Commit Graph

1365 Commits

Author SHA1 Message Date
Rasmus Munk Larsen
71c85df4c1 Clean up the Tensor header and get rid of the EIGEN_SLEEP macro. 2020-12-02 11:04:04 -08:00
Antonio Sanchez
17268b155d Add bit_cast for half/bfloat to/from uint16_t, fix TensorRandom
The existing `TensorRandom.h` implementation makes the assumption that
`half` (`bfloat16`) has a `uint16_t` member `x` (`value`), which is not
always true. This currently fails on arm64, where `x` has type `__fp16`.
Added `bit_cast` specializations to allow casting to/from `uint16_t`
for both `half` and `bfloat16`.  Also added tests in
`half_float`, `bfloat16_float`, and `cxx11_tensor_random` to catch
these errors in the future.
2020-11-18 20:32:35 +00:00
Antonio Sanchez
3669498f5a Fix rule-of-3 for the Tensor module.
Adds copy constructors to Tensor ops, inherits assignment operators from
`TensorBase`.

Addresses #1863
2020-11-18 18:14:53 +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
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
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
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
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
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
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
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
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
Eugene Zhulenev
b9362fb8f7 Convert StridedLinearBufferCopy::Kind to enum class 2020-01-13 11:43:24 -08:00
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
dde279f57d Hide recursive meta templates from Doxygen 2019-12-19 19:47:23 +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
Eugene Zhulenev
788bef6ab5 Reduce block evaluation overhead for small tensor expressions 2019-12-17 19:06:14 +00:00
Eugene Zhulenev
381f8f3139 Initialize non-trivially constructible types when allocating a temp buffer. 2019-12-12 01:31:30 +00:00
Eugene Zhulenev
64272c7f40 Squeeze reads from two inner dimensions in TensorPadding 2019-12-11 16:54:51 -08:00
Eugene Zhulenev
963ba1015b Add back accidentally deleted default constructor to TensorExecutorTilingContext. 2019-12-11 18:47:55 +00:00
Eugene Zhulenev
c9220c035f Remove block memory allocation required by removed block evaluation API 2019-12-10 17:15:55 -08:00
Eugene Zhulenev
1c879eb010 Remove V2 suffix from TensorBlock 2019-12-10 15:40:23 -08:00
Eugene Zhulenev
dbca11e880 Remove TensorBlock.h and old TensorBlock/BlockMapper 2019-12-10 14:31:44 -08:00
Deven Desai
c49f0d851a Fix for HIP breakage detected on 191210
The following commit introduces compile errors when running eigen with hipcc

2918f85ba9

hipcc errors out because it requies the device attribute on the methods within the TensorBlockV2ResourceRequirements struct instroduced by the commit above. The fix is to add the device attribute to those methods
2019-12-10 22:14:05 +00:00
Eugene Zhulenev
2918f85ba9 Do not use std::vector in getResourceRequirements 2019-12-09 16:19:55 -08:00
Artem Belevich
8056a05b54 Undo the block size change.
.z *is* used by the EigenContractionKernelInternal().
2019-12-09 11:10:29 -08:00
Eugene Zhulenev
dbb703d44e Add async evaluation support to TensorSelectOp 2019-12-09 18:36:13 +00:00
Eugene Zhulenev
bb7ccac3af Add recursive work splitting to EvalShardedByInnerDimContext 2019-12-05 14:51:49 -08:00
Artem Belevich
25230d1862 Improve performance of contraction kernels
* Force-inline implementations. They pass around pointers to shared memory
  blocks. Without inlining compiler must operate via generic pointers.
  Inlining allows compiler to detect that we're operating on shared memory
  which allows generation of substantially faster code.

* Fixed a long-standing typo which resulted in launching 8x more kernels
  than we needed (.z dimension of the block is unused by the kernel).
2019-12-05 12:48:34 -08:00