ai.djl.training.optimizer.Adam Maven / Gradle / Ivy
/*
* Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
* with the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0/
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES
* OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package ai.djl.training.optimizer;
import ai.djl.Device;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDList;
import ai.djl.ndarray.internal.NDArrayEx;
import ai.djl.training.tracker.Tracker;
import ai.djl.util.Preconditions;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
/**
* {@code Adam} is a generalization of the AdaGrad {@link Optimizer}.
*
* Adam updates the weights using:
*
* \( m = beta1 * m + (1 - beta1) * grad\)
* \( v = beta2 * v + (1 - beta2) * grad^2 \)
* \( w -= learning_rate * m / (sqrt(v) + epsilon) \)
*
* where g represents the gradient, and m/v are 1st and 2nd order moment estimates (mean and
* variance).
*
* @see The D2L chapter on Adam
*/
public class Adam extends Optimizer {
private Tracker learningRateTracker;
private float beta1;
private float beta2;
private float epsilon;
private Map> means;
private Map> variances;
/**
* Creates a new instance of {@code Adam} optimizer.
*
* @param builder the builder to create a new instance of {@code Adam} optimizer
*/
protected Adam(Builder builder) {
super(builder);
learningRateTracker = builder.learningRateTracker;
beta1 = builder.beta1;
beta2 = builder.beta2;
epsilon = builder.epsilon;
means = new ConcurrentHashMap<>();
variances = new ConcurrentHashMap<>();
}
/** {@inheritDoc} */
@Override
public void update(String parameterId, NDArray weight, NDArray grad) {
int t = updateCount(parameterId);
double coef1 = 1.0 - Math.pow(beta1, t);
double coef2 = 1.0 - Math.pow(beta2, t);
float lr = learningRateTracker.getNewValue(t);
float newLearningRate = (float) (lr * Math.sqrt(coef2) / coef1);
float weightDecay = getWeightDecay();
Preconditions.checkArgument(
!Float.isNaN(newLearningRate)
&& !Float.isNaN(weightDecay)
&& !Float.isInfinite(newLearningRate)
&& !Float.isInfinite(weightDecay),
"learning rate or weight decay is nan or infinite");
NDList inputs =
new NDList(
weight,
grad,
withDefaultState(
means, parameterId, weight.getDevice(), k -> weight.zerosLike()),
withDefaultState(
variances,
parameterId,
weight.getDevice(),
k -> weight.zerosLike()));
NDList weights = new NDList(weight);
NDArrayEx ex = weight.getNDArrayInternal();
ex.adamUpdate(
inputs,
weights,
newLearningRate,
weightDecay,
rescaleGrad,
clipGrad,
beta1,
beta2,
epsilon,
true);
}
/**
* Creates a builder to build a {@code Adam}.
*
* @return a new builder
*/
public static Builder builder() {
return new Builder();
}
/** The Builder to construct an {@link Adam} object. */
public static final class Builder extends OptimizerBuilder {
private Tracker learningRateTracker = Tracker.fixed(0.001f);
private float beta1 = 0.9f;
private float beta2 = 0.999f;
private float epsilon = 1e-8f;
Builder() {}
/** {@inheritDoc} */
@Override
protected Builder self() {
return this;
}
/**
* Sets the {@link Tracker} for this optimizer.
*
* @param learningRateTracker the {@link Tracker} to be set
* @return this {@code Builder}
*/
public Builder optLearningRateTracker(Tracker learningRateTracker) {
this.learningRateTracker = learningRateTracker;
return this;
}
/**
* Sets the decay rate for the first moment estimates.
*
* @param beta1 the deacay rate for the the first moment estimates
* @return this {@code Builder}
*/
public Builder optBeta1(float beta1) {
this.beta1 = beta1;
return this;
}
/**
* Sets the decay rate for the second moment estimates.
*
* @param beta2 the decay rate for the the second moment estimates
* @return this {@code Builder}
*/
public Builder optBeta2(float beta2) {
this.beta2 = beta2;
return this;
}
/**
* Sets \(epsilon\) - a small quantity for numerical stability.
*
* @param epsilon a small quantity for numerical stability
* @return this {@code Builder}
*/
public Builder optEpsilon(float epsilon) {
this.epsilon = epsilon;
return this;
}
/**
* Builds a {@link Adam} block.
*
* @return the {@link Adam} block
*/
public Adam build() {
return new Adam(this);
}
}
}