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add adamw, rms, adagrad, sgd optimizers

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Anastasia Golovina 2022-04-25 22:17:07 +03:00
parent c53bdd38f8
commit b288c4ce59
4 changed files with 224 additions and 3 deletions
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32
kmath-noa/src/main/java/space/kscience/kmath/noa/JNoa.java
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@ -316,6 +316,38 @@ class JNoa {
public static native void zeroGradAdamOptim(long adamOptHandle);
public static native long rmsOptim(long jitModuleHandle, double learningRate);
public static native void disposeRmsOptim(long rmsOptHandle);
public static native void stepRmsOptim(long rmsOptHandle);
public static native void zeroGradRmsOptim(long rmsOptHandle);
public static native long adamWOptim(long jitModuleHandle, double learningRate);
public static native void disposeAdamWOptim(long adamWOptHandle);
public static native void stepAdamWOptim(long adamWOptHandle);
public static native void zeroGradAdamWOptim(long adamWOptHandle);
public static native long adagradOptim(long jitModuleHandle, double learningRate);
public static native void disposeAdagradOptim(long adagradOptHandle);
public static native void stepAdagradOptim(long adagradOptHandle);
public static native void zeroGradAdagradOptim(long adagradOptHandle);
public static native long sgdOptim(long jitModuleHandle, double learningRate);
public static native void disposeSgdOptim(long sgdOptHandle);
public static native void stepSgdOptim(long sgdOptHandle);
public static native void zeroGradSgdOptim(long sgdOptHandle);
public static native void swapTensors(long lhsHandle, long rhsHandle);
public static native long loadTensorDouble(String path, int device);

13
kmath-noa/src/main/kotlin/space/kscience/kmath/noa/algebras.kt
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@ -342,6 +342,18 @@ protected constructor(scope: NoaScope) :
public fun NoaJitModule.adamOptimiser(learningRate: Double): AdamOptimiser =
AdamOptimiser(scope, JNoa.adamOptim(jitModuleHandle, learningRate))
public fun NoaJitModule.rmsOptimiser(learningRate: Double): RMSpropOptimiser =
RMSpropOptimiser(scope, JNoa.rmsOptim(jitModuleHandle, learningRate))
public fun NoaJitModule.adamWOptimiser(learningRate: Double): AdamWOptimiser =
AdamWOptimiser(scope, JNoa.adamWOptim(jitModuleHandle, learningRate))
public fun NoaJitModule.adagradOptimiser(learningRate: Double): AdagradOptimiser =
AdagradOptimiser(scope, JNoa.adagradOptim(jitModuleHandle, learningRate))
public fun NoaJitModule.sgdOptimiser(learningRate: Double): SgdOptimiser =
SgdOptimiser(scope, JNoa.sgdOptim(jitModuleHandle, learningRate))
}
public sealed class NoaDoubleAlgebra
@ -722,3 +734,4 @@ protected constructor(scope: NoaScope) :
override fun NoaIntTensor.set(dim: Int, slice: Slice, array: IntArray): Unit =
JNoa.setSliceBlobInt(tensorHandle, dim, slice.first, slice.second, array)
}

33
kmath-noa/src/main/kotlin/space/kscience/kmath/noa/optim.kt
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@ -23,3 +23,36 @@ internal constructor(scope: NoaScope, internal val optimiserHandle: OptimiserHan
override fun step(): Unit = JNoa.stepAdamOptim(optimiserHandle)
override fun zeroGrad(): Unit = JNoa.zeroGradAdamOptim(optimiserHandle)
}
public class RMSpropOptimiser
internal constructor(scope: NoaScope, internal val optimiserHandle: OptimiserHandle)
: NoaOptimiser(scope) {
override fun dispose(): Unit = JNoa.disposeRmsOptim(optimiserHandle)
override fun step(): Unit = JNoa.stepRmsOptim(optimiserHandle)
override fun zeroGrad(): Unit = JNoa.zeroGradRmsOptim(optimiserHandle)
}
public class AdamWOptimiser
internal constructor(scope: NoaScope, internal val optimiserHandle: OptimiserHandle)
: NoaOptimiser(scope) {
override fun dispose(): Unit = JNoa.disposeAdamWOptim(optimiserHandle)
override fun step(): Unit = JNoa.stepAdamWOptim(optimiserHandle)
override fun zeroGrad(): Unit = JNoa.zeroGradAdamWOptim(optimiserHandle)
}
public class AdagradOptimiser
internal constructor(scope: NoaScope, internal val optimiserHandle: OptimiserHandle)
: NoaOptimiser(scope) {
override fun dispose(): Unit = JNoa.disposeAdagradOptim(optimiserHandle)
override fun step(): Unit = JNoa.stepAdagradOptim(optimiserHandle)
override fun zeroGrad(): Unit = JNoa.zeroGradAdagradOptim(optimiserHandle)
}
public class SgdOptimiser
internal constructor(scope: NoaScope, internal val optimiserHandle: OptimiserHandle)
: NoaOptimiser(scope) {
override fun dispose(): Unit = JNoa.disposeSgdOptim(optimiserHandle)
override fun step(): Unit = JNoa.stepSgdOptim(optimiserHandle)
override fun zeroGrad(): Unit = JNoa.zeroGradSgdOptim(optimiserHandle)
}

145
kmath-noa/src/test/kotlin/space/kscience/kmath/noa/TestJitModules.kt
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@ -17,7 +17,7 @@ class TestJitModules {
private val lossPath = resources.resolve("loss.pt").absolutePath
@Test
fun testOptimisation() = NoaFloat {
fun testOptimisationAdam() = NoaFloat {
setSeed(SEED)
@ -52,4 +52,147 @@ class TestJitModules {
assertTrue(loss.value() < 0.1)
}!!
@Test
fun testOptimisationRms() = NoaFloat {
setSeed(SEED)
val dataModule = loadJitModule(dataPath)
val netModule = loadJitModule(netPath)
val lossModule = loadJitModule(lossPath)
val xTrain = dataModule.getBuffer("x_train")
val yTrain = dataModule.getBuffer("y_train")
val xVal = dataModule.getBuffer("x_val")
val yVal = dataModule.getBuffer("y_val")
netModule.train(true)
lossModule.setBuffer("target", yTrain)
val yPred = netModule.forward(xTrain)
val loss = lossModule.forward(yPred)
val optimiser = netModule.rmsOptimiser(0.005)
repeat(250){
optimiser.zeroGrad()
netModule.forwardAssign(xTrain, yPred)
lossModule.forwardAssign(yPred, loss)
loss.backward()
optimiser.step()
}
netModule.forwardAssign(xVal, yPred)
lossModule.setBuffer("target", yVal)
lossModule.forwardAssign(yPred, loss)
assertTrue(loss.value() < 0.1)
}!!
@Test
fun testOptimisationAdamW() = NoaFloat {
setSeed(SEED)
val dataModule = loadJitModule(dataPath)
val netModule = loadJitModule(netPath)
val lossModule = loadJitModule(lossPath)
val xTrain = dataModule.getBuffer("x_train")
val yTrain = dataModule.getBuffer("y_train")
val xVal = dataModule.getBuffer("x_val")
val yVal = dataModule.getBuffer("y_val")
netModule.train(true)
lossModule.setBuffer("target", yTrain)
val yPred = netModule.forward(xTrain)
val loss = lossModule.forward(yPred)
val optimiser = netModule.adamWOptimiser(0.005)
repeat(250){
optimiser.zeroGrad()
netModule.forwardAssign(xTrain, yPred)
lossModule.forwardAssign(yPred, loss)
loss.backward()
optimiser.step()
}
netModule.forwardAssign(xVal, yPred)
lossModule.setBuffer("target", yVal)
lossModule.forwardAssign(yPred, loss)
assertTrue(loss.value() < 0.1)
}!!
@Test
fun testOptimisationAdagrad() = NoaFloat {
setSeed(SEED)
val dataModule = loadJitModule(dataPath)
val netModule = loadJitModule(netPath)
val lossModule = loadJitModule(lossPath)
val xTrain = dataModule.getBuffer("x_train")
val yTrain = dataModule.getBuffer("y_train")
val xVal = dataModule.getBuffer("x_val")
val yVal = dataModule.getBuffer("y_val")
netModule.train(true)
lossModule.setBuffer("target", yTrain)
val yPred = netModule.forward(xTrain)
val loss = lossModule.forward(yPred)
val optimiser = netModule.adagradOptimiser(0.005)
repeat(250){
optimiser.zeroGrad()
netModule.forwardAssign(xTrain, yPred)
lossModule.forwardAssign(yPred, loss)
loss.backward()
optimiser.step()
}
netModule.forwardAssign(xVal, yPred)
lossModule.setBuffer("target", yVal)
lossModule.forwardAssign(yPred, loss)
assertTrue(loss.value() < 0.1)
}!!
@Test
fun testOptimisationSgd() = NoaFloat {
setSeed(SEED)
val dataModule = loadJitModule(dataPath)
val netModule = loadJitModule(netPath)
val lossModule = loadJitModule(lossPath)
val xTrain = dataModule.getBuffer("x_train")
val yTrain = dataModule.getBuffer("y_train")
val xVal = dataModule.getBuffer("x_val")
val yVal = dataModule.getBuffer("y_val")
netModule.train(true)
lossModule.setBuffer("target", yTrain)
val yPred = netModule.forward(xTrain)
val loss = lossModule.forward(yPred)
val optimiser = netModule.sgdOptimiser(0.005)
repeat(250){
optimiser.zeroGrad()
netModule.forwardAssign(xTrain, yPred)
lossModule.forwardAssign(yPred, loss)
loss.backward()
optimiser.step()
}
netModule.forwardAssign(xVal, yPred)
lossModule.setBuffer("target", yVal)
lossModule.forwardAssign(yPred, loss)
assertTrue(loss.value() < 0.1)
}!!
}