Setting all the optimizers to have useLocking = True (#310)

* Setting all the optimizers to have useLocking = True, like Keras. Adding a determinism test that's currently failing.

* More work on the GradientDescentTest.

* Tidying up the test.

Author: Craigacp

tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/AdaDelta.java

@@ -20,6 +20,7 @@
 import org.tensorflow.Output;
 import org.tensorflow.op.Op;
 import org.tensorflow.op.core.Variable;
+import org.tensorflow.op.train.ApplyAdadelta;
 import org.tensorflow.types.family.TType;
 
 import java.util.List;
@@ -160,7 +161,8 @@ protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable
         tf.dtypes.cast(tf.constant(learningRate), gradient.type()),
         tf.dtypes.cast(tf.constant(rho), gradient.type()),
         tf.dtypes.cast(tf.constant(epsilon), gradient.type()),
-        gradient);
+        gradient,
+        ApplyAdadelta.useLocking(true));
   }
 
   /** {@inheritDoc} */

tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/AdaGrad.java

@@ -19,6 +19,7 @@
 import org.tensorflow.Operand;
 import org.tensorflow.Output;
 import org.tensorflow.op.Op;
+import org.tensorflow.op.train.ApplyAdagrad;
 import org.tensorflow.op.core.Variable;
 import org.tensorflow.types.family.TType;
 
@@ -42,6 +43,9 @@ public class AdaGrad extends Optimizer {
   public static final float LEARNING_RATE_DEFAULT = 0.001f;
   public static final float INITIAL_ACCUMULATOR_DEFAULT = 0.01f;
 
+  private static final ApplyAdagrad.Options[] opts = new ApplyAdagrad.Options[]{
+          ApplyAdagrad.updateSlots(true), ApplyAdagrad.useLocking(true)};
+
   private final float learningRate;
 
   private final float initialAccumulatorValue;
@@ -140,7 +144,7 @@ private <T extends TType> void createAdaGradSlot(Output<T> v) {
   protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable) {
     Variable<T> slot = getSlot(variable, ACCUMULATOR).get();
     return tf.train.applyAdagrad(
-        variable, slot, tf.dtypes.cast(tf.constant(learningRate), gradient.type()), gradient);
+        variable, slot, tf.dtypes.cast(tf.constant(learningRate), gradient.type()), gradient, opts);
   }
 
   /** {@inheritDoc} */

tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/AdaGradDA.java

@@ -22,6 +22,7 @@
 import org.tensorflow.op.Op;
 import org.tensorflow.op.core.Assign;
 import org.tensorflow.op.core.Variable;
+import org.tensorflow.op.train.ApplyAdagradDa;
 import org.tensorflow.types.TInt64;
 import org.tensorflow.types.family.TType;
 
@@ -219,7 +220,8 @@ protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable
         tf.dtypes.cast(tf.constant(learningRate), gradient.type()),
         tf.dtypes.cast(tf.constant(l1Strength), gradient.type()),
         tf.dtypes.cast(tf.constant(l2Strength), gradient.type()),
-        globalStep);
+        globalStep,
+        ApplyAdagradDa.useLocking(true));
   }
 
   /**

tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Adam.java

@@ -26,6 +26,7 @@
 import org.tensorflow.op.core.Assign;
 import org.tensorflow.op.core.Constant;
 import org.tensorflow.op.core.Variable;
+import org.tensorflow.op.train.ApplyAdam;
 import org.tensorflow.types.TFloat32;
 import org.tensorflow.types.family.TType;
 
@@ -237,7 +238,8 @@ protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable
         tf.dtypes.cast(betaOneConst, gradient.type()),
         tf.dtypes.cast(betaTwoConst, gradient.type()),
         tf.dtypes.cast(epsilonConst, gradient.type()),
-        gradient);
+        gradient,
+        ApplyAdam.useLocking(true));
   }
 
   /**

tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Adamax.java

@@ -170,7 +170,8 @@ protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable
         tf.dtypes.cast(betaOneConst, gradient.type()),
         tf.dtypes.cast(betaTwoConst, gradient.type()),
         tf.dtypes.cast(epsilonConst, gradient.type()),
-        gradient);
+        gradient,
+        ApplyAdaMax.useLocking(true));
   }
 
   /** {@inheritDoc} */

tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/GradientDescent.java

@@ -18,6 +18,7 @@
 import org.tensorflow.Graph;
 import org.tensorflow.Output;
 import org.tensorflow.op.Op;
+import org.tensorflow.op.train.ApplyGradientDescent;
 import org.tensorflow.types.family.TType;
 
 /**
@@ -66,7 +67,10 @@ public GradientDescent(Graph graph, String name, float learningRate) {
   @Override
   protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable) {
     return tf.train.applyGradientDescent(
-        variable, tf.dtypes.cast(tf.constant(learningRate), gradient.type()), gradient);
+        variable,
+        tf.dtypes.cast(tf.constant(learningRate), gradient.type()),
+        gradient,
+        ApplyGradientDescent.useLocking(true));
   }
 
   /** {@inheritDoc} */

tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Momentum.java

@@ -139,7 +139,8 @@ protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable
         tf.dtypes.cast(tf.constant(learningRate), gradient.type()),
         gradient,
         tf.dtypes.cast(tf.constant(momentum), gradient.type()),
-        ApplyMomentum.useNesterov(useNesterov));
+        ApplyMomentum.useNesterov(useNesterov),
+        ApplyMomentum.useLocking(true));
   }
 
   /** {@inheritDoc} */

tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/RMSProp.java

@@ -20,6 +20,8 @@
 import org.tensorflow.Output;
 import org.tensorflow.op.Op;
 import org.tensorflow.op.core.Variable;
+import org.tensorflow.op.train.ApplyCenteredRmsProp;
+import org.tensorflow.op.train.ApplyRmsProp;
 import org.tensorflow.types.family.TType;
 
 import java.util.List;
@@ -202,7 +204,8 @@ protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable
           tf.dtypes.cast(tf.constant(decay), gradient.type()),
           tf.dtypes.cast(tf.constant(momentum), gradient.type()),
           tf.dtypes.cast(tf.constant(epsilon), gradient.type()),
-          gradient);
+          gradient,
+          ApplyCenteredRmsProp.useLocking(true));
     }
     return tf.train.applyRmsProp(
         variable,
@@ -212,7 +215,8 @@ protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable
         tf.dtypes.cast(tf.constant(decay), gradient.type()),
         tf.dtypes.cast(tf.constant(momentum), gradient.type()),
         tf.dtypes.cast(tf.constant(epsilon), gradient.type()),
-        gradient);
+        gradient,
+        ApplyRmsProp.useLocking(true));
   }
 
   /** {@inheritDoc} */

tensorflow-framework/src/test/java/org/tensorflow/framework/optimizers/GradientDescentTest.java

@@ -2,19 +2,34 @@
 
 import org.junit.jupiter.api.*;
 import org.tensorflow.Graph;
+import org.tensorflow.Session;
+import org.tensorflow.Tensor;
+import org.tensorflow.framework.initializers.Glorot;
+import org.tensorflow.framework.initializers.VarianceScaling;
 import org.tensorflow.framework.utils.TestSession;
+import org.tensorflow.ndarray.FloatNdArray;
 import org.tensorflow.ndarray.Shape;
+import org.tensorflow.ndarray.buffer.DataBuffers;
 import org.tensorflow.op.Op;
 import org.tensorflow.op.Ops;
 import org.tensorflow.op.core.Assign;
 import org.tensorflow.op.core.Constant;
+import org.tensorflow.op.core.Init;
+import org.tensorflow.op.core.Placeholder;
 import org.tensorflow.op.core.Variable;
+import org.tensorflow.op.math.Add;
+import org.tensorflow.op.math.Mean;
+import org.tensorflow.op.nn.Relu;
+import org.tensorflow.proto.framework.ConfigProto;
+import org.tensorflow.proto.framework.GraphDef;
 import org.tensorflow.types.TFloat32;
 import org.tensorflow.types.family.TType;
 
 import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.List;
 
+import static org.junit.jupiter.api.Assertions.assertArrayEquals;
 import static org.junit.jupiter.api.Assertions.assertEquals;
 
 /** Test cases for GradientDescent Optimizer */
@@ -97,4 +112,149 @@ public void testBasic() {
       session.evaluate(expectedVar1, var1);
     }
   }
+
+  // This test fails due to incorrect gradients being generated some of the time, when
+  // using an identical graph on identical data. It should not, but it seems to be a
+  // problem in TF-core.
+  @Disabled
+  @Test
+  public void testDeterminism() {
+    ConfigProto config =
+        ConfigProto.newBuilder()
+            .setIntraOpParallelismThreads(1)
+            .setInterOpParallelismThreads(1)
+            .build();
+
+    GraphDef def;
+    String initName;
+    String trainName;
+    String lossName;
+
+    String fcWeightName, fcBiasName, outputWeightName, outputBiasName;
+
+    try (Graph g = new Graph()) {
+      Ops tf = Ops.create(g);
+
+      Glorot<TFloat32> initializer =
+          new Glorot<>(tf, VarianceScaling.Distribution.TRUNCATED_NORMAL, 1L);
+      // Inputs
+      Placeholder<TFloat32> input =
+          tf.withName("input").placeholder(TFloat32.class, Placeholder.shape(Shape.of(-1, 20)));
+
+      // Fully connected layer
+      Variable<TFloat32> fcWeights =
+          tf.variable(initializer.call(tf.array(20L, 200L), TFloat32.class));
+      fcWeightName = fcWeights.op().name();
+      Variable<TFloat32> fcBiases = tf.variable(tf.fill(tf.array(200), tf.constant(0.1f)));
+      fcBiasName = fcBiases.op().name();
+      Relu<TFloat32> relu = tf.nn.relu(tf.math.add(tf.linalg.matMul(input, fcWeights), fcBiases));
+
+      // Output layer
+      Variable<TFloat32> outputWeights =
+          tf.variable(initializer.call(tf.array(200L, 2L), TFloat32.class));
+      outputWeightName = outputWeights.op().name();
+      Variable<TFloat32> outputBiases = tf.variable(tf.fill(tf.array(2L), tf.constant(0.1f)));
+      outputBiasName = outputBiases.op().name();
+      Add<TFloat32> output = tf.math.add(tf.linalg.matMul(relu, outputWeights), outputBiases);
+
+      // Loss
+      Placeholder<TFloat32> placeholder =
+          tf.withName("output").placeholder(TFloat32.class, Placeholder.shape(Shape.of(-1, 2)));
+      Mean<TFloat32> loss =
+          tf.math.mean(
+              tf.nn.raw.softmaxCrossEntropyWithLogits(output, placeholder).loss(), tf.constant(0));
+      lossName = loss.op().name();
+
+      GradientDescent gd = new GradientDescent(g, 10.0f);
+      Op trainingOp = gd.minimize(loss);
+      trainName = trainingOp.op().name();
+
+      // Create the init op
+      Init init = tf.init();
+      initName = init.op().name();
+
+      def = g.toGraphDef();
+    }
+
+    float[] data =
+        new float[] {
+          1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, -8.0f, -9.0f, 10.0f, 11.0f, 12.0f, 13.0f,
+          -14.0f, -15.0f, 0.16f, 0.17f, 0.18f, 1.9f, 0.2f
+        };
+    TFloat32 dataTensor = TFloat32.tensorOf(Shape.of(1, 20), DataBuffers.of(data));
+    float[] target = new float[] {0.2f, 0.8f};
+    TFloat32 targetTensor = TFloat32.tensorOf(Shape.of(1, 2), DataBuffers.of(target));
+
+    int numRuns = 20;
+    List<List<Tensor>> initialized = new ArrayList<>(numRuns);
+    List<List<Tensor>> trained = new ArrayList<>(numRuns);
+    float[] initialLoss = new float[numRuns];
+    float[] postTrainingLoss = new float[numRuns];
+
+    for (int i = 0; i < numRuns; i++) {
+      try (Graph g = new Graph();
+          Session s = new Session(g, config)) {
+        g.importGraphDef(def);
+        s.run(initName);
+
+        initialized.add(
+            s.runner()
+                .fetch(fcWeightName)
+                .fetch(fcBiasName)
+                .fetch(outputWeightName)
+                .fetch(outputBiasName)
+                .run());
+
+        TFloat32 lossVal = (TFloat32) s.runner()
+            .addTarget(trainName)
+            .feed("input", dataTensor)
+            .feed("output", targetTensor)
+            .fetch(lossName)
+            .run().get(0);
+        initialLoss[i] = lossVal.getFloat();
+        lossVal.close();
+
+        trained.add(
+            s.runner()
+                .fetch(fcWeightName)
+                .fetch(fcBiasName)
+                .fetch(outputWeightName)
+                .fetch(outputBiasName)
+                .run());
+
+        lossVal = (TFloat32) s.runner()
+                .addTarget(trainName)
+                .feed("input", dataTensor)
+                .feed("output", targetTensor)
+                .fetch(lossName)
+                .run().get(0);
+        postTrainingLoss[i] = lossVal.getFloat();
+        lossVal.close();
+      }
+    }
+
+    for (int i = 1; i < numRuns; i++) {
+      assertEquals(initialLoss[0], initialLoss[i]);
+      assertEquals(postTrainingLoss[0], postTrainingLoss[i]);
+      // Because the weights are references not copies.
+      assertEquals(initialized.get(i), trained.get(i));
+      assertEquals(
+          initialized.get(0),
+          initialized.get(i),
+          "Variables not initialized identically (0," + i + ")");
+      assertEquals(
+          trained.get(0), trained.get(i), "Variables not trained identically (0," + i + ")");
+    }
+
+    for (List<Tensor> curInit : initialized) {
+      for (Tensor t : curInit) {
+        t.close();
+      }
+    }
+    for (List<Tensor> curTrained : trained) {
+      for (Tensor t : curTrained) {
+        t.close();
+      }
+    }
+  }
 }