use of com.simiacryptus.mindseye.lang.cudnn.CudaTensor in project MindsEye by SimiaCryptus.
the class ImgConcatLayer method evalAndFree.
@Nullable
@Override
public Result evalAndFree(@Nonnull final Result... inObj) {
if (!CudaSystem.isEnabled())
return getCompatibilityLayer().evalAndFree(inObj);
// assert Arrays.stream(this.bias).allMatch(Double::isFinite);
// assert Arrays.stream(inObj).flatMapToDouble(input->input.data.stream().flatMapToDouble(x-> Arrays.stream(x.getData()))).allMatch(v->Double.isFinite(v));
int[] dimensions = inObj[0].getData().getDimensions();
assert 3 == dimensions.length;
@Nonnull final int[] outputDimensions = Arrays.copyOf(dimensions, dimensions.length);
final int length = inObj[0].getData().length();
assert Arrays.stream(inObj).allMatch(x -> {
@Nonnull int[] d = x.getData().getDimensions();
return 3 == d.length && d[0] == outputDimensions[0] && d[1] == outputDimensions[1] && x.getData().length() == length;
});
outputDimensions[2] = Arrays.stream(inObj).mapToInt(x -> x.getData().getDimensions()[2]).sum();
if (0 < maxBands && outputDimensions[2] > maxBands) {
outputDimensions[2] = maxBands;
}
return new Result(CudaSystem.run(gpu -> {
final long outputSize = ((long) length * outputDimensions[2] * outputDimensions[1] * outputDimensions[0] * precision.size);
@Nonnull final CudaMemory cudaOutput = gpu.allocate(outputSize, MemoryType.Managed.normalize(), true);
IntStream stream = IntStream.range(0, inObj.length);
// if (!CoreSettings.INSTANCE.isConservative() && parallel) stream = stream.parallel();
stream.forEach(i -> {
assert CudaDevice.isThreadDeviceId(gpu.getDeviceId());
final TensorList input = inObj[i].getData();
@Nonnull final int[] inputDimensions = input.getDimensions();
assert inputDimensions[0] == outputDimensions[0];
assert inputDimensions[1] == outputDimensions[1];
int bandOffset = IntStream.range(0, i).map(j -> inObj[j].getData().getDimensions()[2]).sum();
if (maxBands > 0)
bandOffset = Math.min(bandOffset, maxBands);
int inputBands = inputDimensions[2];
if (maxBands > 0)
inputBands = Math.min(inputBands, maxBands - bandOffset);
if (inputBands > 0) {
@Nullable final CudaTensor cudaInput = gpu.getTensor(input, precision, MemoryType.Device, false);
assert inputBands > 0;
assert maxBands <= 0 || inputBands <= maxBands;
assert inputBands <= inputDimensions[2];
@Nonnull final CudaDevice.CudaTensorDescriptor outputDescriptor = gpu.newTensorDescriptor(//
precision, //
length, //
inputBands, //
outputDimensions[1], //
outputDimensions[0], //
outputDimensions[2] * outputDimensions[1] * outputDimensions[0], //
outputDimensions[1] * outputDimensions[0], //
outputDimensions[0], 1);
@Nonnull final CudaDevice.CudaTensorDescriptor inputDescriptor = gpu.newTensorDescriptor(//
precision, //
length, //
inputBands, //
inputDimensions[1], //
inputDimensions[0], //
cudaInput.descriptor.nStride, //
cudaInput.descriptor.cStride, //
cudaInput.descriptor.hStride, cudaInput.descriptor.wStride);
int byteOffset = outputDescriptor.cStride * bandOffset * precision.size;
CudaMemory cudaInputMemory = cudaInput.getMemory(gpu);
gpu.cudnnTransformTensor(precision.getPointer(1.0), inputDescriptor.getPtr(), cudaInputMemory.getPtr(), precision.getPointer(0.0), outputDescriptor.getPtr(), cudaOutput.getPtr().withByteOffset(byteOffset));
assert CudaDevice.isThreadDeviceId(gpu.getDeviceId());
cudaInputMemory.dirty();
cudaOutput.dirty();
cudaInputMemory.freeRef();
Stream.<ReferenceCounting>of(cudaInput, outputDescriptor, inputDescriptor).forEach(ReferenceCounting::freeRef);
}
});
CudaDevice.CudaTensorDescriptor outDesc = gpu.newTensorDescriptor(precision, length, outputDimensions[2], outputDimensions[1], outputDimensions[0]);
return CudaTensorList.wrap(CudaTensor.wrap(cudaOutput, outDesc, precision), length, outputDimensions, precision);
}, Arrays.stream(inObj).map(Result::getData).toArray()), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
assert delta.getDimensions()[0] == outputDimensions[0];
assert delta.getDimensions()[1] == outputDimensions[1];
assert delta.getDimensions()[2] == outputDimensions[2];
if (!Arrays.equals(delta.getDimensions(), outputDimensions)) {
throw new AssertionError(Arrays.toString(delta.getDimensions()) + " != " + Arrays.toString(outputDimensions));
}
// outputBuffer.freeRef();
// assert error.stream().flatMapToDouble(x-> Arrays.stream(x.getData())).allMatch(Double::isFinite);
@Nonnull IntStream stream = IntStream.range(0, inObj.length);
if (!CoreSettings.INSTANCE.isSingleThreaded() && parallel)
stream = stream.parallel();
stream.forEach(i -> {
final Result input = inObj[i];
int[] inputDimentions = input.getData().getDimensions();
assert 3 == inputDimentions.length;
assert delta.length() == input.getData().length();
assert inputDimentions[0] == outputDimensions[0];
assert inputDimentions[1] == outputDimensions[1];
int bandOffset = IntStream.range(0, i).map(j -> inObj[j].getData().getDimensions()[2]).sum();
int inputBands = maxBands <= 0 ? inputDimentions[2] : Math.min(inputDimentions[2], maxBands - bandOffset);
if (inputBands > 0 && input.isAlive()) {
assert inputBands <= inputDimentions[2];
assert inputBands <= outputDimensions[2];
final TensorList passbackTensorList = CudaSystem.run(gpu -> {
final CudaTensor result;
synchronized (gpu) {
result = gpu.getTensor(delta, precision, MemoryType.Device, true);
}
@Nullable final CudaTensor cudaDelta = result;
CudaMemory cudaDeltaMemory = cudaDelta.getMemory(gpu);
try {
if (inputDimentions[2] == inputBands) {
@Nonnull final CudaDevice.CudaTensorDescriptor viewDescriptor = gpu.newTensorDescriptor(//
precision, //
length, //
inputDimentions[2], //
inputDimentions[1], //
inputDimentions[0], //
cudaDelta.descriptor.nStride, //
cudaDelta.descriptor.cStride, //
cudaDelta.descriptor.hStride, cudaDelta.descriptor.wStride);
int byteOffset = cudaDelta.descriptor.cStride * bandOffset * precision.size;
CudaMemory ptr = cudaDeltaMemory.withByteOffset(byteOffset);
CudaTensor cudaTensor = CudaTensor.wrap(ptr, viewDescriptor, precision);
Stream.<ReferenceCounting>of(cudaDelta).forEach(ReferenceCounting::freeRef);
return CudaTensorList.wrap(cudaTensor, length, inputDimentions, precision);
} else {
@Nonnull final CudaDevice.CudaTensorDescriptor passbackTransferDescriptor = gpu.newTensorDescriptor(//
precision, //
length, //
inputBands, //
inputDimentions[1], //
inputDimentions[0], //
inputDimentions[2] * inputDimentions[1] * inputDimentions[0], //
inputDimentions[1] * inputDimentions[0], //
inputDimentions[0], 1);
@Nonnull final CudaDevice.CudaTensorDescriptor passbackDescriptor = gpu.newTensorDescriptor(//
precision, //
length, //
inputDimentions[2], //
inputDimentions[1], //
inputDimentions[0], //
inputDimentions[2] * inputDimentions[1] * inputDimentions[0], //
inputDimentions[1] * inputDimentions[0], //
inputDimentions[0], 1);
@Nonnull final CudaDevice.CudaTensorDescriptor deltaViewDescriptor = gpu.newTensorDescriptor(//
precision, //
length, //
inputBands, //
inputDimentions[1], //
inputDimentions[0], //
cudaDelta.descriptor.nStride, //
cudaDelta.descriptor.cStride, //
cudaDelta.descriptor.hStride, cudaDelta.descriptor.wStride);
@Nonnull final CudaMemory cudaBackprop = gpu.allocate((long) passbackDescriptor.nStride * length * precision.size, MemoryType.Managed.normalize(), inputBands == inputDimentions[2]);
int byteOffset = cudaDelta.descriptor.cStride * bandOffset * precision.size;
gpu.cudnnTransformTensor(precision.getPointer(1.0), deltaViewDescriptor.getPtr(), cudaDeltaMemory.getPtr().withByteOffset(byteOffset), precision.getPointer(0.0), passbackTransferDescriptor.getPtr(), cudaBackprop.getPtr());
cudaBackprop.dirty();
cudaDeltaMemory.dirty();
Stream.<ReferenceCounting>of(cudaDelta, deltaViewDescriptor, passbackTransferDescriptor).forEach(ReferenceCounting::freeRef);
return CudaTensorList.wrap(CudaTensor.wrap(cudaBackprop, passbackDescriptor, precision), length, inputDimentions, precision);
}
} finally {
cudaDeltaMemory.freeRef();
}
});
input.accumulate(buffer, passbackTensorList);
}
// assert passbackTensorList.stream().flatMapToDouble(x-> Arrays.stream(x.getData())).allMatch(v->Double.isFinite(v));
});
}) {
@Override
protected void _free() {
for (@Nonnull Result result : inObj) {
result.freeRef();
result.getData().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 ImgCropLayer method evalAndFree.
@Nullable
@Override
public Result evalAndFree(@Nonnull final Result... inObj) {
if (!CudaSystem.isEnabled())
return getCompatibilityLayer().evalAndFree(inObj);
assert 1 == inObj.length;
final Result input = inObj[0];
final TensorList inputData = input.getData();
assert 3 == inputData.getDimensions().length;
final int length = inputData.length();
@Nonnull int[] dimIn = inputData.getDimensions();
if (dimIn[0] == sizeX && dimIn[1] == sizeY) {
return input;
}
@Nonnull final int[] dimOut = Arrays.copyOf(dimIn, 3);
dimOut[0] = sizeX;
dimOut[1] = sizeY;
final TensorList outputData = CudaSystem.run(gpu -> {
@Nullable final CudaTensor inputTensor = gpu.getTensor(inputData, precision, MemoryType.Device, false);
inputData.freeRef();
boolean dirty = dimOut[0] <= dimIn[0] && dimOut[1] <= dimIn[1];
assert dimOut[0] > 0;
assert dimOut[1] > 0;
assert dimOut[2] > 0;
CudaTensor cudaTensor = copy(gpu, inputTensor, length, dimIn, dimOut, dirty, precision);
Stream.<ReferenceCounting>of(inputTensor).forEach(ReferenceCounting::freeRef);
return CudaTensorList.wrap(cudaTensor, length, dimOut, precision);
}, inputData);
return new Result(outputData, (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
if (!Arrays.equals(delta.getDimensions(), outputData.getDimensions())) {
throw new AssertionError(Arrays.toString(delta.getDimensions()) + " != " + Arrays.toString(outputData.getDimensions()));
}
if (delta.length() != outputData.length()) {
throw new AssertionError(delta.length() + " != " + outputData.length());
}
assert delta.length() == length;
if (input.isAlive()) {
final TensorList passbackTensorList = CudaSystem.run(gpu -> {
@Nullable final CudaTensor errorPtr = gpu.getTensor(delta, precision, MemoryType.Device, false);
delta.freeRef();
boolean dirty = dimOut[0] >= dimIn[0] && dimOut[1] >= dimIn[1];
CudaTensor cudaTensor = copy(gpu, errorPtr, length, dimOut, dimIn, dirty, precision);
Stream.<ReferenceCounting>of(errorPtr).forEach(ReferenceCounting::freeRef);
return CudaTensorList.wrap(cudaTensor, length, dimIn, precision);
}, delta);
input.accumulate(buffer, passbackTensorList);
} else {
delta.freeRef();
}
}) {
@Override
public void accumulate(final DeltaSet<Layer> buffer, final TensorList delta) {
getAccumulator().accept(buffer, delta);
}
@Override
protected void _free() {
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 ImgTileAssemblyLayer method copy.
/**
* Copy.
*
* @param copyParams the copy params
*/
public void copy(final CopyParams copyParams) {
CudnnHandle gpu = copyParams.gpu;
gpu.initThread();
assert CudaDevice.isThreadDeviceId(gpu.getDeviceId());
@Nullable final CudaTensor inputBuffer = gpu.getTensor(copyParams.inObj[copyParams.inputIndex].getData(), precision, MemoryType.Device, false);
copy(gpu, copyParams.length, copyParams.tileDimensions, inputBuffer, copyParams.outputDims, copyParams.outputBuffer, copyParams.positionX, copyParams.totalHeight);
inputBuffer.freeRef();
}
use of com.simiacryptus.mindseye.lang.cudnn.CudaTensor in project MindsEye by SimiaCryptus.
the class ImgTileAssemblyLayer method evalAndFree.
@Nullable
@Override
public Result evalAndFree(@Nonnull final Result... inObj) {
if (!CudaSystem.isEnabled())
return getCompatibilityLayer().evalAndFree(inObj);
if (1 == inObj.length) {
return inObj[0];
}
int[] inputDimensions = inObj[0].getData().getDimensions();
assert 3 == inputDimensions.length;
final int length = inObj[0].getData().length();
int[] outputDims = getOutputDims(inObj);
final TensorList outputData = CudaSystem.run(gpu -> {
assert CudaDevice.isThreadDeviceId(gpu.getDeviceId());
assert outputDims[0] > 0;
assert outputDims[1] > 0;
assert outputDims[2] > 0;
@Nonnull final CudaMemory outputBuffer = gpu.allocate((long) length * outputDims[2] * outputDims[1] * outputDims[0] * precision.size, MemoryType.Managed.normalize(), false);
int totalWidth = 0;
int totalHeight = 0;
int inputIndex = 0;
List<CopyParams> copies = new ArrayList<>();
for (int row = 0; row < rows; row++) {
int positionX = 0;
int rowHeight = 0;
for (int col = 0; col < columns; col++) {
int[] tileDimensions = inObj[inputIndex].getData().getDimensions();
rowHeight = Math.max(rowHeight, tileDimensions[1]);
copies.add(new CopyParams(gpu, inObj, outputBuffer, length, outputDims, tileDimensions, inputIndex, positionX, totalHeight));
positionX += tileDimensions[0];
inputIndex += 1;
assert CudaDevice.isThreadDeviceId(gpu.getDeviceId());
}
totalHeight += rowHeight;
totalWidth = Math.max(totalWidth, positionX);
}
assert CudaDevice.isThreadDeviceId(gpu.getDeviceId());
Stream<CopyParams> stream = copies.stream();
if (!CoreSettings.INSTANCE.isSingleThreaded() && parallel)
stream = stream.parallel();
stream.forEach(this::copy);
Arrays.stream(inObj).forEach(r -> r.getData().freeRef());
CudaDevice.CudaTensorDescriptor descriptor = gpu.newTensorDescriptor(precision, length, outputDims[2], outputDims[1], outputDims[0]);
CudaTensor ptr = CudaTensor.wrap(outputBuffer, descriptor, precision);
return CudaTensorList.wrap(ptr, length, outputDims, precision);
}, Arrays.stream(inObj).map(Result::getData).toArray());
return new Result(outputData, (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList error) -> {
if (!Arrays.equals(error.getDimensions(), outputData.getDimensions())) {
throw new AssertionError(Arrays.toString(error.getDimensions()) + " != " + Arrays.toString(outputData.getDimensions()));
}
if (error.length() != outputData.length()) {
throw new AssertionError(error.length() + " != " + outputData.length());
}
assert error.length() == length;
int totalHeight = 0;
int inputIndex = 0;
List<BackpropParams> tasks = new ArrayList<>();
for (int row = 0; row < rows; row++) {
int positionX = 0;
int rowHeight = 0;
for (int col = 0; col < columns; col++) {
Result in = inObj[inputIndex];
int[] tileDimensions = in.getData().getDimensions();
rowHeight = Math.max(rowHeight, tileDimensions[1]);
if (inObj[inputIndex].isAlive()) {
tasks.add(new BackpropParams(inObj, buffer, error, outputDims, tileDimensions, length, positionX, totalHeight, inputIndex));
}
positionX += tileDimensions[0];
inputIndex += 1;
}
totalHeight += rowHeight;
}
Stream<BackpropParams> stream = tasks.stream();
if (!CoreSettings.INSTANCE.isSingleThreaded() && parallel)
stream = stream.parallel();
stream.forEach(this::backprop);
}) {
@Override
protected void _free() {
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 ImgTileSelectLayer method evalAndFree.
@Nullable
@Override
public Result evalAndFree(@Nonnull final Result... inObj) {
if (!CudaSystem.isEnabled())
return getCompatibilityLayer().evalAndFree(inObj);
assert 1 == inObj.length;
final Result input = inObj[0];
final TensorList inputData = input.getData();
assert 3 == inputData.getDimensions().length;
final int length = inputData.length();
@Nonnull int[] dimIn = inputData.getDimensions();
if (dimIn[0] == sizeY && dimIn[1] == sizeX) {
return input;
}
@Nonnull final int[] dimOut = getViewDimensions(dimIn, new int[] { sizeY, sizeX, dimIn[2] }, new int[] { positionX, positionY, 0 });
final TensorList outputData = CudaSystem.run(gpu -> {
assert dimOut[0] > 0;
assert dimOut[1] > 0;
assert dimOut[2] > 0;
boolean dirty = dimOut[0] == dimIn[0] && dimOut[1] == dimIn[1];
CudaTensor cudaTensor = copy(gpu, inputData, dimIn, dimOut, precision, this.positionX, this.positionY, dirty);
return CudaTensorList.wrap(cudaTensor, length, dimOut, precision);
}, inputData);
int[] outputDimensions = outputData.getDimensions();
assert length == outputData.length();
return new Result(outputData, (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList error) -> {
if (!Arrays.equals(error.getDimensions(), outputDimensions)) {
throw new AssertionError(Arrays.toString(error.getDimensions()) + " != " + Arrays.toString(outputDimensions));
}
if (error.length() != length) {
throw new AssertionError(error.length() + " != " + length);
}
assert error.length() == inputData.length();
if (input.isAlive()) {
final TensorList passbackTensorList = CudaSystem.run(gpu -> {
boolean dirty = dimOut[0] >= dimIn[0] && dimOut[1] >= dimIn[1];
CudaTensor cudaTensor = copy(gpu, error, dimOut, dimIn, precision, -this.positionX, -this.positionY, dirty);
return CudaTensorList.wrap(cudaTensor, length, dimIn, precision);
}, error);
input.accumulate(buffer, passbackTensorList);
}
}) {
@Override
protected void _free() {
Arrays.stream(inObj).forEach(nnResult -> nnResult.freeRef());
}
@Override
public boolean isAlive() {
return Arrays.stream(inObj).anyMatch(x -> x.isAlive());
}
};
}
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