Examples with Tensor com.simiacryptus.mindseye.lang.Tensor used on opensource projects

Example 26 with Tensor

use of com.simiacryptus.mindseye.lang.Tensor in project MindsEye by SimiaCryptus.

the class L1NormalizationLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... input) {
    Arrays.stream(input).forEach(nnResult -> nnResult.addRef());
    final Result in = input[0];
    final TensorList inData = in.getData();
    inData.addRef();
    return new Result(TensorArray.wrap(IntStream.range(0, inData.length()).mapToObj(dataIndex -> {
        @Nullable final Tensor value = inData.get(dataIndex);
        try {
            final double sum = value.sum();
            if (!Double.isFinite(sum) || 0 == sum) {
                value.addRef();
                return value;
            } else {
                return value.scale(1.0 / sum);
            }
        } finally {
            value.freeRef();
        }
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList outDelta) -> {
        if (in.isAlive()) {
            final Tensor[] passbackArray = IntStream.range(0, outDelta.length()).mapToObj(dataIndex -> {
                Tensor inputTensor = inData.get(dataIndex);
                @Nullable final double[] value = inputTensor.getData();
                Tensor outputTensor = outDelta.get(dataIndex);
                @Nullable final double[] delta = outputTensor.getData();
                final double dot = ArrayUtil.dot(value, delta);
                final double sum = Arrays.stream(value).sum();
                @Nonnull final Tensor passback = new Tensor(outputTensor.getDimensions());
                @Nullable final double[] passbackData = passback.getData();
                if (0 != sum || Double.isFinite(sum)) {
                    for (int i = 0; i < value.length; i++) {
                        passbackData[i] = (delta[i] - dot / sum) / sum;
                    }
                }
                outputTensor.freeRef();
                inputTensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]);
            assert Arrays.stream(passbackArray).flatMapToDouble(x -> Arrays.stream(x.getData())).allMatch(v -> Double.isFinite(v));
            @Nonnull TensorArray tensorArray = TensorArray.wrap(passbackArray);
            in.accumulate(buffer, tensorArray);
        }
    }) {

        @Override
        protected void _free() {
            inData.freeRef();
            Arrays.stream(input).forEach(nnResult -> nnResult.freeRef());
        }

        @Override
        public boolean isAlive() {
            return in.isAlive();
        }
    };
}

Example 27 with Tensor

use of com.simiacryptus.mindseye.lang.Tensor in project MindsEye by SimiaCryptus.

the class VGG16_HDF5 method phase0.

/**
 * Phase 0.
 */
protected void phase0() {
    add(new ImgMinSizeLayer(226, 226));
    add(new ImgBandBiasLayer(3).set(new Tensor(-103.939, -116.779, -123.68)));
}

Example 28 with Tensor

use of com.simiacryptus.mindseye.lang.Tensor in project MindsEye by SimiaCryptus.

the class VGG19_HDF5 method phase0.

/**
 * Phase 0.
 */
protected void phase0() {
    add(new ImgMinSizeLayer(226, 226));
    add(new ImgBandBiasLayer(3).set(new Tensor(-103.939, -116.779, -123.68)));
}

Example 29 with Tensor

use of com.simiacryptus.mindseye.lang.Tensor in project MindsEye by SimiaCryptus.

the class CudnnTest method testTensorListMT.

private void testTensorListMT(@Nonnull NotebookOutput log, @Nonnull int[] dimensions, int length, double tolerance, int accumulations) {
    @Nonnull Supplier<TensorList> factory = () -> TensorArray.wrap(IntStream.range(0, length).mapToObj(j -> {
        @Nonnull Tensor tensor = new Tensor(dimensions);
        Arrays.parallelSetAll(tensor.getData(), this::random);
        return tensor;
    }).toArray(j -> new Tensor[j]));
    log.code(() -> {
        @Nonnull ListeningExecutorService pool = MoreExecutors.listeningDecorator(Executors.newFixedThreadPool(5));
        PrintStream out = SysOutInterceptor.INSTANCE.currentHandler();
        try {
            List<ListenableFuture<Object>> collect = IntStream.range(0, 16).mapToObj(workerNumber -> {
                @Nonnull TimedResult<TensorList> originalTiming = TimedResult.time(() -> factory.get());
                TensorList original = originalTiming.result;
                logger.info(String.format("[%s] Calculated test data in %.4fsec", workerNumber, originalTiming.seconds()));
                @Nonnull ListenableFuture<TensorList> mutableDataFuture = pool.submit(() -> CudaSystem.run(gpu -> {
                    PrintStream oldHandler = SysOutInterceptor.INSTANCE.setCurrentHandler(out);
                    @Nonnull TimedResult<CudaTensor> timedResult = TimedResult.time(() -> {
                        return gpu.getTensor(original, Precision.Double, MemoryType.Managed, false);
                    });
                    logger.info(String.format("[%s] Wrote %s in %.4f seconds, Device %d: %s", workerNumber, Arrays.toString(dimensions), timedResult.seconds(), gpu.getDeviceId(), CudaDevice.getDeviceName(gpu.getDeviceId())));
                    SysOutInterceptor.INSTANCE.setCurrentHandler(oldHandler);
                    return CudaTensorList.wrap(timedResult.result, length, dimensions, Precision.Double);
                }, original));
                @Nonnull TimedResult<List<TensorList>> accumulantTiming = TimedResult.time(() -> IntStream.range(0, accumulations).mapToObj(x -> factory.get()).collect(Collectors.toList()));
                List<TensorList> accumulants = accumulantTiming.result;
                @Nonnull TimedResult<TensorList> finalResultTiming = TimedResult.time(() -> {
                    return accumulants.stream().map(x -> {
                        x.addRef();
                        return x;
                    }).reduce((a, b) -> {
                        TensorList sum = a.addAndFree(b);
                        b.freeRef();
                        return sum;
                    }).map(x -> {
                        TensorList sum = x.add(original);
                        x.freeRef();
                        return sum;
                    }).orElseGet(() -> {
                        original.addRef();
                        return original;
                    });
                });
                logger.info(String.format("[%s] Calculated accumulant in %.4fsec", workerNumber, accumulantTiming.seconds()));
                @Nonnull ListenableFuture<TensorList> accumulated = Futures.transform(mutableDataFuture, (x) -> {
                    PrintStream oldHandler = SysOutInterceptor.INSTANCE.setCurrentHandler(out);
                    @Nonnull AtomicReference<TensorList> mutableGpuData = new AtomicReference<>(x);
                    accumulants.stream().parallel().forEach(delta -> {
                        CudaSystem.run(gpu -> {
                            @Nonnull TimedResult<CudaTensorList> timedWrite = TimedResult.time(() -> {
                                @Nullable CudaTensor cudaMemory = gpu.getTensor(delta, Precision.Double, MemoryType.Managed, false);
                                delta.freeRef();
                                return CudaTensorList.wrap(cudaMemory, length, dimensions, Precision.Double);
                            });
                            @Nonnull TimedResult<Void> timedAccumulation = TimedResult.time(() -> {
                                synchronized (mutableGpuData) {
                                    mutableGpuData.getAndUpdate(y -> {
                                        TensorList add = y.add(timedWrite.result);
                                        y.freeRef();
                                        return add;
                                    });
                                }
                                timedWrite.result.freeRef();
                            });
                            logger.info(String.format("[%s] Wrote in %.4f seconds and accumulated %s in %.4f seconds, Device %d: %s", workerNumber, timedAccumulation.seconds(), Arrays.toString(dimensions), timedWrite.seconds(), gpu.getDeviceId(), CudaDevice.getDeviceName(gpu.getDeviceId())));
                        }, delta);
                    });
                    SysOutInterceptor.INSTANCE.setCurrentHandler(oldHandler);
                    return mutableGpuData.get();
                }, pool);
                TensorList finalResult = finalResultTiming.result;
                logger.info(String.format("[%s] Calculated final data in %.4fsec", workerNumber, finalResultTiming.seconds()));
                return Futures.transform(accumulated, (write) -> {
                    original.freeRef();
                    PrintStream oldHandler = SysOutInterceptor.INSTANCE.setCurrentHandler(out);
                    CudaSystem.run(gpu -> {
                        @Nonnull TimedResult<Boolean> timedVerify = TimedResult.time(() -> {
                            @Nonnull TensorList minus = finalResult.minus(write);
                            double diffVal = minus.stream().mapToDouble(x -> {
                                double v = Arrays.stream(x.getData()).map(Math::abs).max().getAsDouble();
                                x.freeRef();
                                return v;
                            }).max().getAsDouble();
                            minus.freeRef();
                            return diffVal < tolerance;
                        });
                        logger.info(String.format("[%s] Read %s and verified in %.4fs using device %d: %s", workerNumber, Arrays.toString(dimensions), timedVerify.seconds(), gpu.getDeviceId(), CudaDevice.getDeviceName(gpu.getDeviceId())));
                        if (!timedVerify.result)
                            Assert.assertTrue(finalResult.prettyPrint() + " != " + write.prettyPrint(), timedVerify.result);
                        write.freeRef();
                    });
                    SysOutInterceptor.INSTANCE.setCurrentHandler(oldHandler);
                    finalResult.freeRef();
                    return null;
                }, pool);
            }).collect(Collectors.toList());
            List<Object> objects = Futures.allAsList(collect).get();
        } catch (@Nonnull InterruptedException | ExecutionException e) {
            throw new RuntimeException(e);
        } finally {
            pool.shutdown();
        }
    });
}

Example 30 with Tensor

use of com.simiacryptus.mindseye.lang.Tensor in project MindsEye by SimiaCryptus.

the class CudnnTest method memoryTransfer.

private void memoryTransfer(@Nonnull NotebookOutput log, int... size) {
    @Nonnull Supplier<TensorList> factory = () -> TensorArray.wrap(IntStream.range(0, 1).mapToObj(j -> {
        @Nonnull Tensor tensor = new Tensor(size);
        Arrays.parallelSetAll(tensor.getData(), this::random);
        return tensor;
    }).toArray(j -> new Tensor[j]));
    TensorList original = factory.get();
    log.code(() -> {
        CudaTensor write = CudaSystem.run(gpu -> {
            @Nonnull TimedResult<CudaTensor> timedResult = TimedResult.time(() -> {
                return gpu.getTensor(original, Precision.Double, MemoryType.Managed, false);
            });
            int deviceNumber = gpu.getDeviceId();
            logger.info(String.format("Wrote %s bytes in %.4f seconds, Device %d: %s", Arrays.toString(size), timedResult.seconds(), deviceNumber, CudaDevice.getDeviceName(deviceNumber)));
            return timedResult.result;
        }, original);
        CudnnHandle.forEach(gpu -> {
            @Nonnull Tensor readCopy = new Tensor(size);
            @Nonnull TimedResult<CudaMemory> timedResult = TimedResult.time(() -> {
                CudaMemory cudaMemory = write.getMemory(gpu);
                CudaMemory read = cudaMemory.read(Precision.Double, readCopy.getData());
                cudaMemory.freeRef();
                return read;
            });
            @Nonnull TimedResult<Boolean> timedVerify = TimedResult.time(() -> {
                @Nullable Tensor tensor = original.get(0);
                boolean equals = tensor.equals(readCopy);
                tensor.freeRef();
                return equals;
            });
            int deviceNumber = gpu.getDeviceId();
            logger.info(String.format("Read %s bytes in %.4f seconds and verified in %.4fs using device %d: %s", Arrays.toString(size), timedResult.seconds(), timedVerify.seconds(), deviceNumber, CudaDevice.getDeviceName(deviceNumber)));
            if (!timedVerify.result)
                Assert.assertTrue(original.prettyPrint() + " != " + readCopy.prettyPrint(), timedVerify.result);
            readCopy.freeRef();
        });
        write.freeRef();
    });
    original.freeRef();
}