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.
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
