Shyrma gather (#254)
* - profiling gather op for aurora Signed-off-by: Yurii <iuriish@yahoo.com> * - include contiguous memcpy in gather op Signed-off-by: Yurii <iuriish@yahoo.com>master
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72f9cda019
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c5193ecb81
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@ -21,6 +21,8 @@
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#include <ops/declarable/helpers/gather.h>
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#include <numeric>
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#include <execution/Threads.h>
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#include <ShapeUtils.h>
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#include <ConstantTadHelper.h>
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namespace nd4j {
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namespace ops {
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@ -54,39 +56,120 @@ void gather(nd4j::LaunchContext * context, const NDArray* input, const NDArray*
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}
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else {
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std::vector<int> dimsOut(indices->rankOf());
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std::iota(dimsOut.begin(), dimsOut.end(), axis); // fill with axis, axis+1, ... axis+indices->rankOf()-1
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const Nd4jLong numOfSubArrs = indices->lengthOf();
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if(input->rankOf() == 1 && output->rankOf() == 1) {
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i += increment) {
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NDArray subArrOut = (*output)(i, dimsOut);
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NDArray subArrIn = (*input)(indices->e<Nd4jLong>(i), {axis});
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subArrOut.assign(subArrIn);
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i += increment)
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output->p(i, input->e(indices->e<Nd4jLong>(i)));
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};
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samediff::Threads::parallel_for(func, 0, output->lengthOf());
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}
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else {
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std::vector<int> dimsOut;
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for (int i = 0; i < axis; ++i)
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dimsOut.push_back(i);
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for (int i = axis+indices->rankOf(); i < output->rankOf(); ++i)
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dimsOut.push_back(i);
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std::vector<int> dimsIn = ShapeUtils::evalDimsToExclude(input->rankOf(), {axis});
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const Nd4jLong numOfSubArrs = indices->lengthOf();
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auto inTadPack = ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimsIn);
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auto outTadPack = ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimsOut);
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Nd4jLong* inTadShapeInfo = inTadPack.primaryShapeInfo();
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Nd4jLong* outTadShapeInfo = outTadPack.primaryShapeInfo();
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if (shape::order(inTadShapeInfo) == shape::order(outTadShapeInfo) && shape::order(inTadShapeInfo) == 'c' && input->dataType() == output->dataType() && shape::elementWiseStride(inTadShapeInfo) == 1 && shape::elementWiseStride(outTadShapeInfo) == 1) {
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i += increment) {
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void* inBuff = input->bufferWithOffset(inTadPack.primaryOffsets()[indices->e<Nd4jLong>(i)]);
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void* outBuff = output->bufferWithOffset(outTadPack.primaryOffsets()[i]);
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memcpy(outBuff, inBuff, shape::length(inTadShapeInfo) * input->sizeOfT());
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}
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};
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samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
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}
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};
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else {
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i += increment) {
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samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
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void* inBuff = input->bufferWithOffset(inTadPack.primaryOffsets()[indices->e<Nd4jLong>(i)]);
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void* outBuff = output->bufferWithOffset(outTadPack.primaryOffsets()[i]);
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NativeOpExecutioner::execTransformAny(input->getContext(), transform::Assign,
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inBuff, inTadShapeInfo, nullptr/*input specialBuffer*/, nullptr/*input specialShapeInfo*/,
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outBuff, outTadShapeInfo, nullptr/*output specialBuffer*/, nullptr/*output specialShapeInfo*/,
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nullptr, nullptr, nullptr, false/*allowParallelism*/);
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}
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};
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samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
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}
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}
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}
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}
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else {
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// we only allow scalar/vector case here
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if (numOfIntArgs == 2) { // scalar case
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output->assign((*input)(intArgs[1], {axis}));
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}
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else { // vector case
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const Nd4jLong numOfSubArrs = intArgs.size() - 1;
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i += increment) {
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NDArray subArrOut = (*output)(i, {axis});
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NDArray subArrIn = (*input)(intArgs[i + 1], {axis});
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subArrOut.assign(subArrIn);
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}
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};
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std::vector<int> dims = ShapeUtils::evalDimsToExclude(input->rankOf(), {axis});
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auto inTadPack = ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dims);
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auto outTadPack = ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dims);
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Nd4jLong* inTadShapeInfo = inTadPack.primaryShapeInfo();
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Nd4jLong* outTadShapeInfo = outTadPack.primaryShapeInfo();
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if (shape::order(inTadShapeInfo) == shape::order(outTadShapeInfo) && shape::order(inTadShapeInfo) == 'c' && input->dataType() == output->dataType() && shape::elementWiseStride(inTadShapeInfo) == 1 && shape::elementWiseStride(outTadShapeInfo) == 1) {
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i += increment) {
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void* inBuff = input->bufferWithOffset(inTadPack.primaryOffsets()[intArgs[i + 1]]);
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void* outBuff = output->bufferWithOffset(outTadPack.primaryOffsets()[i]);
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std::memcpy(outBuff, inBuff, shape::length(inTadShapeInfo) * input->sizeOfT());
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}
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};
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samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
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}
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else {
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i += increment) {
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void* inBuff = input->bufferWithOffset(inTadPack.primaryOffsets()[intArgs[i + 1]]);
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void* outBuff = output->bufferWithOffset(outTadPack.primaryOffsets()[i]);
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NativeOpExecutioner::execTransformAny(input->getContext(), transform::Assign,
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inBuff, inTadShapeInfo, nullptr/*input specialBuffer*/, nullptr/*input specialShapeInfo*/,
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outBuff, outTadShapeInfo, nullptr/*output specialBuffer*/, nullptr/*output specialShapeInfo*/,
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nullptr, nullptr, nullptr, false/*allowParallelism*/);
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}
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};
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samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
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}
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samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
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}
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}
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}
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@ -279,7 +279,7 @@ PLATFORM_CHECK(matmul, ENGINE_CPU) {
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const DataType zType = z->dataType();
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return block.isUseMKLDNN() &&
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return block.isUseMKLDNN() && x->rankOf() < 3 &&
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(
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(xType==DataType::FLOAT32 && yType==DataType::FLOAT32 && zType==DataType::FLOAT32) ||
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(xType==DataType::HALF && yType==DataType::HALF && zType==DataType::FLOAT32) ||
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