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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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45
unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h
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@@ -154,23 +154,6 @@ class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprT
m_padding_left(padding_left), m_padding_right(padding_right),
m_padding_type(PADDING_VALID), m_padding_value(padding_value) {}
#ifdef EIGEN_USE_SYCL // this is work around for sycl as Eigen could not use c++11 deligate constructor feature
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols,
DenseIndex row_strides, DenseIndex col_strides,
DenseIndex in_row_strides, DenseIndex in_col_strides,
DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
bool padding_explicit, DenseIndex padding_top, DenseIndex padding_bottom,
DenseIndex padding_left, DenseIndex padding_right, PaddingType padding_type,
Scalar padding_value)
: m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
m_row_strides(row_strides), m_col_strides(col_strides),
m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
m_padding_explicit(padding_explicit), m_padding_top(padding_top), m_padding_bottom(padding_bottom),
m_padding_left(padding_left), m_padding_right(padding_right),
m_padding_type(padding_type), m_padding_value(padding_value) {}
#endif
EIGEN_DEVICE_FUNC
DenseIndex patch_rows() const { return m_patch_rows; }
@@ -242,6 +225,8 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
typedef StorageMemory<CoeffReturnType, Device> Storage;
typedef typename Storage::Type EvaluatorPointerType;
enum {
IsAligned = false,
@@ -256,15 +241,8 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
typedef internal::TensorBlock<Scalar, Index, NumDims, Layout>
OutputTensorBlock;
#ifdef __SYCL_DEVICE_ONLY__
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator( const XprType op, const Device& device)
#else
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator( const XprType& op, const Device& device)
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator( const XprType& op, const Device& device)
: m_device(device), m_impl(op.expression(), device)
#ifdef EIGEN_USE_SYCL
, m_op(op)
#endif
{
EIGEN_STATIC_ASSERT((NumDims >= 4), YOU_MADE_A_PROGRAMMING_MISTAKE);
@@ -410,7 +388,7 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, 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 /*data*/) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
@@ -516,13 +494,15 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
return packetWithPossibleZero(index);
}
EIGEN_DEVICE_FUNC typename Eigen::internal::traits<XprType>::PointerType data() const { return NULL; }
EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
#ifdef EIGEN_USE_SYCL
// Required by SYCL in order to construct the expression tree on the device
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const XprType& xpr() const { return m_op; }
// 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
Index rowPaddingTop() const { return m_rowPaddingTop; }
@@ -693,6 +673,7 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
{
EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
EIGEN_UNROLL_LOOP
for (int i = 0; i < PacketSize; ++i) {
values[i] = coeff(index+i);
}
@@ -744,12 +725,8 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
Scalar m_paddingValue;
const Device& m_device;
const Device EIGEN_DEVICE_REF m_device;
TensorEvaluator<ArgType, Device> m_impl;
#ifdef EIGEN_USE_SYCL
// Required for SYCL in order to construct the expression tree on the device
XprType m_op;
#endif
};