Examples with ND4JIllegalStateException org.nd4j.linalg.exception.ND4JIllegalStateException used on opensource projects

Example 96 with ND4JIllegalStateException

use of org.nd4j.linalg.exception.ND4JIllegalStateException in project nd4j by deeplearning4j.

the class NativeOpExecutioner method exec.

@Override
public INDArray exec(BroadcastOp op, int... dimension) {
    long st = profilingHookIn(op);
    if (dimension == null)
        dimension = new int[] { Integer.MAX_VALUE };
    dimension = Shape.normalizeAxis(op.x().rank(), dimension);
    validateDataType(Nd4j.dataType(), op);
    for (int i = 0; i < dimension.length; i++) if (dimension[i] >= op.x().rank() && dimension[i] != Integer.MAX_VALUE)
        throw new ND4JIllegalStateException("Op target dimension " + Arrays.toString(dimension) + " contains element that higher then rank of op.X: [" + op.x().rank() + "]");
    /**
     * Returns the {@link Shape#createShapeInformation(int[], int[], int, int, char)}
     * and the associated offsets for each {@link INDArray#tensorAlongDimension(int, int...)}
     * The first item is the shape information. The second one is the offsets.
     */
    Pair<DataBuffer, DataBuffer> tadBuffers = tadManager.getTADOnlyShapeInfo(op.x(), dimension);
    Pointer hostTadShapeInfo = tadBuffers.getFirst().addressPointer();
    Pointer hostTadOffsets = tadBuffers.getSecond().addressPointer();
    Pointer devTadShapeInfoZ = null;
    Pointer devTadOffsetsZ = null;
    // if (!Arrays.equals(op.x().shape(),op.z().shape()) || !Arrays.equals(op.x().stride(),op.z().stride()) || op.x().ordering() != op.z().ordering()) {
    // that's the place where we're going to have second TAD in place
    Pair<DataBuffer, DataBuffer> tadBuffersZ = tadManager.getTADOnlyShapeInfo(op.z(), dimension);
    devTadShapeInfoZ = tadBuffersZ.getFirst().addressPointer();
    devTadOffsetsZ = tadBuffersZ.getSecond().addressPointer();
    if (extraz.get() == null)
        extraz.set(new PointerPointer(32));
    PointerPointer dummy = extraz.get().put(hostTadShapeInfo, hostTadOffsets, devTadShapeInfoZ, devTadOffsetsZ);
    Pointer dimensionAddress = constantHandler.getConstantBuffer(dimension).addressPointer();
    if (op.x().data().dataType() == DataBuffer.Type.DOUBLE) {
        loop.execBroadcastDouble(dummy, op.opNum(), (DoublePointer) op.x().data().addressPointer(), (IntPointer) op.x().shapeInfoDataBuffer().addressPointer(), (DoublePointer) op.y().data().addressPointer(), (IntPointer) op.y().shapeInfoDataBuffer().addressPointer(), (DoublePointer) op.z().data().addressPointer(), (IntPointer) op.z().shapeInfoDataBuffer().addressPointer(), (IntPointer) dimensionAddress, dimension.length);
    } else {
        loop.execBroadcastFloat(dummy, op.opNum(), (FloatPointer) op.x().data().addressPointer(), (IntPointer) op.x().shapeInfoDataBuffer().addressPointer(), (FloatPointer) op.y().data().addressPointer(), (IntPointer) op.y().shapeInfoDataBuffer().addressPointer(), (FloatPointer) op.z().data().addressPointer(), (IntPointer) op.z().shapeInfoDataBuffer().addressPointer(), (IntPointer) dimensionAddress, dimension.length);
    }
    return op.z();
}

Example 97 with ND4JIllegalStateException

use of org.nd4j.linalg.exception.ND4JIllegalStateException in project nd4j by deeplearning4j.

the class DifferentialFunction method resolvePropertiesFromSameDiffBeforeExecution.

/**
 * Resolve properties and arguments right before execution of
 * this operation.
 */
public void resolvePropertiesFromSameDiffBeforeExecution() {
    val properties = sameDiff.propertiesToResolveForFunction(this);
    val fields = DifferentialFunctionClassHolder.getInstance().getFieldsForFunction(this);
    val currentFields = this.propertiesForFunction();
    for (val property : properties) {
        // just skip  if this is the case
        if (!fields.containsKey(property))
            continue;
        val var = sameDiff.getVarNameForFieldAndFunction(this, property);
        val fieldType = fields.get(property);
        val varArr = sameDiff.getArrForVarName(var);
        // already defined
        if (currentFields.containsKey(property)) {
            continue;
        }
        /**
         * Possible cause:
         * Might be related to output name alignment.
         */
        if (varArr == null) {
            throw new ND4JIllegalStateException("Unable to set null array!");
        }
        if (fieldType.getType().equals(int[].class)) {
            setValueFor(fieldType, varArr.data().asInt());
        } else if (fieldType.equals(double[].class)) {
            setValueFor(fieldType, varArr.data().asDouble());
        } else if (fieldType.equals(int.class)) {
            setValueFor(fieldType, varArr.getInt(0));
        } else if (fieldType.equals(double.class)) {
            setValueFor(fieldType, varArr.getDouble(0));
        }
    }
}

Example 98 with ND4JIllegalStateException

use of org.nd4j.linalg.exception.ND4JIllegalStateException in project nd4j by deeplearning4j.

the class NativeGraphExecutioner method executeGraph.

/**
 * This method executes given graph and returns results
 *
 * @param sd
 * @return
 */
@Override
public INDArray[] executeGraph(SameDiff sd, ExecutorConfiguration configuration) {
    Map<Integer, Node> intermediate = new HashMap<>();
    ByteBuffer buffer = convertToFlatBuffers(sd, configuration, intermediate);
    BytePointer bPtr = new BytePointer(buffer);
    log.info("Buffer length: {}", buffer.limit());
    Pointer res = NativeOpsHolder.getInstance().getDeviceNativeOps().executeFlatGraphFloat(null, bPtr);
    if (res == null)
        throw new ND4JIllegalStateException("Graph execution failed");
    // FIXME: this is BAD
    PagedPointer pagedPointer = new PagedPointer(res, 1024 * 1024L);
    FlatResult fr = FlatResult.getRootAsFlatResult(pagedPointer.asBytePointer().asByteBuffer());
    log.info("VarMap: {}", sd.variableMap());
    INDArray[] results = new INDArray[fr.variablesLength()];
    for (int e = 0; e < fr.variablesLength(); e++) {
        FlatVariable var = fr.variables(e);
        log.info("Var received: id: [{}:{}/<{}>];", var.id().first(), var.id().second(), var.name());
        FlatArray ndarray = var.ndarray();
        INDArray val = Nd4j.createFromFlatArray(ndarray);
        results[e] = val;
        if (var.name() != null && sd.variableMap().containsKey(var.name())) {
            // log.info("VarName: {}; Exists: {}; NDArrayInfo: {};", var.opName(), sd.variableMap().containsKey(var.opName()), sd.getVertexToArray().containsKey(var.opName()));
            // log.info("storing: {}; array: {}", var.name(), val);
            sd.associateArrayWithVariable(val, sd.variableMap().get(var.name()));
        } else {
            // log.info("Original id: {}; out: {}; out2: {}", original, sd.getVertexIdxToInfo().get(original), graph.getVariableForVertex(original));
            if (sd.variableMap().get(var.name()) != null) {
                sd.associateArrayWithVariable(val, sd.getVariable(var.name()));
            } else {
                throw new ND4JIllegalStateException("Unknown variable received as result: [" + var.name() + "]");
            }
        }
    }
    return results;
}

Example 99 with ND4JIllegalStateException

use of org.nd4j.linalg.exception.ND4JIllegalStateException in project nd4j by deeplearning4j.

the class TensorflowDescriptorParser method opDescs.

/**
 * Get the op descriptors for tensorflow
 * @return the op descriptors for tensorflow
 * @throws Exception
 */
public static Map<String, OpDef> opDescs() throws Exception {
    InputStream contents = new ClassPathResource("ops.proto").getInputStream();
    try (BufferedInputStream bis2 = new BufferedInputStream(contents);
        BufferedReader reader = new BufferedReader(new InputStreamReader(bis2))) {
        org.tensorflow.framework.OpList.Builder builder = org.tensorflow.framework.OpList.newBuilder();
        StringBuilder str = new StringBuilder();
        String line = null;
        while ((line = reader.readLine()) != null) {
            // .append("\n");
            str.append(line);
        }
        TextFormat.getParser().merge(str.toString(), builder);
        List<OpDef> list = builder.getOpList();
        Map<String, OpDef> map = new HashMap<>();
        for (OpDef opDef : list) {
            map.put(opDef.getName(), opDef);
        }
        return map;
    } catch (Exception e2) {
        e2.printStackTrace();
    }
    throw new ND4JIllegalStateException("Unable to load tensorflow descriptors!");
}

Example 100 with ND4JIllegalStateException

use of org.nd4j.linalg.exception.ND4JIllegalStateException in project nd4j by deeplearning4j.

the class TFGraphMapper method mapTensorProto.

public INDArray mapTensorProto(TensorProto tfTensor) {
    // building shape first
    int dims = tfTensor.getTensorShape().getDimCount();
    int[] arrayShape = null;
    List<Integer> dimensions = new ArrayList<>();
    for (int e = 0; e < dims; e++) {
        // TODO: eventually we want long shapes :(
        int dim = (int) tfTensor.getTensorShape().getDim(e).getSize();
        dimensions.add(dim);
    }
    arrayShape = Ints.toArray(dimensions);
    if (tfTensor.getDtype() == DataType.DT_INT32 || tfTensor.getDtype() == DataType.DT_INT16 || tfTensor.getDtype() == DataType.DT_INT8) {
        // valueOf
        if (tfTensor.getIntValCount() == 1 || ArrayUtil.prod(arrayShape) == 1) {
            // straight zero case
            if (tfTensor.getIntValCount() < 1)
                return Nd4j.trueScalar(0.0);
            // should be scalar otherwise
            int val = tfTensor.getIntVal(0);
            if (arrayShape == null || arrayShape.length == 0)
                arrayShape = new int[] {};
            INDArray array = Nd4j.valueArrayOf(arrayShape, (double) val);
            return array;
        } else if (tfTensor.getInt64ValCount() > 0) {
            double[] jArray = new double[tfTensor.getIntValCount()];
            for (int e = 0; e < tfTensor.getIntValCount(); e++) {
                jArray[e] = (double) tfTensor.getIntVal(e);
            }
            // TF arrays are always C
            INDArray array = Nd4j.create(jArray, arrayShape, 0, 'c');
            return array;
        } else {
            // FIXME: INT bytebuffers should be converted to floating point
            // throw new UnsupportedOperationException("To be implemented yet");
            long length = ArrayUtil.prodLong(arrayShape);
            // binary representation
            val bb = tfTensor.getTensorContent().asReadOnlyByteBuffer();
            val fb = bb.order(ByteOrder.nativeOrder()).asIntBuffer();
            val fa = new float[fb.capacity()];
            for (int e = 0; e < fb.capacity(); e++) fa[e] = (float) fb.get(e);
            if (fa.length == 0)
                throw new ND4JIllegalStateException("Can't find Tensor values! Probably you've forgot to freeze graph before saving?");
            if (fa.length == 1)
                return Nd4j.trueScalar(fa[0]);
            if (arrayShape.length == 1)
                return Nd4j.trueVector(fa);
            val array = Nd4j.create(fa, arrayShape, 'c', 0);
            // log.debug("Data: {}", Arrays.toString(array.data().asFloat()));
            return array;
        }
    } else if (tfTensor.getDtype() == DataType.DT_FLOAT) {
        if (tfTensor.getFloatValCount() == 1 || ArrayUtil.prod(arrayShape) == 1) {
            // straight zero case
            if (tfTensor.getFloatValCount() < 1)
                return Nd4j.scalar(0.0);
            float val = tfTensor.getFloatVal(0);
            if (arrayShape == null || arrayShape.length == 0)
                arrayShape = new int[] {};
            INDArray array = Nd4j.valueArrayOf(arrayShape, (double) val);
            return array;
        } else if (tfTensor.getFloatValCount() > 0) {
            float[] jArray = new float[tfTensor.getFloatValCount()];
            for (int e = 0; e < tfTensor.getFloatValCount(); e++) {
                jArray[e] = tfTensor.getFloatVal(e);
            }
            // FIXME: we're missing float[] signature
            INDArray array = Nd4j.create(Nd4j.createBuffer(jArray), arrayShape, 'c');
            return array;
        } else if (tfTensor.getTensorContent().size() > 0) {
            // binary representation
            val bb = tfTensor.getTensorContent().asReadOnlyByteBuffer();
            val fb = bb.order(ByteOrder.nativeOrder()).asFloatBuffer();
            val fa = new float[fb.capacity()];
            for (int e = 0; e < fb.capacity(); e++) fa[e] = fb.get(e);
            if (fa.length == 0)
                throw new ND4JIllegalStateException("Can't find Tensor values! Probably you've forgot to freeze graph before saving?");
            if (fa.length == 1)
                return Nd4j.trueScalar(fa[0]);
            if (arrayShape.length == 1)
                return Nd4j.trueVector(fa);
            val array = Nd4j.create(fa, arrayShape, 'c', 0);
            return array;
        }
    } else if (tfTensor.getDtype() == DataType.DT_DOUBLE) {
        if (tfTensor.getDoubleValCount() == 1 || ArrayUtil.prod(arrayShape) == 1) {
            // straight zero case
            if (tfTensor.getDoubleValCount() < 1)
                return Nd4j.trueScalar(0.0);
            double val = tfTensor.getDoubleVal(0);
            INDArray array = Nd4j.trueScalar(val);
            return array;
        } else if (tfTensor.getDoubleValCount() > 0) {
            double[] jArray = new double[tfTensor.getDoubleValCount()];
            for (int e = 0; e < tfTensor.getDoubleValCount(); e++) {
                jArray[e] = tfTensor.getDoubleVal(e);
            }
            // TF arrays are always C
            INDArray array = Nd4j.create(jArray, arrayShape, 0, 'c');
            return array;
        } else if (tfTensor.getTensorContent().size() > 0) {
            // binary representation
            // DataBuffer buffer = Nd4j.createBuffer(tfTensor.getTensorContent().asReadOnlyByteBuffer(), DataBuffer.Type.FLOAT, (int) length);
            // INDArray array = Nd4j.createArrayFromShapeBuffer(buffer, Nd4j.getShapeInfoProvider().createShapeInformation(arrayShape, 'c'));
            // binary representation
            val bb = tfTensor.getTensorContent().asReadOnlyByteBuffer();
            val fb = bb.order(ByteOrder.nativeOrder()).asDoubleBuffer();
            val da = new double[fb.capacity()];
            for (int e = 0; e < fb.capacity(); e++) da[e] = fb.get(e);
            if (da.length == 0)
                throw new ND4JIllegalStateException("Can't find Tensor values! Probably you've forgot to freeze graph before saving?");
            if (da.length == 1)
                return Nd4j.trueScalar(da[0]);
            if (arrayShape.length == 1)
                return Nd4j.trueVector(da);
            val array = Nd4j.create(da, arrayShape, 0, 'c');
            return array;
        }
    } else if (tfTensor.getDtype() == DataType.DT_INT64) {
        if (tfTensor.getInt64ValCount() == 1 || ArrayUtil.prod(arrayShape) == 1) {
            // straight zero case
            if (tfTensor.getDoubleValCount() < 1)
                return Nd4j.trueScalar(0.0);
            double val = (double) tfTensor.getInt64Val(0);
            INDArray array = Nd4j.trueScalar(val);
            return array;
        } else if (tfTensor.getInt64ValCount() > 0) {
            double[] jArray = new double[tfTensor.getInt64ValCount()];
            for (int e = 0; e < tfTensor.getInt64ValCount(); e++) {
                jArray[e] = (double) tfTensor.getInt64Val(e);
            }
            // TF arrays are always C
            INDArray array = Nd4j.create(jArray, arrayShape, 0, 'c');
            return array;
        } else if (tfTensor.getTensorContent().size() > 0) {
            // throw new UnsupportedOperationException("To be implemented yet");
            // Mapping INT bytebuffers should be converted to floating point
            val bb = tfTensor.getTensorContent().asReadOnlyByteBuffer();
            val lb = bb.order(ByteOrder.nativeOrder()).asLongBuffer();
            val fa = new float[lb.capacity()];
            for (int e = 0; e < lb.capacity(); e++) fa[e] = (float) lb.get(e);
            if (fa.length == 0)
                throw new ND4JIllegalStateException("Can't find Tensor values! Probably you've forgot to freeze graph before saving?");
            if (fa.length == 1)
                return Nd4j.trueScalar(fa[0]);
            if (arrayShape.length == 1)
                return Nd4j.trueVector(fa);
            val array = Nd4j.create(fa, arrayShape, 'c', 0);
            // log.debug("Data: {}", Arrays.toString(array.data().asFloat()));
            return array;
        }
    } else {
        throw new UnsupportedOperationException("Unknown dataType found: [" + tfTensor.getDtype() + "]");
    }
    throw new ND4JIllegalStateException("Invalid method state");
}