Removed m_dimension as instance member of TensorStorage with
FixedDimensions and instead use the template parameter. This
means that the sizeof a pure fixed-size storage is exactly
equal to the data it is storing.
For these to exist we would need to define `_USE_MATH_DEFINES` before
`cmath` or `math.h` is first included. However, we don't
control the include order for projects outside Eigen, so even defining
the macro in `Eigen/Core` does not fix the issue for projects that
end up including `<cmath>` before Eigen does (explicitly or transitively).
To fix this, we define `EIGEN_LOG2E` and `EIGEN_LN2` ourselves.
The following commit introduced a breakage in ROCm/HIP support for Eigen.
5ec4907434 (1958e65719641efe5483abc4ce0b61806270f6f3_525_517)
```
Building HIPCC object test/CMakeFiles/gpu_basic.dir/gpu_basic_generated_gpu_basic.cu.o
In file included from /home/rocm-user/eigen/test/gpu_basic.cu:20:
In file included from /home/rocm-user/eigen/test/main.h:356:
In file included from /home/rocm-user/eigen/Eigen/QR:11:
In file included from /home/rocm-user/eigen/Eigen/Core:222:
/home/rocm-user/eigen/Eigen/src/Core/arch/GPU/PacketMath.h:556:10: error: use of undeclared identifier 'half2half2'; did you mean '__half2half2'?
return half2half2(from);
^~~~~~~~~~
__half2half2
/opt/rocm/hip/include/hip/hcc_detail/hip_fp16.h:547:21: note: '__half2half2' declared here
__half2 __half2half2(__half x)
^
1 error generated when compiling for gfx900.
```
The cause seems to be a copy-paster error, and the fix is trivial
The previous code had `__host__ __device__` functions calling `__device__`
functions (e.g. `__low2half`) which caused build failures in tensorflow.
Also tried to simplify the `#ifdef` guards to make them more clear.
In the current `dense_assignment_loop` implementations, if the
destination's inner or outer size is zero at compile time and if the kernel
involves a product, we currently get a compile error (#2080). This is
triggered by attempting to multiply a non-existent row by a column (or
vice-versa).
To address this, we add a specialization for zero-sized assignments
(`AllAtOnceTraversal`) which evaluates to a no-op. We also add a static
check to ensure the size is in-fact zero. This now seems to be the only
existing use of `AllAtOnceTraversal`.
Fixes#2080.
Removed redundant checks and redundant code for CUDA/HIP.
Note: there are several issues here of calling `__device__` functions
from `__host__ __device__` functions, in particular `__low2half`.
We do not address that here -- only modifying this file enough
to get our current tests to compile.
Fixed: #1847
Current implementations fail to consider half-float packets, only
half-float scalars. Added specializations for packets on AVX, AVX512 and
NEON. Added tests to `special_packetmath`.
The current `special_functions` tests would fail for half and bfloat16 due to
lack of precision. The NEON tests also fail with precision issues and
due to different handling of `sqrt(inf)`, so special functions bessel, ndtri
have been disabled.
Tested with AVX, AVX512.
The `shfl*` functions are `__device__` only, and adjusted `#ifdef`s so
they are defined whenever the corresponding CUDA/HIP ones are.
Also changed the HIP/CUDA<9.0 versions to cast to int instead of
doing the conversion `half`<->`float`.
Fixes#2083
Adding the term e*ln(2) is split into two step for no obvious reason.
This dates back to the original Cephes code from which the algorithm is adapted.
It appears that this was done in Cephes to prevent the compiler from reordering
the addition of the 3 terms in the approximation
log(1+x) ~= x - 0.5*x^2 + x^3*P(x)/Q(x)
which must be added in reverse order since |x| < (sqrt(2)-1).
This allows rewriting the code to just 2 pmadd and 1 padd instructions,
which on a Skylake processor speeds up the code by 5-7%.
The current impl corrupts the comparison masks when converting
from float back to bfloat16. The resulting masks are then
no longer all zeros or all ones, which breaks when used with
`pselect` (e.g. in `pmin<PropagateNumbers>`). This was
causing `packetmath_15` to fail on arm.
Introducing a simple `F32MaskToBf16Mask` corrects this (takes
the lower 16-bits for each float mask).
Prior to this fix, `TensorContractionGpu` and the `cxx11_tensor_of_float16_gpu`
test are broken, as well as several ops in Tensorflow. The gpu functions
`__shfl*` became ambiguous now that `Eigen::half` implicitly converts to float.
Here we add the required specializations.
`bit_cast` cannot be `constexpr`, so we need to remove `EIGEN_CONSTEXPR` from
`raw_half_as_uint16(...)`. This shouldn't affect anything else, since
it is only used in `a bit_cast<uint16_t,half>()` which is not itself
`constexpr`.
Fixes#2077.
This allows the `packetmath` tests to pass for AVX512 on skylake.
Made `half` and `bfloat16` consistent in terms of ops they support.
Note the `log` tests are currently disabled for `bfloat16` since
they fail due to poor precision (they were previously disabled for
`Packet8bf` via test function specialization -- I just removed that
specialization and disabled it in the generic test).
part of the class signature is lost due to a problem with forward
declarations. The problem is probably caused by doxygen bug #7689.
It is confirmed to be fixed in doxygen >= 1.8.19.
Allows exclusion of doc and related targets to help when using eigen via add_subdirectory().
Requested by:
https://gitlab.com/libeigen/eigen/-/issues/1842
Also required making EIGEN_TEST_BUILD_DOCUMENTATION a dependent option on EIGEN_BUILD_DOC. This ensures documentation targets are properly defined when EIGEN_TEST_BUILD_DOCUMENTATION is ON.
The `half_float` test was failing with `-mcpu=cortex-a55` (native `__fp16`) due
to a bad NaN bit-pattern comparison (in the case of casting a float to `__fp16`,
the signaling `NaN` is quieted). There was also an inconsistency between
`numeric_limits<half>::quiet_NaN()` and `NumTraits::quiet_NaN()`. Here we
correct the inconsistency and compare NaNs according to the IEEE 754
definition.
Also modified the `bfloat16_float` test to match.
Tested with `cortex-a53` and `cortex-a55`.
