Implement vectorized versions of log1p and expm1 in Eigen using Kahan's formulas, and change the scalar implementations to properly handle infinite arguments.
This actually fixes an issue in unit-test packetmath_2 with pcmp_eq when it is compiled with clang. When pcmp_eq(Packet4f,Packet4f) is used instead of pcmp_eq(Packet2d,Packet2d), the unit-test does not pass due to NaN on ref vector.
Depending on instruction set, significant speedups are observed for the vectorized path:
log1p wall time is reduced 60-93% (2.5x - 15x speedup)
expm1 wall time is reduced 0-85% (1x - 7x speedup)
The scalar path is slower by 20-30% due to the extra branch needed to handle +infinity correctly.
Full benchmarks measured on Intel(R) Xeon(R) Gold 6154 here: https://bitbucket.org/snippets/rmlarsen/MXBkpM
The vec_vsx_ld/vec_vsx_st builtins were wrongly used for aligned load/store. In fact, they perform unaligned memory access and, even when the address is 16-byte aligned, they are much slower (at least 2x) than their aligned counterparts.
For double/Packet2d vec_xl/vec_xst should be prefered over vec_ld/vec_st, although the latter works when casted to float/Packet4f.
Silencing some weird warning with throw but some GCC versions. Such warning are not thrown by Clang.
If no offset is given, them it should be zero.
Also passes full address to vec_vsx_ld/st builtins.
Removes userless _EIGEN_ALIGNED_PTR & _EIGEN_MASK_ALIGNMENT.
Removes unnecessary casts.
The change caused the device struct to be copied for each expression evaluation, and caused, e.g., a 10% regression in the TensorFlow multinomial op on GPU:
Benchmark Time(ns) CPU(ns) Iterations
----------------------------------------------------------------------
BM_Multinomial_gpu_1_100000_4 128173 231326 2922 1.610G items/s
VS
Benchmark Time(ns) CPU(ns) Iterations
----------------------------------------------------------------------
BM_Multinomial_gpu_1_100000_4 146683 246914 2719 1.509G items/s
Not having this attribute results in the following failures in the `--config=rocm` TF build.
```
In file included from tensorflow/core/kernels/cross_op_gpu.cu.cc:20:
In file included from ./tensorflow/core/framework/register_types.h:20:
In file included from ./tensorflow/core/framework/numeric_types.h:20:
In file included from ./third_party/eigen3/unsupported/Eigen/CXX11/Tensor:1:
In file included from external/eigen_archive/unsupported/Eigen/CXX11/Tensor:140:
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h:356:37: error: 'Eigen::constCast': no overloaded function has restriction specifiers that are compatible with the ambient context 'data'
typename Storage::Type result = constCast(m_impl.data());
^
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h:356:37: error: 'Eigen::constCast': no overloaded function has restriction specifiers that are compatible with the ambient context 'data'
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h:148:56: note: in instantiation of member function 'Eigen::TensorEvaluator<const Eigen::TensorChippingOp<1, Eigen::TensorMap<Eigen::Tensor<int, 2, 1, long>, 16, MakePointer> >, Eigen::Gpu\
Device>::data' requested here
return m_rightImpl.evalSubExprsIfNeeded(m_leftImpl.data());
```
Adding the EIGEN_DEVICE_FUNC attribute resolves those errors
* Modifying TensorDeviceSYCL to use `EIGEN_THROW_X`.
* Modifying TensorMacro to use `EIGEN_TRY/CATCH(X)` macro.
* Modifying TensorReverse.h to use `EIGEN_DEVICE_REF` instead of `&`.
* Fixing the SYCL device macro in SpecialFunctionsImpl.h.
