[WIP] reverse improvements (#115)
* initial commit Signed-off-by: raver119 <raver119@gmail.com> * reverse draft Signed-off-by: raver119 <raver119@gmail.com> * reverse kernel Signed-off-by: raver119 <raver119@gmail.com> * reverse kernel Signed-off-by: raver119 <raver119@gmail.com>master
raver119
GitHub
parent
0e8a4f77bc
commit
355c6b6096
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@ -30,6 +30,67 @@ namespace nd4j {
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namespace ops {
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namespace helpers {
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template <typename T>
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static __global__ void reverseTadKernel(void* vinput, Nd4jLong *inputShape, void* voutput, Nd4jLong *outputShape, Nd4jLong *inputTadShape, Nd4jLong *inputTadOffsets, Nd4jLong *outputTadShape, Nd4jLong *outputTadOffsets, uint64_t limit, uint64_t numOfElemsToReverse, uint64_t numTads) {
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auto input = reinterpret_cast<T*>(vinput);
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auto output = reinterpret_cast<T*>(voutput);
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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// this means that we'll have additional cycle, to move middle element
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auto div = numOfElemsToReverse / 2;
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auto odd = numOfElemsToReverse % 2 != 0;
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auto rlimit = odd ? limit / 2 + 1 : limit / 2;
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// all threads operate in the same input/output space
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for (uint64_t e = tid; e < rlimit; e += step) {
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// finding out the TAD we're going to process
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auto tadId = e / div;
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if (tadId >= numTads)
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continue;
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// now finding out element within tad
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auto idx = e % div;
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//printf("TID: %i; numTads: %lld; tadLength: %lld; tadId: %i, idx: %lld\n", tid, numTads, numOfElemsToReverse, tadId, idx);
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auto tadInput = input + inputTadOffsets[tadId];
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auto tadOutput = output + outputTadOffsets[tadId];
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// we're calculating offsets within input TAD
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auto fOffset = shape::getIndexOffset(idx, inputTadShape);
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auto lOffset = shape::getIndexOffset(numOfElemsToReverse - idx - 1, inputTadShape);
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// now we're storing input values
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auto v1 = tadInput[fOffset];
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auto v2 = tadInput[lOffset];
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// now we're calculating offsets within output TAD
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auto zfOffset = shape::getIndexOffset(idx, outputTadShape);
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auto zlOffset = shape::getIndexOffset(numOfElemsToReverse - idx - 1, outputTadShape);
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// and saving values to output arrays
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tadOutput[zfOffset] = v2;
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tadOutput[zlOffset] = v1;
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}
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// moving odd element in blocks
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if (odd && threadIdx.x == 0) {
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for (uint64_t e = blockIdx.x; e < numTads; e += gridDim.x) {
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auto tadInput = input + inputTadOffsets[e];
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auto tadOutput = output + outputTadOffsets[e];
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auto xOffset = shape::getIndexOffset(numOfElemsToReverse / 2, inputTadShape);
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auto zOffset = shape::getIndexOffset(numOfElemsToReverse / 2, outputTadShape);
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tadOutput[zOffset] = tadInput[xOffset];
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}
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}
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}
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template <typename T>
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static __global__ void reverseArrayKernel(void* input, Nd4jLong *inputShape, void* output, Nd4jLong *outputShape, Nd4jLong numOfElemsToReverse) {
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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@ -52,7 +113,7 @@ namespace helpers {
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auto odd = numOfElemsToReverse % 2 != 0;
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auto limit = numOfElemsToReverse / 2;
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for (Nd4jLong e = tid; e < limit; e += step) {
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for (uint64_t e = tid; e < limit; e += step) {
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// we're calculating offsets within input array
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auto fOffset = shape::getIndexOffset(e, inputShape);
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auto lOffset = shape::getIndexOffset(numOfElemsToReverse - e - 1, inputShape);
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@ -80,13 +141,19 @@ namespace helpers {
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}
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template<typename T>
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static void reverseArray(nd4j::LaunchContext * context, NDArray* input, NDArray* output, Nd4jLong numOfElemsToReverse) {
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static void reverseTad(nd4j::LaunchContext * context, const NDArray* input, NDArray* output, Nd4jLong *inputTadShape, Nd4jLong *inputTadOffsets, Nd4jLong *outputTadShape, Nd4jLong *outputTadOffsets, uint64_t tadLength) {
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auto stream = context->getCudaStream();
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reverseTadKernel<T><<<256, 512, 8192, *stream>>>(input->getSpecialBuffer(), input->getSpecialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), inputTadShape, inputTadOffsets, outputTadShape, outputTadOffsets, input->lengthOf(), tadLength, input->lengthOf() / tadLength);
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}
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template<typename T>
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static void reverseArray(nd4j::LaunchContext * context, const NDArray* input, NDArray* output, Nd4jLong numOfElemsToReverse) {
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auto stream = context->getCudaStream();
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Nd4jLong numOfReverse = numOfElemsToReverse;
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if (numOfElemsToReverse == 0)
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numOfReverse = input->lengthOf();
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reverseArrayKernel<T><<<256, 512, 8192, *stream>>>(input->specialBuffer(), input->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), numOfReverse);
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reverseArrayKernel<T><<<256, 512, 8192, *stream>>>(input->getSpecialBuffer(), input->getSpecialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), numOfReverse);
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}
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@ -153,27 +220,23 @@ namespace helpers {
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// we need to reverse axis only if that's new op
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std::vector<int> dimensions = isBackProp ? ShapeUtils::evalDimsToExclude(input->rankOf(), *intArgs) : *intArgs;
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std::vector<int> axis = ShapeUtils::evalDimsToExclude(input->rankOf(), dimensions);
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auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), axis);
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auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), axis);
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auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions);
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auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions);
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auto listOut = output->allTensorsAlongDimension(dimensions);
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auto listIn = input->allTensorsAlongDimension(dimensions);
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NDArray *subArrIn, *subArrOut;
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NDArray::prepareSpecialUse({output}, {input});
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for(int i = 0; i < listIn->size(); ++i) { // listIn->size() = listOut->size()
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subArrIn = listIn->at(i);
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subArrOut = listOut->at(i);
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BUILD_SINGLE_SELECTOR(input->dataType(), reverseArray, (context, subArrIn, subArrOut, 0), LIBND4J_TYPES);
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if (packX.numberOfTads() == 1) {
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BUILD_SINGLE_SELECTOR(input->dataType(), reverseArray, (context, input, output, 0), LIBND4J_TYPES);
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} else {
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BUILD_SINGLE_SELECTOR(input->dataType(), reverseTad, (context, input, output, packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets(), (uint64_t) (input->lengthOf() / packX.numberOfTads())), LIBND4J_TYPES);
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}
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//BUILD_SINGLE_SELECTOR(input->dataType(), reverseArray, (context, const_cast<NDArray*>(input), output, (int)0), LIBND4J_TYPES);
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NDArray::registerSpecialUse({output}, {input});
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delete listOut;
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delete listIn;
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}
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BUILD_SINGLE_TEMPLATE(template void reverseArray, (nd4j::LaunchContext * context, NDArray *inArr, NDArray *outArr, Nd4jLong numOfElemsToReverse), LIBND4J_TYPES);
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BUILD_SINGLE_TEMPLATE(template void reverseArray, (nd4j::LaunchContext * context, const NDArray *inArr, NDArray *outArr, Nd4jLong numOfElemsToReverse), LIBND4J_TYPES);
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}
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}
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@ -3523,7 +3523,8 @@ TEST_F(DeclarableOpsTests1, Reverse_7 ) {
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ASSERT_EQ(ND4J_STATUS_OK, results->status());
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auto result = results->at(0);
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// result->printBuffer();
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//expected.printIndexedBuffer("E");
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//result->printIndexedBuffer("R");
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ASSERT_TRUE(expected.isSameShapeStrict(result));
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ASSERT_TRUE(expected.equalsTo(result));
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@ -197,3 +197,44 @@ TEST_F(DeclarableOpsTests16, test_range_2) {
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delete shapes;
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}
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TEST_F(DeclarableOpsTests16, test_reverse_1) {
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std::vector<Nd4jLong> rows = {3, 5, 7, 8, 9, 10, 119, 211};
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std::vector<Nd4jLong> columns = {6, 5, 10, 100, 153, 171, 635};
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for (auto r : rows) {
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for (auto c : columns) {
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//nd4j_printf("Trying [%i, %i]\n", r, c);
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auto array = NDArrayFactory::create<float>('c', {r, c});
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auto exp = NDArrayFactory::create<float>('c', {r, c});
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auto reversed = NDArrayFactory::create<float>('c', {r, c});
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auto rowOriginal = NDArrayFactory::create<float>('c', {c});
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auto rowReversed = NDArrayFactory::create<float>('c', {c});
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for (int e = 0; e < c; e++) {
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rowOriginal.p(e, (float) e);
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rowReversed.p(c - e - 1, (float) e);
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}
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auto listI = array.allTensorsAlongDimension({1});
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auto listE = exp.allTensorsAlongDimension({1});
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for (int e = 0; e < r; e++) {
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listI->at(e)->assign(rowOriginal);
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listE->at(e)->assign(rowReversed);
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}
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delete listI;
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delete listE;
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nd4j::ops::reverse op;
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Nd4jLong axis = 1;
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auto status = op.execute({&array}, {&reversed}, {}, {axis}, {});
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ASSERT_EQ(Status::OK(), status);
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ASSERT_EQ(exp, reversed);
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}
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}
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}
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@ -24,6 +24,7 @@
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#include <NDArray.h>
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#include <ops/ops.h>
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#include <GradCheck.h>
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#include <chrono>
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using namespace nd4j;
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@ -58,5 +59,20 @@ TEST_F(DeclarableOpsTestsCuda1, Test_CHOOSE_SCALAR_LARGE) {
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//ASSERT_TRUE(exp.isSameShape(z));
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delete result;
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}
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/*
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TEST_F(DeclarableOpsTestsCuda1, Test_Reverse_TAD_1) {
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auto x = NDArrayFactory::create<float>('c', {1, 3, 608, 608});
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auto z = x.like();
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x.linspace(1.0f);
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nd4j::ops::reverse op;
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auto timeStart = std::chrono::system_clock::now();
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auto status = op.execute({&x}, {&z}, {}, {1}, {});
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auto timeEnd = std::chrono::system_clock::now();
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auto outerTime = std::chrono::duration_cast<std::chrono::microseconds> (timeEnd - timeStart).count();
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nd4j_printf("exec time: %lld us\n", outerTime);
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ASSERT_EQ(Status::OK(), status);
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}
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*/
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