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Merged eigen/eigen

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This commit is contained in:
Srinivas Vasudevan 2019-09-04 23:50:52 -04:00
commit a9cf823db7
13 changed files with 97 additions and 63 deletions
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4
Eigen/src/Core/GenericPacketMath.h
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@ -276,12 +276,12 @@ pselect(const Packet& mask, const Packet& a, const Packet& b) {
template<> EIGEN_DEVICE_FUNC inline float pselect<float>(
const float& mask, const float& a, const float&b) {
return mask == 0 ? b : a;
return numext::equal_strict(mask,0.f) ? b : a;
}
template<> EIGEN_DEVICE_FUNC inline double pselect<double>(
const double& mask, const double& a, const double& b) {
return mask == 0 ? b : a;
return numext::equal_strict(mask,0.) ? b : a;
}
/** \internal \returns a <= b as a bit mask */

4
Eigen/src/Core/arch/AVX512/PacketMath.h
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@ -95,8 +95,8 @@ template<> struct packet_traits<float> : default_packet_traits
#if EIGEN_GNUC_AT_LEAST(5, 3) || (!EIGEN_COMP_GNUC_STRICT)
#ifdef EIGEN_VECTORIZE_AVX512DQ
HasLog = 1,
HasLog1p = 1,
HasExpm1 = 1,
HasLog1p = 1,
HasExpm1 = 1,
HasNdtri = 1,
#endif
HasExp = 1,

1
Eigen/src/Core/arch/SSE/PacketMath.h
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@ -112,6 +112,7 @@ template<> struct packet_traits<float> : default_packet_traits
HasLog = 1,
HasLog1p = 1,
HasExpm1 = 1,
HasNdtri = 1,
HasExp = 1,
HasNdtri = 1,
HasSqrt = 1,

2
Eigen/src/SparseCore/SparseCwiseUnaryOp.h
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@ -49,6 +49,7 @@ template<typename UnaryOp, typename ArgType>
class unary_evaluator<CwiseUnaryOp<UnaryOp,ArgType>, IteratorBased>::InnerIterator
: public unary_evaluator<CwiseUnaryOp<UnaryOp,ArgType>, IteratorBased>::EvalIterator
{
protected:
typedef typename XprType::Scalar Scalar;
typedef typename unary_evaluator<CwiseUnaryOp<UnaryOp,ArgType>, IteratorBased>::EvalIterator Base;
public:
@ -99,6 +100,7 @@ template<typename ViewOp, typename ArgType>
class unary_evaluator<CwiseUnaryView<ViewOp,ArgType>, IteratorBased>::InnerIterator
: public unary_evaluator<CwiseUnaryView<ViewOp,ArgType>, IteratorBased>::EvalIterator
{
protected:
typedef typename XprType::Scalar Scalar;
typedef typename unary_evaluator<CwiseUnaryView<ViewOp,ArgType>, IteratorBased>::EvalIterator Base;
public:

1
Eigen/src/SparseCore/SparseView.h
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@ -90,6 +90,7 @@ struct unary_evaluator<SparseView<ArgType>, IteratorBased>
class InnerIterator : public EvalIterator
{
protected:
typedef typename XprType::Scalar Scalar;
public:

12
Eigen/src/plugins/ArrayCwiseUnaryOps.h
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@ -608,16 +608,18 @@ erfc() const
return ErfcReturnType(derived());
}
/** \cpp11 \returns an expression of the coefficient-wise Complementary error
/** \returns an expression of the coefficient-wise inverse of the CDF of the Normal distribution function
* function of *this.
*
* \specialfunctions_module
*
* In other words, considering `x = ndtri(y)`, it returns the argument, x, for which the area under the
* Gaussian probability density function (integrated from minus infinity to x) is equal to y.
*
* \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
* or float/double in non c++11 mode, the user has to provide implementations of ndtri(T) for any scalar
* type T to be supported.
* \note This function supports only float and double scalar types. To support other scalar types,
* the user has to provide implementations of ndtri(T) for any scalar type T to be supported.
*
* \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_ndtri">Math functions</a>, erf()
* \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_ndtri">Math functions</a>
*/
EIGEN_DEVICE_FUNC
inline const NdtriReturnType

12
doc/CoeffwiseMathFunctionsTable.dox
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@ -553,6 +553,18 @@ This also means that, unless specified, if the function \c std::foo is available
</td>
<td></td>
</tr>
<tr>
<td class="code">
\anchor cwisetable_ndtri
a.\link ArrayBase::ndtri ndtri\endlink(); \n
\link Eigen::ndtri ndtri\endlink(a);
</td>
<td>Inverse of the CDF of the Normal distribution function</td>
<td>
built-in for float and double
</td>
<td></td>
</tr>
<tr><td colspan="4"></td></tr>
</table>

8
unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
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@ -1071,12 +1071,12 @@ class TensorBase : public TensorBase<Derived, ReadOnlyAccessors> {
#ifdef EIGEN_USE_THREADS
// Select the async device on which to evaluate the expression.
template <typename DeviceType>
template <typename DeviceType, typename DoneCallback>
typename internal::enable_if<
internal::is_same<DeviceType, ThreadPoolDevice>::value,
TensorAsyncDevice<Derived, DeviceType>>::type
device(const DeviceType& dev, std::function<void()> done) {
return TensorAsyncDevice<Derived, DeviceType>(dev, derived(), std::move(done));
TensorAsyncDevice<Derived, DeviceType, DoneCallback>>::type
device(const DeviceType& dev, DoneCallback done) {
return TensorAsyncDevice<Derived, DeviceType, DoneCallback>(dev, derived(), std::move(done));
}
#endif // EIGEN_USE_THREADS

10
unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h
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@ -73,21 +73,21 @@ template <typename ExpressionType, typename DeviceType> class TensorDevice {
* ThreadPoolDevice).
*
* Example:
* std::function<void()> done = []() {};
* auto done = []() { ... expression evaluation done ... };
* C.device(EIGEN_THREAD_POOL, std::move(done)) = A + B;
*/
template <typename ExpressionType, typename DeviceType>
template <typename ExpressionType, typename DeviceType, typename DoneCallback>
class TensorAsyncDevice {
public:
TensorAsyncDevice(const DeviceType& device, ExpressionType& expression,
std::function<void()> done)
DoneCallback done)
: m_device(device), m_expression(expression), m_done(std::move(done)) {}
template <typename OtherDerived>
EIGEN_STRONG_INLINE TensorAsyncDevice& operator=(const OtherDerived& other) {
typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign;
typedef internal::TensorAsyncExecutor<const Assign, DeviceType> Executor;
typedef internal::TensorAsyncExecutor<const Assign, DeviceType, DoneCallback> Executor;
// WARNING: After assignment 'm_done' callback will be in undefined state.
Assign assign(m_expression, other);
@ -99,7 +99,7 @@ class TensorAsyncDevice {
protected:
const DeviceType& m_device;
ExpressionType& m_expression;
std::function<void()> m_done;
DoneCallback m_done;
};
#endif // EIGEN_USE_THREADS

34
unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
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@ -101,8 +101,8 @@ class TensorExecutor {
* Default async execution strategy is not implemented. Currently it's only
* available for ThreadPoolDevice (see definition below).
*/
template <typename Expression, typename Device, bool Vectorizable,
bool Tileable>
template <typename Expression, typename Device, typename DoneCallback,
bool Vectorizable, bool Tileable>
class TensorAsyncExecutor {};
/**
@ -419,15 +419,17 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, /*Tileable*/ tr
}
};
template <typename Expression, bool Vectorizable, bool Tileable>
class TensorAsyncExecutor<Expression, ThreadPoolDevice, Vectorizable, Tileable> {
template <typename Expression, typename DoneCallback, bool Vectorizable,
bool Tileable>
class TensorAsyncExecutor<Expression, ThreadPoolDevice, DoneCallback,
Vectorizable, Tileable> {
public:
typedef typename Expression::Index StorageIndex;
typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
static EIGEN_STRONG_INLINE void runAsync(const Expression& expr,
const ThreadPoolDevice& device,
std::function<void()> done) {
DoneCallback done) {
TensorAsyncExecutorContext* const ctx =
new TensorAsyncExecutorContext(expr, device, std::move(done));
@ -455,7 +457,7 @@ class TensorAsyncExecutor<Expression, ThreadPoolDevice, Vectorizable, Tileable>
struct TensorAsyncExecutorContext {
TensorAsyncExecutorContext(const Expression& expr,
const ThreadPoolDevice& thread_pool,
std::function<void()> done)
DoneCallback done)
: evaluator(expr, thread_pool), on_done(std::move(done)) {}
~TensorAsyncExecutorContext() {
@ -466,12 +468,13 @@ class TensorAsyncExecutor<Expression, ThreadPoolDevice, Vectorizable, Tileable>
Evaluator evaluator;
private:
std::function<void()> on_done;
DoneCallback on_done;
};
};
template <typename Expression, bool Vectorizable>
class TensorAsyncExecutor<Expression, ThreadPoolDevice, Vectorizable, /*Tileable*/ true> {
template <typename Expression, typename DoneCallback, bool Vectorizable>
class TensorAsyncExecutor<Expression, ThreadPoolDevice, DoneCallback,
Vectorizable, /*Tileable*/ true> {
public:
typedef typename traits<Expression>::Index StorageIndex;
typedef typename traits<Expression>::Scalar Scalar;
@ -485,7 +488,7 @@ class TensorAsyncExecutor<Expression, ThreadPoolDevice, Vectorizable, /*Tileable
static EIGEN_STRONG_INLINE void runAsync(const Expression& expr,
const ThreadPoolDevice& device,
std::function<void()> done) {
DoneCallback done) {
TensorAsyncExecutorContext* const ctx =
new TensorAsyncExecutorContext(expr, device, std::move(done));
@ -494,9 +497,10 @@ class TensorAsyncExecutor<Expression, ThreadPoolDevice, Vectorizable, /*Tileable
if (total_size < cache_size &&
!ExpressionHasTensorBroadcastingOp<Expression>::value) {
internal::TensorAsyncExecutor<Expression, ThreadPoolDevice, Vectorizable,
/*Tileable*/ false>::runAsync(
expr, device, [ctx]() { delete ctx; });
auto delete_ctx = [ctx]() { delete ctx; };
internal::TensorAsyncExecutor<
Expression, ThreadPoolDevice, decltype(delete_ctx), Vectorizable,
/*Tileable*/ false>::runAsync(expr, device, std::move(delete_ctx));
return;
}
@ -532,7 +536,7 @@ class TensorAsyncExecutor<Expression, ThreadPoolDevice, Vectorizable, /*Tileable
struct TensorAsyncExecutorContext {
TensorAsyncExecutorContext(const Expression& expr,
const ThreadPoolDevice& thread_pool,
std::function<void()> done)
DoneCallback done)
: device(thread_pool),
evaluator(expr, thread_pool),
on_done(std::move(done)) {}
@ -548,7 +552,7 @@ class TensorAsyncExecutor<Expression, ThreadPoolDevice, Vectorizable, /*Tileable
TilingContext tiling;
private:
std::function<void()> on_done;
DoneCallback on_done;
};
};

4
unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
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@ -94,7 +94,7 @@ template<typename XprType, template <class> class MakePointer_ = MakePointer> cl
template<typename XprType> class TensorForcedEvalOp;
template<typename ExpressionType, typename DeviceType> class TensorDevice;
template<typename ExpressionType, typename DeviceType> class TensorAsyncDevice;
template<typename ExpressionType, typename DeviceType, typename DoneCallback> class TensorAsyncDevice;
template<typename Derived, typename Device> struct TensorEvaluator;
struct NoOpOutputKernel;
@ -168,7 +168,7 @@ template <typename Expression, typename Device,
bool Tileable = IsTileable<Device, Expression>::value>
class TensorExecutor;
template <typename Expression, typename Device,
template <typename Expression, typename Device, typename DoneCallback,
bool Vectorizable = IsVectorizable<Device, Expression>::value,
bool Tileable = IsTileable<Device, Expression>::value>
class TensorAsyncExecutor;

21
unsupported/test/cxx11_tensor_executor.cpp
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@ -578,11 +578,15 @@ static void test_async_execute_unary_expr(Device d)
src.setRandom();
const auto expr = src.square();
using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
using Executor = internal::TensorAsyncExecutor<const Assign, Device,
Vectorizable, Tileable>;
Eigen::Barrier done(1);
Executor::runAsync(Assign(dst, expr), d, [&done]() { done.Notify(); });
auto on_done = [&done]() { done.Notify(); };
using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
using DoneCallback = decltype(on_done);
using Executor = internal::TensorAsyncExecutor<const Assign, Device, DoneCallback,
Vectorizable, Tileable>;
Executor::runAsync(Assign(dst, expr), d, on_done);
done.Wait();
for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
@ -610,12 +614,15 @@ static void test_async_execute_binary_expr(Device d)
const auto expr = lhs + rhs;
Eigen::Barrier done(1);
auto on_done = [&done]() { done.Notify(); };
using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
using Executor = internal::TensorAsyncExecutor<const Assign, Device,
using DoneCallback = decltype(on_done);
using Executor = internal::TensorAsyncExecutor<const Assign, Device, DoneCallback,
Vectorizable, Tileable>;
Eigen::Barrier done(1);
Executor::runAsync(Assign(dst, expr), d, [&done]() { done.Notify(); });
Executor::runAsync(Assign(dst, expr), d, on_done);
done.Wait();
for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {

47
unsupported/test/cxx11_tensor_thread_pool.cpp
View File

@ -683,34 +683,39 @@ EIGEN_DECLARE_TEST(cxx11_tensor_thread_pool)
CALL_SUBTEST_3(test_multithread_contraction_agrees_with_singlethread<RowMajor>());
CALL_SUBTEST_3(test_multithread_contraction_with_output_kernel<ColMajor>());
CALL_SUBTEST_3(test_multithread_contraction_with_output_kernel<RowMajor>());
CALL_SUBTEST_3(test_async_multithread_contraction_agrees_with_singlethread<ColMajor>());
CALL_SUBTEST_3(test_async_multithread_contraction_agrees_with_singlethread<RowMajor>());
CALL_SUBTEST_4(test_async_multithread_contraction_agrees_with_singlethread<ColMajor>());
CALL_SUBTEST_4(test_async_multithread_contraction_agrees_with_singlethread<RowMajor>());
// Test EvalShardedByInnerDimContext parallelization strategy.
CALL_SUBTEST_4(test_sharded_by_inner_dim_contraction<ColMajor>());
CALL_SUBTEST_4(test_sharded_by_inner_dim_contraction<RowMajor>());
CALL_SUBTEST_4(test_sharded_by_inner_dim_contraction_with_output_kernel<ColMajor>());
CALL_SUBTEST_4(test_sharded_by_inner_dim_contraction_with_output_kernel<RowMajor>());
CALL_SUBTEST_4(test_async_sharded_by_inner_dim_contraction<ColMajor>());
CALL_SUBTEST_4(test_async_sharded_by_inner_dim_contraction<RowMajor>());
CALL_SUBTEST_4(test_async_sharded_by_inner_dim_contraction_with_output_kernel<ColMajor>());
CALL_SUBTEST_4(test_async_sharded_by_inner_dim_contraction_with_output_kernel<RowMajor>());
CALL_SUBTEST_5(test_sharded_by_inner_dim_contraction<ColMajor>());
CALL_SUBTEST_5(test_sharded_by_inner_dim_contraction<RowMajor>());
CALL_SUBTEST_5(test_sharded_by_inner_dim_contraction_with_output_kernel<ColMajor>());
CALL_SUBTEST_5(test_sharded_by_inner_dim_contraction_with_output_kernel<RowMajor>());
CALL_SUBTEST_6(test_async_sharded_by_inner_dim_contraction<ColMajor>());
CALL_SUBTEST_6(test_async_sharded_by_inner_dim_contraction<RowMajor>());
CALL_SUBTEST_6(test_async_sharded_by_inner_dim_contraction_with_output_kernel<ColMajor>());
CALL_SUBTEST_6(test_async_sharded_by_inner_dim_contraction_with_output_kernel<RowMajor>());
// Exercise various cases that have been problematic in the past.
CALL_SUBTEST_5(test_contraction_corner_cases<ColMajor>());
CALL_SUBTEST_5(test_contraction_corner_cases<RowMajor>());
CALL_SUBTEST_7(test_contraction_corner_cases<ColMajor>());
CALL_SUBTEST_7(test_contraction_corner_cases<RowMajor>());
CALL_SUBTEST_6(test_full_contraction<ColMajor>());
CALL_SUBTEST_6(test_full_contraction<RowMajor>());
CALL_SUBTEST_8(test_full_contraction<ColMajor>());
CALL_SUBTEST_8(test_full_contraction<RowMajor>());
CALL_SUBTEST_7(test_multithreaded_reductions<ColMajor>());
CALL_SUBTEST_7(test_multithreaded_reductions<RowMajor>());
CALL_SUBTEST_9(test_multithreaded_reductions<ColMajor>());
CALL_SUBTEST_9(test_multithreaded_reductions<RowMajor>());
CALL_SUBTEST_7(test_memcpy());
CALL_SUBTEST_7(test_multithread_random());
CALL_SUBTEST_10(test_memcpy());
CALL_SUBTEST_10(test_multithread_random());
TestAllocator test_allocator;
CALL_SUBTEST_7(test_multithread_shuffle<ColMajor>(NULL));
CALL_SUBTEST_7(test_multithread_shuffle<RowMajor>(&test_allocator));
CALL_SUBTEST_7(test_threadpool_allocate(&test_allocator));
CALL_SUBTEST_11(test_multithread_shuffle<ColMajor>(NULL));
CALL_SUBTEST_11(test_multithread_shuffle<RowMajor>(&test_allocator));
CALL_SUBTEST_11(test_threadpool_allocate(&test_allocator));
// Force CMake to split this test.
// EIGEN_SUFFIXES;1;2;3;4;5;6;7;8;9;10;11
}