Making them trivially copyable allows using std::memcpy() without undefined
behaviors.
Only Matrix and Array with trivially copyable DenseStorage are marked as
trivially copyable with an additional type trait.
As described in http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2019/p0848r3.html
it requires extremely verbose SFINAE to make the special member functions of
fixed-size Matrix and Array trivial, unless C++20 concepts are available to
simplify the selection of trivial special member functions given template
parameters. Therefore only make this feature available to compilers that support
C++20 P0848R3.
Fix#1855.
This MR fixes a bunch of smaller issues, making the following changes:
* Template parameters in the documentation are documented with `\tparam` instead
of `\param`
* Superfluous semicolon warnings fixed
* Fixed the type of literals used to initialize float variables
- Doing computation with uninitialized (zero-ed ? but thanks Linux) matrix, or
worse NaN on other non-linux systems.
- This commit fixes it by initializing to Random().
VS2017 doesn't like deducing alias types, leading to a bunch of compile
errors for functions involving the `tuple` alias. Replacing with
`TupleImpl` seems to solve this, allowing the test to compile/pass.
Some checks used incorrect values, partly from copy-paste errors,
partly from the change in behaviour introduced in !398.
Modified results to match scipy, simplified tests by updating
`VERIFY_IS_CWISE_APPROX` to work for scalars.
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.
Without this flag, when compiling with nvcc, if the compute architecture of a card does
not exactly match any of those listed for `-gencode arch=compute_<arch>,code=sm_<arch>`,
then the kernel will fail to run with:
```
cudaErrorNoKernelImageForDevice: no kernel image is available for execution on the device.
```
This can happen, for example, when compiling with an older cuda version
that does not support a newer architecture (e.g. T4 is `sm_75`, but cuda
9.2 only supports up to `sm_70`).
With the `-arch=<arch>` flag, the code will compile and run at the
supplied architecture.
- Unify test/CMakeLists.txt and unsupported/test/CMakeLists.txt
- Added `EIGEN_CUDA_FLAGS` that are appended to the set of flags passed
to the cuda compiler (nvcc or clang).
The latter is to support passing custom flags (e.g. `-arch=` to nvcc,
or to disable cuda-specific warnings).
These names are so common, IMO they should not exist directly in the
`Eigen::` namespace. This prevents us from using the `last` or `all`
names for any parameters or local variables, otherwise spitting out
warnings about shadowing or hiding the global values. Many external
projects (and our own examples) also heavily use
```
using namespace Eigen;
```
which means these conflict with external libraries as well, e.g.
`std::fill(first,last,value)`.
It seems originally these were placed in a separate namespace
`Eigen::placeholders`, which has since been deprecated. I propose
to un-deprecate this, and restore the original locations.
These symbols are also imported into `Eigen::indexing`, which
additionally imports `fix` and `seq`. An alternative is to remove the
`placeholders` namespace and stick with `indexing`.
NOTE: this is an API-breaking change.
Fixes#2321.
Duplicating the namespace `tuple_impl` caused a conflict with the
`arch/GPU/Tuple.h` definitions for the `tuple_test`. We can't
just use `Eigen::internal` either, since there exists a different
`Eigen::internal::get`. Renaming the namespace to `test_detail`
fixes the issue.
This introduces new functions:
```
// returns kernel(args...) running on the CPU.
Eigen::run_on_cpu(Kernel kernel, Args&&... args);
// returns kernel(args...) running on the GPU.
Eigen::run_on_gpu(Kernel kernel, Args&&... args);
Eigen::run_on_gpu_with_hint(size_t buffer_capacity_hint, Kernel kernel, Args&&... args);
// returns kernel(args...) running on the GPU if using
// a GPU compiler, or CPU otherwise.
Eigen::run(Kernel kernel, Args&&... args);
Eigen::run_with_hint(size_t buffer_capacity_hint, Kernel kernel, Args&&... args);
```
Running on the GPU is accomplished by:
- Serializing the kernel inputs on the CPU
- Transferring the inputs to the GPU
- Passing the kernel and serialized inputs to a GPU kernel
- Deserializing the inputs on the GPU
- Running `kernel(inputs...)` on the GPU
- Serializing all output parameters and the return value
- Transferring the serialized outputs back to the CPU
- Deserializing the outputs and return value on the CPU
- Returning the deserialized return value
All inputs must be serializable (currently POD types, `Eigen::Matrix`
and `Eigen::Array`). The kernel must also be POD (though usually
contains no actual data).
Tested on CUDA 9.1, 10.2, 11.3, with g++-6, g++-8, g++-10 respectively.
This MR depends on !622, !623, !624.
An analogue of `std::tuple` that works on device.
Context: I've tried `std::tuple` in various versions of NVCC and clang,
and although code seems to compile, it often fails to run - generating
"illegal memory access" errors, or "illegal instruction" errors.
This replacement does work on device.
The `Serializer<T>` class implements a binary serialization that
can write to (`serialize`) and read from (`deserialize`) a byte
buffer. Also added convenience routines for serializing
a list of arguments.
This will mainly be for testing, specifically to transfer data to
and from the GPU.
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
Removed all configurations that explicitly test or set the c++ standard
flags. The only place the standard is now configured is at the top of
the main `CMakeLists.txt` file, which can easily be updated (e.g. if
we decide to move to c++14+). This can also be set via command-line using
```
> cmake -DCMAKE_CXX_STANDARD 14
```
Kept the `EIGEN_TEST_CXX11` flag for now - that still controls whether to
build/run the `cxx11_*` tests. We will likely end up renaming these
tests and removing the `CXX11` subfolder.
Manually constructing an unaligned object declared as aligned
invokes UB, so we cannot technically check for alignment from
within the constructor. Newer versions of clang optimize away
this check.
Removing the affected tests.
The `memset` function and bitwise manipulation only apply to POD types
that do not require initialization, otherwise resulting in UB. We currently
violate this in `ptrue` and `pzero`, we assume bitmasks for `pselect`, and
bitwise operations are applied byte-by-byte in the generic implementations.
This is causing issues for scalar types that do require initialization
or that contain non-POD info such as pointers (#2201). We either break
them, or force specializations of these functions for custom scalars,
even if they are not vectorized.
Here we modify these functions for scalars only - instead using only
scalar operations:
- `pzero`: `Scalar(0)` for all scalars.
- `ptrue`: `Scalar(1)` for non-trivial scalars, bitset to one bits for trivial scalars.
- `pselect`: ternary select comparing mask to `Scalar(0)` for all scalars
- `pand`, `por`, `pxor`, `pnot`: use operators `&`, `|`, `^`, `~` for all integer or non-trivial scalars, otherwise apply bytewise.
For non-scalar types, the original implementations are used to maintain
compatibility and minimize the number of changes.
Fixes#2201.
Since `std::equal_to::operator()` is not a device function, it
fails on GPU. On my device, I seem to get a silent crash in the
kernel (no reported error, but the kernel does not complete).
Replacing this with a portable version enables comparisons on device.
Addresses #2292 - would need to be cherry-picked. The 3.3 branch
also requires adding `EIGEN_DEVICE_FUNC` in `BooleanRedux.h` to get
fully working.
For custom scalars, zero is not necessarily represented by
a zeroed-out memory block (e.g. gnu MPFR). We therefore
cannot rely on `memset` if we want to fill a matrix or tensor
with zeroes. Instead, we should rely on `fill`, which for trivial
types does end up getting converted to a `memset` under-the-hood
(at least with gcc/clang).
Requires adding a `fill(begin, end, v)` to `TensorDevice`.
Replaced all potentially bad instances of memset with fill.
Fixes#2245.
For empty or single-column matrices, the current `PartialPivLU`
currently dereferences a `nullptr` or accesses memory out-of-bounds.
Here we adjust the checks to avoid this.
When calling conservativeResize() on a matrix with DontAlign flag, the
temporary variable used to perform the resize should have the same
Options as the original matrix to ensure that the correct override of
swap is called (i.e. PlainObjectBase::swap(DenseBase<OtherDerived> &
other). Calling the base class swap (i.e in DenseBase) results in
assertions errors or memory corruption.
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.
Fixes#2229.
For dynamic matrices with fixed-sized storage, only copy/swap
elements that have been set. Otherwise, this leads to inefficient
copying, and potential UB for non-initialized elements.
The namespace declaration for googlehash is a configurable macro that
can be disabled. In particular, it is disabled within google, causing
compile errors since `dense_hash_map`/`sparse_hash_map` are then in
the global namespace instead of in `::google`.
Here we play a bit of gynastics to allow for both `google::*_hash_map`
and `*_hash_map`, while limiting namespace polution. Symbols within
the `::google` namespace are imported into `Eigen::google`.
We also remove checks based on `_SPARSE_HASH_MAP_H_`, as this is
fragile, and instead require `EIGEN_GOOGLEHASH_SUPPORT` to be
defined.
Clang-tidy complains that full specializations in headers can cause ODR
violations. Marked these as `inline` to fix.
It also complains about renaming arguments in specializations. Set the
argument names to match.
Some CUDA/HIP constants fail on device with `constexpr` since they
internally rely on non-constexpr functions, e.g.
```
\#define CUDART_INF_F __int_as_float(0x7f800000)
```
This fails for cuda-clang (though passes with nvcc). These constants are
currently used by `device::numeric_limits`. For portability, we
need to remove `constexpr` from the affected functions.
For C++11 or higher, we should be able to rely on the `std::numeric_limits`
versions anyways, since the methods themselves are now `constexpr`, so
should be supported on device (clang/hipcc natively, nvcc with
`--expr-relaxed-constexpr`).
`g_called` is not used in subtest 7, so was generating a
`-Wunneeded-internal-declaration` warnings. Here we silence
it by initializing the static variable.
The original fails with nvcc+msvc - there's a static order of initialization
issue leading to registered tests being cleared. The test then fails on
```
VERIFY(EigenTest::all().size()>0);
```
since `EigenTest` no longer contains any tests. The singleton pattern
fixes this.
Replace usage of `std::numeric_limits<...>::min/max_exponent` in
codebase where possible. Also replaced some other `numeric_limits`
usages in affected tests with the `NumTraits` equivalent.
The previous MR !443 failed for c++03 due to lack of `constexpr`.
Because of this, we need to keep around the `std::numeric_limits`
version in enum expressions until the switch to c++11.
Fixes#2148
Replace usage of `std::numeric_limits<...>::min/max_exponent` in
codebase. Also replaced some other `numeric_limits` usages in
affected tests with the `NumTraits` equivalent.
Fixes#2148
NVCC does not understand `__forceinline`, so we need to use `inline`
when compiling for GPU.
ICC specializes `std::complex` operators for `float` and `double`
by default, which cannot be used on device and conflict with Eigen's
workaround in CUDA/Complex.h. This can be prevented by defining
`_OVERRIDE_COMPLEX_SPECIALIZATION_` before including `<complex>`.
Added this define to the tests and to `Eigen/Core`, but this will
not work if the user includes `<complex>` before `<Eigen/Core>`.
ICC also seems to generate a duplicate `Map` symbol in
`PlainObjectBase`:
```
error: "Map" has already been declared in the current scope
static ConstMapType Map(const Scalar *data)
```
I tracked this down to `friend class Eigen::Map`. Putting the `friend`
statements at the bottom of the class seems to resolve this issue.
Fixes#2180
The original swap approach leads to potential undefined behavior (reading
uninitialized memory) and results in unnecessary copying of data for static
storage.
Here we pass down the move assignment to the underlying storage. Static
storage does a one-way copy, dynamic storage does a swap.
Modified the tests to no longer read from the moved-from matrix/tensor,
since that can lead to UB. Added a test to ensure we do not access
uninitialized memory in a move.
Fixes: #2119
The macro `__cplusplus` is not defined correctly in MSVC unless building
with the the `/Zc:__cplusplus` flag. Instead, it defines `_MSVC_LANG` to the
specified c++ standard version number.
Here we introduce `EIGEN_CPLUSPLUS` which will contain the c++ version
number both for MSVC and otherwise. This simplifies checks for supported
features.
Also replaced most instances of standard version checking via `__cplusplus`
with the existing `EIGEN_COMP_CXXVER` macro for better clarity.
Fixes: #2170
This is a new version of !423, which failed for MSVC.
Defined `EIGEN_OPTIMIZATION_BARRIER(X)` that uses inline assembly to
prevent operations involving `X` from crossing that barrier. Should
work on most `GNUC` compatible compilers (MSVC doesn't seem to need
this). This is a modified version adapted from what was used in
`psincos_float` and tested on more platforms
(see #1674, https://godbolt.org/z/73ezTG).
Modified `rint` to use the barrier to prevent the add/subtract rounding
trick from being optimized away.
Also fixed an edge case for large inputs that get bumped up a power of two
and ends up rounding away more than just the fractional part. If we are
over `2^digits` then just return the input. This edge case was missed in
the test since the test was comparing approximate equality, which was still
satisfied. Adding a strict equality option catches it.
It seems *sometimes* with aggressive optimizations the combination
`psub(padd(a, b), b)` trick to force rounding is compiled away. Here
we replace with inline assembly to prevent this (I tried `volatile`,
but that leads to additional loads from memory).
Also fixed an edge case for large inputs `a` where adding `b` bumps
the value up a power of two and ends up rounding away more than
just the fractional part. If we are over `2^digits` then just return
the input. This edge case was missed in the test since the test was
comparing approximate equality, which was still satisfied. Adding
a strict equality option catches it.
In SSE, by adding/subtracting 2^MantissaBits, we force rounding according to the
current rounding mode.
For NEON, we use the provided intrinsics for rint/floor/ceil if
available (armv8).
Related to #1969.
Since `numeric_limits<half>::max_exponent` is a static inline constant,
it cannot be directly passed by reference. This triggers a linker error
in recent versions of `g++-powerpc64le`.
Changing `half` to take inputs by value fixes this. Wrapping
`max_exponent` with `int(...)` to make an addressable integer also fixes this
and may help with other custom `Scalar` types down-the-road.
Also eliminated some compile warnings for powerpc.
With !406, we accidentally broke arm 32-bit NEON builds, since
`vsqrt_f32` is only available for 64-bit.
Here we add back the `rsqrt` implementation for 32-bit, relying
on a `prsqrt` implementation with better handling of edge cases.
Note that several of the 32-bit NEON packet tests are currently
failing - either due to denormal handling (NEON versions flush
to zero, but scalar paths don't) or due to accuracy (e.g. sin/cos).
The original will saturate if the input does not fit into an integer
type. Here we fix this, returning the input if it doesn't have
enough precision to have a fractional part.
Also added `pceil` for NEON.
Fixes#1969.
