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* Remove legacy block evaluation supportGravatar Eugene Zhulenev2019-11-12
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* Fix a race in async tensor evaluation: Don't run on_done() until after ↵Gravatar Rasmus Munk Larsen2019-11-11
| | | | device.deallocate() / evaluator.cleanup() complete, since the device might be destroyed after on_done() runs.
* Break loop dependence in TensorGenerator block accessGravatar Eugene Zhulenev2019-11-11
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* Fix data race in css11_tensor_notification test.Gravatar Rasmus Munk Larsen2019-11-08
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* 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.
* Merged in ezhulenev/eigen-01 (pull request PR-746)Gravatar Rasmus Larsen2019-11-04
|\ | | | | | | Remove internal::smart_copy and replace with std::copy
* | Cleanup includes in Tensor module after switch to C++11 and aboveGravatar Eugene Zhulenev2019-10-29
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| * Remove internal::smart_copy and replace with std::copyGravatar Eugene Zhulenev2019-10-29
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* Fix CXX11Meta compilation with MSVCGravatar Eugene Zhulenev2019-10-28
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* Prevent potential ODR in TensorExecutorGravatar Eugene Zhulenev2019-10-28
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* This PR fixes:Gravatar Mehdi Goli2019-10-23
| | | | | * The specialization of array class in the different namespace for GCC<=6.4 * The implicit call to `std::array` constructor using the initializer list for GCC <=6.1
* Merged in deven-amd/eigen-hip-fix-191018 (pull request PR-738)Gravatar Rasmus Larsen2019-10-22
|\ | | | | | | Fix for the HIP build+test errors.
* | Add block evaluation V2 to TensorAsyncExecutor.Gravatar Rasmus Munk Larsen2019-10-22
| | | | | | | | Add async evaluation to a number of ops.
| * Fix for the HIP build+test errors.Gravatar Deven Desai2019-10-22
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The errors were introduced by this commit : After the above mentioned commit, some of the tests started failing with the following error ``` Built target cxx11_tensor_reduction 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:117: /home/rocm-user/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBlockV2.h:155:5: error: the field type is not amp-compatible DestinationBufferKind m_kind; ^ /home/rocm-user/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBlockV2.h:211:3: error: the field type is not amp-compatible DestinationBuffer m_destination; ^ ``` For some reason HIPCC does not like device code to contain enum types which do not have the base-type explicitly declared. The fix is trivial, explicitly state "int" as the basetype
* | Drop support for c++03 in Eigen tensor. Get rid of some code used to emulate ↵Gravatar Rasmus Munk Larsen2019-10-18
|/ | | | c++11 functionality with older compilers.
* Propagate block evaluation preference through rvalue tensor expressionsGravatar Eugene Zhulenev2019-10-17
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* Cleanup Tensor block destination and materialized block storage allocationGravatar Eugene Zhulenev2019-10-16
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* TensorBroadcasting support for random/uniform blocksGravatar Eugene Zhulenev2019-10-16
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* Block evaluation for TensorGenerator/TensorReverse/TensorShufflingGravatar Eugene Zhulenev2019-10-14
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* bug #1747: fix compilation with MSVCGravatar Gael Guennebaud2019-10-14
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* Block evaluation for TensorGenerator + TensorReverse + fixed bug in tensor ↵Gravatar Eugene Zhulenev2019-10-10
| | | | reverse op
* Block evaluation for TensorChipping + fixed bugs in TensorPadding and ↵Gravatar Eugene Zhulenev2019-10-09
| | | | TensorSlicing
* Implement c++03 compatible fix for changeset ↵Gravatar Gael Guennebaud2019-10-09
| | | | 7a43af1a335da2c0489b4119a33ee1cbff0c15d6
* Fix compilation of FFTW unit testGravatar Gael Guennebaud2019-10-08
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* Add block evaluation to TensorEvalTo and fix few small bugsGravatar Eugene Zhulenev2019-10-07
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* Fixing incorrect size in Tensor documentation.Gravatar Brian Zhao2019-10-04
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* Use "pdiv" rather than operator/ to support packet types.Gravatar Rasmus Munk Larsen2019-10-04
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* Fix compilation warnings and errors with clang in TensorBlockV2 code and testsGravatar Eugene Zhulenev2019-10-04
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* Add block evaluation to TensorReshaping/TensorCasting/TensorPadding/TensorSelectGravatar Eugene Zhulenev2019-10-02
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* Add beta to TensorContractionKernel and make memset optionalGravatar Eugene Zhulenev2019-10-02
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* Move implementation of vectorized error function erf() to ↵Gravatar Rasmus Munk Larsen2019-09-27
| | | | SpecialFunctionsImpl.h.
* Fix cxx11_tensor_block_io testGravatar Eugene Zhulenev2019-09-25
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* Fix compilation warnings and errors with clang in TensorBlockV2Gravatar 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
* Fix a bug in a packed block type in TensorContractionThreadPoolGravatar Eugene Zhulenev2019-09-24
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* Merged in rmlarsen/eigen (pull request PR-704)Gravatar Rasmus Larsen2019-09-24
|\ | | | | | | Add generic PacketMath implementation of the Error Function (erf).
| * Add TODO to cleanup FMA cost modelling.Gravatar Rasmus Munk Larsen2019-09-24
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* | Choose TensorBlock StridedLinearCopy type staticallyGravatar Eugene Zhulenev2019-09-24
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* | Add new TensorBlock api implementation + testsGravatar Eugene Zhulenev2019-09-24
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* | Tensor block evaluation V2 support for unary/binary/broadcstingGravatar Eugene Zhulenev2019-09-24
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* | Fix implicit conversion warnings and use pnegate to negate packetsGravatar Christoph Hertzberg2019-09-23
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* | Fix (or mask away) conversion warnings introduced in ↵Gravatar Christoph Hertzberg2019-09-23
| | | | | | | | | | | | 553caeb6a3bb545aef895f8fc9f219be44679017 .
* | Add support for asynchronous evaluation of tensor casting expressions.Gravatar Rasmus Munk Larsen2019-09-19
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| * Add generic PacketMath implementation of the Error Function (erf).Gravatar Rasmus Munk Larsen2019-09-19
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* Merging eigen/eigen.Gravatar Srinivas Vasudevan2019-09-16
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* | 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
| * Fix maybe-unitialized warnings in TensorContractionThreadPoolGravatar Eugene Zhulenev2019-09-13
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| * Use ThreadLocal container in TensorContractionThreadPoolGravatar Eugene Zhulenev2019-09-13
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* 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.
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