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authorGravatar Vamsi Sripathi <vamsi.sripathi@intel.com>2018-05-23 14:02:05 -0700
committerGravatar Vamsi Sripathi <vamsi.sripathi@intel.com>2018-05-23 14:02:05 -0700
commit6293ad3f392a7b97ebb9f9f874682505c1391f2d (patch)
tree27e8306ff9ee047f8fce7bcd853fca06294b9783
parent7134fa7a2eea469f35ea12899e693a633b5b42e5 (diff)
Performance improvements to tensor broadcast operation
1. Added new packet functions using SIMD for NByOne, OneByN cases 2. Modified existing packet functions to reduce index calculations when input stride is non-SIMD 3. Added 4 test cases to cover the new packet functions
-rw-r--r--unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h110
-rw-r--r--unsupported/test/cxx11_tensor_broadcasting.cpp62
2 files changed, 168 insertions, 4 deletions
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h b/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
index b6c93aff9..9ab6b3565 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
@@ -105,6 +105,7 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
+ bool nByOne = false, oneByN = false;
enum {
IsAligned = true,
@@ -142,6 +143,24 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
}
}
+
+ if (input_dims[0] == 1) {
+ oneByN = true;
+ for (int i = 1; i < NumDims; ++i) {
+ if (broadcast[i] != 1) {
+ oneByN = false;
+ break;
+ }
+ }
+ } else if (input_dims[NumDims-1] == 1) {
+ nByOne = true;
+ for (int i = 0; i < NumDims-1; ++i) {
+ if (broadcast[i] != 1) {
+ nByOne = false;
+ break;
+ }
+ }
+ }
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
@@ -237,9 +256,84 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
}
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
- return packetColMajor<LoadMode>(index);
+ if (oneByN) {
+ return packetNByOne<LoadMode>(index);
+ } else if (nByOne) {
+ return packetOneByN<LoadMode>(index);
+ } else {
+ return packetColMajor<LoadMode>(index);
+ }
} else {
- return packetRowMajor<LoadMode>(index);
+ if (oneByN) {
+ return packetOneByN<LoadMode>(index);
+ } else if (nByOne) {
+ return packetNByOne<LoadMode>(index);
+ } else {
+ return packetRowMajor<LoadMode>(index);
+ }
+ }
+ }
+
+ template<int LoadMode>
+ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetOneByN(Index index) const
+ {
+ EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+ eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+ Index dim, inputIndex;
+
+ if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+ dim = NumDims - 1;
+ } else {
+ dim = 0;
+ }
+
+ inputIndex = index % m_inputStrides[dim];
+ if (inputIndex + PacketSize <= m_inputStrides[dim]) {
+ return m_impl.template packet<Unaligned>(inputIndex);
+ } else {
+ EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+ for (int i = 0; i < PacketSize; ++i) {
+ if (inputIndex > m_inputStrides[dim]-1) {
+ inputIndex = 0;
+ }
+ values[i] = m_impl.coeff(inputIndex++);
+ }
+ return internal::pload<PacketReturnType>(values);
+ }
+ }
+
+ template<int LoadMode>
+ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetNByOne(Index index) const
+ {
+ EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+ eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+ EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+ Index dim, inputIndex, outputOffset;
+
+ if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+ dim = 1;
+ } else {
+ dim = NumDims - 2;
+ }
+
+ inputIndex = index / m_outputStrides[dim];
+ outputOffset = index % m_outputStrides[dim];
+ if (outputOffset + PacketSize <= m_outputStrides[dim]) {
+ values[0] = m_impl.coeff(inputIndex);
+ return internal::pload1<PacketReturnType>(values);
+ } else {
+ for (int i = 0, cur = 0; i < PacketSize; ++i, ++cur) {
+ if (outputOffset + cur < m_outputStrides[dim]) {
+ values[i] = m_impl.coeff(inputIndex);
+ } else {
+ values[i] = m_impl.coeff(++inputIndex);
+ outputOffset = 0;
+ cur = 0;
+ }
+ }
+ return internal::pload<PacketReturnType>(values);
}
}
@@ -290,7 +384,11 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
values[0] = m_impl.coeff(inputIndex);
for (int i = 1; i < PacketSize; ++i) {
- values[i] = coeffColMajor(originalIndex+i);
+ if (innermostLoc + i < m_impl.dimensions()[0]) {
+ values[i] = m_impl.coeff(inputIndex+i);
+ } else {
+ values[i] = coeffColMajor(originalIndex+i);
+ }
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
@@ -342,7 +440,11 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
values[0] = m_impl.coeff(inputIndex);
for (int i = 1; i < PacketSize; ++i) {
- values[i] = coeffRowMajor(originalIndex+i);
+ if (innermostLoc + i < m_impl.dimensions()[NumDims-1]) {
+ values[i] = m_impl.coeff(inputIndex+i);
+ } else {
+ values[i] = coeffRowMajor(originalIndex+i);
+ }
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
diff --git a/unsupported/test/cxx11_tensor_broadcasting.cpp b/unsupported/test/cxx11_tensor_broadcasting.cpp
index 5c0ea5889..a9d268ea6 100644
--- a/unsupported/test/cxx11_tensor_broadcasting.cpp
+++ b/unsupported/test/cxx11_tensor_broadcasting.cpp
@@ -180,6 +180,64 @@ static void test_fixed_size_broadcasting()
#endif
}
+template <int DataLayout>
+static void test_simple_broadcasting_one_by_n()
+{
+ Tensor<float, 4, DataLayout> tensor(1,13,5,7);
+ tensor.setRandom();
+ array<ptrdiff_t, 4> broadcasts;
+ broadcasts[0] = 9;
+ broadcasts[1] = 1;
+ broadcasts[2] = 1;
+ broadcasts[3] = 1;
+ Tensor<float, 4, DataLayout> broadcast;
+ broadcast = tensor.broadcast(broadcasts);
+
+ VERIFY_IS_EQUAL(broadcast.dimension(0), 9);
+ VERIFY_IS_EQUAL(broadcast.dimension(1), 13);
+ VERIFY_IS_EQUAL(broadcast.dimension(2), 5);
+ VERIFY_IS_EQUAL(broadcast.dimension(3), 7);
+
+ for (int i = 0; i < 9; ++i) {
+ for (int j = 0; j < 13; ++j) {
+ for (int k = 0; k < 5; ++k) {
+ for (int l = 0; l < 7; ++l) {
+ VERIFY_IS_EQUAL(tensor(i%1,j%13,k%5,l%7), broadcast(i,j,k,l));
+ }
+ }
+ }
+ }
+}
+
+template <int DataLayout>
+static void test_simple_broadcasting_n_by_one()
+{
+ Tensor<float, 4, DataLayout> tensor(7,3,5,1);
+ tensor.setRandom();
+ array<ptrdiff_t, 4> broadcasts;
+ broadcasts[0] = 1;
+ broadcasts[1] = 1;
+ broadcasts[2] = 1;
+ broadcasts[3] = 19;
+ Tensor<float, 4, DataLayout> broadcast;
+ broadcast = tensor.broadcast(broadcasts);
+
+ VERIFY_IS_EQUAL(broadcast.dimension(0), 7);
+ VERIFY_IS_EQUAL(broadcast.dimension(1), 3);
+ VERIFY_IS_EQUAL(broadcast.dimension(2), 5);
+ VERIFY_IS_EQUAL(broadcast.dimension(3), 19);
+
+ for (int i = 0; i < 7; ++i) {
+ for (int j = 0; j < 3; ++j) {
+ for (int k = 0; k < 5; ++k) {
+ for (int l = 0; l < 19; ++l) {
+ VERIFY_IS_EQUAL(tensor(i%7,j%3,k%5,l%1), broadcast(i,j,k,l));
+ }
+ }
+ }
+ }
+}
+
void test_cxx11_tensor_broadcasting()
{
@@ -191,4 +249,8 @@ void test_cxx11_tensor_broadcasting()
CALL_SUBTEST(test_static_broadcasting<RowMajor>());
CALL_SUBTEST(test_fixed_size_broadcasting<ColMajor>());
CALL_SUBTEST(test_fixed_size_broadcasting<RowMajor>());
+ CALL_SUBTEST(test_simple_broadcasting_one_by_n<RowMajor>());
+ CALL_SUBTEST(test_simple_broadcasting_n_by_one<RowMajor>());
+ CALL_SUBTEST(test_simple_broadcasting_one_by_n<ColMajor>());
+ CALL_SUBTEST(test_simple_broadcasting_n_by_one<ColMajor>());
}