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Workaroun mixed device issue in multiple engine case #1123

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merged 1 commit into from Jul 27, 2021
Merged

Workaroun mixed device issue in multiple engine case #1123

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14 changes: 11 additions & 3 deletions integration/src/main/java/ai/djl/integration/tests/nn/BlockCoreTest.java
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Expand Up @@ -12,7 +12,6 @@
*/
package ai.djl.integration.tests.nn;

import ai.djl.Device;
import ai.djl.MalformedModelException;
import ai.djl.Model;
import ai.djl.engine.Engine;
Expand Down Expand Up @@ -57,6 +56,7 @@
import java.nio.file.Files;
import java.util.Arrays;
import org.testng.Assert;
import org.testng.SkipException;
import org.testng.annotations.Test;

public class BlockCoreTest {
Expand Down Expand Up @@ -207,12 +207,16 @@ public void testBatchNorm() throws IOException, MalformedModelException {
@SuppressWarnings("try")
@Test
public void testLayerNorm() throws IOException, MalformedModelException {
if (!"PyTorch".equals(Engine.getInstance().getEngineName())) {
throw new SkipException("Only works for PyTorch engine.");
}

TrainingConfig config =
new DefaultTrainingConfig(Loss.l2Loss())
.optInitializer(Initializer.ONES, Parameter.Type.WEIGHT);

Block block = LayerNorm.builder().build();
try (Model model = Model.newInstance("model", Device.cpu(), "PyTorch")) {
try (Model model = Model.newInstance("model")) {
model.setBlock(block);

try (Trainer trainer = model.newTrainer(config)) {
Expand All @@ -234,12 +238,16 @@ public void testLayerNorm() throws IOException, MalformedModelException {
@SuppressWarnings("try")
@Test
public void test2LayerNorm() throws IOException, MalformedModelException {
if (!"PyTorch".equals(Engine.getInstance().getEngineName())) {
throw new SkipException("Only works for PyTorch engine.");
}

TrainingConfig config =
new DefaultTrainingConfig(Loss.l2Loss())
.optInitializer(Initializer.ONES, Parameter.Type.WEIGHT);

Block block = LayerNorm.builder().axis(2, 3).build();
try (Model model = Model.newInstance("model", Device.cpu(), "PyTorch")) {
try (Model model = Model.newInstance("model")) {
model.setBlock(block);

try (Trainer trainer = model.newTrainer(config)) {
Expand Down