Lin_space operation improve (#373)
* libnd4j update linspace op Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j #8513 update lin_space op, tests added Signed-off-by: Oleg <oleg.semeniv@gmail.com> * - minor linspace tweaks (num_elements now iArg) - java linspace updates - couple of additional tests for linspace Signed-off-by: raver119 <raver119@gmail.com> * roll back timeout change Signed-off-by: raver119 <raver119@gmail.com> Co-authored-by: raver119 <raver119@gmail.com>master
Oleh
GitHub
parent
12ba1fa406
commit
3d15706ffa
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@ -26,24 +26,38 @@
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namespace sd {
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namespace ops {
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CUSTOM_OP_IMPL(lin_space, 3, 1, false, 0, 0) {
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auto output = OUTPUT_VARIABLE(0);
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auto start = INPUT_VARIABLE(0);
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auto finish = INPUT_VARIABLE(1);
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auto numOfElements = INPUT_VARIABLE(2);
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CUSTOM_OP_IMPL(lin_space, 0, 1, false, 0, 0) {
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if (numOfElements->e<Nd4jLong>(0) == 1) {
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auto output = OUTPUT_VARIABLE(0);
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const int nInputs = block.width();
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bool bInputs = (3 == nInputs || 3 == block.numI() || (2 == block.numT() && block.numI() > 0));
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REQUIRE_TRUE(bInputs, 0, "lin_space OP: Have to be supplied correct inputs, input size or T_ARG size have to be equal 3, but got inputs - %i, T_ARGS - %i!", nInputs, block.numT());
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auto start = (nInputs > 0) ? INPUT_VARIABLE(0)->e<double>(0) : static_cast<double>(T_ARG(0));
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auto finish = (nInputs > 0) ? INPUT_VARIABLE(1)->e<double>(0) : static_cast<double>(T_ARG(1));
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auto numOfElements = (nInputs > 0) ? INPUT_VARIABLE(2)->e<Nd4jLong>(0) : static_cast<Nd4jLong>(I_ARG(0));
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if (numOfElements == 1) {
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output->assign(start);
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return Status::OK();
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}
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output->linspace(start->e<double>(0), (finish->e<double>(0) - start->e<double>(0)) / (numOfElements->e<Nd4jLong>(0) - 1.));
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output->linspace(start, (finish - start) / ( numOfElements - 1.0 ));
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return Status::OK();
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}
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DECLARE_SHAPE_FN(lin_space) {
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auto dataType = ArrayOptions::dataType(inputShape->at(0));
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Nd4jLong steps = INPUT_VARIABLE(2)->e<Nd4jLong>(0);
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const int nInputs = block.width();
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bool bInputs = (3 == nInputs || 3 == block.numI() || (2 == block.numT() && block.numI() > 0));
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REQUIRE_TRUE(bInputs, 0, "lin_space OP: Have to be supplied correct inputs, input size or T_ARG size have to be equal 3, but got inputs - %i, T_ARGS - %i!", nInputs, block.numT() );
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auto dataType = (nInputs > 0) ? ArrayOptions::dataType(inputShape->at(0)) : ( block.numD() > 0 ? static_cast<DataType>(D_ARG(0)) : DataType::FLOAT32) ;
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Nd4jLong steps = (nInputs > 0) ? INPUT_VARIABLE(2)->e<Nd4jLong>(0) : static_cast<Nd4jLong>(I_ARG(0));
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return SHAPELIST(ConstantShapeHelper::getInstance()->vectorShapeInfo(steps, dataType));
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}
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@ -1433,16 +1433,20 @@ namespace sd {
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/**
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* lin_space - op porting from TF (https://www.tensorflow.org/api_docs/python/tf/lin_space)
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*
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* input params:
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* optional input params:
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* 0 - startVal - NDArray scalar (float point)
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* 1 - finishVal - NDArray scalar (float point)
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* 2 - numOfElements - NDArray scalar (integer)
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*
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* Optional:
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* T args
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* 0 - startVal
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* 1 - finishVal]
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* 2 - numOfElements
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* output:
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* 0 - 1D NDArray with the same type as input and length as given with numOfElements param.
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*/
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#if NOT_EXCLUDED(OP_lin_space)
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DECLARE_CUSTOM_OP(lin_space, 3, 1, false, 0, 0);
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DECLARE_CUSTOM_OP(lin_space, 0, 1, false, 0, 0);
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#endif
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/**
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@ -2010,6 +2010,34 @@ TEST_F(DeclarableOpsTests10, LinSpace_Test1) {
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ASSERT_TRUE(expect.equalsTo(res));
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}
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////////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests10, LinSpace_Test2) {
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NDArray expect = NDArrayFactory::create<float>({1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5, 5., 5.5, 6., 6.5, 7., 7.5,
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8., 8.5, 9., 9.5, 10., 10.5, 11., 11.5, 12.});
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sd::ops::lin_space op;
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auto result = op.evaluate({}, {1, 12}, {23});
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ASSERT_EQ(result.status(), ND4J_STATUS_OK);
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auto res = result.at(0);
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ASSERT_EQ( res->dataType(), sd::DataType::FLOAT32 );
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ASSERT_TRUE(expect.equalsTo(res));
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}
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////////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests10, LinSpace_Test3) {
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NDArray expect('c', { 23 }, {1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5, 5., 5.5, 6., 6.5, 7., 7.5, 8., 8.5, 9., 9.5, 10., 10.5, 11., 11.5, 12.}, sd::DataType::DOUBLE );
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sd::ops::lin_space op;
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auto result = op.evaluate({}, {1, 12}, {23}, {}, { sd::DOUBLE });
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ASSERT_EQ(result.status(), ND4J_STATUS_OK);
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auto res = result.at(0);
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ASSERT_EQ( res->dataType(), expect.dataType());
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ASSERT_TRUE(expect.equalsTo(res));
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}
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////////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests10, ImageResizeNeighbor_Test1) {
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@ -1334,6 +1334,20 @@ TEST_F(JavaInteropTests, test_workspace_backed_arrays_1) {
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ASSERT_EQ(Status::OK(), status);
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}
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TEST_F(JavaInteropTests, test_linspace_shape_1) {
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if (!Environment::getInstance()->isCPU())
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return;
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sd::ops::lin_space op;
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double tArgs[2] = {1.0, 10.0};
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Nd4jLong iArgs = 10L;
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int dArg = (int) sd::DataType::FLOAT32;
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auto result = ::calculateOutputShapes2(nullptr, op.getOpHash(), nullptr, nullptr, 0, tArgs, 2, &iArgs, 1, nullptr, 0, &dArg, 1);
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ASSERT_EQ(1, result->size());
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delete result;
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}
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/*
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TEST_F(JavaInteropTests, Test_Results_Conversion_1) {
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auto pl = sd::graph::readFlatBuffers("./resources/gru_dynamic_mnist.fb");
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@ -42,6 +42,9 @@ import java.util.Map;
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public class Linspace extends DynamicCustomOp {
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private DataType dataType;
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private double start;
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private double stop;
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private long elements;
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public Linspace(SameDiff sameDiff, DataType dataType, double start, double stop, long number) {
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this(sameDiff, sameDiff.constant(start), sameDiff.constant(stop), sameDiff.constant(number), dataType);
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@ -54,7 +57,7 @@ public class Linspace extends DynamicCustomOp {
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}
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public Linspace(DataType dataType, double start, double stop, long number) {
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this(dataType, Nd4j.scalar(start), Nd4j.scalar(stop), Nd4j.scalar(number));
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this(start, stop, number, dataType);
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}
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public Linspace(DataType dataType, INDArray start, INDArray stop, INDArray number) {
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@ -67,6 +70,19 @@ public class Linspace extends DynamicCustomOp {
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addDArgument(dataType);
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}
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public Linspace(double start, double stop, long number, @NonNull DataType dataType) {
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super(new INDArray[]{}, null);
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this.dataType = dataType;
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addDArgument(dataType);
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this.start = start;
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this.stop = stop;
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this.elements = number;
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addTArgument(this.start, this.stop);
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addIArgument(elements);
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}
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public Linspace(){ }
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@Override
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@ -1947,7 +1947,7 @@ public class CudaExecutioner extends DefaultOpExecutioner {
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val result = new ArrayList<LongShapeDescriptor>();
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int nIn = opContext != null ? opContext.numInputArguments() : op.numInputArguments();
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if(nIn == 0 && op.getDescriptor().getNumInputs() != -2) {
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if(nIn == 0 && op.getDescriptor().getNumInputs() >= 1) {
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if(log.isTraceEnabled()){
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log.trace("Could not calculate output shape for op {}: number of input args was 0",
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op.getClass().getName());
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@ -1754,7 +1754,7 @@ public class NativeOpExecutioner extends DefaultOpExecutioner {
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val result = new ArrayList<LongShapeDescriptor>();
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int nIn = opContext != null ? opContext.numInputArguments() : op.numInputArguments();
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if(nIn == 0 && op.getDescriptor().getNumInputs() != -2) {
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if(nIn == 0 && op.getDescriptor().getNumInputs() >= 1) {
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if(log.isTraceEnabled()){
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log.trace("Could not calculate output shape for op {}: number of input args was 0",
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op.getClass().getName());
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@ -20475,11 +20475,15 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
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/**
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* lin_space - op porting from TF (https://www.tensorflow.org/api_docs/python/tf/lin_space)
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*
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* input params:
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* optional input params:
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* 0 - startVal - NDArray scalar (float point)
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* 1 - finishVal - NDArray scalar (float point)
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* 2 - numOfElements - NDArray scalar (integer)
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*
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* Optional:
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* T args
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* 0 - startVal
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* 1 - finishVal]
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* 2 - numOfElements
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* output:
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* 0 - 1D NDArray with the same type as input and length as given with numOfElements param.
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*/
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@ -37,6 +37,7 @@ import org.nd4j.linalg.api.ops.impl.image.ResizeBilinear;
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import org.nd4j.linalg.api.ops.impl.reduce.Mmul;
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import org.nd4j.linalg.api.ops.impl.reduce.MmulBp;
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import org.nd4j.linalg.api.ops.impl.shape.Create;
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import org.nd4j.linalg.api.ops.impl.shape.Linspace;
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import org.nd4j.linalg.api.ops.impl.shape.OnesLike;
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import org.nd4j.linalg.api.ops.impl.shape.SequenceMask;
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import org.nd4j.linalg.api.ops.impl.transforms.Cholesky;
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@ -1803,6 +1804,16 @@ public class CustomOpsTests extends BaseNd4jTest {
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assertEquals(ret[0], in);
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}
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@Test
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public void testLinspaceSignature_1() throws Exception {
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val array1 = Nd4j.exec(new Linspace(DataType.FLOAT, Nd4j.scalar(1.0f), Nd4j.scalar(10.f), Nd4j.scalar(10L)))[0];
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val array2 = Nd4j.exec(new Linspace(DataType.FLOAT, 1.0f, 10.f, 10L))[0];
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assertEquals(array1.dataType(), array2.dataType());
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assertEquals(array1, array2);
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}
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@Test
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public void testLogdet() {
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INDArray x = Nd4j.createFromArray(new double[]{
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