If EIGEN_DONT_VECTORIZE is defined, immintrin.h is not included even if F16C is available. Trying to use F16C intrinsics thus fails.
This fixes issue #2395.
(cherry picked from commit c06c3e52a082e403e7a241350fd867e907c833dc)
Fixes compiler errors in expressions that look like
Eigen::Matrix<Eigen::half, 3, 1>::Random().maxCoeff()
The error comes from the code that creates the initial value for
vectorized reductions. The fix is to specify the scalar type of the
reduction's initial value.
The cahnge is necessary for Eigen::half because unlike other types,
Eigen::half scalars cannot be implicitly created from integers.
(cherry picked from commit d0e3791b1a0e2db9edd5f1d1befdb2ac5a40efe0)
We currently have plenty of type definitions with the alignment
qualifier coming after the type. The compiler warns about ignoring
them:
int EIGEN_ALIGN16 ai[4];
Turn this into:
EIGEN_ALIGN16 int ai[4];
(cherry picked from commit 8faafc3aaa2b45e234cfe0bef085c1134ceffc42)
Cross-compiled via `s390x-linux-gnu-g++`, run via qemu. This allows the
packetmath tests to pass.
(cherry picked from commit 40bbe8a4d0eb3ec2bfd472fa30cac19e6e743b46)
MSVC does not support specializing compound assignments for
`std::complex`, since it already specializes them (contrary to the
standard).
Trying to use one of these on device will currently lead to a
duplicate definition error. This is still probably preferable
to no error though. If we remove the definitions for MSVC, then
it will compile, but the kernel will fail silently.
The only proper solution would be to define our own custom `Complex`
type.
(cherry picked from commit f0f1d7938b7083800ff75fe88e15092f08a4e67e)
The 2979 warning is yet another "calling a __host__ function from a
__host__ device__ function. Although we probably should eventually
address these, they are flooding the logs. Most of these are
harmless since we only call the original from the host.
In cases where these are actually called from device, an error is generated
instead anyways.
The 2977 warning is a bit strange - although the warning suggests the
`__device__` annotation is ignored, this doesn't actually seem to be
the case. Without the `__device__` declarations, the kernel actually
fails to run when attempting to construct such objects. Again,
these warnings are flooding the logs, so disabling for now.
(cherry picked from commit 86c0decc480147d109b1dd8b968bcbc509b7a2e6)
reinterpret_cast between unrelated types is undefined behavior and leads
to misoptimizations on some platforms.
Use the safer (and faster) version via bit_cast
(cherry picked from commit b5eaa4269503f77d0aa58d2f8ed9419e1ba7784d)
Packet loading is skipped due to aliasing violation, leading to nullopt matrix
multiplication.
Fixes#2327.
(cherry picked from commit 3c724c44cff3f9e2e9e35351abff0b5c022b320d)
The `Options` of the new `hCoeffs` vector do not necessarily match
those of the `MatrixType`, leading to build errors. Having the
`CoeffVectorType` be a template parameter relieves this restriction.
(cherry picked from commit ebd4b17d2f5ca29a5c16ebd35d54d7aeda587820)
CUDA 9 seems to require labelling defaulted constructors as
`EIGEN_DEVICE_FUNC`, despite giving warnings that such labels are
ignored. Without these labels, the `gpu_basic` test fails to
compile, with errors about calling `__host__` functions from
`__host__ __device__` functions.
(cherry picked from commit 998bab4b04f26552b9875acfe113e69c7adccec4)
Clang doesn't like !621, needs the "g" constraint back.
The "g" constraint also works for GCC >= 5.
This fixes our gitlab CI.
(cherry picked from commit 3a6296d4f198ffbcccda4303919b3b14d5e54524)
GCC 4.8 doesn't seem to like the `g` register constraint, failing to
compile with "error: 'asm' operand requires impossible reload".
Tested `r` instead, and that seems to work, even with latest compilers.
Also fixed some minor macro issues to eliminate warnings on armv7.
Fixes#2315.
(cherry picked from commit ff07a8a63945d89301d1b29ac59d170ff9be3955)
All cuda `__half` functions are device-only in CUDA 9, including
conversions. Host-side conversions were added in CUDA 10.
The existing code doesn't build prior to 10.0.
All arithmetic functions are always device-only, so there's
therefore no reason to use vectorization on the host at all.
Modified the code to disable vectorization for `__half` on host,
which required also updating the `TensorReductionGpu` implementation
which previously made assumptions about available packets.
(cherry picked from commit cc3573ab4451853774cd5c3497373d5fe8914774)
There were some typos that checked `EIGEN_HAS_CXX14` that should have
checked `EIGEN_HAS_CXX14_VARIABLE_TEMPLATES`, causing a mismatch
in some of the `Eigen::fix<N>` assumptions.
Also fixed the `symbolic_index` test when
`EIGEN_HAS_CXX14_VARIABLE_TEMPLATES` is 0.
Fixes#2308
(cherry picked from commit 5db9e5c77958997856ddbccfa4a52ff22e83bef9)
