Nd4j refactoring (#101)
* cleanup Signed-off-by: Robert Altena <Rob@Ra-ai.com> * wip Signed-off-by: Robert Altena <Rob@Ra-ai.com> * wip Signed-off-by: Robert Altena <Rob@Ra-ai.com>master
Robert Altena
GitHub
parent
b8846113bd
commit
a438434b1f
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@ -54,7 +54,6 @@ import org.nd4j.linalg.api.ops.impl.indexaccum.IMax;
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import org.nd4j.linalg.api.ops.impl.indexaccum.IMin;
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import org.nd4j.linalg.api.ops.impl.reduce.Mmul;
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import org.nd4j.linalg.api.ops.impl.scalar.ReplaceNans;
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import org.nd4j.linalg.api.ops.impl.reduce.TensorMmul;
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import org.nd4j.linalg.api.ops.impl.scatter.ScatterUpdate;
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import org.nd4j.linalg.api.ops.impl.shape.Diag;
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import org.nd4j.linalg.api.ops.impl.shape.DiagPart;
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@ -90,7 +89,6 @@ import org.nd4j.tools.PropertyParser;
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import org.nd4j.versioncheck.VersionCheck;
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import java.io.*;
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import java.lang.ref.ReferenceQueue;
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import java.lang.reflect.Constructor;
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import java.math.BigDecimal;
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import java.nio.ByteBuffer;
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@ -114,29 +112,29 @@ import java.util.logging.Logger;
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*/
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public class Nd4j {
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public final static String DATA_BUFFER_OPS = "databufferfactory";
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public final static String CONVOLUTION_OPS = "convops";
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private final static String DATA_BUFFER_OPS = "databufferfactory";
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private final static String CONVOLUTION_OPS = "convops";
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/**@deprecated Use {@link ND4JSystemProperties#DTYPE}*/
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@Deprecated
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public final static String DTYPE = ND4JSystemProperties.DTYPE;
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public final static String BLAS_OPS = "blas.ops";
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public final static String SPARSE_BLAS_OPS = "sparseblas.ops";
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private final static String BLAS_OPS = "blas.ops";
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private final static String SPARSE_BLAS_OPS = "sparseblas.ops";
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public final static String NATIVE_OPS = "native.ops";
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public final static String ORDER_KEY = "ndarray.order";
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public final static String NDARRAY_FACTORY_CLASS = "ndarrayfactory.class";
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public final static String SPARSE_NDARRAY_FACTORY_CLASS = "sparsendarrayfactory.class";
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public final static String OP_EXECUTIONER = "opexec";
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public final static String OP_FACTORY = "opfactory";
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private final static String ORDER_KEY = "ndarray.order";
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private final static String NDARRAY_FACTORY_CLASS = "ndarrayfactory.class";
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private final static String SPARSE_NDARRAY_FACTORY_CLASS = "sparsendarrayfactory.class";
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private final static String OP_EXECUTIONER = "opexec";
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public final static String DISTRIBUTION = "dist";
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public final static String SHAPEINFO_PROVIDER = "shapeinfoprovider";
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public final static String SPARSEINFO_PROVIDER = "sparseinfoprovider";
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public final static String CONSTANT_PROVIDER = "constantsprovider";
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public final static String AFFINITY_MANAGER = "affinitymanager";
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private final static String SHAPEINFO_PROVIDER = "shapeinfoprovider";
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private final static String SPARSEINFO_PROVIDER = "sparseinfoprovider";
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private final static String CONSTANT_PROVIDER = "constantsprovider";
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private final static String AFFINITY_MANAGER = "affinitymanager";
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//disable toString() on compressed arrays for debugging. Should be off by default.
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public final static String COMPRESSION_DEBUG = "compressiondebug";
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public final static String MEMORY_MANAGER = "memorymanager";
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public final static String WORKSPACE_MANAGER = "workspacemanager";
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public final static String RANDOM_PROVIDER = "random";
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private final static String COMPRESSION_DEBUG = "compressiondebug";
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private final static String MEMORY_MANAGER = "memorymanager";
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private final static String WORKSPACE_MANAGER = "workspacemanager";
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private final static String RANDOM_PROVIDER = "random";
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/**@deprecated Use {@link ND4JSystemProperties#LOG_INITIALIZATION}*/
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@Deprecated
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public static final String LOG_INIT_ENV_PROPERTY = ND4JSystemProperties.LOG_INITIALIZATION;
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@ -144,8 +142,7 @@ public class Nd4j {
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//the datatype used for allocating buffers
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protected static DataType dtype = DataType.FLOAT;
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//the allocation mode for the heap
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public static DataBuffer.AllocationMode alloc = DataBuffer.AllocationMode.HEAP;
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public static char ORDER = 'c';
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public static DataBuffer.AllocationMode alloc = DataBuffer.AllocationMode.MIXED_DATA_TYPES;
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public static double EPS_THRESHOLD = 1e-5;
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private static boolean allowsOrder = false;
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public static boolean compressDebug = false;
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@ -157,45 +154,27 @@ public class Nd4j {
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private static final AtomicInteger numThreads = new AtomicInteger(-1);
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private static AtomicReference<DataType> defaultFloatingPointDataType;
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protected static Class<? extends MemoryWorkspaceManager> workspaceManagerClazz;
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protected static Class<? extends BlasWrapper> blasWrapperClazz;
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protected static Class<? extends BlasWrapper> sparseBlasWrapperClazz;
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protected static Class<? extends NDArrayFactory> ndArrayFactoryClazz;
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protected static Class<? extends NDArrayFactory> sparseNDArrayClazz;
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protected static Class<? extends ConvolutionInstance> convolutionInstanceClazz;
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protected static Class<? extends DataBufferFactory> dataBufferFactoryClazz;
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protected static Class<? extends OpExecutioner> opExecutionerClazz;
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protected static Class<? extends org.nd4j.linalg.api.rng.Random> randomClazz;
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protected static Class<? extends DistributionFactory> distributionFactoryClazz;
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protected static Class<? extends BaseShapeInfoProvider> shapeInfoProviderClazz;
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protected static Class<? extends BaseSparseInfoProvider> sparseInfoProviderClazz;
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protected static Class<? extends BasicConstantHandler> constantProviderClazz;
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protected static Class<? extends BasicAffinityManager> affinityManagerClazz;
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protected static Class<? extends BasicMemoryManager> memoryManagerClazz;
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protected static DataBufferFactory DATA_BUFFER_FACTORY_INSTANCE;
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protected static BlasWrapper BLAS_WRAPPER_INSTANCE;
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protected static BlasWrapper SPARSE_BLAS_WRAPPER_INSTANCE;
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private static DataBufferFactory DATA_BUFFER_FACTORY_INSTANCE;
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private static BlasWrapper BLAS_WRAPPER_INSTANCE;
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private static BlasWrapper SPARSE_BLAS_WRAPPER_INSTANCE;
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protected static NDArrayFactory INSTANCE;
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protected static NDArrayFactory SPARSE_INSTANCE;
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protected static ConvolutionInstance CONVOLUTION_INSTANCE;
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protected static OpExecutioner OP_EXECUTIONER_INSTANCE;
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protected static DistributionFactory DISTRIBUTION_FACTORY;
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protected static ShapeInfoProvider shapeInfoProvider;
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protected static SparseInfoProvider sparseInfoProvider;
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protected static ConstantHandler constantHandler;
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protected static AffinityManager affinityManager;
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protected static MemoryManager memoryManager;
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private static NDArrayFactory SPARSE_INSTANCE;
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private static ConvolutionInstance CONVOLUTION_INSTANCE;
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private static OpExecutioner OP_EXECUTIONER_INSTANCE;
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private static DistributionFactory DISTRIBUTION_FACTORY;
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private static ShapeInfoProvider shapeInfoProvider;
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private static SparseInfoProvider sparseInfoProvider;
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private static ConstantHandler constantHandler;
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private static AffinityManager affinityManager;
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private static MemoryManager memoryManager;
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protected static AtomicBoolean fallbackMode;
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private static AtomicBoolean fallbackMode;
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protected static Properties props = new Properties();
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protected static ReferenceQueue<INDArray> referenceQueue = new ReferenceQueue<>();
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protected static ReferenceQueue<DataBuffer> bufferQueue = new ReferenceQueue<>();
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private final static Logger logger = Logger.getLogger(Nd4j.class.getName());
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protected static final INDArray[] EMPTY_ARRAYS = new INDArray[DataType.values().length];
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private static final INDArray[] EMPTY_ARRAYS = new INDArray[DataType.values().length];
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static {
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fallbackMode = new AtomicBoolean(false);
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@ -385,7 +364,6 @@ public class Nd4j {
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* @param toShuffle the ndarray to shuffle
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* @param random the random to use
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* @param dimension the dimension to do the shuffle
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* @return
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*/
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public static void shuffle(INDArray toShuffle, Random random, @NonNull int... dimension) {
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INSTANCE.shuffle(toShuffle, random, dimension);
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@ -396,10 +374,8 @@ public class Nd4j {
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* along a specified set of dimensions
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* @param toShuffle the ndarray to shuffle
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* @param dimension the dimension to do the shuffle
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* @return
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*/
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public static void shuffle(INDArray toShuffle, @NonNull int... dimension) {
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//shuffle(toShuffle, new Random(), dimension);
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INSTANCE.shuffle(toShuffle, new Random(), dimension);
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}
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@ -408,10 +384,8 @@ public class Nd4j {
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* along a specified set of dimensions
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* @param toShuffle the ndarray to shuffle
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* @param dimension the dimension to do the shuffle
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* @return
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*/
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public static void shuffle(Collection<INDArray> toShuffle, @NonNull int... dimension) {
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//shuffle(toShuffle, new Random(), dimension);
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INSTANCE.shuffle(toShuffle, new Random(), dimension);
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}
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@ -420,10 +394,8 @@ public class Nd4j {
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* along a specified set of dimensions
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* @param toShuffle the ndarray to shuffle
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* @param dimension the dimension to do the shuffle
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* @return
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*/
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public static void shuffle(Collection<INDArray> toShuffle, Random rnd, @NonNull int... dimension) {
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//shuffle(toShuffle, new Random(), dimension);
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INSTANCE.shuffle(toShuffle, rnd, dimension);
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}
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@ -433,33 +405,11 @@ public class Nd4j {
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*
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* @param toShuffle the ndarray to shuffle
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* @param dimensions the dimension to do the shuffle. Please note - order matters here.
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* @return
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*/
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public static void shuffle(List<INDArray> toShuffle, Random rnd, List<int[]> dimensions) {
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INSTANCE.shuffle(toShuffle, rnd, dimensions);
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}
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/**
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* The reference queue used for cleaning up
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* ndarrays
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*
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* @return the reference queue for cleaning up ndarrays
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*/
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public static ReferenceQueue<INDArray> refQueue() {
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return referenceQueue;
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}
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/**
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* The reference queue used for cleaning up
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* databuffers
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*
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* @return the reference queue for cleaning up databuffers
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*/
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public static ReferenceQueue<DataBuffer> bufferRefQueue() {
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return bufferQueue;
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}
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/**
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* Get the primary distributions
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* factory
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@ -480,9 +430,9 @@ public class Nd4j {
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}
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/**
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* This method returns RandomFactory instance
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* Get the RandomFactory instance
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*
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* @return
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* @return the RandomFactory instance
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*/
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public static RandomFactory getRandomFactory() {
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return randomFactory;
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@ -500,7 +450,7 @@ public class Nd4j {
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/**
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* Set a convolution instance
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*
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* @param convolutionInstance
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* @param convolutionInstance the new convolution instance
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*/
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public static void setConvolution(ConvolutionInstance convolutionInstance) {
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if (convolutionInstance == null)
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@ -526,7 +476,6 @@ public class Nd4j {
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* slice is the specified shape
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*/
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public static INDArray create(int[] sliceShape, float[]... arrays) {
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//TODO: Remove duplicate code.
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int slices = arrays.length;
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INDArray ret = Nd4j.createUninitialized(DataType.FLOAT, ArrayUtil.toLongArray(ArrayUtil.combine(new int[] {slices}, sliceShape)));
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for (int i = 0; i < ret.slices(); i++)
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@ -586,30 +535,6 @@ public class Nd4j {
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return DATA_BUFFER_FACTORY_INSTANCE;
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}
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/**
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* Given a sequence of Iterators over a transform of matrices, fill in all of
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* the matrices with the entries in the theta vector. Errors are
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* thrown if the theta vector does not exactly fill the matrices.
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*
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* TODO: unused method.
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*/
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public static void setParams(INDArray theta, Collection<INDArray>... matrices) {
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int index = 0;
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for (Collection<INDArray> matrixCollection : matrices) {
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for (INDArray matrix : matrixCollection) {
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INDArray linear = matrix.reshape(-1);
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for (int i = 0; i < matrix.length(); i++) {
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linear.putScalar(i, theta.getDouble(index));
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index++;
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}
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}
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}
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if (index != theta.length()) {
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throw new AssertionError("Did not entirely use the theta vector");
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}
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}
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/**
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* Roll the specified axis backwards,
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* until it lies in a given position.
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@ -681,7 +606,7 @@ public class Nd4j {
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* @param b the right tensor
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* @param result the result array
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* @param axes the axes for each array to do matrix multiply along
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* @return
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* @return the result array
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*/
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public static INDArray tensorMmul(INDArray a, INDArray b,INDArray result, int[][] axes) {
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int validationLength = Math.min(axes[0].length, axes[1].length);
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@ -720,15 +645,7 @@ public class Nd4j {
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//if listA and listB are empty these donot initialize.
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//so initializing with {1} which will then get overriden if not empty
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long[] newShapeA = {-1, n2};
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//TODO: remove duplicate code.
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long[] oldShapeA;
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if (listA.size() == 0) {
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oldShapeA = new long[] {1};
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} else {
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oldShapeA = Longs.toArray(listA);
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for (int i = 0; i < oldShapeA.length; i++)
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oldShapeA[i] = a.size((int) oldShapeA[i]);
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}
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long[] oldShapeA = getOldShape(listA, a);
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int n3 = 1;
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int bNax = Math.min(b.rank(), axes[1].length);
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@ -737,14 +654,7 @@ public class Nd4j {
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}
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long[] newShapeB = {n3, -1};
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long[] oldShapeB;
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if (listB.size() == 0) {
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oldShapeB = new long[] {1};
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} else {
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oldShapeB = Longs.toArray(listB);
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for (int i = 0; i < oldShapeB.length; i++)
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oldShapeB[i] = b.size((int) oldShapeB[i]);
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}
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long[] oldShapeB = getOldShape(listB, b);
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INDArray at = a.permute(newAxesA).reshape(newShapeA);
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INDArray bt = b.permute(newAxesB).reshape(newShapeB);
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@ -754,6 +664,19 @@ public class Nd4j {
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return ret.reshape(aPlusB);
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}
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// Some duplicate code that refactored out:
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private static long[] getOldShape(List<Integer> list, INDArray x){
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long[] res;
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if (list.size() == 0) {
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res = new long[] {1};
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} else {
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res= Longs.toArray(list);
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for (int i = 0; i < res.length; i++)
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res[i] = x.size((int) res[i]);
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}
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return res;
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}
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/**
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* Tensor matrix multiplication.
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* Both tensors must be the same rank
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@ -761,7 +684,7 @@ public class Nd4j {
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* @param a the left tensor
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* @param b the right tensor
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* @param axes the axes for each array to do matrix multiply along
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* @return
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* @return the multiplication result.
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*/
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public static INDArray tensorMmul(INDArray a, INDArray b, int[][] axes) {
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CustomOp op = DynamicCustomOp.builder("tensordot")
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@ -892,29 +815,6 @@ public class Nd4j {
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return matmul(a,b, null);
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}
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/**
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* Given a sequence of Iterators over a transform of matrices, fill in all of
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* the matrices with the entries in the theta vector. Errors are
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* thrown if the theta vector does not exactly fill the matrices.
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* TODO: unused method.
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*/
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public static void setParams(INDArray theta, Iterator<? extends INDArray>... matrices) {
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int index = 0;
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for (Iterator<? extends INDArray> matrixIterator : matrices) {
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while (matrixIterator.hasNext()) {
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INDArray matrix = matrixIterator.next().reshape(-1);
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for (int i = 0; i < matrix.length(); i++) {
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matrix.putScalar(i, theta.getDouble(index));
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index++;
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}
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}
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}
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if (index != theta.length()) {
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throw new AssertionError("Did not entirely use the theta vector");
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}
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}
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/**
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* The factory used for creating ndarrays
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*
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@ -5766,45 +5666,45 @@ public class Nd4j {
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}
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compressDebug = pp.toBoolean(COMPRESSION_DEBUG);
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ORDER = pp.toChar(ORDER_KEY, NDArrayFactory.C);
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char ORDER = pp.toChar(ORDER_KEY, NDArrayFactory.C);
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affinityManagerClazz = (Class<? extends BasicAffinityManager>) Class
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Class<? extends BasicAffinityManager> affinityManagerClazz = (Class<? extends BasicAffinityManager>) Class
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.forName(pp.toString(AFFINITY_MANAGER));
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affinityManager = affinityManagerClazz.newInstance();
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ndArrayFactoryClazz = (Class<? extends NDArrayFactory>) Class.forName(
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Class<? extends NDArrayFactory> ndArrayFactoryClazz = (Class<? extends NDArrayFactory>) Class.forName(
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pp.toString(NDARRAY_FACTORY_CLASS));
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sparseNDArrayClazz = (Class<? extends NDArrayFactory>) Class.forName(
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Class<? extends NDArrayFactory> sparseNDArrayClazz = (Class<? extends NDArrayFactory>) Class.forName(
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pp.toString(SPARSE_NDARRAY_FACTORY_CLASS));
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convolutionInstanceClazz = (Class<? extends ConvolutionInstance>) Class
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Class<? extends ConvolutionInstance> convolutionInstanceClazz = (Class<? extends ConvolutionInstance>) Class
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.forName(pp.toString(CONVOLUTION_OPS, DefaultConvolutionInstance.class.getName()));
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String defaultName = pp.toString(DATA_BUFFER_OPS, DefaultDataBufferFactory.class.getName());
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dataBufferFactoryClazz = (Class<? extends DataBufferFactory>) Class
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Class<? extends DataBufferFactory> dataBufferFactoryClazz = (Class<? extends DataBufferFactory>) Class
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.forName(pp.toString(DATA_BUFFER_OPS, defaultName));
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shapeInfoProviderClazz = (Class<? extends BaseShapeInfoProvider>) Class
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Class<? extends BaseShapeInfoProvider> shapeInfoProviderClazz = (Class<? extends BaseShapeInfoProvider>) Class
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.forName(pp.toString(SHAPEINFO_PROVIDER));
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sparseInfoProviderClazz = (Class<? extends BaseSparseInfoProvider>) Class.forName(
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Class<? extends BaseSparseInfoProvider> sparseInfoProviderClazz = (Class<? extends BaseSparseInfoProvider>) Class.forName(
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pp.toString(SPARSEINFO_PROVIDER));
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constantProviderClazz = (Class<? extends BasicConstantHandler>) Class
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Class<? extends BasicConstantHandler> constantProviderClazz = (Class<? extends BasicConstantHandler>) Class
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.forName(pp.toString(CONSTANT_PROVIDER));
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memoryManagerClazz = (Class<? extends BasicMemoryManager>) Class
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Class<? extends BasicMemoryManager> memoryManagerClazz = (Class<? extends BasicMemoryManager>) Class
|
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.forName(pp.toString(MEMORY_MANAGER));
|
||||
|
||||
allowsOrder = backend.allowsOrder();
|
||||
String rand = pp.toString(RANDOM_PROVIDER, DefaultRandom.class.getName());
|
||||
randomClazz = (Class<? extends org.nd4j.linalg.api.rng.Random>) Class.forName(rand);
|
||||
Class<? extends org.nd4j.linalg.api.rng.Random> randomClazz = (Class<? extends org.nd4j.linalg.api.rng.Random>) Class.forName(rand);
|
||||
randomFactory = new RandomFactory(randomClazz);
|
||||
|
||||
workspaceManagerClazz = (Class<? extends MemoryWorkspaceManager>) Class
|
||||
Class<? extends MemoryWorkspaceManager> workspaceManagerClazz = (Class<? extends MemoryWorkspaceManager>) Class
|
||||
.forName(pp.toString(WORKSPACE_MANAGER));
|
||||
|
||||
blasWrapperClazz = (Class<? extends BlasWrapper>) Class
|
||||
Class<? extends BlasWrapper> blasWrapperClazz = (Class<? extends BlasWrapper>) Class
|
||||
.forName(pp.toString(BLAS_OPS));
|
||||
sparseBlasWrapperClazz = (Class<? extends BlasWrapper>) Class
|
||||
Class<? extends BlasWrapper> sparseBlasWrapperClazz = (Class<? extends BlasWrapper>) Class
|
||||
.forName(pp.toString(SPARSE_BLAS_OPS));
|
||||
String clazzName = pp.toString(DISTRIBUTION, DefaultDistributionFactory.class.getName());
|
||||
distributionFactoryClazz = (Class<? extends DistributionFactory>) Class.forName(clazzName);
|
||||
Class<? extends DistributionFactory> distributionFactoryClazz = (Class<? extends DistributionFactory>) Class.forName(clazzName);
|
||||
|
||||
|
||||
memoryManager = memoryManagerClazz.newInstance();
|
||||
|
|
@ -5813,7 +5713,7 @@ public class Nd4j {
|
|||
sparseInfoProvider = sparseInfoProviderClazz.newInstance();
|
||||
workspaceManager = workspaceManagerClazz.newInstance();
|
||||
|
||||
opExecutionerClazz = (Class<? extends OpExecutioner>) Class
|
||||
Class<? extends OpExecutioner> opExecutionerClazz = (Class<? extends OpExecutioner>) Class
|
||||
.forName(pp.toString(OP_EXECUTIONER, DefaultOpExecutioner.class.getName()));
|
||||
|
||||
OP_EXECUTIONER_INSTANCE = opExecutionerClazz.newInstance();
|
||||
|
|
|
|||
Loading…
Reference in New Issue