm)$ ctpu up --project=${PROJECT_ID} \ --tpu-only \ --tf-version=1.15.5 \ --name=transformer-tutorial
Run the training script.
(vm)$ t2t-trainer \ --model=transformer_encoder \ --hparams_set=transformer_tiny_tpu \ --problem=sentiment_imdb \ --eval_steps=1 \ --data_dir=${DATA_DIR} \ --output_dir=${MODEL_DIR}/sentiment_classifier \ --use_tpu=True \ --cloud_tpu_name=${TPU_NAME} \ --train_steps=10
The above command runs 10 training steps, then 3 evaluation steps. It runs in approximately 5 minutes on a v3-8 TPU node. This model achieves approximately 85% accuracy after approximately 2,000 steps.
Cleaning up
To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.
Disconnect from the Compute Engine instance, if you have not already done so:
(vm)$ exit
Your prompt should now be
username@projectname
, showing you are in the Cloud Shell.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 --project=${PROJECT_ID} \ --zone=europe-west4-a \ --name=transformer-tutorial
Run the following command to verify the Compute Engine VM and Cloud TPU have been shut down:
$ ctpu status --project=${PROJECT_ID} \ --zone=europe-west4-a
The deletion might take several minutes. A response like the one below indicates there are no more allocated instances:
2018/04/28 16:16:23 WARNING: Setting zone to "europe-west4-a" No instances currently exist. Compute Engine VM: -- Cloud TPU: --
Run
gsutil
as shown, replacing bucket-name with the name of the Cloud Storage bucket you created for this tutorial:$ gsutil rm -r gs://bucket-name
What's next
In this tutorial you have trained the Transformer model using a sample dataset. The results of this training are (in most cases) not usable for inference. To use a model for inference you can train the data on a publicly available dataset or your own data set. Models trained on Cloud TPUs require datasets to be in TFRecord format.You can use the dataset conversion tool sample to convert an image classification dataset into TFRecord format. If you are not using an image classification model, you will have to convert your dataset to TFRecord format yourself. For more information, see TFRecord and tf.Example
Hyperparameter tuning
To improve the model's performance with your dataset, you can tune the model's hyperparameters. You can find information about hyperparameters common to all TPU supported models on GitHub. Information about model-specific hyperparameters can be found in the source code for each model. For more information on hyperparameter tuning, see Overview of hyperparameter tuning, Using the Hyperparameter tuning service and Tune hyperparameters.
Inference
Once you have trained your model you can use it for inference (also called prediction). AI Platform is a cloud-based solution for developing, training, and deploying machine learning models. Once a model is deployed, you can use the AI Platform Prediction service.
- 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.