Keras documentation: Writing a training loop from scratch in JAX (original) (raw)

Author: fchollet
Date created: 2023/06/25
Last modified: 2023/06/25
Description: Writing low-level training & evaluation loops in JAX.

View in Colab • GitHub source

Setup

`import os

This guide can only be run with the jax backend.

os.environ["KERAS_BACKEND"] = "jax"

import jax

We import TF so we can use tf.data.

import tensorflow as tf import keras import numpy as np `

Introduction

Keras provides default training and evaluation loops, fit() and evaluate(). Their usage is covered in the guideTraining & evaluation with the built-in methods.

If you want to customize the learning algorithm of your model while still leveraging the convenience of fit()(for instance, to train a GAN using fit()), you can subclass the Model class and implement your own train_step() method, which is called repeatedly during fit().

Now, if you want very low-level control over training & evaluation, you should write your own training & evaluation loops from scratch. This is what this guide is about.

A first end-to-end example

To write a custom training loop, we need the following ingredients:

Let's line them up.

First, let's get the model and the MNIST dataset:

`def get_model(): inputs = keras.Input(shape=(784,), name="digits") x1 = keras.layers.Dense(64, activation="relu")(inputs) x2 = keras.layers.Dense(64, activation="relu")(x1) outputs = keras.layers.Dense(10, name="predictions")(x2) model = keras.Model(inputs=inputs, outputs=outputs) return model

model = get_model()

Prepare the training dataset.

batch_size = 32 (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() x_train = np.reshape(x_train, (-1, 784)).astype("float32") x_test = np.reshape(x_test, (-1, 784)).astype("float32") y_train = keras.utils.to_categorical(y_train) y_test = keras.utils.to_categorical(y_test)

Reserve 10,000 samples for validation.

x_val = x_train[-10000:] y_val = y_train[-10000:] x_train = x_train[:-10000] y_train = y_train[:-10000]

Prepare the training dataset.

train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)) train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size)

Prepare the validation dataset.

val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val)) val_dataset = val_dataset.batch(batch_size) `

Next, here's the loss function and the optimizer. We'll use a Keras optimizer in this case.

`# Instantiate a loss function. loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)

Instantiate an optimizer.

optimizer = keras.optimizers.Adam(learning_rate=1e-3) `

Getting gradients in JAX

Let's train our model using mini-batch gradient with a custom training loop.

In JAX, gradients are computed via metaprogramming: you call the jax.grad (orjax.value_and_grad on a function in order to create a gradient-computing function for that first function.

So the first thing we need is a function that returns the loss value. That's the function we'll use to generate the gradient function. Something like this:

def compute_loss(x, y): ... return loss

Once you have such a function, you can compute gradients via metaprogramming as such:

grad_fn = jax.grad(compute_loss) grads = grad_fn(x, y)

Typically, you don't just want to get the gradient values, you also want to get the loss value. You can do this by using jax.value_and_grad instead of jax.grad:

grad_fn = jax.value_and_grad(compute_loss) loss, grads = grad_fn(x, y)

JAX computation is purely stateless

In JAX, everything must be a stateless function – so our loss computation function must be stateless as well. That means that all Keras variables (e.g. weight tensors) must be passed as function inputs, and any variable that has been updated during the forward pass must be returned as function output. The function have no side effect.

During the forward pass, the non-trainable variables of a Keras model might get updated. These variables could be, for instance, RNG seed state variables or BatchNormalization statistics. We're going to need to return those. So we need something like this:

def compute_loss_and_updates(trainable_variables, non_trainable_variables, x, y): ... return loss, non_trainable_variables

Once you have such a function, you can get the gradient function by specifying has_aux in value_and_grad: it tells JAX that the loss computation function returns more outputs than just the loss. Note that the loss should always be the first output.

grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True) (loss, non_trainable_variables), grads = grad_fn( trainable_variables, non_trainable_variables, x, y )

Now that we have established the basics, let's implement this compute_loss_and_updates function. Keras models have a stateless_call method which will come in handy here. It works just like model.__call__, but it requires you to explicitly pass the value of all the variables in the model, and it returns not just the __call__ outputs but also the (potentially updated) non-trainable variables.

def compute_loss_and_updates(trainable_variables, non_trainable_variables, x, y): y_pred, non_trainable_variables = model.stateless_call( trainable_variables, non_trainable_variables, x, training=True ) loss = loss_fn(y, y_pred) return loss, non_trainable_variables

Let's get the gradient function:

grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True)

The training step function

Next, let's implement the end-to-end training step, the function that will both run the forward pass, compute the loss, compute the gradients, but also use the optimizer to update the trainable variables. This function also needs to be stateless, so it will get as input a state tuple that includes every state element we're going to use:

To update the trainable variables, we use the optimizer's stateless methodstateless_apply. It's equivalent to optimizer.apply(), but it requires always passing trainable_variables and optimizer_variables. It returns both the updated trainable variables and the updated optimizer_variables.

def train_step(state, data): trainable_variables, non_trainable_variables, optimizer_variables = state x, y = data (loss, non_trainable_variables), grads = grad_fn( trainable_variables, non_trainable_variables, x, y ) trainable_variables, optimizer_variables = optimizer.stateless_apply( optimizer_variables, grads, trainable_variables ) # Return updated state return loss, ( trainable_variables, non_trainable_variables, optimizer_variables, )

Make it fast with jax.jit

By default, JAX operations run eagerly, just like in TensorFlow eager mode and PyTorch eager mode. And just like TensorFlow eager mode and PyTorch eager mode, it's pretty slow – eager mode is better used as a debugging environment, not as a way to do any actual work. So let's make our train_step fast by compiling it.

When you have a stateless JAX function, you can compile it to XLA via the@jax.jit decorator. It will get traced during its first execution, and in subsequent executions you will be executing the traced graph (this is just like @tf.function(jit_compile=True). Let's try it:

@jax.jit def train_step(state, data): trainable_variables, non_trainable_variables, optimizer_variables = state x, y = data (loss, non_trainable_variables), grads = grad_fn( trainable_variables, non_trainable_variables, x, y ) trainable_variables, optimizer_variables = optimizer.stateless_apply( optimizer_variables, grads, trainable_variables ) # Return updated state return loss, ( trainable_variables, non_trainable_variables, optimizer_variables, )

We're now ready to train our model. The training loop itself is trivial: we just repeatedly call loss, state = train_step(state, data).

Note:

`# Build optimizer variables. optimizer.build(model.trainable_variables)

trainable_variables = model.trainable_variables non_trainable_variables = model.non_trainable_variables optimizer_variables = optimizer.variables state = trainable_variables, non_trainable_variables, optimizer_variables

Training loop

for step, data in enumerate(train_dataset): data = (data[0].numpy(), data[1].numpy()) loss, state = train_step(state, data) # Log every 100 batches. if step % 100 == 0: print(f"Training loss (for 1 batch) at step {step}: {float(loss):.4f}") print(f"Seen so far: {(step + 1) * batch_size} samples") `

Training loss (for 1 batch) at step 0: 96.2726 Seen so far: 32 samples Training loss (for 1 batch) at step 100: 2.0853 Seen so far: 3232 samples Training loss (for 1 batch) at step 200: 0.6535 Seen so far: 6432 samples Training loss (for 1 batch) at step 300: 1.2679 Seen so far: 9632 samples Training loss (for 1 batch) at step 400: 0.7563 Seen so far: 12832 samples Training loss (for 1 batch) at step 500: 0.7154 Seen so far: 16032 samples Training loss (for 1 batch) at step 600: 1.0267 Seen so far: 19232 samples Training loss (for 1 batch) at step 700: 0.6860 Seen so far: 22432 samples Training loss (for 1 batch) at step 800: 0.7306 Seen so far: 25632 samples Training loss (for 1 batch) at step 900: 0.4571 Seen so far: 28832 samples Training loss (for 1 batch) at step 1000: 0.6023 Seen so far: 32032 samples Training loss (for 1 batch) at step 1100: 0.9140 Seen so far: 35232 samples Training loss (for 1 batch) at step 1200: 0.4224 Seen so far: 38432 samples Training loss (for 1 batch) at step 1300: 0.6696 Seen so far: 41632 samples Training loss (for 1 batch) at step 1400: 0.1399 Seen so far: 44832 samples Training loss (for 1 batch) at step 1500: 0.5761 Seen so far: 48032 samples

A key thing to notice here is that the loop is entirely stateless – the variables attached to the model (model.weights) are never getting updated during the loop. Their new values are only stored in the state tuple. That means that at some point, before saving the model, you should be attaching the new variable values back to the model.

Just call variable.assign(new_value) on each model variable you want to update:

trainable_variables, non_trainable_variables, optimizer_variables = state for variable, value in zip(model.trainable_variables, trainable_variables): variable.assign(value) for variable, value in zip(model.non_trainable_variables, non_trainable_variables): variable.assign(value)

Low-level handling of metrics

Let's add metrics monitoring to this basic training loop.

You can readily reuse built-in Keras metrics (or custom ones you wrote) in such training loops written from scratch. Here's the flow:

Let's use this knowledge to compute CategoricalAccuracy on training and validation data at the end of training:

`# Get a fresh model model = get_model()

Instantiate an optimizer to train the model.

optimizer = keras.optimizers.Adam(learning_rate=1e-3)

Instantiate a loss function.

loss_fn = keras.losses.CategoricalCrossentropy(from_logits=True)

Prepare the metrics.

train_acc_metric = keras.metrics.CategoricalAccuracy() val_acc_metric = keras.metrics.CategoricalAccuracy()

def compute_loss_and_updates( trainable_variables, non_trainable_variables, metric_variables, x, y ): y_pred, non_trainable_variables = model.stateless_call( trainable_variables, non_trainable_variables, x ) loss = loss_fn(y, y_pred) metric_variables = train_acc_metric.stateless_update_state( metric_variables, y, y_pred ) return loss, (non_trainable_variables, metric_variables)

grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True)

@jax.jit def train_step(state, data): ( trainable_variables, non_trainable_variables, optimizer_variables, metric_variables, ) = state x, y = data (loss, (non_trainable_variables, metric_variables)), grads = grad_fn( trainable_variables, non_trainable_variables, metric_variables, x, y ) trainable_variables, optimizer_variables = optimizer.stateless_apply( optimizer_variables, grads, trainable_variables ) # Return updated state return loss, ( trainable_variables, non_trainable_variables, optimizer_variables, metric_variables, ) `

We'll also prepare an evaluation step function:

@jax.jit def eval_step(state, data): trainable_variables, non_trainable_variables, metric_variables = state x, y = data y_pred, non_trainable_variables = model.stateless_call( trainable_variables, non_trainable_variables, x ) loss = loss_fn(y, y_pred) metric_variables = val_acc_metric.stateless_update_state( metric_variables, y, y_pred ) return loss, ( trainable_variables, non_trainable_variables, metric_variables, )

Here are our loops:

`# Build optimizer variables. optimizer.build(model.trainable_variables)

trainable_variables = model.trainable_variables non_trainable_variables = model.non_trainable_variables optimizer_variables = optimizer.variables metric_variables = train_acc_metric.variables state = ( trainable_variables, non_trainable_variables, optimizer_variables, metric_variables, )

Training loop

for step, data in enumerate(train_dataset): data = (data[0].numpy(), data[1].numpy()) loss, state = train_step(state, data) # Log every 100 batches. if step % 100 == 0: print(f"Training loss (for 1 batch) at step {step}: {float(loss):.4f}") _, _, _, metric_variables = state for variable, value in zip(train_acc_metric.variables, metric_variables): variable.assign(value) print(f"Training accuracy: {train_acc_metric.result()}") print(f"Seen so far: {(step + 1) * batch_size} samples")

metric_variables = val_acc_metric.variables ( trainable_variables, non_trainable_variables, optimizer_variables, metric_variables, ) = state state = trainable_variables, non_trainable_variables, metric_variables

Eval loop

for step, data in enumerate(val_dataset): data = (data[0].numpy(), data[1].numpy()) loss, state = eval_step(state, data) # Log every 100 batches. if step % 100 == 0: print(f"Validation loss (for 1 batch) at step {step}: {float(loss):.4f}") _, _, metric_variables = state for variable, value in zip(val_acc_metric.variables, metric_variables): variable.assign(value) print(f"Validation accuracy: {val_acc_metric.result()}") print(f"Seen so far: {(step + 1) * batch_size} samples") `

Training loss (for 1 batch) at step 0: 70.8851 Training accuracy: 0.09375 Seen so far: 32 samples Training loss (for 1 batch) at step 100: 2.1930 Training accuracy: 0.6596534848213196 Seen so far: 3232 samples Training loss (for 1 batch) at step 200: 3.0249 Training accuracy: 0.7352300882339478 Seen so far: 6432 samples Training loss (for 1 batch) at step 300: 0.6004 Training accuracy: 0.7588247656822205 Seen so far: 9632 samples Training loss (for 1 batch) at step 400: 1.4633 Training accuracy: 0.7736907601356506 Seen so far: 12832 samples Training loss (for 1 batch) at step 500: 1.3367 Training accuracy: 0.7826846241950989 Seen so far: 16032 samples Training loss (for 1 batch) at step 600: 0.8767 Training accuracy: 0.7930532693862915 Seen so far: 19232 samples Training loss (for 1 batch) at step 700: 0.3479 Training accuracy: 0.8004636168479919 Seen so far: 22432 samples Training loss (for 1 batch) at step 800: 0.3608 Training accuracy: 0.8066869378089905 Seen so far: 25632 samples Training loss (for 1 batch) at step 900: 0.7582 Training accuracy: 0.8117369413375854 Seen so far: 28832 samples Training loss (for 1 batch) at step 1000: 1.3135 Training accuracy: 0.8142170310020447 Seen so far: 32032 samples Training loss (for 1 batch) at step 1100: 1.0202 Training accuracy: 0.8186308145523071 Seen so far: 35232 samples Training loss (for 1 batch) at step 1200: 0.6766 Training accuracy: 0.822023332118988 Seen so far: 38432 samples Training loss (for 1 batch) at step 1300: 0.7606 Training accuracy: 0.8257110118865967 Seen so far: 41632 samples Training loss (for 1 batch) at step 1400: 0.7657 Training accuracy: 0.8290283679962158 Seen so far: 44832 samples Training loss (for 1 batch) at step 1500: 0.6563 Training accuracy: 0.831653892993927 Seen so far: 48032 samples Validation loss (for 1 batch) at step 0: 0.1622 Validation accuracy: 0.8329269289970398 Seen so far: 32 samples Validation loss (for 1 batch) at step 100: 0.7455 Validation accuracy: 0.8338780999183655 Seen so far: 3232 samples Validation loss (for 1 batch) at step 200: 0.2738 Validation accuracy: 0.836174488067627 Seen so far: 6432 samples Validation loss (for 1 batch) at step 300: 0.1255 Validation accuracy: 0.8390461206436157 Seen so far: 9632 samples

Low-level handling of losses tracked by the model

Layers & models recursively track any losses created during the forward pass by layers that call self.add_loss(value). The resulting list of scalar loss values are available via the property model.lossesat the end of the forward pass.

If you want to be using these loss components, you should sum them and add them to the main loss in your training step.

Consider this layer, that creates an activity regularization loss:

class ActivityRegularizationLayer(keras.layers.Layer): def call(self, inputs): self.add_loss(1e-2 * jax.numpy.sum(inputs)) return inputs

Let's build a really simple model that uses it:

`inputs = keras.Input(shape=(784,), name="digits") x = keras.layers.Dense(64, activation="relu")(inputs)

Insert activity regularization as a layer

x = ActivityRegularizationLayer()(x) x = keras.layers.Dense(64, activation="relu")(x) outputs = keras.layers.Dense(10, name="predictions")(x)

model = keras.Model(inputs=inputs, outputs=outputs) `

Here's what our compute_loss_and_updates function should look like now:

def compute_loss_and_updates( trainable_variables, non_trainable_variables, metric_variables, x, y ): y_pred, non_trainable_variables, losses = model.stateless_call( trainable_variables, non_trainable_variables, x, return_losses=True ) loss = loss_fn(y, y_pred) if losses: loss += jax.numpy.sum(losses) metric_variables = train_acc_metric.stateless_update_state( metric_variables, y, y_pred ) return loss, non_trainable_variables, metric_variables

That's it!