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* Fixed output of complex matricesGravatar Jens Wehner2021-03-15
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* Re-implement move assignments.Gravatar Antonio Sanchez2021-03-10
| | | | | | | | | | | | | | | 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
* Define EIGEN_CPLUSPLUS and replace most __cplusplus checks.Gravatar Antonio Sanchez2021-03-05
| | | | | | | | | | | | | | | 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
* Revert "Adds EIGEN_CONSTEXPR and EIGEN_NOEXCEPT to rows(), cols(), ↵Gravatar David Tellenbach2021-03-05
| | | | | | | innerStride(), outerStride(), and size()" This reverts commit 6cbb3038ac48cb5fe17eba4dfbf26e3e798041f1 because it breaks clang-10 builds on x86 and aarch64 when C++11 is enabled.
* Adds EIGEN_CONSTEXPR and EIGEN_NOEXCEPT to rows(), cols(), innerStride(), ↵Gravatar Steve Bronder2021-03-04
| | | | outerStride(), and size()
* Add log2 operation to TensorBaseGravatar Eugene Zhulenev2021-03-04
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* Inherit from `no_assignment_operator` to avoid implicit copy constructor ↵Gravatar Christoph Hertzberg2021-02-27
| | | | | | warnings (cherry picked from commit 9bbb7ea4b54b1f307863be4ed8d105c38cdefe50)
* Fix some enum-enum conversion warningsGravatar Christoph Hertzberg2021-02-27
| | | | (cherry picked from commit 838f3d8ce22a5549ef10c7386fb03040721749a0)
* ReturnByValue is already non-copyableGravatar Christoph Hertzberg2021-02-27
| | | | (cherry picked from commit abbf95045009619f37bd92b45433eedbfcbe41cf)
* Fix double-promotion warningsGravatar Christoph Hertzberg2021-02-27
| | | | (cherry picked from commit c22c103e932e511e96645186831363585a44b7a3)
* Idrs iterative linear solverGravatar Jens Wehner2021-02-27
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* Don't crash when attempting to slice an empty tensor.Gravatar Rasmus Munk Larsen2021-02-24
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* Some improvements for kissfft from Martin Reinecke(pocketfft author):Gravatar Guoqiang QI2021-02-24
| | | | | | 1.Only computing about half of the factors and use complex conjugate symmetry for the rest instead of all to save time. 2.All twiddles are calculated in double because that gives the maximum achievable precision when doing float transforms. 3.Reducing all angles to the range 0<angle<pi/4 which gives even more precision.
* Add missing adolc isinf/isnan.Gravatar Antonio Sanchez2021-02-19
| | | | | | | Also modified cmake/FindAdolc.cmake to eliminate warnings, and added search paths to match install layout. Fixed: #2157
* Return nan at poles of polygamma, digamma, and zeta if limit is not definedGravatar frgossen2021-02-19
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* Remove vim specific comments to recognoize correct file-type.Gravatar David Tellenbach2021-02-09
| | | | As discussed in #2143 we remove editor specific comments.
* add specialization of check_sparse_solving() for SuperLU solver, in order to ↵Gravatar Ralf Hannemann-Tamas2021-02-08
| | | | test adjoint and transpose solves
* Include `<cstdint>` in one place, remove custom typedefsGravatar Antonio Sanchez2021-01-26
| | | | | | | | | | | | | | 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.
* Remove std::cerr in iterative solver since we don't have iostream.Gravatar David Tellenbach2021-01-21
| | | | This fixes #2123
* fix paddings of TensorVolumePatchOpGravatar Maozhou, Ge2021-01-15
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* Add CUDA complex sqrt.Gravatar Antonio Sanchez2020-12-22
| | | | | | | | | | | | | | | This is to support scalar `sqrt` of complex numbers `std::complex<T>` on device, requested by Tensorflow folks. Technically `std::complex` is not supported by NVCC on device (though it is by clang), so the default `sqrt(std::complex<T>)` function only works on the host. Here we create an overload to add back the functionality. Also modified the CMake file to add `--relaxed-constexpr` (or equivalent) flag for NVCC to allow calling constexpr functions from device functions, and added support for specifying compute architecture for NVCC (was already available for clang).
* Replace call to FixedDimensions() with a singleton instance ofGravatar Turing Eret2020-12-16
| | | | FixedDimensions.
* TensorStorage with FixedDimensions now has zero instance memory overhead.Gravatar Turing Eret2020-12-14
| | | | | | | Removed m_dimension as instance member of TensorStorage with FixedDimensions and instead use the template parameter. This means that the sizeof a pure fixed-size storage is exactly equal to the data it is storing.
* Fix bad NEON fp16 checkGravatar Antonio Sanchez2020-12-04
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* Special function implementations for half/bfloat16 packets.Gravatar Antonio Sanchez2020-12-04
| | | | | | | | | | | | | Current implementations fail to consider half-float packets, only half-float scalars. Added specializations for packets on AVX, AVX512 and NEON. Added tests to `special_packetmath`. The current `special_functions` tests would fail for half and bfloat16 due to lack of precision. The NEON tests also fail with precision issues and due to different handling of `sqrt(inf)`, so special functions bessel, ndtri have been disabled. Tested with AVX, AVX512.
* Clean up the Tensor header and get rid of the EIGEN_SLEEP macro.Gravatar Rasmus Munk Larsen2020-12-02
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* Add bit_cast for half/bfloat to/from uint16_t, fix TensorRandomGravatar Antonio Sanchez2020-11-18
| | | | | | | | | | The existing `TensorRandom.h` implementation makes the assumption that `half` (`bfloat16`) has a `uint16_t` member `x` (`value`), which is not always true. This currently fails on arm64, where `x` has type `__fp16`. Added `bit_cast` specializations to allow casting to/from `uint16_t` for both `half` and `bfloat16`. Also added tests in `half_float`, `bfloat16_float`, and `cxx11_tensor_random` to catch these errors in the future.
* Fix rule-of-3 for the Tensor module.Gravatar Antonio Sanchez2020-11-18
| | | | | | | Adds copy constructors to Tensor ops, inherits assignment operators from `TensorBase`. Addresses #1863
* [SYCL clean up the code] : removing exrta #pragma unroll in SYCL which was ↵Gravatar mehdi-goli2020-10-28
| | | | causing issues in embeded systems
* Get rid of nested template specialization in TensorReductionGpu.h, which was ↵Gravatar Rasmus Munk Larsen2020-10-13
| | | | broken by c6953f799b01d36f4236b64f351cc1446e0abe17.
* Add packet generic ops `predux_fmin`, `predux_fmin_nan`, `predux_fmax`, and ↵Gravatar Rasmus Munk Larsen2020-10-13
| | | | `predux_fmax_nan` that implement reductions with `PropagateNaN`, and `PropagateNumbers` semantics. Add (slow) generic implementations for most reductions.
* Add EIGEN prefix for HAS_LGAMMA_RGravatar David Tellenbach2020-10-08
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* Use lgamma_r if it is available (update check for glibc 2.19+)Gravatar Eugene Zhulenev2020-10-08
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* Don't make assumptions about NaN-propagation for pmin/pmax - it various ↵Gravatar Rasmus Munk Larsen2020-10-07
| | | | | | across platforms. Change test to only test for NaN-propagation for pfmin/pfmax.
* Fix Eigen::ThreadPool::CurrentThreadId returning wrong thread id when ↵Gravatar Zhuyie2020-09-25
| | | | EIGEN_AVOID_THREAD_LOCAL and NDEBUG are defined
* 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.
* 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
* 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)
* Inherit alignment trait from argument in TensorBroadcasting to avoid ↵Gravatar Rasmus Munk Larsen2020-07-28
| | | | segfault when the argument is unaligned.
* 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.
* Support BFloat16 in EigenGravatar Teng Lu2020-06-20
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* Run two independent chains, when reducing tensors.Gravatar Ilya Tokar2020-06-16
| | | | | | | | | | | | | | | | | | | | | | | | | | Running two chains exposes more instruction level parallelism, by allowing to execute both chains at the same time. Results are a bit noisy, but for medium length we almost hit theoretical upper bound of 2x. BM_fullReduction_16T/3 [using 16 threads] 17.3ns ±11% 17.4ns ± 9% ~ (p=0.178 n=18+19) BM_fullReduction_16T/4 [using 16 threads] 17.6ns ±17% 17.0ns ±18% ~ (p=0.835 n=20+19) BM_fullReduction_16T/7 [using 16 threads] 18.9ns ±12% 18.2ns ±10% ~ (p=0.756 n=20+18) BM_fullReduction_16T/8 [using 16 threads] 19.8ns ±13% 19.4ns ±21% ~ (p=0.512 n=20+20) BM_fullReduction_16T/10 [using 16 threads] 23.5ns ±15% 20.8ns ±24% -11.37% (p=0.000 n=20+19) BM_fullReduction_16T/15 [using 16 threads] 35.8ns ±21% 26.9ns ±17% -24.76% (p=0.000 n=20+19) BM_fullReduction_16T/16 [using 16 threads] 38.7ns ±22% 27.7ns ±18% -28.40% (p=0.000 n=20+19) BM_fullReduction_16T/31 [using 16 threads] 146ns ±17% 74ns ±11% -49.05% (p=0.000 n=20+18) BM_fullReduction_16T/32 [using 16 threads] 154ns ±19% 84ns ±30% -45.79% (p=0.000 n=20+19) BM_fullReduction_16T/64 [using 16 threads] 603ns ± 8% 308ns ±12% -48.94% (p=0.000 n=17+17) BM_fullReduction_16T/128 [using 16 threads] 2.44µs ±13% 1.22µs ± 1% -50.29% (p=0.000 n=17+17) BM_fullReduction_16T/256 [using 16 threads] 9.84µs ±14% 5.13µs ±30% -47.82% (p=0.000 n=19+19) BM_fullReduction_16T/512 [using 16 threads] 78.0µs ± 9% 56.1µs ±17% -28.02% (p=0.000 n=18+20) BM_fullReduction_16T/1k [using 16 threads] 325µs ± 5% 263µs ± 4% -19.00% (p=0.000 n=20+16) BM_fullReduction_16T/2k [using 16 threads] 1.09ms ± 3% 0.99ms ± 1% -9.04% (p=0.000 n=20+20) BM_fullReduction_16T/4k [using 16 threads] 7.66ms ± 3% 7.57ms ± 3% -1.24% (p=0.017 n=20+20) BM_fullReduction_16T/10k [using 16 threads] 65.3ms ± 4% 65.0ms ± 3% ~ (p=0.718 n=20+20)
* Eigen moved the `scanLauncehr` function inside the internal namespace.Gravatar mehdi-goli2020-05-11
| | | | | | | This commit applies the following changes: - Moving the `scamLauncher` specialization inside internal namespace to fix compiler crash on TensorScan for SYCL backend. - Replacing `SYCL/sycl.hpp` to `CL/sycl.hpp` in order to follow SYCL 1.2.1 standard. - minor fixes: commenting out an unused variable to avoid compiler warnings.
* Add parallelization of TensorScanOp for types without packet ops.Gravatar Rasmus Munk Larsen2020-05-06
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Clean up the code a bit and do a few micro-optimizations to improve performance for small tensors. Benchmark numbers for Tensor<uint32_t>: name old time/op new time/op delta BM_cumSumRowReduction_1T/8 [using 1 threads] 76.5ns ± 0% 61.3ns ± 4% -19.80% (p=0.008 n=5+5) BM_cumSumRowReduction_1T/64 [using 1 threads] 2.47µs ± 1% 2.40µs ± 1% -2.77% (p=0.008 n=5+5) BM_cumSumRowReduction_1T/256 [using 1 threads] 39.8µs ± 0% 39.6µs ± 0% -0.60% (p=0.008 n=5+5) BM_cumSumRowReduction_1T/4k [using 1 threads] 13.9ms ± 0% 13.4ms ± 1% -4.19% (p=0.008 n=5+5) BM_cumSumRowReduction_2T/8 [using 2 threads] 76.8ns ± 0% 59.1ns ± 0% -23.09% (p=0.016 n=5+4) BM_cumSumRowReduction_2T/64 [using 2 threads] 2.47µs ± 1% 2.41µs ± 1% -2.53% (p=0.008 n=5+5) BM_cumSumRowReduction_2T/256 [using 2 threads] 39.8µs ± 0% 34.7µs ± 6% -12.74% (p=0.008 n=5+5) BM_cumSumRowReduction_2T/4k [using 2 threads] 13.8ms ± 1% 7.2ms ± 6% -47.74% (p=0.008 n=5+5) BM_cumSumRowReduction_8T/8 [using 8 threads] 76.4ns ± 0% 61.8ns ± 3% -19.02% (p=0.008 n=5+5) BM_cumSumRowReduction_8T/64 [using 8 threads] 2.47µs ± 1% 2.40µs ± 1% -2.84% (p=0.008 n=5+5) BM_cumSumRowReduction_8T/256 [using 8 threads] 39.8µs ± 0% 28.3µs ±11% -28.75% (p=0.008 n=5+5) BM_cumSumRowReduction_8T/4k [using 8 threads] 13.8ms ± 0% 2.7ms ± 5% -80.39% (p=0.008 n=5+5) BM_cumSumColReduction_1T/8 [using 1 threads] 59.1ns ± 0% 80.3ns ± 0% +35.94% (p=0.029 n=4+4) BM_cumSumColReduction_1T/64 [using 1 threads] 3.06µs ± 0% 3.08µs ± 1% ~ (p=0.114 n=4+4) BM_cumSumColReduction_1T/256 [using 1 threads] 175µs ± 0% 176µs ± 0% ~ (p=0.190 n=4+5) BM_cumSumColReduction_1T/4k [using 1 threads] 824ms ± 1% 844ms ± 1% +2.37% (p=0.008 n=5+5) BM_cumSumColReduction_2T/8 [using 2 threads] 59.0ns ± 0% 90.7ns ± 0% +53.74% (p=0.029 n=4+4) BM_cumSumColReduction_2T/64 [using 2 threads] 3.06µs ± 0% 3.10µs ± 0% +1.08% (p=0.016 n=4+5) BM_cumSumColReduction_2T/256 [using 2 threads] 176µs ± 0% 189µs ±18% ~ (p=0.151 n=5+5) BM_cumSumColReduction_2T/4k [using 2 threads] 836ms ± 2% 611ms ±14% -26.92% (p=0.008 n=5+5) BM_cumSumColReduction_8T/8 [using 8 threads] 59.3ns ± 2% 90.6ns ± 0% +52.79% (p=0.008 n=5+5) BM_cumSumColReduction_8T/64 [using 8 threads] 3.07µs ± 0% 3.10µs ± 0% +0.99% (p=0.016 n=5+4) BM_cumSumColReduction_8T/256 [using 8 threads] 176µs ± 0% 80µs ±19% -54.51% (p=0.008 n=5+5) BM_cumSumColReduction_8T/4k [using 8 threads] 827ms ± 2% 180ms ±14% -78.24% (p=0.008 n=5+5)
* Fix accidental copy of loop variable.Gravatar Rasmus Munk Larsen2020-05-05
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* Vectorize and parallelize TensorScanOp.Gravatar Rasmus Munk Larsen2020-05-05
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | TensorScanOp is used in TensorFlow for a number of operations, such as cumulative logexp reduction and cumulative sum and product reductions. The benchmarks numbers below are for cumulative row- and column reductions of NxN matrices. name old time/op new time/op delta BM_cumSumRowReduction_1T/4 [using 1 threads ] 25.1ns ± 1% 35.2ns ± 1% +40.45% BM_cumSumRowReduction_1T/8 [using 1 threads ] 73.4ns ± 0% 82.7ns ± 3% +12.74% BM_cumSumRowReduction_1T/32 [using 1 threads ] 988ns ± 0% 832ns ± 0% -15.77% BM_cumSumRowReduction_1T/64 [using 1 threads ] 4.07µs ± 2% 3.47µs ± 0% -14.70% BM_cumSumRowReduction_1T/128 [using 1 threads ] 18.0µs ± 0% 16.8µs ± 0% -6.58% BM_cumSumRowReduction_1T/512 [using 1 threads ] 287µs ± 0% 281µs ± 0% -2.22% BM_cumSumRowReduction_1T/2k [using 1 threads ] 4.78ms ± 1% 4.78ms ± 2% ~ BM_cumSumRowReduction_1T/10k [using 1 threads ] 117ms ± 1% 117ms ± 1% ~ BM_cumSumRowReduction_8T/4 [using 8 threads ] 25.0ns ± 0% 35.2ns ± 0% +40.82% BM_cumSumRowReduction_8T/8 [using 8 threads ] 77.2ns ±16% 81.3ns ± 0% ~ BM_cumSumRowReduction_8T/32 [using 8 threads ] 988ns ± 0% 833ns ± 0% -15.67% BM_cumSumRowReduction_8T/64 [using 8 threads ] 4.08µs ± 2% 3.47µs ± 0% -14.95% BM_cumSumRowReduction_8T/128 [using 8 threads ] 18.0µs ± 0% 17.3µs ±10% ~ BM_cumSumRowReduction_8T/512 [using 8 threads ] 287µs ± 0% 58µs ± 6% -79.92% BM_cumSumRowReduction_8T/2k [using 8 threads ] 4.79ms ± 1% 0.64ms ± 1% -86.58% BM_cumSumRowReduction_8T/10k [using 8 threads ] 117ms ± 1% 18ms ± 6% -84.50% BM_cumSumColReduction_1T/4 [using 1 threads ] 23.9ns ± 0% 33.4ns ± 1% +39.68% BM_cumSumColReduction_1T/8 [using 1 threads ] 71.6ns ± 1% 49.1ns ± 3% -31.40% BM_cumSumColReduction_1T/32 [using 1 threads ] 973ns ± 0% 165ns ± 2% -83.10% BM_cumSumColReduction_1T/64 [using 1 threads ] 4.06µs ± 1% 0.57µs ± 1% -85.94% BM_cumSumColReduction_1T/128 [using 1 threads ] 33.4µs ± 1% 4.1µs ± 1% -87.67% BM_cumSumColReduction_1T/512 [using 1 threads ] 1.72ms ± 4% 0.21ms ± 5% -87.91% BM_cumSumColReduction_1T/2k [using 1 threads ] 119ms ±53% 11ms ±35% -90.42% BM_cumSumColReduction_1T/10k [using 1 threads ] 1.59s ±67% 0.35s ±49% -77.96% BM_cumSumColReduction_8T/4 [using 8 threads ] 23.8ns ± 0% 33.3ns ± 0% +40.06% BM_cumSumColReduction_8T/8 [using 8 threads ] 71.6ns ± 1% 49.2ns ± 5% -31.33% BM_cumSumColReduction_8T/32 [using 8 threads ] 1.01µs ±12% 0.17µs ± 3% -82.93% BM_cumSumColReduction_8T/64 [using 8 threads ] 4.15µs ± 4% 0.58µs ± 1% -86.09% BM_cumSumColReduction_8T/128 [using 8 threads ] 33.5µs ± 0% 4.1µs ± 4% -87.65% BM_cumSumColReduction_8T/512 [using 8 threads ] 1.71ms ± 3% 0.06ms ±16% -96.21% BM_cumSumColReduction_8T/2k [using 8 threads ] 97.1ms ±14% 3.0ms ±23% -96.88% BM_cumSumColReduction_8T/10k [using 8 threads ] 1.97s ± 8% 0.06s ± 2% -96.74%
* 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)
* Add async evaluation support to TensorSlicingOp.Gravatar Eugene Zhulenev2020-04-22
| | | Device::memcpy is not async-safe and might lead to deadlocks. Always evaluate slice expression in async mode.
* Fix a bug in TensorIndexList.hGravatar Changming Sun2020-04-13
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* Resolve C4346 when building eigen on windowsGravatar jangsoopark2020-04-08
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