Added `EIGEN_HAS_STD_HASH` macro, checking for C++11 support and not
running on GPU.
`std::hash<float>` is not a device function, so cannot be used by
`std::hash<bfloat16>`. Removed `EIGEN_DEVICE_FUNC` and only
define if `EIGEN_HAS_STD_HASH`. Same for `half`.
Added `EIGEN_CUDA_HAS_FP16_ARITHMETIC` to improve readability,
eliminate warnings about `EIGEN_CUDA_ARCH` not being defined.
Replaced a couple C-style casts with `reinterpret_cast` for aligned
loading of `half*` to `half2*`. This eliminates `-Wcast-align`
warnings in clang. Although not ideal due to potential type aliasing,
this is how CUDA handles these conversions internally.
macOS defines int64_t as long long even for C++03 and therefore expects
a template specialization
internal::make_unsigned<long long>,
for C++03. Since other platforms define int64_t as long for C++03 we
cannot add the specialization for all cases.
In two places in SuperLUSupport.h, a local variable 'size' is
created that is used only inside an eigen_assert. Remove these,
just fetch the required values inside the assert statements.
This avoids annoying -Wunused warnings (and -Werror=unused errors)
in NDEBUG builds.
The original clamping bounds on `_x` actually produce finite values:
```
exp(88.3762626647950) = 2.40614e+38 < 3.40282e+38
exp(709.437) = 1.27226e+308 < 1.79769e+308
```
so with an accurate `ldexp` implementation, `pexp` fails for large
inputs, producing finite values instead of `inf`.
This adjusts the bounds slightly outside the finite range so that
the output will overflow to +/- `inf` as expected.
The previous implementations produced garbage values if the exponent did
not fit within the exponent bits. See #2131 for a complete discussion,
and !375 for other possible implementations.
Here we implement the 4-factor version. See `pldexp_impl` in
`GenericPacketMathFunctions.h` for a full description.
The SSE `pcmp*` methods were moved down since `pcmp_le<Packet4i>`
requires `por`.
Left as a "TODO" is to delegate to a faster version if we know the
exponent does fit within the exponent bits.
Fixes#2131.
Currently if compiled by NVCC, the `MatrixBase::bdcSvd()` implementation
is skipped, leading to a linker error. This prevents it from running on
the host as well.
Seems it was disabled 6 years ago (5384e891) to match `jacobiSvd`, but
`jacobiSvd` is now enabled on host. Tested and runs fine on host, but
will not compile/run for device (though it's not labelled as a device
function, so this should be fine).
Fixes#2139
We are potentially seeing some accuracy issues with these. Ideally we
would hand off to `float`, but that's not trivial with the current
setup.
We may want to consider adding `ppow<Packet>` and `HasPow`, so
implementations can more easily specialize this.
Clang does a poor job of optimizing the GEBP microkernel on 32-bit ARM,
leading to excessive 16-byte register spills, slowing down basic f32
matrix multiplication by approx 50%.
By specializing `gebp_traits`, we can eliminate the register spills.
Volatile inline ASM both acts as a barrier to prevent reordering and
enforces strict register use. In a simple f32 matrix multiply example,
this modification reduces 16-byte spills from 109 instances to zero,
leading to a 1.5x speed increase (search for `16-byte Spill` in the
assembly in https://godbolt.org/z/chsPbE).
This is a replacement of !379. See there for further discussion.
Also moved `gebp_traits` specializations for NEON to
`Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h` to be alongside
other NEON-specific code.
Fixes#2138.
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.
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.
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.
