use of com.simiacryptus.mindseye.lang.cudnn.CudaTensor in project MindsEye by SimiaCryptus.
the class GramianLayer method evalAndFree.
@Nullable
@Override
public Result evalAndFree(final Result... inObj) {
assert 1 == inObj.length;
TensorList inputData = inObj[0].getData();
int[] inputDimensions = inputData.getDimensions();
assert 3 == inputDimensions.length;
return new Result(CudaSystem.run(gpu -> {
CudaTensor tensor = gpu.getTensor(inputData, precision, MemoryType.Device, false);
CudaTensorList output = getOutput(gpu, tensor);
tensor.freeRef();
return output;
}, inputData), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
@Nonnull final int[] outputDimensions = { 1, 1, inputDimensions[2] * inputDimensions[2] };
if (!Arrays.equals(delta.getDimensions(), outputDimensions)) {
throw new AssertionError(Arrays.toString(delta.getDimensions()) + " != " + Arrays.toString(outputDimensions));
}
if (inObj[0].isAlive()) {
final TensorList passbackTensorList = CudaSystem.run(gpu -> {
@Nullable final CudaTensor inputTensor = gpu.getTensor(inputData, precision, MemoryType.Device, false);
CudaTensor deltaTensor = gpu.getTensor(delta, precision, MemoryType.Device, true);
delta.freeRef();
CudaTensorList feedback = getFeedback(gpu, inputTensor, deltaTensor);
deltaTensor.freeRef();
inputTensor.freeRef();
return feedback;
}, delta);
inObj[0].accumulate(buffer, passbackTensorList);
} else {
delta.freeRef();
}
}) {
@Override
public final void accumulate(DeltaSet<Layer> buffer, TensorList delta) {
getAccumulator().accept(buffer, delta);
}
@Override
protected void _free() {
inputData.freeRef();
Arrays.stream(inObj).forEach(nnResult -> nnResult.freeRef());
}
@Override
public boolean isAlive() {
return Arrays.stream(inObj).anyMatch(x -> x.isAlive());
}
};
}
use of com.simiacryptus.mindseye.lang.cudnn.CudaTensor in project MindsEye by SimiaCryptus.
the class ImgBandBiasLayer method evalAndFree.
@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 leftData = input.getData();
@Nonnull final int[] inputDimensions = leftData.getDimensions();
final int length = leftData.length();
if (3 != inputDimensions.length) {
throw new IllegalArgumentException("dimensions=" + Arrays.toString(inputDimensions));
}
// assert !right.isAlive();
return new Result(CudaSystem.run(gpu -> {
@Nonnull final CudaResource<cudnnOpTensorDescriptor> 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(leftData, 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);
}, leftData), (@Nonnull final DeltaSet<Layer> 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.<ReferenceCounting>of(biasMem, deltaTensorMemory, deltaTensor, biasDescriptor).forEach(ReferenceCounting::freeRef);
return biasV;
}, delta);
buffer.get(ImgBandBiasLayer.this, bias).addInPlace(biasDelta).freeRef();
}
if (input.isAlive()) {
input.accumulate(buffer, delta);
} else {
delta.freeRef();
}
}) {
@Override
public final void accumulate(DeltaSet<Layer> buffer, TensorList delta) {
getAccumulator().accept(buffer, delta);
}
@Override
protected void _free() {
leftData.freeRef();
input.freeRef();
}
@Override
public boolean isAlive() {
for (@Nonnull final Result element : inObj) if (element.isAlive()) {
return true;
}
return false;
}
};
}
use of com.simiacryptus.mindseye.lang.cudnn.CudaTensor in project MindsEye by SimiaCryptus.
the class ImgLinearSubnetLayer method evalAndFree.
@Nullable
@Override
public Result evalAndFree(@Nonnull final Result... inObj) {
assert 1 == inObj.length;
Result input = inObj[0];
TensorList inputData = input.getData();
@Nonnull final int[] inputDims = inputData.getDimensions();
assert 3 == inputDims.length;
int length = inputData.length();
int maxBand = legs.stream().mapToInt(x -> x.toBand).max().getAsInt();
assert maxBand == inputDims[2] : maxBand + " != " + inputDims[2];
assert IntStream.range(0, maxBand).allMatch(i -> 1 == legs.stream().filter(x -> x.fromBand <= i && x.toBand > i).count());
CudaTensor passback = CudaSystem.run(gpu -> {
return CudaTensor.wrap(gpu.allocate(inputData.getElements() * precision.size, MemoryType.Device, true), gpu.newTensorDescriptor(precision, length, inputDims[2], inputDims[1], inputDims[0]), precision);
});
try {
AtomicInteger counter = new AtomicInteger(0);
SumInputsLayer sumInputsLayer = new SumInputsLayer();
try {
Result[] legResults = legs.stream().map(leg -> {
passback.addRef();
ImgBandSelectLayer imgBandSelectLayer = new ImgBandSelectLayer(leg.fromBand, leg.toBand);
input.addRef();
TensorList legData = imgBandSelectLayer.eval(input).getDataAndFree();
imgBandSelectLayer.freeRef();
return leg.inner.evalAndFree(new Result(legData, (DeltaSet<Layer> ctx, TensorList delta) -> {
int[] outputDimensions = delta.getDimensions();
int[] inputDimensions = inputDims;
synchronized (passback) {
CudaSystem.run(gpu -> {
@Nonnull final CudaDevice.CudaTensorDescriptor viewDescriptor = gpu.newTensorDescriptor(//
precision, //
length, //
outputDimensions[2], //
outputDimensions[1], //
outputDimensions[0], //
inputDimensions[2] * inputDimensions[1] * inputDimensions[0], //
inputDimensions[1] * inputDimensions[0], //
inputDimensions[0], 1);
final int byteOffset = viewDescriptor.cStride * leg.fromBand * precision.size;
assert delta.length() == inputData.length();
assert passback.getDeviceId() == gpu.getDeviceId();
// assert error.stream().flatMapToDouble(x-> Arrays.stream(x.getData())).allMatch(Double::isFinite);
@Nullable final CudaTensor deltaTensor = gpu.getTensor(delta, precision, MemoryType.Device, true);
@Nonnull final CudaMemory passbackBuffer = passback.getMemory(gpu);
CudaMemory errorPtrMemory = deltaTensor.getMemory(gpu);
passbackBuffer.synchronize();
gpu.cudnnTransformTensor(precision.getPointer(1.0), deltaTensor.descriptor.getPtr(), errorPtrMemory.getPtr(), precision.getPointer(0.0), viewDescriptor.getPtr(), passbackBuffer.getPtr().withByteOffset(byteOffset));
errorPtrMemory.dirty();
passbackBuffer.dirty();
Stream.<ReferenceCounting>of(deltaTensor, viewDescriptor, passbackBuffer, errorPtrMemory).forEach(ReferenceCounting::freeRef);
}, passback);
}
if (counter.incrementAndGet() >= legs.size()) {
counter.set(0);
input.accumulate(ctx, CudaTensorList.create(passback, length, inputDims, precision));
}
}) {
@Override
protected void _free() {
super._free();
input.freeRef();
passback.freeRef();
}
});
}).toArray(i -> new Result[i]);
return sumInputsLayer.setParallel(parallel).setPrecision(precision).evalAndFree(legResults);
} finally {
sumInputsLayer.freeRef();
input.freeRef();
}
} finally {
passback.freeRef();
}
}
use of com.simiacryptus.mindseye.lang.cudnn.CudaTensor in project MindsEye by SimiaCryptus.
the class ImgTileAssemblyLayer method backprop.
/**
* Backprop.
*
* @param backpropParams the backprop params
*/
public void backprop(final BackpropParams backpropParams) {
final TensorList passbackTensorList = CudaSystem.run(gpu -> {
CudaTensor ptr = copy(gpu, backpropParams.error, backpropParams.tileDimensions, backpropParams.outputDims, backpropParams.length, -backpropParams.positionX, -backpropParams.totalHeight);
return CudaTensorList.wrap(ptr, backpropParams.length, backpropParams.tileDimensions, precision);
}, backpropParams.error);
backpropParams.inObj[backpropParams.inputIndex].accumulate(backpropParams.buffer, passbackTensorList);
}
use of com.simiacryptus.mindseye.lang.cudnn.CudaTensor in project MindsEye by SimiaCryptus.
the class ImgTileAssemblyLayer method copy.
/**
* Copy cuda tensor.
*
* @param gpu the gpu
* @param error the error
* @param tileDimensions the tile dimensions
* @param outputDims the output dims
* @param length the length
* @param positionX the position x
* @param positionY the position y
* @return the cuda tensor
*/
public CudaTensor copy(final CudnnHandle gpu, final TensorList error, final int[] tileDimensions, final int[] outputDims, final int length, final int positionX, final int positionY) {
@Nullable final CudaTensor errorPtr = gpu.getTensor(error, precision, MemoryType.Device, false);
@Nonnull final CudaMemory passbackBuffer = gpu.allocate((long) length * tileDimensions[2] * tileDimensions[1] * tileDimensions[0] * precision.size, MemoryType.Managed.normalize(), false);
copy(gpu, length, outputDims, errorPtr, tileDimensions, passbackBuffer, positionX, positionY);
errorPtr.freeRef();
CudaDevice.CudaTensorDescriptor descriptor = gpu.newTensorDescriptor(precision, length, tileDimensions[2], tileDimensions[1], tileDimensions[0]);
return CudaTensor.wrap(passbackBuffer, descriptor, precision);
}
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