Unfortunately `std::bit_and` and the like are host-only functions prior
to c++14 (since they are not `constexpr`). They also never exist in the
global namespace, so the current implementation always fails to compile via
NVCC - since `EIGEN_USING_STD` tries to import the symbol from the global
namespace on device.
To overcome these limitations, we implement these functionals here.
Allows the altivec packetmath tests to pass. There were a few issues:
- `pstoreu` was missing MSQ on `_BIG_ENDIAN` systems
- `cmp_*` didn't properly handle conversion of bool flags (0x7FC instead
of 0xFFFF)
- `pfrexp` needed to set the `exponent` argument.
Related to !370, #2128
cc: @ChipKerchner @pdrocaldeira
Tested on `_BIG_ENDIAN` running on QEMU with VSX. Couldn't figure out build
flags to get it to work for little endian.
The workaround_9220 function was introduced a long time ago to
workaround a CMake issue with enable_language(OPTIONAL). Since then
CMake has clarified that the OPTIONAL keywords has not been
implemented[0].
A CheckLanguage module is now provided with CMake to check if a language
can be enabled. Use that instead.
[0] https://cmake.org/cmake/help/v3.18/command/enable_language.html
Originating from
[this SO issue](https://stackoverflow.com/questions/65901014/how-to-solve-this-all-error-2-in-this-case),
some win32 compilers define `__int32` as a `long`, but MinGW defines
`std::int32_t` as an `int`, leading to a type conflict.
To avoid this, we remove the custom `typedef` definitions for win32. The
Tensor module requires C++11 anyways, so we are guaranteed to have
included `<cstdint>` already in `Eigen/Core`.
Also re-arranged the headers to only include `<cstdint>` in one place to
avoid this type of error again.
The new `generic_pow` implementation was failing for half/bfloat16 since
their construction from int/float is not `constexpr`. Modified
in `GenericPacketMathFunctions` to remove `constexpr`.
While adding tests for half/bfloat16, found other issues related to
implicit conversions.
Also needed to implement `numext::arg` for non-integer, non-complex,
non-float/double/long double types. These seem to be implicitly
converted to `std::complex<T>`, which then fails for half/bfloat16.
NVCC and older versions of clang do not fully support `std::complex` on device,
leading to either compile errors (Cannot call `__host__` function) or worse,
runtime errors (Illegal instruction). For most functions, we can
implement specialized `numext` versions. Here we specialize the standard
operators (with the exception of stream operators and member function operators
with a scalar that are already specialized in `<complex>`) so they can be used
in device code as well.
To import these operators into the current scope, use
`EIGEN_USING_STD_COMPLEX_OPERATORS`. By default, these are imported into
the `Eigen`, `Eigen:internal`, and `Eigen::numext` namespaces.
This allow us to remove specializations of the
sum/difference/product/quotient ops, and allow us to treat complex
numbers like most other scalars (e.g. in tests).
This patch adds support for Arm's new vector extension SVE (Scalable Vector Extension). In contrast to other vector extensions that are supported by Eigen, SVE types are inherently *sizeless*. For the use in Eigen we fix their size at compile-time (note that this is not necessary in general, SVE is *length agnostic*).
During compilation the flag `-msve-vector-bits=N` has to be set where `N` is a power of two in the range of `128`to `2048`, indicating the length of an SVE vector.
Since SVE is rather young, we decided to disable it by default even if it would be available. A user has to enable it explicitly by defining `EIGEN_ARM64_USE_SVE`.
This patch introduces the packet types `PacketXf` and `PacketXi` for packets of `float` and `int32_t` respectively. The size of these packets depends on the SVE vector length. E.g. if `-msve-vector-bits=512` is set, `PacketXf` will contain `512/32 = 16` elements.
This MR is joint work with Miguel Tairum <miguel.tairum@arm.com>.
The recent addition of vectorized pow (!330) relies on `pfrexp` and
`pldexp`. This was missing for `Eigen::half` and `Eigen::bfloat16`.
Adding tests for these packet ops also exposed an issue with handling
negative values in `pfrexp`, returning an incorrect exponent.
Added the missing implementations, corrected the exponent in `pfrexp1`,
and added `packetmath` tests.
Test enters an infinite loop if size is 1x1 when choosing to select
unique indices for adding `inf` and `NaN` to the input. Here we
revert to non-unique indices, and split the `hypotNorm` check into
two cases: one where both `inf` and `NaN` are added, and one where
only `NaN` is added.
Hex literals are interpreted as unsigned, leading to a comparison between
signed max supported function `abcd[0]` (which was negative) to the unsigned
literal `0x80000006`. Should not change result since signed is
implicitly converted to unsigned for the comparison, but eliminates the
warning.
I ran some testing (comparing to `std::pow(double(x), double(y)))` for `x` in the set of all (positive) floats in the interval `[std::sqrt(std::numeric_limits<float>::min()), std::sqrt(std::numeric_limits<float>::max())]`, and `y` in `{2, sqrt(2), -sqrt(2)}` I get the following error statistics:
```
max_rel_error = 8.34405e-07
rms_rel_error = 2.76654e-07
```
If I widen the range to all normal float I see lower accuracy for arguments where the result is subnormal, e.g. for `y = sqrt(2)`:
```
max_rel_error = 0.666667
rms = 6.8727e-05
count = 1335165689
argmax = 2.56049e-32, 2.10195e-45 != 1.4013e-45
```
which seems reasonable, since these results are subnormals with only couple of significant bits left.
Apparently `inf` is a macro on iOS for `std::numeric_limits<T>::infinity()`,
causing a compile error here. We don't need the local anyways since it's
only used in one spot.
Upon investigation, `JacobiSVD` is significantly faster than `BDCSVD`
for small matrices (twice as fast for 2x2, 20% faster for 3x3,
1% faster for 10x10). Since the majority of cases will be small,
let's stick with `JacobiSVD`. See !361.
In the previous code, in attempting to correct for a negative
determinant, we end up multiplying and dividing by a number that
is often very near, but not exactly +/-1. By flushing to +/-1,
we can replace a division with a multiplication, and results
are more numerically consistent.
The following commit breaks ROCm support for Eigen
f149e0ebc3
All unit tests fail with the following error
```
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:19:
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:166:
/home/rocm-user/eigen/Eigen/src/Core/MathFunctionsImpl.h:105:35: error: __host__ __device__ function 'complex_sqrt' cannot overload __host__ function 'complex_sqrt'
EIGEN_DEVICE_FUNC std::complex<T> complex_sqrt(const std::complex<T>& z) {
^
/home/rocm-user/eigen/Eigen/src/Core/MathFunctions.h:342:38: note: previous declaration is here
template<typename T> std::complex<T> complex_sqrt(const std::complex<T>& a_x);
^
1 error generated when compiling for gfx900.
CMake Error at gpu_basic_generated_gpu_basic.cu.o.cmake:192 (message):
Error generating file
/home/rocm-user/eigen/build/test/CMakeFiles/gpu_basic.dir//./gpu_basic_generated_gpu_basic.cu.o
test/CMakeFiles/gpu_basic.dir/build.make:63: recipe for target 'test/CMakeFiles/gpu_basic.dir/gpu_basic_generated_gpu_basic.cu.o' failed
make[3]: *** [test/CMakeFiles/gpu_basic.dir/gpu_basic_generated_gpu_basic.cu.o] Error 1
CMakeFiles/Makefile2:16618: recipe for target 'test/CMakeFiles/gpu_basic.dir/all' failed
make[2]: *** [test/CMakeFiles/gpu_basic.dir/all] Error 2
CMakeFiles/Makefile2:16625: recipe for target 'test/CMakeFiles/gpu_basic.dir/rule' failed
make[1]: *** [test/CMakeFiles/gpu_basic.dir/rule] Error 2
Makefile:5401: recipe for target 'gpu_basic' failed
make: *** [gpu_basic] Error 2
```
The error message is accurate, and the fix (provided in thsi commit) is trivial.
MSVC incorrectly handles `inf` cases for `std::sqrt<std::complex<T>>`.
Here we replace it with a custom version (currently used on GPU).
Also fixed the `packetmath` test, which previously skipped several
corner cases since `CHECK_CWISE1` only tests the first `PacketSize`
elements.
MSVC's uniform random number generator is not quite as uniform as
others, requiring a slightly wider threshold on the histogram test.
After inspecting histograms for several runs, there's no obvious
bias -- just some bins end up having slightly more less elements
(often > 2% but less than 2.5%).
Since `eigen_assert` is a macro, the statements can become noops (e.g.
when compiling for GPU), so they may not execute the contained logic -- which
in this case is the entire `Ref` construction. We need to separate the assert
from statements which have consequences.
Fixes#2113
The existing `Ref` class failed to consider cases where the Ref's
`Stride` setting *could* match the underlying referred object's stride,
but **didn't** at runtime. This led to trying to set invalid stride values,
causing runtime failures in some cases, and garbage due to mismatched
strides in others.
Here we add the missing runtime checks. This involves computing the
strides necessary to align with the referred object's storage, and
verifying we can actually set those strides at runtime.
In the `const` case, if it *may* be possible to refer to the original
storage at compile-time but fails at runtime, then we defer to the
`construct(...)` method that makes a copy.
Added more tests to check these cases.
Fixes#2093.
This is to support scalar `sqrt` of complex numbers `std::complex<T>` on
device, requested by Tensorflow folks.
Technically `std::complex` is not supported by NVCC on device
(though it is by clang), so the default `sqrt(std::complex<T>)` function only
works on the host. Here we create an overload to add back the
functionality.
Also modified the CMake file to add `--relaxed-constexpr` (or
equivalent) flag for NVCC to allow calling constexpr functions from
device functions, and added support for specifying compute architecture for
NVCC (was already available for clang).
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.
