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1262 Commits

Author SHA1 Message Date
Gael Guennebaud
ea0d5dc956 bug #1741: fix C.noalias() = A*C; with C.innerStride()!=1 2019-09-10 16:25:24 +02:00
David Tellenbach
3031d57200 PR 621: Fix documentation of EIGEN_COMP_EMSCRIPTEN 2019-03-21 02:21:04 +01:00
Srinivas Vasudevan
e38dd48a27 PR 681: Add ndtri function, the inverse of the normal distribution function. 2019-08-12 19:26:29 -04:00
João P. L. de Carvalho
66d073c38e bug #1718: Add cast to successfully compile with clang on PowerPC 
Ignoring -Wc11-extensions warnings thrown by clang at Altivec/PacketMath.h
 2019-08-09 15:56:26 -06:00
Justin Carpentier
ffaf658ecd PR 655: Fix missing Eigen namespace in Macros 2019-06-05 09:51:59 +02:00
Mehdi Goli
16a56b2ddd [SYCL] This PR adds the minimum modifications to Eigen core required to run Eigen unsupported modules on devices supporting SYCL. 
* Adding SYCL memory model
* Enabling/Disabling SYCL  backend in Core
*  Supporting Vectorization
 2019-06-27 12:25:09 +01:00
Christoph Hertzberg
e0be7f30e1 bug #1724: Mask buggy warnings with g++-7 
(grafted from 427f2f66d6
)
 2019-06-14 14:57:46 +02:00
Rasmus Munk Larsen
6d432eae5d Make is_valid_index_type return false for float and double when EIGEN_HAS_TYPE_TRAITS is off. 2019-06-05 16:42:27 -07:00
Rasmus Munk Larsen
f715f6e816 Add workaround for choosing the right include files with FP16C support with clang. 2019-06-05 13:36:37 -07:00
Rasmus Munk Larsen
b08527b0c1 Clean up CUDA/NVCC version macros and their use in Eigen, and a few other CUDA build failures. 2019-05-31 15:26:06 -07:00
Rasmus Munk Larsen
3eb5ad0ed0 Enable support for F16C with Clang. The required intrinsics were added here: https://reviews.llvm.org/D16177 
and are part of LLVM 3.8.0.
 2019-05-20 17:19:20 -07:00
Rasmus Larsen
e92486b8c3 Merged in rmlarsen/eigen (pull request PR-643) 
Make Eigen build with cuda 10 and clang.

Approved-by: Justin Lebar <justin.lebar@gmail.com>
 2019-05-20 17:02:39 +00:00
Rasmus Munk Larsen
ab0a30e429 Make Eigen build with cuda 10 and clang. 2019-05-15 13:32:15 -07:00
Scott Ramsby
ff06ef7584 Eigen: Fix MSVC C++17 language standard detection logic 
To detect C++17 support, use _MSVC_LANG macro instead of _MSC_VER. _MSC_VER can indicate whether the current compiler version could support the C++17 language standard, but not whether that standard is actually selected (i.e. via /std:c++17).
See these web pages for more details:
https://devblogs.microsoft.com/cppblog/msvc-now-correctly-reports-__cplusplus/
https://docs.microsoft.com/en-us/cpp/preprocessor/predefined-macros
 2019-05-03 14:14:09 -07:00
Eugene Zhulenev
e9f0eb8a5e Add masked_store_available to unpacket_traits 2019-05-02 14:52:58 -07:00
Anuj Rawat
8c7a6feb8e Adding lowlevel APIs for optimized RHS packet load in TensorFlow 
SpatialConvolution

Low-level APIs are added in order to optimized packet load in gemm_pack_rhs
in TensorFlow SpatialConvolution. The optimization is for scenario when a
packet is split across 2 adjacent columns. In this case we read it as two
'partial' packets and then merge these into 1. Currently this only works for
Packet16f (AVX512) and Packet8f (AVX2). We plan to add this for other
packet types (such as Packet8d) also.

This optimization shows significant speedup in SpatialConvolution with
certain parameters. Some examples are below.

Benchmark parameters are specified as:
Batch size, Input dim, Depth, Num of filters, Filter dim

Speedup numbers are specified for number of threads 1, 2, 4, 8, 16.

AVX512:

Parameters                  | Speedup (Num of threads: 1, 2, 4, 8, 16)
----------------------------|------------------------------------------
128,   24x24,  3, 64,   5x5 |2.18X, 2.13X, 1.73X, 1.64X, 1.66X
128,   24x24,  1, 64,   8x8 |2.00X, 1.98X, 1.93X, 1.91X, 1.91X
 32,   24x24,  3, 64,   5x5 |2.26X, 2.14X, 2.17X, 2.22X, 2.33X
128,   24x24,  3, 64,   3x3 |1.51X, 1.45X, 1.45X, 1.67X, 1.57X
 32,   14x14, 24, 64,   5x5 |1.21X, 1.19X, 1.16X, 1.70X, 1.17X
128, 128x128,  3, 96, 11x11 |2.17X, 2.18X, 2.19X, 2.20X, 2.18X

AVX2:

Parameters                  | Speedup (Num of threads: 1, 2, 4, 8, 16)
----------------------------|------------------------------------------
128,   24x24,  3, 64,   5x5 | 1.66X, 1.65X, 1.61X, 1.56X, 1.49X
 32,   24x24,  3, 64,   5x5 | 1.71X, 1.63X, 1.77X, 1.58X, 1.68X
128,   24x24,  1, 64,   5x5 | 1.44X, 1.40X, 1.38X, 1.37X, 1.33X
128,   24x24,  3, 64,   3x3 | 1.68X, 1.63X, 1.58X, 1.56X, 1.62X
128, 128x128,  3, 96, 11x11 | 1.36X, 1.36X, 1.37X, 1.37X, 1.37X

In the higher level benchmark cifar10, we observe a runtime improvement
of around 6% for AVX512 on Intel Skylake server (8 cores).

On lower level PackRhs micro-benchmarks specified in TensorFlow
tensorflow/core/kernels/eigen_spatial_convolutions_test.cc, we observe
the following runtime numbers:

AVX512:

Parameters                                                     | Runtime without patch (ns) | Runtime with patch (ns) | Speedup
---------------------------------------------------------------|----------------------------|-------------------------|---------
BM_RHS_NAME(PackRhs, 128, 24, 24, 3, 64, 5, 5, 1, 1, 256, 56)  |  41350                     | 15073                   | 2.74X
BM_RHS_NAME(PackRhs, 32, 64, 64, 32, 64, 5, 5, 1, 1, 256, 56)  |   7277                     |  7341                   | 0.99X
BM_RHS_NAME(PackRhs, 32, 64, 64, 32, 64, 5, 5, 2, 2, 256, 56)  |   8675                     |  8681                   | 1.00X
BM_RHS_NAME(PackRhs, 32, 64, 64, 30, 64, 5, 5, 1, 1, 256, 56)  |  24155                     | 16079                   | 1.50X
BM_RHS_NAME(PackRhs, 32, 64, 64, 30, 64, 5, 5, 2, 2, 256, 56)  |  25052                     | 17152                   | 1.46X
BM_RHS_NAME(PackRhs, 32, 256, 256, 4, 16, 8, 8, 1, 1, 256, 56) |  18269                     | 18345                   | 1.00X
BM_RHS_NAME(PackRhs, 32, 256, 256, 4, 16, 8, 8, 2, 4, 256, 56) |  19468                     | 19872                   | 0.98X
BM_RHS_NAME(PackRhs, 32, 64, 64, 4, 16, 3, 3, 1, 1, 36, 432)   | 156060                     | 42432                   | 3.68X
BM_RHS_NAME(PackRhs, 32, 64, 64, 4, 16, 3, 3, 2, 2, 36, 432)   | 132701                     | 36944                   | 3.59X

AVX2:

Parameters                                                     | Runtime without patch (ns) | Runtime with patch (ns) | Speedup
---------------------------------------------------------------|----------------------------|-------------------------|---------
BM_RHS_NAME(PackRhs, 128, 24, 24, 3, 64, 5, 5, 1, 1, 256, 56)  | 26233                      | 12393                   | 2.12X
BM_RHS_NAME(PackRhs, 32, 64, 64, 32, 64, 5, 5, 1, 1, 256, 56)  |  6091                      |  6062                   | 1.00X
BM_RHS_NAME(PackRhs, 32, 64, 64, 32, 64, 5, 5, 2, 2, 256, 56)  |  7427                      |  7408                   | 1.00X
BM_RHS_NAME(PackRhs, 32, 64, 64, 30, 64, 5, 5, 1, 1, 256, 56)  | 23453                      | 20826                   | 1.13X
BM_RHS_NAME(PackRhs, 32, 64, 64, 30, 64, 5, 5, 2, 2, 256, 56)  | 23167                      | 22091                   | 1.09X
BM_RHS_NAME(PackRhs, 32, 256, 256, 4, 16, 8, 8, 1, 1, 256, 56) | 23422                      | 23682                   | 0.99X
BM_RHS_NAME(PackRhs, 32, 256, 256, 4, 16, 8, 8, 2, 4, 256, 56) | 23165                      | 23663                   | 0.98X
BM_RHS_NAME(PackRhs, 32, 64, 64, 4, 16, 3, 3, 1, 1, 36, 432)   | 72689                      | 44969                   | 1.62X
BM_RHS_NAME(PackRhs, 32, 64, 64, 4, 16, 3, 3, 2, 2, 36, 432)   | 61732                      | 39779                   | 1.55X

All benchmarks on Intel Skylake server with 8 cores.
 2019-04-20 06:46:43 +00:00
Deven Desai
51e399fc15 updates requested in the PR feedback. Also droping coded within #ifdef EIGEN_HAS_OLD_HIP_FP16 2019-03-19 21:45:25 +00:00
Deven Desai
2dbea5510f Merged eigen/eigen into default 2019-03-19 16:52:38 -04:00
Sam Hasinoff
9ba81cf0ff Fully qualify Eigen::internal::aligned_free 
This helps avoids a conflict on certain Windows toolchains
(potentially due to some ADL name resolution bug) in the case
where aligned_free is defined in the global namespace. In any
case, tightening this up is harmless.
 2019-03-02 17:42:16 +00:00
Gael Guennebaud
edd413c184 bug #1409: make EIGEN_MAKE_ALIGNED_OPERATOR_NEW* macros empty in c++17 mode: 
- this helps clang 5 and 6 to support alignas in STL's containers.
 - this makes the public API of our (and users) classes cleaner
 2019-02-20 13:52:11 +01:00
Christoph Hertzberg
a1646fc960 Commas at the end of enumerator lists are not allowed in C++03 2019-02-19 14:32:25 +01:00
Gael Guennebaud
ab78cabd39 Add C++17 detection macro, and make sure throw(xpr) is not used if the compiler is in c++17 mode. 2019-02-19 14:04:35 +01:00
Gael Guennebaud
512b74aaa1 GEMM: catch all scalar-multiple variants when falling-back to a coeff-based product. 
Before only s*A*B was caught which was both inconsistent with GEMM, sub-optimal,
and could even lead to compilation-errors (https://stackoverflow.com/questions/54738495).
 2019-02-18 11:47:54 +01:00
Gael Guennebaud
83309068b4 bug #1680: improve MSVC inlining by declaring many triavial constructors and accessors as STRONG_INLINE. 2019-02-15 16:35:35 +01:00
Gael Guennebaud
559320745e bug #1678: Fix lack of __FMA__ macro on MSVC with AVX512 2019-02-15 10:30:28 +01:00
Gael Guennebaud
b3c4344a68 bug #1676: workaround GCC's bug in c++17 mode. 2019-02-07 15:21:35 +01:00
Gael Guennebaud
eb4c6bb22d Fix conflicts and merge 2019-01-30 15:57:08 +01:00
nluehr
92774f0275 Replace host_define.h with cuda_runtime_api.h 2019-01-18 16:10:09 -06:00
Patrick Peltzer
15e53d5d93 PR 567: makes all dense solvers inherit SoverBase (LU,Cholesky,QR,SVD). 
This changeset also includes:
 * add HouseholderSequence::conjugateIf
 * define int as the StorageIndex type for all dense solvers
 * dedicated unit tests, including assertion checking
 * _check_solve_assertion(): this method can be implemented in derived solver classes to implement custom checks
 * CompleteOrthogonalDecompositions: add applyZOnTheLeftInPlace, fix scalar type in applyZAdjointOnTheLeftInPlace(), add missing assertions
 * Cholesky: add missing assertions
 * FullPivHouseholderQR: Corrected Scalar type in _solve_impl()
 * BDCSVD: Unambiguous return type for ternary operator
 * SVDBase: Corrected Scalar type in _solve_impl()
 2019-01-17 01:17:39 +01:00
Gael Guennebaud
6cf7afa3d9 Typo 2019-01-15 11:04:37 +01:00
Gael Guennebaud
4356a55a61 PR 571: Implements an accurate argument reduction algorithm for huge inputs of sin/cos and call it instead of falling back to std::sin/std::cos. 
This makes both the small and huge argument cases faster because:
- for small inputs this removes the last pselect
- for large inputs only the reduction part follows a scalar path,
the rest use the same SIMD path as the small-argument case.
 2019-01-14 13:54:01 +01:00
Gael Guennebaud
9005f0111f Replace compiler's alignas/alignof extension by respective c++11 keywords when available. This also fix a compilation issue with gcc-4.7. 2019-01-11 17:10:54 +01:00
Gael Guennebaud
d812f411c3 bug #1654: fix compilation with cuda and no c++11 2019-01-09 18:00:05 +01:00
Gustavo Lima Chaves
e763fcd09e Introducing "vectorized" byte on unpacket_traits structs 
This is a preparation to a change on gebp_traits, where a new template
argument will be introduced to dictate the packet size, so it won't be
bound to the current/max packet size only anymore.

By having packet types defined early on gebp_traits, one has now to
act on packet types, not scalars anymore, for the enum values defined
on that class. One approach for reaching the vectorizable/size
properties one needs there could be getting the packet's scalar again
with unpacket_traits<>, then the size/Vectorizable enum entries from
packet_traits<>. It turns out guards like "#ifndef
EIGEN_VECTORIZE_AVX512" at AVX/PacketMath.h will hide smaller packet
variations of packet_traits<> for some types (and it makes sense to
keep that). In other words, one can't go back to the scalar and create
a new PacketType, as this will always lead to the maximum packet type
for the architecture.

The less costly/invasive solution for that, thus, is to add the
vectorizable info on every unpacket_traits struct as well.
 2018-12-19 14:24:44 -08:00
Gael Guennebaud
7b6d0ff1f6 Enable FMA with MSVC (through /arch:AVX2). To make this possible, I also has to turn the #warning regarding AVX512-FMA to a #error. 2018-12-07 15:14:50 +01:00
Gael Guennebaud
ae59a7652b bug #1638: add a warning if avx512 is enabled without SSE/AVX FMA 2018-12-07 09:23:28 +01:00
Rasmus Munk Larsen
47d8b741b2 #elif -> #else to fix GPU build. 2018-12-05 13:19:31 -08:00
Eugene Zhulenev
0bb15bb6d6 Update checks in ConfigureVectorization.h 2018-12-03 17:10:40 -08:00
Eugene Zhulenev
fd0fbfa9b5 Do not disable alignment with EIGEN_GPUCC 2018-12-03 15:54:10 -08:00
Patrik Huber
95566eeed4 Fix typos 2018-11-23 22:22:14 +00:00
Deven Desai
e7e6809e6b ROCm/HIP specfic fixes + updates 
1. Eigen/src/Core/arch/GPU/Half.h

   Updating the HIPCC implementation half so that it can declared as a __shared__ variable


2. Eigen/src/Core/util/Macros.h, Eigen/src/Core/util/Memory.h

   introducing a EIGEN_USE_STD(func) macro that calls
   - std::func be default
   - ::func when eigen is being compiled with HIPCC

   This change was requested in the previous HIP PR
   (https://bitbucket.org/eigen/eigen/pull-requests/518/pr-with-hip-specific-fixes-for-the-eigen/diff)


3. unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h

   Removing EIGEN_DEVICE_FUNC attribute from pure virtual methods as it is not supported by HIPCC


4. unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h

   Disabling the template specializations of InnerMostDimReducer as they run into HIPCC link errors
 2018-11-19 18:13:59 +00:00
Nikolaus Demmel
3dc0845046 Fix typo in comment on EIGEN_MAX_STATIC_ALIGN_BYTES 2018-11-14 18:11:30 +01:00
Gael Guennebaud
a368848473 Recent xcode versions does support EIGEN_HAS_STATIC_ARRAY_TEMPLATE 2018-11-09 10:33:17 +01:00
Eugene Zhulenev
d9392f9e55 Fix code format 2018-11-02 14:51:35 -07:00
Eugene Zhulenev
118520f04a Workaround nbcc+msvc compiler bug 2018-11-02 14:48:28 -07:00
Christoph Hertzberg
66b28e290d bug #1618: Use different power-of-2 check to avoid MSVC warning 2018-11-01 13:23:19 +01:00
Rasmus Munk Larsen
14054e217f Do not rely on the compiler generating __device__ functions for constexpr in Cuda (via EIGEN_CONSTEXPR_ARE_DEVICE_FUNC. This breaks several target in the TensorFlow Cuda build, e.g., 
INFO: From Compiling tensorflow/core/kernels/maxpooling_op_gpu.cu.cc:
/b/f/w/run/external/eigen_archive/Eigen/src/Core/arch/GPU/Half.h(197): error: calling a __host__ function("std::equal_to<float> ::operator () const") from a __global__ function("tensorflow::_NV_ANON_NAMESPACE::MaxPoolGradBackwardNoMaskNHWC< ::Eigen::half> ") is not allowed

/b/f/w/run/external/eigen_archive/Eigen/src/Core/arch/GPU/Half.h(197): error: identifier "std::equal_to<float> ::operator () const" is undefined in device code"

/b/f/w/run/external/eigen_archive/Eigen/src/Core/arch/GPU/Half.h(197): error: calling a __host__ function("std::equal_to<float> ::operator () const") from a __global__ function("tensorflow::_NV_ANON_NAMESPACE::MaxPoolGradBackwardNoMaskNCHW< ::Eigen::half> ") is not allowed

/b/f/w/run/external/eigen_archive/Eigen/src/Core/arch/GPU/Half.h(197): error: identifier "std::equal_to<float> ::operator () const" is undefined in device code

4 errors detected in the compilation of "/tmp/tmpxft_00000011_00000000-6_maxpooling_op_gpu.cu.cpp1.ii".
ERROR: /tmpfs/tensor_flow/tensorflow/core/kernels/BUILD:3753:1: output 'tensorflow/core/kernels/_objs/pooling_ops_gpu/maxpooling_op_gpu.cu.pic.o' was not created
ERROR: /tmpfs/tensor_flow/tensorflow/core/kernels/BUILD:3753:1: Couldn't build file tensorflow/core/kernels/_objs/pooling_ops_gpu/maxpooling_op_gpu.cu.pic.o: not all outputs were created or valid
 2018-10-22 16:18:24 -07:00
Rasmus Munk Larsen
d8f285852b Only set EIGEN_CONSTEXPR_ARE_DEVICE_FUNC for clang++ if cxx_relaxed_constexpr is available. 2018-10-18 16:55:02 -07:00
Eugene Zhulenev
2bf1a31d81 Use void type if stl-style iterators are not supported 2018-10-10 10:31:40 -07:00
Eugene Zhulenev
c0ca8a9fa3 Compile time detection for unimplemented stl-style iterators 2018-10-09 15:28:23 -07:00