Commit Graph

11031 Commits

Author SHA1 Message Date
Gael Guennebaud
39fb9eeccf bug #1747: fix compilation with MSVC 2019-10-14 22:50:23 +02:00
Eugene Zhulenev
a411e9f344 Block evaluation for TensorGenerator + TensorReverse + fixed bug in tensor reverse op 2019-10-10 10:56:58 -07:00
Rasmus Larsen
b03eb63d7c Merged in ezhulenev/eigen-01 (pull request PR-726)
Block evaluation for TensorChipping + fixed bugs in TensorPadding and TensorSlicing
2019-10-10 16:58:11 +00:00
Gael Guennebaud
e7d8ba747c bug #1752: make is_convertible equivalent to the std c++11 equivalent and fallback to std::is_convertible when c++11 is enabled. 2019-10-10 17:41:47 +02:00
Gael Guennebaud
fb557aec5c bug #1752: disable some is_convertible tests for recent compilers. 2019-10-10 11:40:21 +02:00
Eugene Zhulenev
33e1746139 Block evaluation for TensorChipping + fixed bugs in TensorPadding and TensorSlicing 2019-10-09 12:45:31 -07:00
Gael Guennebaud
f0a4642bab Implement c++03 compatible fix for changeset 7a43af1a33 2019-10-09 16:00:57 +02:00
Gael Guennebaud
196de2efe3 Explicitly bypass resize and memmoves when there is already the exact right number of elements available. 2019-10-08 21:44:33 +02:00
Gael Guennebaud
36da231a41 Disable an expected warning in unit test 2019-10-08 16:28:14 +02:00
Gael Guennebaud
d1def335dc fix one more possible conflicts with real/imag 2019-10-08 16:19:10 +02:00
Gael Guennebaud
87427d2eaa PR 719: fix real/imag namespace conflict 2019-10-08 09:15:17 +02:00
Gael Guennebaud
7a43af1a33 Fix compilation of FFTW unit test 2019-10-08 08:58:35 +02:00
Eugene Zhulenev
f74ab8cb8d Add block evaluation to TensorEvalTo and fix few small bugs 2019-10-07 15:34:26 -07:00
Brian Zhao
3afb640b56 Fixing incorrect size in Tensor documentation. 2019-10-04 21:30:35 -07:00
Rasmus Munk Larsen
20c4a9118f Use "pdiv" rather than operator/ to support packet types. 2019-10-04 16:54:03 -07:00
Rasmus Larsen
d1dd51cb5f Merged in ezhulenev/eigen-01 (pull request PR-723)
Add block evaluation to TensorReshaping/TensorCasting/TensorPadding/TensorSelect

Approved-by: Rasmus Larsen <rmlarsen@google.com>
2019-10-04 17:19:13 +00:00
Eugene Zhulenev
98bdd7252e Fix compilation warnings and errors with clang in TensorBlockV2 code and tests 2019-10-04 10:15:33 -07:00
Rasmus Munk Larsen
fab4e3a753 Address comments on Chebyshev evaluation code:
1. Use pmadd when possible.
2. Add casts to avoid c++03 warnings.
2019-10-02 12:48:17 -07:00
Eugene Zhulenev
60ae24ee1a Add block evaluation to TensorReshaping/TensorCasting/TensorPadding/TensorSelect 2019-10-02 12:44:06 -07:00
Eugene Zhulenev
6e40454a6e Add beta to TensorContractionKernel and make memset optional 2019-10-02 11:06:02 -07:00
Rasmus Munk Larsen
bd0fac456f Prevent infinite loop in the nvcc compiler while unrolling the recurrent templates for Chebyshev polynomial evaluation. 2019-10-01 13:15:30 -07:00
Gael Guennebaud
9549ba8313 Fix perf issue in SimplicialLDLT::solve for complexes (again, m_diag is real) 2019-10-01 12:54:25 +02:00
Gael Guennebaud
c8b2c603b0 Fix speed issue with SimplicialLDLT for complexes: the diagonal is real! 2019-09-30 16:14:34 +02:00
Rasmus Munk Larsen
13ef08e5ac Move implementation of vectorized error function erf() to SpecialFunctionsImpl.h. 2019-09-27 13:56:04 -07:00
Eugene Zhulenev
7c8bc0d928 Fix cxx11_tensor_block_io test 2019-09-25 11:48:11 -07:00
Eugene Zhulenev
0c845e28c9 Fix erf in c++03 2019-09-25 11:31:45 -07:00
Eugene Zhulenev
71d5bedf72 Fix compilation warnings and errors with clang in TensorBlockV2 2019-09-25 11:25:22 -07:00
Deven Desai
5e186b1987 Fix for the HIP build+test errors.
The errors were introduced by this commit : d38e6fbc27


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
2019-09-25 15:39:13 +00:00
Eugene Zhulenev
f35b9ab510 Fix a bug in a packed block type in TensorContractionThreadPool 2019-09-24 16:54:36 -07:00
Rasmus Larsen
d38e6fbc27 Merged in rmlarsen/eigen (pull request PR-704)
Add generic PacketMath implementation of the Error Function (erf).
2019-09-24 23:40:29 +00:00
Rasmus Munk Larsen
591a554c68 Add TODO to cleanup FMA cost modelling. 2019-09-24 16:39:25 -07:00
Eugene Zhulenev
c64396b4c6 Choose TensorBlock StridedLinearCopy type statically 2019-09-24 16:04:29 -07:00
Eugene Zhulenev
c97b208468 Add new TensorBlock api implementation + tests 2019-09-24 15:17:35 -07:00
Eugene Zhulenev
ef9dfee7bd Tensor block evaluation V2 support for unary/binary/broadcsting 2019-09-24 12:52:45 -07:00
Christoph Hertzberg
efd9867ff0 bug #1746: Removed implementation of standard copy-constructor and standard copy-assign-operator from PermutationMatrix and Transpositions to allow malloc-less std::move. Added unit-test to rvalue_types 2019-09-24 11:09:58 +02:00
Christoph Hertzberg
e4c1b3c1d2 Fix implicit conversion warnings and use pnegate to negate packets 2019-09-23 16:07:43 +02:00
Christoph Hertzberg
ba0736fa8e Fix (or mask away) conversion warnings introduced in 553caeb6a3
.
2019-09-23 15:58:05 +02:00
Rasmus Munk Larsen
1d5af0693c Add support for asynchronous evaluation of tensor casting expressions. 2019-09-19 13:54:49 -07:00
Rasmus Munk Larsen
6de5ed08d8 Add generic PacketMath implementation of the Error Function (erf). 2019-09-19 12:48:30 -07:00
Rasmus Munk Larsen
28b6786498 Fix build on setups without AVX512DQ. 2019-09-19 12:36:09 -07:00
Deven Desai
e02d429637 Fix for the HIP build+test errors.
The errors were introduced by this commit : 6e215cf109


The fix is switching to using ::<math_func> instead std::<math_func> when compiling for GPU
2019-09-18 18:44:20 +00:00
Srinivas Vasudevan
df0816b71f Merging eigen/eigen. 2019-09-16 19:33:29 -04:00
Srinivas Vasudevan
6e215cf109 Add Bessel functions to SpecialFunctions.
- 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
2019-09-14 12:16:47 -04:00
Eugene Zhulenev
7c73296849 Revert accidental change to GCC diagnostics 2019-09-13 14:30:58 -07:00
Eugene Zhulenev
bf8866b466 Fix maybe-unitialized warnings in TensorContractionThreadPool 2019-09-13 14:29:55 -07:00
Eugene Zhulenev
553caeb6a3 Use ThreadLocal container in TensorContractionThreadPool 2019-09-13 12:14:44 -07:00
Srinivas Vasudevan
facdec5aa7 Add packetized versions of i0e and i1e special functions.
- 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.
2019-09-11 18:34:02 -07:00
Srinivas Vasudevan
b052ec6992 Merged eigen/eigen into default 2019-09-11 18:01:54 -07:00
Deven Desai
cdb377d0cb Fix for the HIP build+test errors introduced by the ndtri support.
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!)
2019-09-06 16:03:49 +00:00
Gael Guennebaud
747c6a51ca bug #1736: fix compilation issue with A(all,{1,2}).col(j) by implementing true compile-time "if" for block_evaluator<>::coeff(i)/coeffRef(i) 2019-09-11 15:40:07 +02:00