Commit Graph

2863 Commits

Author SHA1 Message Date
Antonio Sanchez
6961468915 Address issues with openglsupport test.
The existing test fails on several systems due to GL runtime version mismatches,
the use of deprecated features, and memory errors due to improper use of GLUT.
The test was modified to:

- Run within a display function, allowing proper GLUT cleanup.
- Generate dynamic shaders with a supported GLSL version string and output variables.
- Report shader compilation errors.
- Check GL context version before launching version-specific tests.

Note that most of the existing `OpenGLSupport` module and tests rely on deprecated
features (e.g. fixed-function pipeline). The test was modified to allow it to
pass on various systems. We might want to consider removing the module or re-writing
it entirely to support modern OpenGL.  This is beyond the scope of this patch.

Testing of legacy GL (for platforms that support it) can be enabled by defining
`EIGEN_LEGACY_OPENGL`.  Otherwise, the test will try to create a modern context.

Tested on
- MacBook Air (2019), macOS Catalina 10.15.7 (OpenGL 2.1, 4.1)
- Debian 10.6, NVidia Quadro K1200 (OpenGL 3.1, 3.3)
2020-11-11 15:54:43 -08:00
Deven Desai
9d11e2c03e CMakefile update for ROCm 4.0
Starting with ROCm 4.0, the `hipconfig --platform` command will return `amd` (prior return value was `hcc`). Updating the CMakeLists.txt files in the test dirs to account for this change.
2020-10-29 18:06:31 +00:00
mehdi-goli
a725a3233c [SYCL clean up the code] : removing exrta #pragma unroll in SYCL which was causing issues in embeded systems 2020-10-28 08:34:49 +00:00
Rasmus Munk Larsen
274ef12b61 Remove leftover debug print statement in cxx11_tensor_expr.cpp 2020-10-14 22:59:51 +00:00
Rasmus Munk Larsen
61fc78bbda Get rid of nested template specialization in TensorReductionGpu.h, which was broken by c6953f799b. 2020-10-13 23:53:11 +00:00
Rasmus Munk Larsen
c6953f799b Add packet generic ops predux_fmin, predux_fmin_nan, predux_fmax, and predux_fmax_nan that implement reductions with PropagateNaN, and PropagateNumbers semantics. Add (slow) generic implementations for most reductions. 2020-10-13 21:48:31 +00:00
David Tellenbach
8f8d77b516 Add EIGEN prefix for HAS_LGAMMA_R 2020-10-08 18:32:19 +02:00
Eugene Zhulenev
2279f2c62f Use lgamma_r if it is available (update check for glibc 2.19+) 2020-10-08 00:26:45 +00:00
Rasmus Munk Larsen
b431024404 Don't make assumptions about NaN-propagation for pmin/pmax - it various across platforms.
Change test to only test for NaN-propagation for pfmin/pfmax.
2020-10-07 19:05:18 +00:00
Zhuyie
e4b24e7fb2 Fix Eigen::ThreadPool::CurrentThreadId returning wrong thread id when EIGEN_AVOID_THREAD_LOCAL and NDEBUG are defined 2020-09-25 09:36:43 +00:00
Rasmus Munk Larsen
e55182ac09 Get rid of initialization logic for blueNorm by making the computed constants static const or constexpr.
Move macro definition EIGEN_CONSTEXPR to Core and make all methods in NumTraits constexpr when EIGEN_HASH_CONSTEXPR is 1.
2020-09-18 17:38:58 +00:00
Deven Desai
603e213d13 Fixing a CUDA / P100 regression introduced by PR 181
PR 181 ( https://gitlab.com/libeigen/eigen/-/merge_requests/181 ) adds `__launch_bounds__(1024)` attribute to GPU kernels, that did not have that attribute explicitly specified.

That PR seems to cause regressions on the CUDA platform. This PR/commit makes the changes in PR 181, to be applicable for HIP only
2020-08-20 00:29:57 +00:00
David Tellenbach
d4a727d092 Disable min/max NaN propagation in test cxx11_tensor_expr
The current pmin/pmax implementation for Arm Neon propagate NaNs
differently than std::min/std::max.

See issue https://gitlab.com/libeigen/eigen/-/issues/1937
2020-08-14 16:16:27 +00:00
Deven Desai
46f8a18567 Adding an explicit launch_bounds(1024) attribute for GPU kernels.
Starting with ROCm 3.5, the HIP compiler will change from HCC to hip-clang.

This compiler change introduce a change in the default value of the `__launch_bounds__` attribute associated with a GPU kernel. (default value means the value assumed by the compiler as the `__launch_bounds attribute__` value, when it is not explicitly specified by the user)

Currently (i.e. for HIP with ROCm 3.3 and older), the default value is 1024. That changes to 256 with ROCm 3.5 (i.e. hip-clang compiler). As a consequence of this change, if a GPU kernel with a `__luanch_bounds__` attribute of 256 is launched at runtime with a threads_per_block value > 256, it leads to a runtime error. This is leading to a couple of Eigen unit test failures with ROCm 3.5.

This commit adds an explicit `__launch_bounds(1024)__` attribute to every GPU kernel that currently does not have it explicitly specified (and hence will end up getting the default value of 256 with the change to hip-clang)
2020-08-05 01:46:34 +00:00
Rasmus Munk Larsen
b92206676c Inherit alignment trait from argument in TensorBroadcasting to avoid segfault when the argument is unaligned. 2020-07-28 19:19:37 +00:00
Rasmus Munk Larsen
ac2eca6b11 Update tensor reduction test to avoid undefined division of bfloat16 by int. 2020-07-22 00:35:51 +00:00
Antonio Sanchez
9cb8771e9c Fix tensor casts for large packets and casts to/from std::complex
The original tensor casts were only defined for
`SrcCoeffRatio`:`TgtCoeffRatio` 1:1, 1:2, 2:1, 4:1. Here we add the
missing 1:N and 8:1.

We also add casting `Eigen::half` to/from `std::complex<T>`, which
was missing to make it consistent with `Eigen:bfloat16`, and
generalize the overload to work for any complex type.

Tests were added to `basicstuff`, `packetmath`, and
`cxx11_tensor_casts` to test all cast configurations.
2020-06-30 18:53:55 +00:00
Teng Lu
386d809bde Support BFloat16 in Eigen 2020-06-20 19:16:24 +00:00
Ilya Tokar
231ce21535 Run two independent chains, when reducing tensors.
Running two chains exposes more instruction level parallelism,
by allowing to execute both chains at the same time.

Results are a bit noisy, but for medium length we almost hit
theoretical upper bound of 2x.

BM_fullReduction_16T/3        [using 16 threads]       17.3ns ±11%        17.4ns ± 9%        ~           (p=0.178 n=18+19)
BM_fullReduction_16T/4        [using 16 threads]       17.6ns ±17%        17.0ns ±18%        ~           (p=0.835 n=20+19)
BM_fullReduction_16T/7        [using 16 threads]       18.9ns ±12%        18.2ns ±10%        ~           (p=0.756 n=20+18)
BM_fullReduction_16T/8        [using 16 threads]       19.8ns ±13%        19.4ns ±21%        ~           (p=0.512 n=20+20)
BM_fullReduction_16T/10       [using 16 threads]       23.5ns ±15%        20.8ns ±24%     -11.37%        (p=0.000 n=20+19)
BM_fullReduction_16T/15       [using 16 threads]       35.8ns ±21%        26.9ns ±17%     -24.76%        (p=0.000 n=20+19)
BM_fullReduction_16T/16       [using 16 threads]       38.7ns ±22%        27.7ns ±18%     -28.40%        (p=0.000 n=20+19)
BM_fullReduction_16T/31       [using 16 threads]        146ns ±17%          74ns ±11%     -49.05%        (p=0.000 n=20+18)
BM_fullReduction_16T/32       [using 16 threads]        154ns ±19%          84ns ±30%     -45.79%        (p=0.000 n=20+19)
BM_fullReduction_16T/64       [using 16 threads]        603ns ± 8%         308ns ±12%     -48.94%        (p=0.000 n=17+17)
BM_fullReduction_16T/128      [using 16 threads]       2.44µs ±13%        1.22µs ± 1%     -50.29%        (p=0.000 n=17+17)
BM_fullReduction_16T/256      [using 16 threads]       9.84µs ±14%        5.13µs ±30%     -47.82%        (p=0.000 n=19+19)
BM_fullReduction_16T/512      [using 16 threads]       78.0µs ± 9%        56.1µs ±17%     -28.02%        (p=0.000 n=18+20)
BM_fullReduction_16T/1k       [using 16 threads]        325µs ± 5%         263µs ± 4%     -19.00%        (p=0.000 n=20+16)
BM_fullReduction_16T/2k       [using 16 threads]       1.09ms ± 3%        0.99ms ± 1%      -9.04%        (p=0.000 n=20+20)
BM_fullReduction_16T/4k       [using 16 threads]       7.66ms ± 3%        7.57ms ± 3%      -1.24%        (p=0.017 n=20+20)
BM_fullReduction_16T/10k      [using 16 threads]       65.3ms ± 4%        65.0ms ± 3%        ~           (p=0.718 n=20+20)
2020-06-16 15:55:11 -04:00
Antonio Sanchez
a7d2552af8 Remove HasCast and fix packetmath cast tests.
The use of the `packet_traits<>::HasCast` field is currently inconsistent with
`type_casting_traits<>`, and is unused apart from within
`test/packetmath.cpp`. In addition, those packetmath cast tests do not
currently reflect how casts are performed in practice: they ignore the
`SrcCoeffRatio` and `TgtCoeffRatio` fields, assuming a 1:1 ratio.

Here we remove the unsed `HasCast`, and modify the packet cast tests to
better reflect their usage.
2020-06-11 17:26:56 +00:00
Thales Sabino
1fcaaf460f Update FindComputeCpp.cmake to fix build problems on Windows
- Use standard types in SYCL/PacketMath.h to avoid compilation problems on Windows
- Add EIGEN_HAS_CONSTEXPR to cxx11_tensor_argmax_sycl.cpp to fix build problems on Windows
2020-06-05 20:51:20 +00:00
Antonio Sánchez
8719b9c5bc Disable test for 32-bit systems (e.g. ARM, i386)
Both i386 and 32-bit ARM do not define __uint128_t. On most systems, if
__uint128_t is defined, then so is the macro __SIZEOF_INT128__.

https://stackoverflow.com/questions/18531782/how-to-know-if-uint128-t-is-defined1
2020-05-28 17:40:15 +00:00
mehdi-goli
d3e81db6c5 Eigen moved the scanLauncehr function inside the internal namespace.
This commit applies the following changes:
    - Moving the `scamLauncher` specialization inside internal namespace to fix compiler crash on TensorScan for SYCL backend.
    - Replacing  `SYCL/sycl.hpp` to `CL/sycl.hpp` in order to follow SYCL 1.2.1 standard.
    - minor fixes: commenting out an unused variable to avoid compiler warnings.
2020-05-11 16:10:33 +01:00
Rasmus Munk Larsen
2fd8a5a08f Add parallelization of TensorScanOp for types without packet ops.
Clean up the code a bit and do a few micro-optimizations to improve performance for small tensors.

Benchmark numbers for Tensor<uint32_t>:

name                                                       old time/op             new time/op             delta
BM_cumSumRowReduction_1T/8   [using 1 threads]             76.5ns ± 0%             61.3ns ± 4%    -19.80%          (p=0.008 n=5+5)
BM_cumSumRowReduction_1T/64  [using 1 threads]             2.47µs ± 1%             2.40µs ± 1%     -2.77%          (p=0.008 n=5+5)
BM_cumSumRowReduction_1T/256 [using 1 threads]             39.8µs ± 0%             39.6µs ± 0%     -0.60%          (p=0.008 n=5+5)
BM_cumSumRowReduction_1T/4k  [using 1 threads]             13.9ms ± 0%             13.4ms ± 1%     -4.19%          (p=0.008 n=5+5)
BM_cumSumRowReduction_2T/8   [using 2 threads]             76.8ns ± 0%             59.1ns ± 0%    -23.09%          (p=0.016 n=5+4)
BM_cumSumRowReduction_2T/64  [using 2 threads]             2.47µs ± 1%             2.41µs ± 1%     -2.53%          (p=0.008 n=5+5)
BM_cumSumRowReduction_2T/256 [using 2 threads]             39.8µs ± 0%             34.7µs ± 6%    -12.74%          (p=0.008 n=5+5)
BM_cumSumRowReduction_2T/4k  [using 2 threads]             13.8ms ± 1%              7.2ms ± 6%    -47.74%          (p=0.008 n=5+5)
BM_cumSumRowReduction_8T/8   [using 8 threads]             76.4ns ± 0%             61.8ns ± 3%    -19.02%          (p=0.008 n=5+5)
BM_cumSumRowReduction_8T/64  [using 8 threads]             2.47µs ± 1%             2.40µs ± 1%     -2.84%          (p=0.008 n=5+5)
BM_cumSumRowReduction_8T/256 [using 8 threads]             39.8µs ± 0%             28.3µs ±11%    -28.75%          (p=0.008 n=5+5)
BM_cumSumRowReduction_8T/4k  [using 8 threads]             13.8ms ± 0%              2.7ms ± 5%    -80.39%          (p=0.008 n=5+5)
BM_cumSumColReduction_1T/8   [using 1 threads]             59.1ns ± 0%             80.3ns ± 0%    +35.94%          (p=0.029 n=4+4)
BM_cumSumColReduction_1T/64  [using 1 threads]             3.06µs ± 0%             3.08µs ± 1%       ~             (p=0.114 n=4+4)
BM_cumSumColReduction_1T/256 [using 1 threads]              175µs ± 0%              176µs ± 0%       ~             (p=0.190 n=4+5)
BM_cumSumColReduction_1T/4k  [using 1 threads]              824ms ± 1%              844ms ± 1%     +2.37%          (p=0.008 n=5+5)
BM_cumSumColReduction_2T/8   [using 2 threads]             59.0ns ± 0%             90.7ns ± 0%    +53.74%          (p=0.029 n=4+4)
BM_cumSumColReduction_2T/64  [using 2 threads]             3.06µs ± 0%             3.10µs ± 0%     +1.08%          (p=0.016 n=4+5)
BM_cumSumColReduction_2T/256 [using 2 threads]              176µs ± 0%              189µs ±18%       ~             (p=0.151 n=5+5)
BM_cumSumColReduction_2T/4k  [using 2 threads]              836ms ± 2%              611ms ±14%    -26.92%          (p=0.008 n=5+5)
BM_cumSumColReduction_8T/8   [using 8 threads]             59.3ns ± 2%             90.6ns ± 0%    +52.79%          (p=0.008 n=5+5)
BM_cumSumColReduction_8T/64  [using 8 threads]             3.07µs ± 0%             3.10µs ± 0%     +0.99%          (p=0.016 n=5+4)
BM_cumSumColReduction_8T/256 [using 8 threads]              176µs ± 0%               80µs ±19%    -54.51%          (p=0.008 n=5+5)
BM_cumSumColReduction_8T/4k  [using 8 threads]              827ms ± 2%              180ms ±14%    -78.24%          (p=0.008 n=5+5)
2020-05-06 14:48:37 -07:00
Rasmus Munk Larsen
0e59f786e1 Fix accidental copy of loop variable. 2020-05-05 21:35:38 +00:00
Rasmus Munk Larsen
7b76c85daf Vectorize and parallelize TensorScanOp.
TensorScanOp is used in TensorFlow for a number of operations, such as cumulative logexp reduction and cumulative sum and product reductions.

The benchmarks numbers below are for cumulative row- and column reductions of NxN matrices.

name                                                         old time/op             new time/op     delta
BM_cumSumRowReduction_1T/4    [using 1 threads ]             25.1ns ± 1%             35.2ns ± 1%    +40.45%
BM_cumSumRowReduction_1T/8    [using 1 threads ]             73.4ns ± 0%             82.7ns ± 3%    +12.74%
BM_cumSumRowReduction_1T/32   [using 1 threads ]              988ns ± 0%              832ns ± 0%    -15.77%
BM_cumSumRowReduction_1T/64   [using 1 threads ]             4.07µs ± 2%             3.47µs ± 0%    -14.70%
BM_cumSumRowReduction_1T/128  [using 1 threads ]             18.0µs ± 0%             16.8µs ± 0%     -6.58%
BM_cumSumRowReduction_1T/512  [using 1 threads ]              287µs ± 0%              281µs ± 0%     -2.22%
BM_cumSumRowReduction_1T/2k   [using 1 threads ]             4.78ms ± 1%             4.78ms ± 2%       ~
BM_cumSumRowReduction_1T/10k  [using 1 threads ]              117ms ± 1%              117ms ± 1%       ~
BM_cumSumRowReduction_8T/4    [using 8 threads ]             25.0ns ± 0%             35.2ns ± 0%    +40.82%
BM_cumSumRowReduction_8T/8    [using 8 threads ]             77.2ns ±16%             81.3ns ± 0%       ~
BM_cumSumRowReduction_8T/32   [using 8 threads ]              988ns ± 0%              833ns ± 0%    -15.67%
BM_cumSumRowReduction_8T/64   [using 8 threads ]             4.08µs ± 2%             3.47µs ± 0%    -14.95%
BM_cumSumRowReduction_8T/128  [using 8 threads ]             18.0µs ± 0%             17.3µs ±10%       ~
BM_cumSumRowReduction_8T/512  [using 8 threads ]              287µs ± 0%               58µs ± 6%    -79.92%
BM_cumSumRowReduction_8T/2k   [using 8 threads ]             4.79ms ± 1%             0.64ms ± 1%    -86.58%
BM_cumSumRowReduction_8T/10k  [using 8 threads ]              117ms ± 1%               18ms ± 6%    -84.50%

BM_cumSumColReduction_1T/4    [using 1 threads ]             23.9ns ± 0%             33.4ns ± 1%    +39.68%
BM_cumSumColReduction_1T/8    [using 1 threads ]             71.6ns ± 1%             49.1ns ± 3%    -31.40%
BM_cumSumColReduction_1T/32   [using 1 threads ]              973ns ± 0%              165ns ± 2%    -83.10%
BM_cumSumColReduction_1T/64   [using 1 threads ]             4.06µs ± 1%             0.57µs ± 1%    -85.94%
BM_cumSumColReduction_1T/128  [using 1 threads ]             33.4µs ± 1%              4.1µs ± 1%    -87.67%
BM_cumSumColReduction_1T/512  [using 1 threads ]             1.72ms ± 4%             0.21ms ± 5%    -87.91%
BM_cumSumColReduction_1T/2k   [using 1 threads ]              119ms ±53%               11ms ±35%    -90.42%
BM_cumSumColReduction_1T/10k  [using 1 threads ]              1.59s ±67%              0.35s ±49%    -77.96%
BM_cumSumColReduction_8T/4    [using 8 threads ]             23.8ns ± 0%             33.3ns ± 0%    +40.06%
BM_cumSumColReduction_8T/8    [using 8 threads ]             71.6ns ± 1%             49.2ns ± 5%    -31.33%
BM_cumSumColReduction_8T/32   [using 8 threads ]             1.01µs ±12%             0.17µs ± 3%    -82.93%
BM_cumSumColReduction_8T/64   [using 8 threads ]             4.15µs ± 4%             0.58µs ± 1%    -86.09%
BM_cumSumColReduction_8T/128  [using 8 threads ]             33.5µs ± 0%              4.1µs ± 4%    -87.65%
BM_cumSumColReduction_8T/512  [using 8 threads ]             1.71ms ± 3%             0.06ms ±16%    -96.21%
BM_cumSumColReduction_8T/2k   [using 8 threads ]             97.1ms ±14%              3.0ms ±23%    -96.88%
BM_cumSumColReduction_8T/10k  [using 8 threads ]              1.97s ± 8%              0.06s ± 2%    -96.74%
2020-05-05 00:19:43 +00:00
Rasmus Munk Larsen
ab773c7e91 Extend support for Packet16b:
* Add ptranspose<*,4> to support matmul and add unit test for Matrix<bool> * Matrix<bool>
* work around a bug in slicing of Tensor<bool>.
* Add tensor tests

This speeds up matmul for boolean matrices by about 10x

name                            old time/op             new time/op             delta
BM_MatMul<bool>/8                267ns ± 0%              479ns ± 0%  +79.25%          (p=0.008 n=5+5)
BM_MatMul<bool>/32              6.42µs ± 0%             0.87µs ± 0%  -86.50%          (p=0.008 n=5+5)
BM_MatMul<bool>/64              43.3µs ± 0%              5.9µs ± 0%  -86.42%          (p=0.008 n=5+5)
BM_MatMul<bool>/128              315µs ± 0%               44µs ± 0%  -85.98%          (p=0.008 n=5+5)
BM_MatMul<bool>/256             2.41ms ± 0%             0.34ms ± 0%  -85.68%          (p=0.008 n=5+5)
BM_MatMul<bool>/512             18.8ms ± 0%              2.7ms ± 0%  -85.53%          (p=0.008 n=5+5)
BM_MatMul<bool>/1k               149ms ± 0%               22ms ± 0%  -85.40%          (p=0.008 n=5+5)
2020-04-28 16:12:47 +00:00
Eugene Zhulenev
3c02fefec5 Add async evaluation support to TensorSlicingOp.
Device::memcpy is not async-safe and might lead to deadlocks. Always evaluate slice expression in async mode.
2020-04-22 19:55:01 +00:00
Rasmus Munk Larsen
2f6ddaa25c Add partial vectorization for matrices and tensors of bool. This speeds up boolean operations on Tensors by up to 25x.
Benchmark numbers for the logical and of two NxN tensors:

name                                               old time/op             new time/op             delta
BM_booleanAnd_1T/3   [using 1 threads]             14.6ns ± 0%             14.4ns ± 0%   -0.96%
BM_booleanAnd_1T/4   [using 1 threads]             20.5ns ±12%              9.0ns ± 0%  -56.07%
BM_booleanAnd_1T/7   [using 1 threads]             41.7ns ± 0%             10.5ns ± 0%  -74.87%
BM_booleanAnd_1T/8   [using 1 threads]             52.1ns ± 0%             10.1ns ± 0%  -80.59%
BM_booleanAnd_1T/10  [using 1 threads]             76.3ns ± 0%             13.8ns ± 0%  -81.87%
BM_booleanAnd_1T/15  [using 1 threads]              167ns ± 0%               16ns ± 0%  -90.45%
BM_booleanAnd_1T/16  [using 1 threads]              188ns ± 0%               16ns ± 0%  -91.57%
BM_booleanAnd_1T/31  [using 1 threads]              667ns ± 0%               34ns ± 0%  -94.83%
BM_booleanAnd_1T/32  [using 1 threads]              710ns ± 0%               35ns ± 0%  -95.01%
BM_booleanAnd_1T/64  [using 1 threads]             2.80µs ± 0%             0.11µs ± 0%  -95.93%
BM_booleanAnd_1T/128 [using 1 threads]             11.2µs ± 0%              0.4µs ± 0%  -96.11%
BM_booleanAnd_1T/256 [using 1 threads]             44.6µs ± 0%              2.5µs ± 0%  -94.31%
BM_booleanAnd_1T/512 [using 1 threads]              178µs ± 0%               10µs ± 0%  -94.35%
BM_booleanAnd_1T/1k  [using 1 threads]              717µs ± 0%               78µs ± 1%  -89.07%
BM_booleanAnd_1T/2k  [using 1 threads]             2.87ms ± 0%             0.31ms ± 1%  -89.08%
BM_booleanAnd_1T/4k  [using 1 threads]             11.7ms ± 0%              1.9ms ± 4%  -83.55%
BM_booleanAnd_1T/10k [using 1 threads]             70.3ms ± 0%             17.2ms ± 4%  -75.48%
2020-04-20 20:16:28 +00:00
Changming Sun
b1aa07a8d3 Fix a bug in TensorIndexList.h 2020-04-13 18:22:03 +00:00
jangsoopark
39142904cc Resolve C4346 when building eigen on windows 2020-04-08 14:55:39 +09:00
Aaron Franke
5c22c7a7de Make file formatting comply with POSIX and Unix standards
UTF-8, LF, no BOM, and newlines at the end of files
2020-03-23 18:09:02 +00:00
Deven Desai
7158ed4e0e Fixing HIP breakage caused by the recent commit that introduces Packet4h2 as the Eigen::Half packet type 2020-03-12 01:06:24 +00:00
Sami Kama
b733b8b680 remove duplicate pset1 for half and add some comments about why we need expose pmul/add/div/min/max on host 2020-03-10 20:28:43 +00:00
Cédric Hubert
98bfc5aaa8 Update MarketIO.h 2020-02-28 12:41:51 +00:00
Ilya Tokar
eb6cc29583 Avoid a division in NonBlockingThreadPool::Steal.
Looking at profiles we spend ~10-20% of Steal on simply computing
random % size. We can reduce random 32-bit int into [0, size) range with
a single multiplication and shift. This transformation is described in
https://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/
2020-02-14 16:02:57 -05:00
Eugene Zhulenev
f584bd9b30 Fail at compile time if default executor tries to use non-default device 2020-02-06 22:43:24 +00:00
Eugene Zhulenev
3fda850c46 Remove dead code from TensorReduction.h 2020-01-29 18:45:31 +00:00
Jeff Daily
b5df8cabd7 fix hip-clang compilation due to new HIP scalar accessor 2020-01-20 21:08:52 +00:00
Deven Desai
6d284bb1b7 Fix for HIP breakage - 200115. Adding a missing EIGEN_DEVICE_FUNC attr 2020-01-16 00:51:43 +00:00
Srinivas Vasudevan
f6c6de5d63 Ensure Igamma does not NaN or Inf for large values. 2020-01-14 21:32:48 +00:00
Eugene Zhulenev
b9362fb8f7 Convert StridedLinearBufferCopy::Kind to enum class 2020-01-13 11:43:24 -08:00
Srinivas Vasudevan
2e099e8d8f Added special_packetmath test and tweaked bounds on tests.
Refactor shared packetmath code to header file.
(Squashed from PR !38)
2020-01-11 10:31:21 +00:00
Matthew Powelson
2ea5a715cf Properly initialize b vector in SplineFitting
InterpolateWithDerivative does not initialize the be vector correctly. This issue is discussed In stackoverflow question 48382939.
2020-01-09 21:29:04 +00:00
Ilya Tokar
19876ced76 Bug #1785: Introduce numext::rint.
This provides a new op that matches std::rint and previous behavior of
pround. Also adds corresponding unsupported/../Tensor op.
Performance is the same as e. g. floor (tested SSE/AVX).
2020-01-07 21:22:44 +00:00
mehdi-goli
d0ae052da4 [SYCL Backend]
* Adding Missing operations for vector comparison in SYCL. This caused compiler error for vector comparison when compiling SYCL
 * Fixing the compiler error for placement new in TensorForcedEval.h This caused compiler error when compiling SYCL backend
 * Reducing the SYCL warning by  removing the abort function inside the kernel
 * Adding Strong inline to functions inside SYCL interop.
2020-01-07 15:13:37 +00:00
Deven Desai
636e2bb3fa Fix for HIP breakage - 191220
The breakage was introduced by the following commit :

ae07801dd8

After the commit, HIPCC errors out on some tests with the following error

```
Building HIPCC object unsupported/test/CMakeFiles/cxx11_tensor_device_1.dir/cxx11_tensor_device_1_generated_cxx11_tensor_device.cu.o
In file included from /home/rocm-user/eigen/unsupported/test/cxx11_tensor_device.cu:17:
In file included from /home/rocm-user/eigen/unsupported/Eigen/CXX11/Tensor💯
/home/rocm-user/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h:129:12: error: no matching constructor for initialization of 'Eigen::internal::TensorBlockResourceRequirements'
    return {merge(lhs.shape_type, rhs.shape_type),           // shape_type
           ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/rocm-user/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h:75:8: note: candidate constructor (the implicit copy constructor) not viable: requires 1 argument, but 3 were provided
struct TensorBlockResourceRequirements {
       ^
/home/rocm-user/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h:75:8: note: candidate constructor (the implicit move constructor) not viable: requires 1 argument, but 3 were provided
/home/rocm-user/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h:75:8: note: candidate constructor (the implicit copy constructor) not viable: requires 5 arguments, but 3 were provided
/home/rocm-user/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h:75:8: note: candidate constructor (the implicit default constructor) not viable: requires 0 arguments, but 3 were provided
...
...
```

The fix is to explicitly decalre the (implicitly called) constructor as a device func
2019-12-20 21:28:00 +00:00
Christoph Hertzberg
1e9664b147 Bug #1796: Make matrix squareroot usable for Map and Ref types 2019-12-20 18:10:22 +01:00
Christoph Hertzberg
d86544d654 Reduce code duplication and avoid confusing Doxygen 2019-12-19 19:48:39 +01:00
Christoph Hertzberg
dde279f57d Hide recursive meta templates from Doxygen 2019-12-19 19:47:23 +01:00