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.
