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* Bug 1785: fix pround on x86 to use the same rounding mode as std::round.Gravatar Ilya Tokar2019-12-12
| | | | | | This also adds pset1frombits helper to Packet[24]d. Makes round ~45% slower for SSE: 1.65µs ± 1% before vs 2.45µs ± 2% after, stil an order of magnitude faster than scalar version: 33.8µs ± 2%.
* Clamp tanh approximation outside [-c, c] where c is the smallest value where ↵Gravatar Rasmus Munk Larsen2019-12-12
| | | | the approximation is exactly +/-1. Without FMA, c = 7.90531110763549805, with FMA c = 7.99881172180175781.
* Fix implementation of complex expm1. Add tests that fail with previous ↵Gravatar Srinivas Vasudevan2019-12-12
| | | | implementation, but pass with the current one.
* IO: Fixed printing of char and unsigned char matricesGravatar Joel Holdsworth2019-12-11
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* Added Eigen::numext typedefs for uint8_t, int8_t, uint16_t and int16_tGravatar Joel Holdsworth2019-12-11
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* Fix for HIP breakage detected on 191210Gravatar Deven Desai2019-12-10
| | | | | | | | The following commit introduces compile errors when running eigen with hipcc https://gitlab.com/libeigen/eigen/commit/2918f85ba976dbfbf72f7d4c1961a577f5850148 hipcc errors out because it requies the device attribute on the methods within the TensorBlockV2ResourceRequirements struct instroduced by the commit above. The fix is to add the device attribute to those methods
* Update old links to bitbucket to point to gitlab.comGravatar Gael Guennebaud2019-12-04
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* Merged in ↵Gravatar Rasmus Larsen2019-12-04
|\ | | | | | | | | | | | | | | anshuljl/eigen-2/Anshul-Jaiswal/update-configurevectorizationh-to-not-op-1573079916090 (pull request PR-754) Update ConfigureVectorization.h to not optimize fp16 routines when compiling with cuda. Approved-by: Deven Desai <deven.desai.amd@gmail.com>
* | bug #1776: fix vector-wise STL iterator's operator-> using a proxy as ↵Gravatar Gael Guennebaud2019-12-03
| | | | | | | | | | | | pointer type. This changeset fixes also the value_type definition.
* | Revert the specialization for scalar_logistic_op<float> introduced in:Gravatar Rasmus Munk Larsen2019-12-02
| | | | | | | | | | | | | | https://bitbucket.org/eigen/eigen/commits/77b447c24e3344e43ff64eb932d4bb35a2db01ce While providing a 50% speedup on Haswell+ processors, the large relative error outside [-18, 18] in this approximation causes problems, e.g., when computing gradients of activation functions like softplus in neural networks.
* | Merged in ezhulenev/eigen-02 (pull request PR-767)Gravatar Rasmus Larsen2019-12-02
|\ \ | | | | | | | | | Fix shadow warnings in AlignedBox and SparseBlock
* | | Fix for the HIP build+test errors.Gravatar Deven Desai2019-12-02
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Recent changes have introduced the following build error when compiling with HIPCC --------- unsupported/test/../../Eigen/src/Core/GenericPacketMath.h:254:58: error: 'ldexp': no overloaded function has restriction specifiers that are compatible with the ambient context 'pldexp' --------- The fix for the error is to pick the math function(s) from the global namespace (where they are declared as device functions in the HIP header files) when compiling with HIPCC.
* | | [SYCL] Rebasing the SYCL support branch on top of the Einge upstream master ↵Gravatar Mehdi Goli2019-11-28
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | branch. * Unifying all loadLocalTile from lhs and rhs to an extract_block function. * Adding get_tensor operation which was missing in TensorContractionMapper. * Adding the -D method missing from cmake for Disable_Skinny Contraction operation. * Wrapping all the indices in TensorScanSycl into Scan parameter struct. * Fixing typo in Device SYCL * Unifying load to private register for tall/skinny no shared * Unifying load to vector tile for tensor-vector/vector-tensor operation * Removing all the LHS/RHS class for extracting data from global * Removing Outputfunction from TensorContractionSkinnyNoshared. * Combining the local memory version of tall/skinny and normal tensor contraction into one kernel. * Combining the no-local memory version of tall/skinny and normal tensor contraction into one kernel. * Combining General Tensor-Vector and VectorTensor contraction into one kernel. * Making double buffering optional for Tensor contraction when local memory is version is used. * Modifying benchmark to accept custom Reduction Sizes * Disabling AVX optimization for SYCL backend on the host to allow SSE optimization to the host * Adding Test for SYCL * Modifying SYCL CMake
| * | Fix shadow warnings in AlignedBox and SparseBlockGravatar Eugene Zhulenev2019-11-27
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* | Add missing EIGEN_DEVICE_FUNC attribute to template specializations for pexp ↵Gravatar Rasmus Munk Larsen2019-11-27
| | | | | | | | to fix GPU build.
* | Fix warning due to missing cast for exponent arguments for std::frexp and ↵Gravatar Rasmus Munk Larsen2019-11-26
| | | | | | | | std::lexp.
* | SparseRef: Fixed alignment warning on ARM GCCGravatar Joel Holdsworth2019-11-07
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| * Update ConfigureVectorization.h to not optimize fp16 routines when compiling ↵Gravatar Anshul Jaiswal2019-11-06
| | | | | | | | with cuda.
* | Fix duplicate symbol linking error.Gravatar Gael Guennebaud2019-11-20
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* | 1. Fix a bug in psqrt and make it return 0 for +inf arguments.Gravatar Rasmus Munk Larsen2019-11-15
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2. Simplify handling of special cases by taking advantage of the fact that the builtin vrsqrt approximation handles negative, zero and +inf arguments correctly. This speeds up the SSE and AVX implementations by ~20%. 3. Make the Newton-Raphson formula used for rsqrt more numerically robust: Before: y = y * (1.5 - x/2 * y^2) After: y = y * (1.5 - y * (x/2) * y) Forming y^2 can overflow for very large or very small (denormalized) values of x, while x*y ~= 1. For AVX512, this makes it possible to compute accurate results for denormal inputs down to ~1e-42 in single precision. 4. Add a faster double precision implementation for Knights Landing using the vrsqrt28 instruction and a single Newton-Raphson iteration. Benchmark results: https://bitbucket.org/snippets/rmlarsen/5LBq9o
* | bug #1744: fix compilation with MSVC 2017 and AVX512, plog1p/pexpm1 require ↵Gravatar Gael Guennebaud2019-11-15
| | | | | | | | plog/pexp, but the later was disabled on some compilers
* | bug #1774: fix VectorwiseOp::begin()/end() return types regarding constness.Gravatar Gael Guennebaud2019-11-14
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* | PR 751: Fixed compilation issue when compiling using MSVC with /arch:AVX512 flagGravatar Sakshi Goynar2019-10-31
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* | Enable CompleteOrthogonalDecomposition::pseudoInverse with non-square ↵Gravatar Gael Guennebaud2019-11-13
| | | | | | | | fixed-size matrices.
* | Disable AVX on broken xcode versions. See PR 748.Gravatar Gael Guennebaud2019-11-12
|/ | | | Patch adapted from Hans Johnson's PR 748.
* Add EIGEN_HAS_INTRINSIC_INT128 macroGravatar Rasmus Munk Larsen2019-11-06
| | | | Add a new EIGEN_HAS_INTRINSIC_INT128 macro, and use this instead of __SIZEOF_INT128__. This fixes related issues with TensorIntDiv.h when building with Clang for Windows, where support for 128-bit integer arithmetic is advertised but broken in practice.
* Rollback or PR-746 and partial rollback of ↵Gravatar Rasmus Munk Larsen2019-11-05
| | | | | | | | https://bitbucket.org/eigen/eigen/commits/668ab3fc474e54c7919eda4fbaf11f3a99246494 . std::array is still not supported in CUDA device code on Windows.
* Remove internal::smart_copy and replace with std::copyGravatar Eugene Zhulenev2019-10-29
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* bug #1752: make is_convertible equivalent to the std c++11 equivalent and ↵Gravatar Gael Guennebaud2019-10-10
| | | | fallback to std::is_convertible when c++11 is enabled.
* fix one more possible conflicts with real/imagGravatar Gael Guennebaud2019-10-08
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* PR 719: fix real/imag namespace conflictGravatar Gael Guennebaud2019-10-08
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* Address comments on Chebyshev evaluation code:Gravatar Rasmus Munk Larsen2019-10-02
| | | | | 1. Use pmadd when possible. 2. Add casts to avoid c++03 warnings.
* Prevent infinite loop in the nvcc compiler while unrolling the recurrent ↵Gravatar Rasmus Munk Larsen2019-10-01
| | | | templates for Chebyshev polynomial evaluation.
* Move implementation of vectorized error function erf() to ↵Gravatar Rasmus Munk Larsen2019-09-27
| | | | SpecialFunctionsImpl.h.
* Fix erf in c++03Gravatar Eugene Zhulenev2019-09-25
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* Fix for the HIP build+test errors.Gravatar Deven Desai2019-09-25
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The errors were introduced by this commit : https://bitbucket.org/eigen/eigen/commits/d38e6fbc27abe0c354ffe90928f6741c378e76e1 After the above mentioned commit, some of the tests started failing with the following error ``` Building HIPCC object unsupported/test/CMakeFiles/cxx11_tensor_reduction_gpu_5.dir/cxx11_tensor_reduction_gpu_5_generated_cxx11_tensor_reduction_gpu.cu.o In file included from /home/rocm-user/eigen/unsupported/test/cxx11_tensor_reduction_gpu.cu:16: In file included from /home/rocm-user/eigen/unsupported/Eigen/CXX11/Tensor:29: In file included from /home/rocm-user/eigen/unsupported/Eigen/CXX11/../SpecialFunctions:70: /home/rocm-user/eigen/unsupported/Eigen/CXX11/../src/SpecialFunctions/SpecialFunctionsHalf.h:28:22: error: call to 'erf' is ambiguous return Eigen::half(Eigen::numext::erf(static_cast<float>(a))); ^~~~~~~~~~~~~~~~~~ /home/rocm-user/eigen/unsupported/test/../../Eigen/src/Core/MathFunctions.h:1600:7: note: candidate function [with T = float] float erf(const float &x) { return ::erff(x); } ^ /home/rocm-user/eigen/unsupported/Eigen/CXX11/../src/SpecialFunctions/SpecialFunctionsImpl.h:1897:5: note: candidate function [with Scalar = float] erf(const Scalar& x) { ^ In file included from /home/rocm-user/eigen/unsupported/test/cxx11_tensor_reduction_gpu.cu:16: In file included from /home/rocm-user/eigen/unsupported/Eigen/CXX11/Tensor:29: In file included from /home/rocm-user/eigen/unsupported/Eigen/CXX11/../SpecialFunctions:75: /home/rocm-user/eigen/unsupported/Eigen/CXX11/../src/SpecialFunctions/arch/GPU/GpuSpecialFunctions.h:87:23: error: call to 'erf' is ambiguous return make_double2(erf(a.x), erf(a.y)); ^~~ /home/rocm-user/eigen/unsupported/test/../../Eigen/src/Core/MathFunctions.h:1603:8: note: candidate function [with T = double] double erf(const double &x) { return ::erf(x); } ^ /home/rocm-user/eigen/unsupported/Eigen/CXX11/../src/SpecialFunctions/SpecialFunctionsImpl.h:1897:5: note: candidate function [with Scalar = double] erf(const Scalar& x) { ^ In file included from /home/rocm-user/eigen/unsupported/test/cxx11_tensor_reduction_gpu.cu:16: In file included from /home/rocm-user/eigen/unsupported/Eigen/CXX11/Tensor:29: In file included from /home/rocm-user/eigen/unsupported/Eigen/CXX11/../SpecialFunctions:75: /home/rocm-user/eigen/unsupported/Eigen/CXX11/../src/SpecialFunctions/arch/GPU/GpuSpecialFunctions.h:87:33: error: call to 'erf' is ambiguous return make_double2(erf(a.x), erf(a.y)); ^~~ /home/rocm-user/eigen/unsupported/test/../../Eigen/src/Core/MathFunctions.h:1603:8: note: candidate function [with T = double] double erf(const double &x) { return ::erf(x); } ^ /home/rocm-user/eigen/unsupported/Eigen/CXX11/../src/SpecialFunctions/SpecialFunctionsImpl.h:1897:5: note: candidate function [with Scalar = double] erf(const Scalar& x) { ^ 3 errors generated. ``` This PR fixes the compile error by removing the "old" implementation for "erf" (assuming that the "new" implementation is what we want going forward. from a GPU point-of-view both implementations are the same). This PR also fixes what seems like a cut-n-paste error in the aforementioned commit
* Merged in rmlarsen/eigen (pull request PR-704)Gravatar Rasmus Larsen2019-09-24
|\ | | | | | | Add generic PacketMath implementation of the Error Function (erf).
* | Tensor block evaluation V2 support for unary/binary/broadcstingGravatar Eugene Zhulenev2019-09-24
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* | bug #1746: Removed implementation of standard copy-constructor and standard ↵Gravatar Christoph Hertzberg2019-09-24
| | | | | | | | copy-assign-operator from PermutationMatrix and Transpositions to allow malloc-less std::move. Added unit-test to rvalue_types
| * Add generic PacketMath implementation of the Error Function (erf).Gravatar Rasmus Munk Larsen2019-09-19
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* | Fix build on setups without AVX512DQ.Gravatar Rasmus Munk Larsen2019-09-19
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* Fix for the HIP build+test errors.Gravatar Deven Desai2019-09-18
| | | | | | | The errors were introduced by this commit : https://bitbucket.org/eigen/eigen/commits/6e215cf109073da9ffb5b491171613b8db24fd9d The fix is switching to using ::<math_func> instead std::<math_func> when compiling for GPU
* Add Bessel functions to SpecialFunctions.Gravatar Srinivas Vasudevan2019-09-14
| | | | | | | | | - Split SpecialFunctions files in to a separate BesselFunctions file. In particular add: - Modified bessel functions of the second kind k0, k1, k0e, k1e - Bessel functions of the first kind j0, j1 - Bessel functions of the second kind y0, y1
* Add packetized versions of i0e and i1e special functions.Gravatar Srinivas Vasudevan2019-09-11
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - In particular refactor the i0e and i1e code so scalar and vectorized path share code. - Move chebevl to GenericPacketMathFunctions. A brief benchmark with building Eigen with FMA, AVX and AVX2 flags Before: CPU: Intel Haswell with HyperThreading (6 cores) Benchmark Time(ns) CPU(ns) Iterations ----------------------------------------------------------------- BM_eigen_i0e_double/1 57.3 57.3 10000000 BM_eigen_i0e_double/8 398 398 1748554 BM_eigen_i0e_double/64 3184 3184 218961 BM_eigen_i0e_double/512 25579 25579 27330 BM_eigen_i0e_double/4k 205043 205042 3418 BM_eigen_i0e_double/32k 1646038 1646176 422 BM_eigen_i0e_double/256k 13180959 13182613 53 BM_eigen_i0e_double/1M 52684617 52706132 10 BM_eigen_i0e_float/1 28.4 28.4 24636711 BM_eigen_i0e_float/8 75.7 75.7 9207634 BM_eigen_i0e_float/64 512 512 1000000 BM_eigen_i0e_float/512 4194 4194 166359 BM_eigen_i0e_float/4k 32756 32761 21373 BM_eigen_i0e_float/32k 261133 261153 2678 BM_eigen_i0e_float/256k 2087938 2088231 333 BM_eigen_i0e_float/1M 8380409 8381234 84 BM_eigen_i1e_double/1 56.3 56.3 10000000 BM_eigen_i1e_double/8 397 397 1772376 BM_eigen_i1e_double/64 3114 3115 223881 BM_eigen_i1e_double/512 25358 25361 27761 BM_eigen_i1e_double/4k 203543 203593 3462 BM_eigen_i1e_double/32k 1613649 1613803 428 BM_eigen_i1e_double/256k 12910625 12910374 54 BM_eigen_i1e_double/1M 51723824 51723991 10 BM_eigen_i1e_float/1 28.3 28.3 24683049 BM_eigen_i1e_float/8 74.8 74.9 9366216 BM_eigen_i1e_float/64 505 505 1000000 BM_eigen_i1e_float/512 4068 4068 171690 BM_eigen_i1e_float/4k 31803 31806 21948 BM_eigen_i1e_float/32k 253637 253692 2763 BM_eigen_i1e_float/256k 2019711 2019918 346 BM_eigen_i1e_float/1M 8238681 8238713 86 After: CPU: Intel Haswell with HyperThreading (6 cores) Benchmark Time(ns) CPU(ns) Iterations ----------------------------------------------------------------- BM_eigen_i0e_double/1 15.8 15.8 44097476 BM_eigen_i0e_double/8 99.3 99.3 7014884 BM_eigen_i0e_double/64 777 777 886612 BM_eigen_i0e_double/512 6180 6181 100000 BM_eigen_i0e_double/4k 48136 48140 14678 BM_eigen_i0e_double/32k 385936 385943 1801 BM_eigen_i0e_double/256k 3293324 3293551 228 BM_eigen_i0e_double/1M 12423600 12424458 57 BM_eigen_i0e_float/1 16.3 16.3 43038042 BM_eigen_i0e_float/8 30.1 30.1 23456931 BM_eigen_i0e_float/64 169 169 4132875 BM_eigen_i0e_float/512 1338 1339 516860 BM_eigen_i0e_float/4k 10191 10191 68513 BM_eigen_i0e_float/32k 81338 81337 8531 BM_eigen_i0e_float/256k 651807 651984 1000 BM_eigen_i0e_float/1M 2633821 2634187 268 BM_eigen_i1e_double/1 16.2 16.2 42352499 BM_eigen_i1e_double/8 110 110 6316524 BM_eigen_i1e_double/64 822 822 851065 BM_eigen_i1e_double/512 6480 6481 100000 BM_eigen_i1e_double/4k 51843 51843 10000 BM_eigen_i1e_double/32k 414854 414852 1680 BM_eigen_i1e_double/256k 3320001 3320568 212 BM_eigen_i1e_double/1M 13442795 13442391 53 BM_eigen_i1e_float/1 17.6 17.6 41025735 BM_eigen_i1e_float/8 35.5 35.5 19597891 BM_eigen_i1e_float/64 240 240 2924237 BM_eigen_i1e_float/512 1424 1424 485953 BM_eigen_i1e_float/4k 10722 10723 65162 BM_eigen_i1e_float/32k 86286 86297 8048 BM_eigen_i1e_float/256k 691821 691868 1000 BM_eigen_i1e_float/1M 2777336 2777747 256 This shows anywhere from a 50% to 75% improvement on these operations. I've also benchmarked without any of these flags turned on, and got similar performance to before (if not better). Also tested packetmath.cpp + special_functions to ensure no regressions.
* Merged eigen/eigen into defaultGravatar Srinivas Vasudevan2019-09-11
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| * Fix for the HIP build+test errors introduced by the ndtri support.Gravatar Deven Desai2019-09-06
| | | | | | | | | | | | | | The fixes needed are * adding EIGEN_DEVICE_FUNC attribute to a couple of funcs (else HIPCC will error out when non-device funcs are called from global/device funcs) * switching to using ::<math_func> instead std::<math_func> (only for HIPCC) in cases where the std::<math_func> is not recognized as a device func by HIPCC * removing an errant "j" from a testcase (don't know how that made it in to begin with!)
| * bug #1736: fix compilation issue with A(all,{1,2}).col(j) by implementing ↵Gravatar Gael Guennebaud2019-09-11
| | | | | | | | true compile-time "if" for block_evaluator<>::coeff(i)/coeffRef(i)
| * bug #1741: fix self-adjoint*matrix, triangular*matrix, and ↵Gravatar Gael Guennebaud2019-09-11
| | | | | | | | triangular^1*matrix with a destination having a non-trivial inner-stride
| * Fix compilation of BLAS backend and frontendGravatar Gael Guennebaud2019-09-11
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| * bug #1741: fix SelfAdjointView::rankUpdate and product to triangular part ↵Gravatar Gael Guennebaud2019-09-10
| | | | | | | | for destination with non-trivial inner stride
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