com.simiacryptus.mindseye.layers.cudnn.ImgBandBiasLayer Maven / Gradle / Ivy
/*
* Copyright (c) 2018 by Andrew Charneski.
*
* The author licenses this file to you under the
* Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance
* with the License. You may obtain a copy
* of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License 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 com.simiacryptus.mindseye.layers.cudnn;
import com.google.gson.JsonObject;
import com.simiacryptus.mindseye.lang.*;
import com.simiacryptus.mindseye.lang.cudnn.*;
import com.simiacryptus.mindseye.layers.java.ProductInputsLayer;
import com.simiacryptus.util.FastRandom;
import com.simiacryptus.util.Util;
import jcuda.jcudnn.cudnnOpTensorDescriptor;
import jcuda.jcudnn.cudnnOpTensorOp;
import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
import java.util.UUID;
import java.util.function.DoubleSupplier;
import java.util.function.IntToDoubleFunction;
import java.util.stream.Stream;
/**
* This key multiplies together the inputs, element-by-element. It can be used to implement integer-power activation
* layers, such as the square needed in MeanSqLossLayer.
*/
@SuppressWarnings("serial")
public class ImgBandBiasLayer extends LayerBase implements MultiPrecision {
private Precision precision = Precision.Double;
private Tensor bias;
/**
* Instantiates a new Product inputs key.
*
* @param bands the bands
*/
public ImgBandBiasLayer(int bands) {
this(new Tensor(1, 1, bands));
this.bias.freeRef();
}
/**
* Instantiates a new Product inputs key.
*
* @param bias the bias
*/
public ImgBandBiasLayer(final Tensor bias) {
this.bias = bias;
this.bias.addRef();
}
/**
* Instantiates a new Product inputs key.
*
* @param id the id
* @param rs the rs
*/
protected ImgBandBiasLayer(@Nonnull final JsonObject id, final Map rs) {
super(id);
this.precision = Precision.valueOf(id.getAsJsonPrimitive("precision").getAsString());
this.bias = Tensor.fromJson(id.get("bias"), rs);
}
/**
* From json product inputs key.
*
* @param json the json
* @param rs the rs
* @return the product inputs key
*/
public static ImgBandBiasLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new ImgBandBiasLayer(json, rs);
}
/**
* Gets compatibility key.
*
* @return the compatibility key
*/
@Nonnull
public Layer getCompatibilityLayer() {
return this.as(ProductInputsLayer.class);
}
@Nullable
@Override
public Result evalAndFree(@Nonnull final Result... inObj) {
if (!CudaSystem.isEnabled()) return getCompatibilityLayer().evalAndFree(inObj);
if (inObj.length != 1) {
throw new IllegalArgumentException("inObj.length=" + inObj.length);
}
Result input = inObj[0];
final TensorList inputData = input.getData();
@Nonnull final int[] inputDimensions = inputData.getDimensions();
final int length = inputData.length();
if (3 != inputDimensions.length) {
throw new IllegalArgumentException("dimensions=" + Arrays.toString(inputDimensions));
}
if (0 == Tensor.length(inputData.getDimensions())) {
return input;
}
if (0 == bias.length()) {
return input;
}
// assert !right.isAlive();
return new Result(CudaSystem.run(gpu -> {
@Nonnull final CudaResource opDescriptor = gpu.newOpDescriptor(cudnnOpTensorOp.CUDNN_OP_TENSOR_ADD, precision);
@Nonnull final CudaDevice.CudaTensorDescriptor outputDescriptor = gpu.newTensorDescriptor(precision, length,
inputDimensions[2], inputDimensions[1], inputDimensions[0],
inputDimensions[2] * inputDimensions[1] * inputDimensions[0],
inputDimensions[1] * inputDimensions[0],
inputDimensions[0],
1);
@Nullable final CudaTensor inputTensor = gpu.getTensor(inputData, precision, MemoryType.Device, false);
CudaMemory biasMem = gpu.allocate(bias.length() * precision.size, MemoryType.Device, true).write(precision, bias.getData());
int[] biasDim = bias.getDimensions();
CudaDevice.CudaTensorDescriptor biasDescriptor = gpu.newTensorDescriptor(precision, 1, biasDim[2], biasDim[1], biasDim[0],
biasDim[2] * biasDim[1] * biasDim[0], biasDim[1] * biasDim[0], biasDim[0], 1);
//assert lPtr.size == rPtr.size;
@Nonnull final CudaMemory outputPtr = gpu.allocate((long) precision.size * outputDescriptor.nStride * length, MemoryType.Managed.normalize(), true);
CudaMemory inputMemory = inputTensor.getMemory(gpu);
CudaSystem.handle(gpu.cudnnOpTensor(opDescriptor.getPtr(),
precision.getPointer(1.0), inputTensor.descriptor.getPtr(), inputMemory.getPtr(),
precision.getPointer(1.0), biasDescriptor.getPtr(), biasMem.getPtr(),
precision.getPointer(0.0), outputDescriptor.getPtr(), outputPtr.getPtr()));
assert CudaDevice.isThreadDeviceId(gpu.getDeviceId());
inputMemory.dirty();
biasMem.dirty();
outputPtr.dirty();
inputMemory.freeRef();
biasMem.freeRef();
biasDescriptor.freeRef();
inputTensor.freeRef();
opDescriptor.freeRef();
CudaTensor cudaTensor = CudaTensor.wrap(outputPtr, outputDescriptor, precision);
return CudaTensorList.wrap(cudaTensor, length, inputDimensions, precision);
}, inputData), (@Nonnull final DeltaSet buffer, @Nonnull final TensorList delta) -> {
if (!isFrozen()) {
@Nonnull double[] biasDelta = CudaSystem.run(gpu -> {
@Nullable final CudaTensor deltaTensor = gpu.getTensor(delta, precision, MemoryType.Device, false);
CudaMemory biasMem = gpu.allocate(bias.length() * precision.size, MemoryType.Device, true).write(precision, bias.getData());
int[] biasDim = bias.getDimensions();
CudaDevice.CudaTensorDescriptor biasDescriptor = gpu.newTensorDescriptor(precision,
1, biasDim[2], biasDim[1], biasDim[0],
biasDim[2] * biasDim[1] * biasDim[0], biasDim[1] * biasDim[0], biasDim[0], 1);
CudaMemory deltaTensorMemory = deltaTensor.getMemory(gpu);
gpu.cudnnConvolutionBackwardBias(precision.getPointer(1.0), deltaTensor.descriptor.getPtr(), deltaTensorMemory.getPtr(),
precision.getPointer(0.0), biasDescriptor.getPtr(), biasMem.getPtr());
assert CudaDevice.isThreadDeviceId(gpu.getDeviceId());
biasMem.dirty();
double[] biasV = new double[bias.length()];
biasMem.read(precision, biasV);
Stream.of(biasMem, deltaTensorMemory, deltaTensor, biasDescriptor).forEach(ReferenceCounting::freeRef);
return biasV;
}, delta);
buffer.get(ImgBandBiasLayer.this.getId(), bias).addInPlace(biasDelta).freeRef();
}
if (input.isAlive()) {
input.accumulate(buffer, delta);
} else {
delta.freeRef();
}
}) {
@Override
public final void accumulate(DeltaSet buffer, TensorList delta) {
getAccumulator().accept(buffer, delta);
}
@Override
protected void _free() {
inputData.freeRef();
input.freeRef();
}
@Override
public boolean isAlive() {
for (@Nonnull final Result element : inObj)
if (element.isAlive()) {
return true;
}
return false;
}
};
}
@Nonnull
@Override
public JsonObject getJson(Map resources, DataSerializer dataSerializer) {
@Nonnull JsonObject json = super.getJsonStub();
json.addProperty("precision", precision.name());
json.add("bias", bias.toJson(resources, dataSerializer));
return json;
}
@Override
public Precision getPrecision() {
return precision;
}
@Nonnull
@Override
public ImgBandBiasLayer setPrecision(final Precision precision) {
this.precision = precision;
return this;
}
@Nonnull
@Override
public List state() {
return Arrays.asList(bias.getData());
}
/**
* Add weights img band bias key.
*
* @param f the f
* @return the img band bias key
*/
@Nonnull
public ImgBandBiasLayer addWeights(@Nonnull final DoubleSupplier f) {
Util.add(f, getBias());
return this;
}
/**
* Add weights img band bias key.
*
* @param f the f
* @return the img band bias key
*/
@Nonnull
public ImgBandBiasLayer setWeights(@Nonnull final IntToDoubleFunction f) {
bias.setByCoord(c -> f.applyAsDouble(c.getIndex()));
return this;
}
/**
* Sets weights log.
*
* @param value the value
* @return the weights log
*/
@Nonnull
public ImgBandBiasLayer setWeightsLog(final double value) {
bias.setByCoord(c -> (FastRandom.INSTANCE.random() - 0.5) * Math.pow(10, value));
return this;
}
/**
* Sets and free.
*
* @param tensor the tensor
* @return the and free
*/
public ImgBandBiasLayer setAndFree(final Tensor tensor) {
set(tensor);
tensor.freeRef();
return this;
}
/**
* Set img band bias key.
*
* @param tensor the tensor
* @return the img band bias key
*/
public ImgBandBiasLayer set(final Tensor tensor) {
bias.set(tensor);
return this;
}
/**
* Get bias double [ ].
*
* @return the double [ ]
*/
public double[] getBias() {
return bias.getData();
}
/**
* Sets bias.
*
* @param bias the bias
* @return the bias
*/
public ImgBandBiasLayer setBias(Tensor bias) {
if (this.bias != null) {
this.bias.freeRef();
}
this.bias = bias;
this.bias.addRef();
return this;
}
@Override
protected void _free() {
if (this.bias != null) {
bias.freeRef();
bias = null;
}
super._free();
}
}
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