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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
Yurii Shyrma 2020-02-19 08:35:52 +02:00 committed by GitHub
parent 72f9cda019
commit c5193ecb81
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 107 additions and 24 deletions
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129
libnd4j/include/ops/declarable/helpers/cpu/gather.cpp
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@ -21,6 +21,8 @@
#include <ops/declarable/helpers/gather.h>
#include <numeric>
#include <execution/Threads.h>
#include <ShapeUtils.h>
#include <ConstantTadHelper.h>
namespace nd4j {
namespace ops {
@ -36,7 +38,7 @@ void gather(nd4j::LaunchContext * context, const NDArray* input, const NDArray*
const int numOfIntArgs = intArgs.size();
if (indices != nullptr) {
if (indices != nullptr) {
// first case: indices consist of only one scalar
if(indices->isScalar()) {
@ -46,7 +48,7 @@ void gather(nd4j::LaunchContext * context, const NDArray* input, const NDArray*
auto idx = indices->e<Nd4jLong>(0);
auto scalarNDArray = input->e(idx);
output->assign(scalarNDArray);
}
}
else {
NDArray inSubArr = (*input)(indices->e<Nd4jLong>(0), {axis});
output->assign(inSubArr);
@ -54,41 +56,122 @@ void gather(nd4j::LaunchContext * context, const NDArray* input, const NDArray*
}
else {
std::vector<int> dimsOut(indices->rankOf());
std::iota(dimsOut.begin(), dimsOut.end(), axis); // fill with axis, axis+1, ... axis+indices->rankOf()-1
const Nd4jLong numOfSubArrs = indices->lengthOf();
if(input->rankOf() == 1 && output->rankOf() == 1) {
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i += increment) {
NDArray subArrOut = (*output)(i, dimsOut);
NDArray subArrIn = (*input)(indices->e<Nd4jLong>(i), {axis});
subArrOut.assign(subArrIn);
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i += increment)
output->p(i, input->e(indices->e<Nd4jLong>(i)));
};
samediff::Threads::parallel_for(func, 0, output->lengthOf());
}
else {
std::vector<int> dimsOut;
for (int i = 0; i < axis; ++i)
dimsOut.push_back(i);
for (int i = axis+indices->rankOf(); i < output->rankOf(); ++i)
dimsOut.push_back(i);
std::vector<int> dimsIn = ShapeUtils::evalDimsToExclude(input->rankOf(), {axis});
const Nd4jLong numOfSubArrs = indices->lengthOf();
auto inTadPack = ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimsIn);
auto outTadPack = ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimsOut);
Nd4jLong* inTadShapeInfo = inTadPack.primaryShapeInfo();
Nd4jLong* outTadShapeInfo = outTadPack.primaryShapeInfo();
if (shape::order(inTadShapeInfo) == shape::order(outTadShapeInfo) && shape::order(inTadShapeInfo) == 'c' && input->dataType() == output->dataType() && shape::elementWiseStride(inTadShapeInfo) == 1 && shape::elementWiseStride(outTadShapeInfo) == 1) {
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i += increment) {
void* inBuff = input->bufferWithOffset(inTadPack.primaryOffsets()[indices->e<Nd4jLong>(i)]);
void* outBuff = output->bufferWithOffset(outTadPack.primaryOffsets()[i]);
memcpy(outBuff, inBuff, shape::length(inTadShapeInfo) * input->sizeOfT());
}
};
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
}
};
else {
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i += increment) {
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
void* inBuff = input->bufferWithOffset(inTadPack.primaryOffsets()[indices->e<Nd4jLong>(i)]);
void* outBuff = output->bufferWithOffset(outTadPack.primaryOffsets()[i]);
NativeOpExecutioner::execTransformAny(input->getContext(), transform::Assign,
inBuff, inTadShapeInfo, nullptr/*input specialBuffer*/, nullptr/*input specialShapeInfo*/,
outBuff, outTadShapeInfo, nullptr/*output specialBuffer*/, nullptr/*output specialShapeInfo*/,
nullptr, nullptr, nullptr, false/*allowParallelism*/);
}
};
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
}
}
}
}
}
else {
// we only allow scalar/vector case here
if (numOfIntArgs == 2) { // scalar case
output->assign((*input)(intArgs[1], {axis}));
}
else { // vector case
const Nd4jLong numOfSubArrs = intArgs.size() - 1;
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i += increment) {
NDArray subArrOut = (*output)(i, {axis});
NDArray subArrIn = (*input)(intArgs[i + 1], {axis});
subArrOut.assign(subArrIn);
}
};
std::vector<int> dims = ShapeUtils::evalDimsToExclude(input->rankOf(), {axis});
auto inTadPack = ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dims);
auto outTadPack = ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dims);
Nd4jLong* inTadShapeInfo = inTadPack.primaryShapeInfo();
Nd4jLong* outTadShapeInfo = outTadPack.primaryShapeInfo();
if (shape::order(inTadShapeInfo) == shape::order(outTadShapeInfo) && shape::order(inTadShapeInfo) == 'c' && input->dataType() == output->dataType() && shape::elementWiseStride(inTadShapeInfo) == 1 && shape::elementWiseStride(outTadShapeInfo) == 1) {
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i += increment) {
void* inBuff = input->bufferWithOffset(inTadPack.primaryOffsets()[intArgs[i + 1]]);
void* outBuff = output->bufferWithOffset(outTadPack.primaryOffsets()[i]);
std::memcpy(outBuff, inBuff, shape::length(inTadShapeInfo) * input->sizeOfT());
}
};
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
}
else {
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i += increment) {
void* inBuff = input->bufferWithOffset(inTadPack.primaryOffsets()[intArgs[i + 1]]);
void* outBuff = output->bufferWithOffset(outTadPack.primaryOffsets()[i]);
NativeOpExecutioner::execTransformAny(input->getContext(), transform::Assign,
inBuff, inTadShapeInfo, nullptr/*input specialBuffer*/, nullptr/*input specialShapeInfo*/,
outBuff, outTadShapeInfo, nullptr/*output specialBuffer*/, nullptr/*output specialShapeInfo*/,
nullptr, nullptr, nullptr, false/*allowParallelism*/);
}
};
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
}
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
}
}
}
}

2
libnd4j/include/ops/declarable/platform/mkldnn/matmul.cpp
View File

@ -279,7 +279,7 @@ PLATFORM_CHECK(matmul, ENGINE_CPU) {
const DataType zType = z->dataType();
return block.isUseMKLDNN() &&
return block.isUseMKLDNN() && x->rankOf() < 3 &&
(
(xType==DataType::FLOAT32 && yType==DataType::FLOAT32 && zType==DataType::FLOAT32) ||
(xType==DataType::HALF && yType==DataType::HALF && zType==DataType::FLOAT32) ||