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[SYCL] This PR adds the minimum modifications to the Eigen unsupported module required to run it on devices supporting SYCL.

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* Abstracting the pointer type so that both SYCL memory and pointer can be captured.
* Converting SYCL virtual pointer to SYCL device memory in Eigen evaluator class.
* Binding SYCL placeholder accessor to command group handler by using bind method in Eigen evaluator node.
* Adding SYCL macro for controlling loop unrolling.
* Modifying the TensorDeviceSycl.h and SYCL executor method to adopt the above changes.
This commit is contained in:
Mehdi Goli
2019-06-28 10:08:23 +01:00
parent 16a56b2ddd
commit 7d08fa805a
47 changed files with 1983 additions and 951 deletions
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33
unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
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@@ -37,7 +37,7 @@ struct traits<TensorBroadcastingOp<Broadcast, XprType> > : public traits<XprType
template<typename Broadcast, typename XprType>
struct eval<TensorBroadcastingOp<Broadcast, XprType>, Eigen::Dense>
{
typedef const TensorBroadcastingOp<Broadcast, XprType>& type;
typedef const TensorBroadcastingOp<Broadcast, XprType> EIGEN_DEVICE_REF type;
};
template<typename Broadcast, typename XprType>
@@ -105,7 +105,11 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
protected: // all the non-static fields must have the same access control, otherwise the TensorEvaluator wont be standard layout;
bool isCopy, nByOne, oneByN;
public:
typedef StorageMemory<CoeffReturnType, Device> Storage;
typedef typename Storage::Type EvaluatorPointerType;
enum {
IsAligned = true,
@@ -205,7 +209,7 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
@@ -238,6 +242,7 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
// TODO: attempt to speed this up. The integer divisions and modulo are slow
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index indexColMajor(Index index) const {
Index inputIndex = 0;
EIGEN_UNROLL_LOOP
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_outputStrides[i];
if (internal::index_statically_eq<Broadcast>(i, 1)) {
@@ -272,6 +277,7 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index indexRowMajor(Index index) const {
Index inputIndex = 0;
EIGEN_UNROLL_LOOP
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = index / m_outputStrides[i];
if (internal::index_statically_eq<Broadcast>(i, 1)) {
@@ -376,6 +382,7 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
values[0] = m_impl.coeff(inputIndex);
return internal::pload1<PacketReturnType>(values);
} else {
EIGEN_UNROLL_LOOP
for (int i = 0, cur = 0; i < PacketSize; ++i, ++cur) {
if (outputOffset + cur < m_outputStrides[endDim]) {
values[i] = m_impl.coeff(inputIndex);
@@ -410,6 +417,7 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
return m_impl.template packet<Unaligned>(inputIndex);
} else {
EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
EIGEN_UNROLL_LOOP
for (int i = 0; i < PacketSize; ++i) {
if (inputIndex > m_inputStrides[dim]-1) {
inputIndex = 0;
@@ -441,6 +449,7 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
values[0] = m_impl.coeff(inputIndex);
return internal::pload1<PacketReturnType>(values);
} else {
EIGEN_UNROLL_LOOP
for (int i = 0, cur = 0; i < PacketSize; ++i, ++cur) {
if (outputOffset + cur < m_outputStrides[dim]) {
values[i] = m_impl.coeff(inputIndex);
@@ -465,6 +474,7 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
const Index originalIndex = index;
Index inputIndex = 0;
EIGEN_UNROLL_LOOP
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_outputStrides[i];
if (internal::index_statically_eq<Broadcast>(i, 1)) {
@@ -500,6 +510,7 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
} else {
EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
values[0] = m_impl.coeff(inputIndex);
EIGEN_UNROLL_LOOP
for (int i = 1; i < PacketSize; ++i) {
if (innermostLoc + i < m_impl.dimensions()[0]) {
values[i] = m_impl.coeff(inputIndex+i);
@@ -521,6 +532,7 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
const Index originalIndex = index;
Index inputIndex = 0;
EIGEN_UNROLL_LOOP
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = index / m_outputStrides[i];
if (internal::index_statically_eq<Broadcast>(i, 1)) {
@@ -556,6 +568,7 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
} else {
EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
values[0] = m_impl.coeff(inputIndex);
EIGEN_UNROLL_LOOP
for (int i = 1; i < PacketSize; ++i) {
if (innermostLoc + i < m_impl.dimensions()[NumDims-1]) {
values[i] = m_impl.coeff(inputIndex+i);
@@ -572,6 +585,7 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
costPerCoeff(bool vectorized) const {
double compute_cost = TensorOpCost::AddCost<Index>();
if (!isCopy && NumDims > 0) {
EIGEN_UNROLL_LOOP
for (int i = NumDims - 1; i > 0; --i) {
compute_cost += TensorOpCost::DivCost<Index>();
if (internal::index_statically_eq<Broadcast>(i, 1)) {
@@ -845,12 +859,17 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
}
}
EIGEN_DEVICE_FUNC typename Eigen::internal::traits<XprType>::PointerType data() const { return NULL; }
EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
Broadcast functor() const { return m_broadcast; }
#ifdef EIGEN_USE_SYCL
// binding placeholder accessors to a command group handler for SYCL
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
m_impl.bind(cgh);
}
#endif
private:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void BroadcastBlock(
const Dimensions& input_block_sizes,
@@ -874,9 +893,9 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
BroadcastTensorBlockReader::Run(&broadcast_block, input_block.data());
}
protected:
const Device& m_device;
const Broadcast m_broadcast;
protected:
const Device EIGEN_DEVICE_REF m_device;
const typename internal::remove_reference<Broadcast>::type m_broadcast;
Dimensions m_dimensions;
array<Index, NumDims> m_outputStrides;
array<Index, NumDims> m_inputStrides;