This commit re-enables the use of FMA for the FAST sqrt functions.
Doing so improves the performance of both algorithms. The float32
version is now 88% the speed of the original function, while the
double version is 90%.
"... always use {NonBlocking}ThreadPool". It seems the non-blocking
implementation was me the default/only one, but a reference to the old
name was left unmodified. Fix that.
block
Builds configured without the -DEIGEN_TEST_CXX11=ON flag would fail
right away without this, as this test seems to rely on those language
features. The skip under compilation with MSVC was kept.
Without explicit conversion Tensorflow fails to compile, pset1 template deduction fails.
cannot convert '((const Eigen::internal::MeanReducer<Eigen::half>*)this)
->Eigen::internal::MeanReducer<Eigen::half>::packetCount_'
(type 'const DenseIndex {aka const long int}')
to type 'const type& {aka const Eigen::half&}'
return pdiv(vaccum, pset1<Packet>(packetCount_));
Honestly I’m not sure why it works in Eigen tests, because Eigen::half constructor is explicit, and why it stopped working in TF, I didn’t find any relevant changes since previous Eigen upgrade.
static_cast<T>(packetCount_) - breaks cxx11_tensor_reductions test for Eigen::half, also quite surprising.
This commit fixes the AVX512 implementations of psqrt in the same
way that 3ed67cb0bb
fixed the AVX2 version of this function. The
AVX512 versions of psqrt incorrectly return -0.0 for negative
values, instead of NaN. Fixing the issues requires adding
some additional instructions that slow down the algorithms. A
similar test to the one used in 3ed67cb0bb
shows that the
corrected Packet16f code runs at 73% of the speed of the existing code,
while the corrected Packed8d function runs at 68% of the original.
Also, a few minor fixes for GPU tests running in HIP mode.
1. Adding an include for hip/hip_runtime.h in the Macros.h file
For HIP __host__ and __device__ are macros which are defined in hip headers.
Their definitions need to be included before their use in the file.
2. Fixing the compile failure in TensorContractionGpu introduced by the commit to
"Fuse computations into the Tensor contractions using output kernel"
3. Fixing a HIP/clang specific compile error by making the struct-member assignment explicit
This provide several advantages:
- more flexibility in designing unit tests
- unit tests can be glued to speed up compilation
- unit tests are compiled with same predefined macros, which is a requirement for zapcc
