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)
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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.
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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
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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.
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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
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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.
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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💯
/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
Christoph Hertzberg
c21771ac04
Use double-braces initialization (as everywhere else in the test-suite).
2019-12-19 19:20:48 +01:00
Christoph Hertzberg
a3273aeff8
Fix trivial shadow warning
2019-12-19 19:13:11 +01:00
Eugene Zhulenev
7a65219a2e
Fix TensorPadding bug in squeezed reads from inner dimension
2019-12-19 05:43:57 +00:00
Eugene Zhulenev
73e55525e5
Return const data pointer from TensorRef evaluator.data()
2019-12-18 23:19:36 +00:00
Eugene Zhulenev
ae07801dd8
Tensor block evaluation cost model
2019-12-18 20:07:00 +00:00
Jeff Daily
de07c4d1c2
fix compilation due to new HIP scalar accessor
2019-12-17 20:27:30 +00:00
