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47 Commits

Author SHA1 Message Date
Deven Desai
ba506d5bd2 fix for a ROCm/HIP specificcompile errror introduced by a recent commit. 2019-06-22 00:06:05 +00:00
Rasmus Munk Larsen
c9394d7a0e Remove extra "one" in comment. 2019-06-20 16:23:19 -07:00
Rasmus Munk Larsen
b8f8dac4eb Update comment as suggested by tra@google.com. 2019-06-20 16:18:37 -07:00
Rasmus Munk Larsen
e5e63c2cad Fix grammar. 2019-06-20 16:03:59 -07:00
Rasmus Munk Larsen
302a404b7e Added comment explaining the surprising EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC clause. 2019-06-20 15:59:08 -07:00
Rasmus Munk Larsen
b5237f53b1 Fix CUDA build on Mac. 2019-06-20 15:44:14 -07:00
Rasmus Munk Larsen
988f24b730 Various fixes for packet ops. 
1. Fix buggy pcmp_eq and unit test for half types.
2. Add unit test for pselect and add specializations for SSE 4.1, AVX512, and half types.
3. Get rid of FIXME: Implement faster pnegate for half by XOR'ing with a sign bit mask.
 2019-06-20 11:47:49 -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
Deven Desai
2c38930161 fix for HIP build errors that were introduced by a commit earlier this week 2019-05-24 14:25:32 +00:00
Rasmus Munk Larsen
ab0a30e429 Make Eigen build with cuda 10 and clang. 2019-05-15 13:32:15 -07:00
Anuj Rawat
ad372084f5 Removing unused API to fix compile error in TensorFlow due to 
AVX512VL, AVX512BW usage
 2019-05-12 14:43:10 +00:00
Eugene Zhulenev
45b40d91ca Fix AVX512 & GCC 6.3 compilation 2019-05-07 16:44:55 -07:00
Eugene Zhulenev
e9f0eb8a5e Add masked_store_available to unpacket_traits 2019-05-02 14:52:58 -07:00
Eugene Zhulenev
96e30e936a Add masked pstoreu for Packet16h 2019-05-02 14:11:01 -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
Rasmus Munk Larsen
8450a6d519 Clean up half packet traits and add a few more missing packet ops. 2019-03-14 15:18:06 -07:00
Rasmus Munk Larsen
77f7d4a894 Clean up PacketMathHalf.h and add a few missing logical packet ops. 2019-03-11 17:51:16 -07:00
Rasmus Munk Larsen
4d7f317102 Add a few missing packet ops: cmp_eq for NEON. pfloor for GPU. 2019-02-21 13:32:13 -08:00
Gael Guennebaud
eb4c6bb22d Fix conflicts and merge 2019-01-30 15:57:08 +01:00
Rasmus Munk Larsen
2eccbaf3f7 Add missing logical packet ops for GPU and NEON. 2019-01-17 17:45:08 -08:00
Rasmus Munk Larsen
7401e2541d Fix compilation error for logical packet ops with older compilers. 2019-01-16 14:43:33 -08:00
Rasmus Munk Larsen
89c4001d6f Fix warnings in ptrue for complex and half types. 2019-01-11 14:10:57 -08:00
Rasmus Munk Larsen
df29511ac0 Fix merge. 2019-01-11 10:36:36 -08:00
Rasmus Munk Larsen
9396ace46b Merge. 2019-01-11 10:28:52 -08:00
Rasmus Munk Larsen
fcfced13ed Rename pones -> ptrue. Use _CMP_TRUE_UQ where appropriate. 2019-01-09 17:20:33 -08:00
Rasmus Munk Larsen
e15bb785ad Collapsed revision 
* Add packet up "pones". Write pnot(a) as pxor(pones(a), a).
* Collapsed revision
* Simplify a bit.
* Undo useless diffs.
* Fix typo.
 2019-01-09 16:34:23 -08:00
Rasmus Munk Larsen
8f04442526 Collapsed revision 
* Collapsed revision
* Add packet up "pones". Write pnot(a) as pxor(pones(a), a).
* Collapsed revision
* Simplify a bit.
* Undo useless diffs.
* Fix typo.
 2019-01-09 16:34:23 -08:00
Rasmus Munk Larsen
cb955df9a6 Add packet up "pones". Write pnot(a) as pxor(pones(a), a). 2019-01-09 16:17:08 -08:00
Rasmus Munk Larsen
055f0b73db Add support for pcmp_eq and pnot, including for complex types. 2019-01-07 16:53:36 -08:00
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
1ac2695ef7 bug #1636: fix compilation with some ABI versions. 2018-12-06 00:05:10 +01:00
Gael Guennebaud
69ace742be Several improvements regarding packet-bitwise operations: 
- add unit tests
- optimize their AVX512f implementation
- add missing implementations (half, Packet4f, ...)
 2018-11-30 15:56:08 +01:00
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
Christian von Schultz
4a40b3785d Collapsed revision (based on pull request PR-325) 
* Support compiling without IO streams

Add the preprocessor definition EIGEN_NO_IO which, if defined,
disables all use of the IO streams part of the standard library.
 2018-10-22 21:14:40 +02:00
Gael Guennebaud
e38d1ab4d1 Workaround increases required alignment warning 2018-09-20 17:07:33 +02:00
Deven Desai
c64fe9ea1f Updates to fix HIP-clang specific compile errors. 
Compiling the eigen unittests with hip-clang (HIP with clang as the underlying compiler instead of hcc or nvcc), results in compile errors. The changes in this commit fix those compile errors. The main change is to convert a few instances of "__device__" to "EIGEN_DEVICE_FUNC"
 2018-08-30 20:22:16 +00:00
luz.paz"
43fd42a33b Fix doxy and misc. typos 
Found via `codespell -q 3 -I ../eigen-word-whitelist.txt`
---
 Eigen/src/Core/ProductEvaluators.h |  4 ++--
 Eigen/src/Core/arch/GPU/Half.h     |  2 +-
 Eigen/src/Core/util/Memory.h       |  2 +-
 Eigen/src/Geometry/Hyperplane.h    |  2 +-
 Eigen/src/Geometry/Transform.h     |  2 +-
 Eigen/src/Geometry/Translation.h   | 12 ++++++------
 doc/PreprocessorDirectives.dox     |  2 +-
 doc/TutorialGeometry.dox           |  2 +-
 test/boostmultiprec.cpp            |  2 +-
 test/triangular.cpp                |  2 +-
 10 files changed, 16 insertions(+), 16 deletions(-)
 2018-08-01 21:34:47 -04:00
Jiandong Ruan
6dcd2642aa bug #1526 - CUDA compilation fails on CUDA 9.x SDK when arch is set to compute_60 and/or above 2018-09-08 12:05:33 -07:00
Gael Guennebaud
befaf83f5f bug #1590: fix collision with some system headers defining the macro FP32 2018-08-28 13:21:28 +02:00
Mehdi Goli
01358300d5 Creating separate SYCL required PR for uncontroversial files. 2018-08-03 16:59:15 +01:00
Deven Desai
876f392c39 Updates corresponding to the latest round of PR feedback 
The major changes are

1. Moving CUDA/PacketMath.h to GPU/PacketMath.h
2. Moving CUDA/MathFunctions.h to GPU/MathFunction.h
3. Moving CUDA/CudaSpecialFunctions.h to GPU/GpuSpecialFunctions.h
    The above three changes effectively enable the Eigen "Packet" layer for the HIP platform

4. Merging the "hip_basic" and "cuda_basic" unit tests into one ("gpu_basic")
5. Updating the "EIGEN_DEVICE_FUNC" marking in some places

The change has been tested on the HIP and CUDA platforms.
 2018-07-11 10:39:54 -04:00
Deven Desai
471cfe5ff7 renaming CUDA* to GPU* for some header files 2018-07-11 09:22:04 -04:00
Deven Desai
38807a2575 merging updates from upstream 2018-07-11 09:17:33 -04:00
Deven Desai
b6cc0961b1 updates based on PR feedback 
There are two major changes (and a few minor ones which are not listed here...see PR discussion for details)

1. Eigen::half implementations for HIP and CUDA have been merged.
This means that
- `CUDA/Half.h` and `HIP/hcc/Half.h` got merged to a new file `GPU/Half.h`
- `CUDA/PacketMathHalf.h` and `HIP/hcc/PacketMathHalf.h` got merged to a new file `GPU/PacketMathHalf.h`
- `CUDA/TypeCasting.h` and `HIP/hcc/TypeCasting.h` got merged to a new file `GPU/TypeCasting.h`

After this change the `HIP/hcc` directory only contains one file `math_constants.h`. That will go away too once that file becomes a part of the HIP install.

2. new macros EIGEN_GPUCC, EIGEN_GPU_COMPILE_PHASE and EIGEN_HAS_GPU_FP16 have been added and the code has been updated to use them where appropriate.
- `EIGEN_GPUCC` is the same as `(EIGEN_CUDACC || EIGEN_HIPCC)`
- `EIGEN_GPU_DEVICE_COMPILE` is the same as `(EIGEN_CUDA_ARCH || EIGEN_HIP_DEVICE_COMPILE)`
- `EIGEN_HAS_GPU_FP16` is the same as `(EIGEN_HAS_CUDA_FP16 or EIGEN_HAS_HIP_FP16)`
 2018-06-14 10:21:54 -04:00
Deven Desai
ba972fb6b4 moving Half headers from CUDA dir to GPU dir, removing the HIP versions 2018-06-13 12:26:18 -04:00