In VS 2017, `std::arg` for real inputs always returns 0, even for
negative inputs. It should return `PI` for negative real values.
This seems to be fixed in VS 2019 (MSVC 1920).
The boost library unfortunately specializes `conj` for various types and
assumes the original two-template-parameter version. This changes
restores the second parameter. This also restores ABI compatibility.
The specialization for `std::complex` is because `std::conj` is not
a device function. For custom complex scalar types, users should provide
their own `conj` implementation.
We may consider removing the unnecessary second parameter in the future - but
this will require modifying boost as well.
Fixes#2112.
The cxx11 path for `numext::arg` incorrectly returned the complex type
instead of the real type, leading to compile errors. Fixed this and
added tests.
Related to !477, which uncovered the issue.
This is to resolve an issue for large inputs when +0.5 can
actually lead to +1 if the input doesn't have enough precision
to resolve the addition - leading to an off-by-one error.
See discussion on 9a663973.
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.
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.
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).
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.
This fixes some gcc warnings such as:
```
Eigen/src/Core/GenericPacketMath.h:655:63: warning: implicit conversion turns floating-point number into bool: 'typename __gnu_cxx::__enable_if<__is_integer<bool>::__value, double>::__type' (aka 'double') to 'bool' [-Wimplicit-conversion-floating-point-to-bool]
Packet psqrt(const Packet& a) { EIGEN_USING_STD(sqrt); return sqrt(a); }
```
Details:
- Added `scalar_sqrt_op<bool>` (`-Wimplicit-conversion-floating-point-to-bool`).
- Added `scalar_square_op<bool>` and `scalar_cube_op<bool>`
specializations (`-Wint-in-bool-context`)
- Deprecated above specialized ops for bool.
- Modified `cxx11_tensor_block_eval` to specialize generator for
booleans (`-Wint-in-bool-context`) and to use `abs` instead of `square` to
avoid deprecated bool ops.
