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Add tiled evaluation for TensorForcedEvalOp

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This commit is contained in:
Eugene Zhulenev 2019-03-04 16:02:22 -08:00
parent 694084ecbd
commit b95941e5c2
2 changed files with 58 additions and 6 deletions
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25
unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h
View File

@ -90,14 +90,21 @@ struct TensorEvaluator<const TensorForcedEvalOp<ArgType>, Device>
static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
enum {
IsAligned = true,
PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
BlockAccess = false,
IsAligned = true,
PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
BlockAccess = internal::is_arithmetic<CoeffReturnType>::value,
PreferBlockAccess = false,
Layout = TensorEvaluator<ArgType, Device>::Layout,
RawAccess = true
Layout = TensorEvaluator<ArgType, Device>::Layout,
RawAccess = true
};
typedef typename internal::TensorBlock<
CoeffReturnType, Index, internal::traits<ArgType>::NumDimensions, Layout>
TensorBlock;
typedef typename internal::TensorBlockReader<
CoeffReturnType, Index, internal::traits<ArgType>::NumDimensions, Layout>
TensorBlockReader;
EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device)
/// op_ is used for sycl
: m_impl(op.expression(), device), m_op(op.expression()), m_device(device), m_buffer(NULL)
@ -139,6 +146,14 @@ struct TensorEvaluator<const TensorForcedEvalOp<ArgType>, Device>
return internal::ploadt<PacketReturnType, LoadMode>(m_buffer + index);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void getResourceRequirements(
std::vector<internal::TensorOpResourceRequirements>*) const {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void block(TensorBlock* block) const {
assert(m_buffer != NULL);
TensorBlockReader::Run(block, m_buffer);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
}

39
unsupported/test/cxx11_tensor_executor.cpp
View File

@ -452,6 +452,38 @@ static void test_execute_slice_lvalue(Device d)
}
}
template <typename T, int NumDims, typename Device, bool Vectorizable,
bool Tileable, int Layout>
static void test_execute_broadcasting_of_forced_eval(Device d)
{
static constexpr int Options = 0 | Layout;
auto dims = RandomDims<NumDims>(1, 10);
Tensor<T, NumDims, Options, Index> src(dims);
src.setRandom();
const auto broadcasts = RandomDims<NumDims>(1, 7);
const auto expr = src.square().eval().broadcast(broadcasts);
// We assume that broadcasting on a default device is tested and correct, so
// we can rely on it to verify correctness of tensor executor and tiling.
Tensor<T, NumDims, Options, Index> golden;
golden = expr;
// Now do the broadcasting using configured tensor executor.
Tensor<T, NumDims, Options, Index> dst(golden.dimensions());
using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
using Executor =
internal::TensorExecutor<const Assign, Device, Vectorizable, Tileable>;
Executor::run(Assign(dst, expr), d);
for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
}
}
#define CALL_SUBTEST_PART(PART) \
CALL_SUBTEST_##PART
@ -528,8 +560,13 @@ EIGEN_DECLARE_TEST(cxx11_tensor_executor) {
CALL_SUBTEST_COMBINATIONS(11, test_execute_slice_lvalue, float, 4);
CALL_SUBTEST_COMBINATIONS(11, test_execute_slice_lvalue, float, 5);
CALL_SUBTEST_COMBINATIONS(12, test_execute_broadcasting_of_forced_eval, float, 2);
CALL_SUBTEST_COMBINATIONS(12, test_execute_broadcasting_of_forced_eval, float, 3);
CALL_SUBTEST_COMBINATIONS(12, test_execute_broadcasting_of_forced_eval, float, 4);
CALL_SUBTEST_COMBINATIONS(12, test_execute_broadcasting_of_forced_eval, float, 5);
// Force CMake to split this test.
// EIGEN_SUFFIXES;1;2;3;4;5;6;7;8;9;10;11
// EIGEN_SUFFIXES;1;2;3;4;5;6;7;8;9;10;11;12
}
#undef CALL_SUBTEST_COMBINATIONS