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* Add a generic packet ops corresponding to {std}::fmin and {std}::fmax. The ↵Gravatar Rasmus Munk Larsen2020-10-01
| | | | non-sensical NaN-propagation rules for std::min std::max implemented by pmin and pmax in Eigen is a longstanding source og confusion and bug report. This change is a first step towards addressing it, as discussing in issue #564.
* Fix alignedbox 32-bit precision test failure.Gravatar Antonio Sanchez2020-09-30
| | | | | | | | | | | | | | | The current `test/geo_alignedbox` tests fail on 32-bit arm due to small floating-point errors. In particular, the following is not guaranteed to hold: ``` IsometryTransform identity = IsometryTransform::Identity(); BoxType transformedC; transformedC.extend(c.transformed(identity)); VERIFY(transformedC.contains(c)); ``` since `c.transformed(identity)` is ever-so-slightly different from `c`. Instead, we replace this test with one that checks an identity transform is within floating-point precision of `c`. Also updated the condition on `AlignedBox::transform(...)` to only accept `Affine`, `AffineCompact`, and `Isometry` modes explicitly. Otherwise, invalid combinations of modes would also incorrectly pass the assertion.
* Added AlignedBox::transform(AffineTransform).Gravatar Martin Pecka2020-09-28
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* Remove EIGEN_CONSTEXPR from NumTraits<boost::multiprecision::number<...>>Gravatar David Tellenbach2020-09-21
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* Get rid of initialization logic for blueNorm by making the computed ↵Gravatar Rasmus Munk Larsen2020-09-18
| | | | | | constants static const or constexpr. Move macro definition EIGEN_CONSTEXPR to Core and make all methods in NumTraits constexpr when EIGEN_HASH_CONSTEXPR is 1.
* Make bfloat16(float(-nan)) produce -nan, not nan.Gravatar Tim Shen2020-09-15
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* Add missing functions for Packet8bf in Altivec architecture.Gravatar Pedro Caldeira2020-09-08
| | | | | Including new tests for bfloat16 Packets. Fix prsqrt on GenericPacketMath.
* MatrixProuct enhancements:Gravatar Everton Constantino2020-09-02
| | | | | | | | | | | | | - Changes to Altivec/MatrixProduct Adapting code to gcc 10. Generic code style and performance enhancements. Adding PanelMode support. Adding stride/offset support. Enabling float64, std::complex and std::complex. Fixing lack of symm_pack. Enabling mixedtypes. - Adding std::complex tests to blasutil. - Adding an implementation of storePacketBlock when Incr!= 1.
* Fix #1974: assertion when reserving an empty sparse matrixGravatar Gael Guennebaud2020-08-26
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* Fixing a CUDA / P100 regression introduced by PR 181Gravatar Deven Desai2020-08-20
| | | | | | PR 181 ( https://gitlab.com/libeigen/eigen/-/merge_requests/181 ) adds `__launch_bounds__(1024)` attribute to GPU kernels, that did not have that attribute explicitly specified. That PR seems to cause regressions on the CUDA platform. This PR/commit makes the changes in PR 181, to be applicable for HIP only
* Add possibility to split test suit build targets and improved CI configurationGravatar David Tellenbach2020-08-19
| | | | | | - Introduce CMake option `EIGEN_SPLIT_TESTSUITE` that allows to divide the single test build target into several subtargets - Add CI pipeline for merge request that can be run by GitLab's shared runners - Add nightly CI pipeline
* Fix compilation error in blasutil testGravatar David Tellenbach2020-08-14
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* Replace the call to int64_t in the blasutil test by explicit typesGravatar David Tellenbach2020-08-14
| | | | | | | | | Some platforms define int64_t to be long long even for C++03. If this is the case we miss the definition of internal::make_unsigned for this type. If we just define the template we get duplicated definitions errors for platforms defining int64_t as signed long for C++03. We need to find a way to distinguish both cases at compile-time.
* Add support for Bfloat16 to use vector instructions on AltivecGravatar Pedro Caldeira2020-08-10
| | | | architecture
* Adding an explicit launch_bounds(1024) attribute for GPU kernels.Gravatar Deven Desai2020-08-05
| | | | | | | | | | Starting with ROCm 3.5, the HIP compiler will change from HCC to hip-clang. This compiler change introduce a change in the default value of the `__launch_bounds__` attribute associated with a GPU kernel. (default value means the value assumed by the compiler as the `__launch_bounds attribute__` value, when it is not explicitly specified by the user) Currently (i.e. for HIP with ROCm 3.3 and older), the default value is 1024. That changes to 256 with ROCm 3.5 (i.e. hip-clang compiler). As a consequence of this change, if a GPU kernel with a `__luanch_bounds__` attribute of 256 is launched at runtime with a threads_per_block value > 256, it leads to a runtime error. This is leading to a couple of Eigen unit test failures with ROCm 3.5. This commit adds an explicit `__launch_bounds(1024)__` attribute to every GPU kernel that currently does not have it explicitly specified (and hence will end up getting the default value of 256 with the change to hip-clang)
* Fix bfloat16 castsGravatar David Tellenbach2020-07-23
| | | | | | | If we have explicit conversion operators available (C++11) we define explicit casts from bfloat16 to other types. If not (C++03), we don't define conversion operators but rely on implicit conversion chains from bfloat16 over float to other types.
* Revert change that made conversion from bfloat16 to {float, double} implicit.Gravatar Rasmus Munk Larsen2020-07-22
| | | | Add roundtrip tests for casting between bfloat16 and complex types.
* Faster conversion from integer types to bfloat16Gravatar Niels Dekker2020-07-22
| | | | | | Specialized `bfloat16_impl::float_to_bfloat16_rtne(float)` for normal floating point numbers, infinity and zero, in order to improve the performance of `bfloat16::bfloat16(const T&)` for integer argument types. A reduction of more than 20% of the runtime duration of conversion from int to bfloat16 was observed, using Visual C++ 2019 on Windows 10.
* Allow implicit conversion from bfloat16 to float and doubleGravatar Niels Dekker2020-07-11
| | | | | | Conversion from `bfloat16` to `float` and `double` is lossless. It seems natural to allow the conversion to be implicit, as the C++ language also support implicit conversion from a smaller to a larger floating point type. Intel's OneDLL bfloat16 implementation also has an implicit `operator float()`: https://github.com/oneapi-src/oneDNN/blob/v1.5/src/common/bfloat16.hpp
* Guard operator<< test by EIGEN_NO_IO.Gravatar Rasmus Munk Larsen2020-07-09
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* Add operator<< to print a quaternion.Gravatar Rasmus Munk Larsen2020-07-09
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* Fix test basic stuffGravatar David Tellenbach2020-07-09
| | | | | | - Guard fundamental types that are not available pre C++11 - Separate subsequent angle brackets >> by spaces - Allow casting of Eigen::half and Eigen::bfloat16 to complex types
* Change the sign operator in Eigen to return NaN for NaN arguments, not zero.Gravatar Rasmus Munk Larsen2020-07-07
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* Make test packetmath C++98 compliantGravatar David Tellenbach2020-07-01
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* Delete duplicate test cases in vectorization_logic.cppGravatar Kan Chen2020-07-01
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* Fix tensor casts for large packets and casts to/from std::complexGravatar Antonio Sanchez2020-06-30
| | | | | | | | | | | | | The original tensor casts were only defined for `SrcCoeffRatio`:`TgtCoeffRatio` 1:1, 1:2, 2:1, 4:1. Here we add the missing 1:N and 8:1. We also add casting `Eigen::half` to/from `std::complex<T>`, which was missing to make it consistent with `Eigen:bfloat16`, and generalize the overload to work for any complex type. Tests were added to `basicstuff`, `packetmath`, and `cxx11_tensor_casts` to test all cast configurations.
* Fix denormal check pre c++11.Gravatar Antonio Sanchez2020-06-30
| | | | | `float_denorm_style` is an old-style `enum`, so the `denorm_present` symbol only exists in the `std` namespace prior to c++11.
* Report custom C++ flags in CMake testing summaryGravatar David Tellenbach2020-06-30
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* Fix packetmath_1 float tests for arm/aarch64.Gravatar Antonio Sanchez2020-06-24
| | | | | | | | | | | | | | | | | | | Added missing `pmadd<Packet2f>` for NEON. This leads to significant improvement in precision than previous `pmul+padd`, which was causing the `pcos` tests to fail. Also added an approx test with `std::sin`/`std::cos` since otherwise returning any `a^2+b^2=1` would pass. Modified `log(denorm)` tests. Denorms are not always supported by all systems (returns `::min`), are always flushed to zero on 32-bit arm, and configurably flush to zero on sse/avx/aarch64. This leads to inconsistent results across different systems (i.e. `-inf` vs `nan`). Added a check for existence and exclude ARM. Removed logistic exactness test, since scalar and vectorized versions follow different code-paths due to differences in `pexp` and `pmadd`, which result in slightly different values. For example, exactness always fails on arm, aarch64, and altivec.
* Added missing NEON pcasts, update packetmath tests.Gravatar Antonio Sanchez2020-06-21
| | | | | | | | | | | | | | | | | | | | | | | | | The NEON `pcast` operators are all implemented and tested for existing packets. This requires adding a `pcast(a,b,c,d,e,f,g,h)` for casting between `int64_t` and `int8_t` in `GenericPacketMath.h`. Removed incorrect `HasHalfPacket` definition for NEON's `Packet2l`/`Packet2ul`. Adjustments were also made to the `packetmath` tests. These include - minor bug fixes for cast tests (i.e. 4:1 casts, only casting for packets that are vectorizable) - added 8:1 cast tests - random number generation - original had uninteresting 0 to 0 casts for many casts between floating-point and integers, and exhibited signed overflow undefined behavior Tested: ``` $ aarch64-linux-gnu-g++ -static -I./ '-DEIGEN_TEST_PART_ALL=1' test/packetmath.cpp -o packetmath $ adb push packetmath /data/local/tmp/ $ adb shell "/data/local/tmp/packetmath" ```
* Support BFloat16 in EigenGravatar Teng Lu2020-06-20
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* Fix #1911: add benchmark for move semantics with fixed-size matrixGravatar Sebastien Boisvert2020-06-11
| | | | | | | | | | | $ clang++ -O3 bench/bench_move_semantics.cpp -I. -std=c++11 \ -o bench_move_semantics $ ./bench_move_semantics float copy semantics: 1755.97 ms float move semantics: 55.063 ms double copy semantics: 2457.65 ms double move semantics: 55.034 ms
* Remove HasCast and fix packetmath cast tests.Gravatar Antonio Sanchez2020-06-11
| | | | | | | | | | | The use of the `packet_traits<>::HasCast` field is currently inconsistent with `type_casting_traits<>`, and is unused apart from within `test/packetmath.cpp`. In addition, those packetmath cast tests do not currently reflect how casts are performed in practice: they ignore the `SrcCoeffRatio` and `TgtCoeffRatio` fields, assuming a 1:1 ratio. Here we remove the unsed `HasCast`, and modify the packet cast tests to better reflect their usage.
* Fix #1757: remove the word 'suicide'Gravatar Sebastien Boisvert2020-06-11
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* Fix broken packetmath test for logistic on Arm.Gravatar Rasmus Munk Larsen2020-06-04
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* Bug #1777: make the scalar and packet path consistent for the logistic ↵Gravatar Gael Guennebaud2020-05-31
| | | | function + respective unit test
* Save one extra temporary when assigning a sparse product to a row-major ↵Gravatar Gael Guennebaud2020-05-30
| | | | sparse matrix
* Guard usage of decltype since it's a C++11 featureGravatar David Tellenbach2020-05-20
| | | | This fixes https://gitlab.com/libeigen/eigen/-/issues/1897
* Add guard around specialization for bool, which is only currently ↵Gravatar Rasmus Munk Larsen2020-05-19
| | | | implemented for SSE.
* - Vectorizing MMA packing.Gravatar Everton Constantino2020-05-19
| | | | | - Optimizing MMA kernel. - Adding PacketBlock store to blas_data_mapper.
* Add missing packet ops for bool, and make it pass the same packet op unit ↵Gravatar Rasmus Munk Larsen2020-05-14
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | tests as other arithmetic types. This change also contains a few minor cleanups: 1. Remove packet op pnot, which is not needed for anything other than pcmp_le_or_nan, which can be done in other ways. 2. Remove the "HasInsert" enum, which is no longer needed since we removed the corresponding packet ops. 3. Add faster pselect op for Packet4i when SSE4.1 is supported. Among other things, this makes the fast transposeInPlace() method available for Matrix<bool>. Run on ************** (72 X 2994 MHz CPUs); 2020-05-09T10:51:02.372347913-07:00 CPU: Intel Skylake Xeon with HyperThreading (36 cores) dL1:32KB dL2:1024KB dL3:24MB Benchmark Time(ns) CPU(ns) Iterations ----------------------------------------------------------------------- BM_TransposeInPlace<float>/4 9.77 9.77 71670320 BM_TransposeInPlace<float>/8 21.9 21.9 31929525 BM_TransposeInPlace<float>/16 66.6 66.6 10000000 BM_TransposeInPlace<float>/32 243 243 2879561 BM_TransposeInPlace<float>/59 844 844 829767 BM_TransposeInPlace<float>/64 933 933 750567 BM_TransposeInPlace<float>/128 3944 3945 177405 BM_TransposeInPlace<float>/256 16853 16853 41457 BM_TransposeInPlace<float>/512 204952 204968 3448 BM_TransposeInPlace<float>/1k 1053889 1053861 664 BM_TransposeInPlace<bool>/4 14.4 14.4 48637301 BM_TransposeInPlace<bool>/8 36.0 36.0 19370222 BM_TransposeInPlace<bool>/16 31.5 31.5 22178902 BM_TransposeInPlace<bool>/32 111 111 6272048 BM_TransposeInPlace<bool>/59 626 626 1000000 BM_TransposeInPlace<bool>/64 428 428 1632689 BM_TransposeInPlace<bool>/128 1677 1677 417377 BM_TransposeInPlace<bool>/256 7126 7126 96264 BM_TransposeInPlace<bool>/512 29021 29024 24165 BM_TransposeInPlace<bool>/1k 116321 116330 6068
* Added support for reverse iterators for Vectorwise operations.Gravatar Felipe Attanasio2020-05-14
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* Indexed view should have RowMajorBit when there is staticly a single rowGravatar Christopher Moore2020-05-14
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* Resolve "IndexedView of a vector should allow linear access"Gravatar Christopher Moore2020-05-13
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* Remove packet ops pinsertfirst and pinsertlast that are only used in a ↵Gravatar Rasmus Munk Larsen2020-05-08
| | | | | | | | | | | | | | | | single place, and can be replaced by other ops when constructing the first/final packet in linspaced_op_impl::packetOp. I cannot measure any performance changes for SSE, AVX, or AVX512. name old time/op new time/op delta BM_LinSpace<float>/1 1.63ns ± 0% 1.63ns ± 0% ~ (p=0.762 n=5+5) BM_LinSpace<float>/8 4.92ns ± 3% 4.89ns ± 3% ~ (p=0.421 n=5+5) BM_LinSpace<float>/64 34.6ns ± 0% 34.6ns ± 0% ~ (p=0.841 n=5+5) BM_LinSpace<float>/512 217ns ± 0% 217ns ± 0% ~ (p=0.421 n=5+5) BM_LinSpace<float>/4k 1.68µs ± 0% 1.68µs ± 0% ~ (p=1.000 n=5+5) BM_LinSpace<float>/32k 13.3µs ± 0% 13.3µs ± 0% ~ (p=0.905 n=5+4) BM_LinSpace<float>/256k 107µs ± 0% 107µs ± 0% ~ (p=0.841 n=5+5) BM_LinSpace<float>/1M 427µs ± 0% 427µs ± 0% ~ (p=0.690 n=5+5)
* Remove unused packet op "palign".Gravatar Rasmus Munk Larsen2020-05-07
| | | | Clean up a compiler warning in c++03 mode in AVX512/Complex.h.
* Make size odd for transposeInPlace test to make sure we hit the scalar path.Gravatar Rasmus Munk Larsen2020-05-07
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* Extend support for Packet16b:Gravatar Rasmus Munk Larsen2020-04-28
| | | | | | | | | | | | | | | | | * Add ptranspose<*,4> to support matmul and add unit test for Matrix<bool> * Matrix<bool> * work around a bug in slicing of Tensor<bool>. * Add tensor tests This speeds up matmul for boolean matrices by about 10x name old time/op new time/op delta BM_MatMul<bool>/8 267ns ± 0% 479ns ± 0% +79.25% (p=0.008 n=5+5) BM_MatMul<bool>/32 6.42µs ± 0% 0.87µs ± 0% -86.50% (p=0.008 n=5+5) BM_MatMul<bool>/64 43.3µs ± 0% 5.9µs ± 0% -86.42% (p=0.008 n=5+5) BM_MatMul<bool>/128 315µs ± 0% 44µs ± 0% -85.98% (p=0.008 n=5+5) BM_MatMul<bool>/256 2.41ms ± 0% 0.34ms ± 0% -85.68% (p=0.008 n=5+5) BM_MatMul<bool>/512 18.8ms ± 0% 2.7ms ± 0% -85.53% (p=0.008 n=5+5) BM_MatMul<bool>/1k 149ms ± 0% 22ms ± 0% -85.40% (p=0.008 n=5+5)
* Block transposeInPlace() when the matrix is real and square. This yields a ↵Gravatar Rasmus Munk Larsen2020-04-28
| | | | | | | | | | | | | | | | | | | | large speedup because we transpose in registers (or L1 if we spill), instead of one packet at a time, which in the worst case makes the code write to the same cache line PacketSize times instead of once. rmlarsen@rmlarsen4:.../eigen_bench/google3$ benchy --benchmarks=.*TransposeInPlace.*float.* --reference=srcfs experimental/users/rmlarsen/bench:matmul_bench 10 / 10 [====================================================================================================================================================================================================================] 100.00% 2m50s (Generated by http://go/benchy. Settings: --runs 5 --benchtime 1s --reference "srcfs" --benchmarks ".*TransposeInPlace.*float.*" experimental/users/rmlarsen/bench:matmul_bench) name old time/op new time/op delta BM_TransposeInPlace<float>/4 9.84ns ± 0% 6.51ns ± 0% -33.80% (p=0.008 n=5+5) BM_TransposeInPlace<float>/8 23.6ns ± 1% 17.6ns ± 0% -25.26% (p=0.016 n=5+4) BM_TransposeInPlace<float>/16 78.8ns ± 0% 60.3ns ± 0% -23.50% (p=0.029 n=4+4) BM_TransposeInPlace<float>/32 302ns ± 0% 229ns ± 0% -24.40% (p=0.008 n=5+5) BM_TransposeInPlace<float>/59 1.03µs ± 0% 0.84µs ± 1% -17.87% (p=0.016 n=5+4) BM_TransposeInPlace<float>/64 1.20µs ± 0% 0.89µs ± 1% -25.81% (p=0.008 n=5+5) BM_TransposeInPlace<float>/128 8.96µs ± 0% 3.82µs ± 2% -57.33% (p=0.008 n=5+5) BM_TransposeInPlace<float>/256 152µs ± 3% 17µs ± 2% -89.06% (p=0.008 n=5+5) BM_TransposeInPlace<float>/512 837µs ± 1% 208µs ± 0% -75.15% (p=0.008 n=5+5) BM_TransposeInPlace<float>/1k 4.28ms ± 2% 1.08ms ± 2% -74.72% (p=0.008 n=5+5)
* Remove unused packet op "preduxp".Gravatar Rasmus Munk Larsen2020-04-23
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