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2743 Commits

Author SHA1 Message Date
Eugene Zhulenev
c64396b4c6 Choose TensorBlock StridedLinearCopy type statically 2019-09-24 16:04:29 -07:00
Eugene Zhulenev
c97b208468 Add new TensorBlock api implementation + tests 2019-09-24 15:17:35 -07:00
Eugene Zhulenev
ef9dfee7bd Tensor block evaluation V2 support for unary/binary/broadcsting 2019-09-24 12:52:45 -07:00
Christoph Hertzberg
e4c1b3c1d2 Fix implicit conversion warnings and use pnegate to negate packets 2019-09-23 16:07:43 +02:00
Christoph Hertzberg
ba0736fa8e Fix (or mask away) conversion warnings introduced in 553caeb6a3bb545aef895f8fc9f219be44679017 
.
 2019-09-23 15:58:05 +02:00
Rasmus Munk Larsen
1d5af0693c Add support for asynchronous evaluation of tensor casting expressions. 2019-09-19 13:54:49 -07:00
Srinivas Vasudevan
df0816b71f Merging eigen/eigen. 2019-09-16 19:33:29 -04:00
Srinivas Vasudevan
6e215cf109 Add Bessel functions to SpecialFunctions. 
- Split SpecialFunctions files in to a separate BesselFunctions file.

In particular add:
    - Modified bessel functions of the second kind k0, k1, k0e, k1e
    - Bessel functions of the first kind j0, j1
    - Bessel functions of the second kind y0, y1
 2019-09-14 12:16:47 -04:00
Eugene Zhulenev
bf8866b466 Fix maybe-unitialized warnings in TensorContractionThreadPool 2019-09-13 14:29:55 -07:00
Eugene Zhulenev
553caeb6a3 Use ThreadLocal container in TensorContractionThreadPool 2019-09-13 12:14:44 -07:00
Srinivas Vasudevan
facdec5aa7 Add packetized versions of i0e and i1e special functions. 
- In particular refactor the i0e and i1e code so scalar and vectorized path share code.
  - Move chebevl to GenericPacketMathFunctions.


A brief benchmark with building Eigen with FMA, AVX and AVX2 flags

Before:

CPU: Intel Haswell with HyperThreading (6 cores)
Benchmark                  Time(ns)        CPU(ns)     Iterations
-----------------------------------------------------------------
BM_eigen_i0e_double/1            57.3           57.3     10000000
BM_eigen_i0e_double/8           398            398        1748554
BM_eigen_i0e_double/64         3184           3184         218961
BM_eigen_i0e_double/512       25579          25579          27330
BM_eigen_i0e_double/4k       205043         205042           3418
BM_eigen_i0e_double/32k     1646038        1646176            422
BM_eigen_i0e_double/256k   13180959       13182613             53
BM_eigen_i0e_double/1M     52684617       52706132             10
BM_eigen_i0e_float/1             28.4           28.4     24636711
BM_eigen_i0e_float/8             75.7           75.7      9207634
BM_eigen_i0e_float/64           512            512        1000000
BM_eigen_i0e_float/512         4194           4194         166359
BM_eigen_i0e_float/4k         32756          32761          21373
BM_eigen_i0e_float/32k       261133         261153           2678
BM_eigen_i0e_float/256k     2087938        2088231            333
BM_eigen_i0e_float/1M       8380409        8381234             84
BM_eigen_i1e_double/1            56.3           56.3     10000000
BM_eigen_i1e_double/8           397            397        1772376
BM_eigen_i1e_double/64         3114           3115         223881
BM_eigen_i1e_double/512       25358          25361          27761
BM_eigen_i1e_double/4k       203543         203593           3462
BM_eigen_i1e_double/32k     1613649        1613803            428
BM_eigen_i1e_double/256k   12910625       12910374             54
BM_eigen_i1e_double/1M     51723824       51723991             10
BM_eigen_i1e_float/1             28.3           28.3     24683049
BM_eigen_i1e_float/8             74.8           74.9      9366216
BM_eigen_i1e_float/64           505            505        1000000
BM_eigen_i1e_float/512         4068           4068         171690
BM_eigen_i1e_float/4k         31803          31806          21948
BM_eigen_i1e_float/32k       253637         253692           2763
BM_eigen_i1e_float/256k     2019711        2019918            346
BM_eigen_i1e_float/1M       8238681        8238713             86


After:

CPU: Intel Haswell with HyperThreading (6 cores)
Benchmark                  Time(ns)        CPU(ns)     Iterations
-----------------------------------------------------------------
BM_eigen_i0e_double/1            15.8           15.8     44097476
BM_eigen_i0e_double/8            99.3           99.3      7014884
BM_eigen_i0e_double/64          777            777         886612
BM_eigen_i0e_double/512        6180           6181         100000
BM_eigen_i0e_double/4k        48136          48140          14678
BM_eigen_i0e_double/32k      385936         385943           1801
BM_eigen_i0e_double/256k    3293324        3293551            228
BM_eigen_i0e_double/1M     12423600       12424458             57
BM_eigen_i0e_float/1             16.3           16.3     43038042
BM_eigen_i0e_float/8             30.1           30.1     23456931
BM_eigen_i0e_float/64           169            169        4132875
BM_eigen_i0e_float/512         1338           1339         516860
BM_eigen_i0e_float/4k         10191          10191          68513
BM_eigen_i0e_float/32k        81338          81337           8531
BM_eigen_i0e_float/256k      651807         651984           1000
BM_eigen_i0e_float/1M       2633821        2634187            268
BM_eigen_i1e_double/1            16.2           16.2     42352499
BM_eigen_i1e_double/8           110            110        6316524
BM_eigen_i1e_double/64          822            822         851065
BM_eigen_i1e_double/512        6480           6481         100000
BM_eigen_i1e_double/4k        51843          51843          10000
BM_eigen_i1e_double/32k      414854         414852           1680
BM_eigen_i1e_double/256k    3320001        3320568            212
BM_eigen_i1e_double/1M     13442795       13442391             53
BM_eigen_i1e_float/1             17.6           17.6     41025735
BM_eigen_i1e_float/8             35.5           35.5     19597891
BM_eigen_i1e_float/64           240            240        2924237
BM_eigen_i1e_float/512         1424           1424         485953
BM_eigen_i1e_float/4k         10722          10723          65162
BM_eigen_i1e_float/32k        86286          86297           8048
BM_eigen_i1e_float/256k      691821         691868           1000
BM_eigen_i1e_float/1M       2777336        2777747            256


This shows anywhere from a 50% to 75% improvement on these operations.

I've also benchmarked without any of these flags turned on, and got similar
performance to before (if not better).

Also tested packetmath.cpp + special_functions to ensure no regressions.
 2019-09-11 18:34:02 -07:00
Deven Desai
cdb377d0cb Fix for the HIP build+test errors introduced by the ndtri support. 
The fixes needed are
 * adding EIGEN_DEVICE_FUNC attribute to a couple of funcs (else HIPCC will error out when non-device funcs are called from global/device funcs)
 * switching to using ::<math_func> instead std::<math_func> (only for HIPCC) in cases where the std::<math_func> is not recognized as a device func by HIPCC
 * removing an errant "j" from a testcase (don't know how that made it in to begin with!)
 2019-09-06 16:03:49 +00:00
Eugene Zhulenev
d918bd9a8b Update ThreadLocal to use separate Initialize/Release callables 2019-09-10 16:13:32 -07:00
Eugene Zhulenev
e3dec4dcc1 ThreadLocal container that does not rely on thread local storage 2019-09-09 15:18:14 -07:00
Srinivas Vasudevan
e38dd48a27 PR 681: Add ndtri function, the inverse of the normal distribution function. 2019-08-12 19:26:29 -04:00
Eugene Zhulenev
47fefa235f Allow move-only done callback in TensorAsyncDevice 2019-09-03 17:20:56 -07:00
Eugene Zhulenev
a8d264fa9c Add test for const TensorMap underlying data mutation 2019-09-03 11:38:39 -07:00
Eugene Zhulenev
f68f2bba09 TensorMap constness should not change underlying storage constness 2019-09-03 11:08:09 -07:00
Alberto Luaces
c694be1214 Fixed Tensor documentation formatting. 2019-07-23 09:24:06 +00:00
Eugene Zhulenev
79c402e40e Fix shadow warnings in TensorContractionThreadPool 2019-08-30 15:38:31 -07:00
Eugene Zhulenev
edf2ec28d8 Fix block mapper type name in TensorExecutor 2019-08-30 15:29:25 -07:00
Eugene Zhulenev
f0b36fb9a4 evalSubExprsIfNeededAsync + async TensorContractionThreadPool 2019-08-30 15:13:38 -07:00
Eugene Zhulenev
619cea9491 Revert accidentally removed <memory> header from ThreadPool 2019-08-30 14:51:17 -07:00
Eugene Zhulenev
66665e7e76 Asynchronous expression evaluation with TensorAsyncDevice 2019-08-30 14:49:40 -07:00
Eugene Zhulenev
bc40d4522c Const correctness in TensorMap<const Tensor<T, ...>> expressions 2019-08-28 17:46:05 -07:00
Eugene Zhulenev
6e77f9bef3 Remove shadow warnings in TensorDeviceThreadPool 2019-08-28 10:32:19 -07:00
Rasmus Larsen
84fefdf321 Merged in ezhulenev/eigen-01 (pull request PR-683) 
Asynchronous parallelFor in Eigen ThreadPoolDevice
 2019-08-26 21:49:17 +00:00
maratek
8b5ab0e4dd Fix get_random_seed on Native Client 
Newlib in Native Client SDK does not provide ::random function.
Implement get_random_seed for NaCl using ::rand, similarly to Windows version.
 2019-08-23 15:25:56 -07:00
Eugene Zhulenev
6901788013 Asynchronous parallelFor in Eigen ThreadPoolDevice 2019-08-22 10:50:51 -07:00
Eugene Zhulenev
071311821e Remove XSMM support from Tensor module 2019-08-19 11:44:25 -07:00
Rasmus Munk Larsen
facc4e4536 Disable tests for contraction with output kernels when using libxsmm, which does not support this. 2019-08-07 14:11:15 -07:00
Rasmus Munk Larsen
eab7e52db2 [Eigen] Vectorize evaluation of coefficient-wise functions over tensor blocks if the strides are known to be 1. Provides up to 20-25% speedup of the TF cross entropy op with AVX. 
A few benchmark numbers:

name                              old time/op             new time/op             delta
BM_Xent_16_10000_cpu              448µs ± 3%              389µs ± 2%  -13.21%
(p=0.008 n=5+5)
BM_Xent_32_10000_cpu              575µs ± 6%              454µs ± 3%  -21.00%          (p=0.008 n=5+5)
BM_Xent_64_10000_cpu              933µs ± 4%              712µs ± 1%  -23.71%          (p=0.008 n=5+5)
 2019-08-07 12:57:42 -07:00
Rasmus Munk Larsen
0987126165 Clean up unnecessary namespace specifiers in TensorBlock.h. 2019-08-07 12:12:52 -07:00
Rasmus Munk Larsen
e2999d4c38 Fix performance regressions due to https://bitbucket.org/eigen/eigen/pull-requests/662. 
The change caused the device struct to be copied for each expression evaluation, and caused, e.g., a 10% regression in the TensorFlow multinomial op on GPU:


Benchmark                       Time(ns)        CPU(ns)     Iterations
----------------------------------------------------------------------
BM_Multinomial_gpu_1_100000_4     128173         231326           2922  1.610G items/s

VS

Benchmark                       Time(ns)        CPU(ns)     Iterations
----------------------------------------------------------------------
BM_Multinomial_gpu_1_100000_4     146683         246914           2719  1.509G items/s
 2019-08-02 11:18:13 -07:00
Eugene Zhulenev
3cd148f983 Fix expression evaluation heuristic for TensorSliceOp 2019-07-09 12:10:26 -07:00
Eugene Zhulenev
6083014594 Add outer/inner chipping optimization for chipping dimension specified at runtime 2019-07-03 11:35:25 -07:00
Deven Desai
7eb2e0a95b adding the EIGEN_DEVICE_FUNC attribute to the constCast routine. 
Not having this attribute results in the following failures in the `--config=rocm` TF build.

```
In file included from tensorflow/core/kernels/cross_op_gpu.cu.cc:20:
In file included from ./tensorflow/core/framework/register_types.h:20:
In file included from ./tensorflow/core/framework/numeric_types.h:20:
In file included from ./third_party/eigen3/unsupported/Eigen/CXX11/Tensor:1:
In file included from external/eigen_archive/unsupported/Eigen/CXX11/Tensor:140:
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h:356:37: error:  'Eigen::constCast':  no overloaded function has restriction specifiers that are compatible with the ambient context 'data'
    typename Storage::Type result = constCast(m_impl.data());
                                    ^
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h:356:37: error:  'Eigen::constCast':  no overloaded function has restriction specifiers that are compatible with the ambient context 'data'
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h:148:56: note: in instantiation of member function 'Eigen::TensorEvaluator<const Eigen::TensorChippingOp<1, Eigen::TensorMap<Eigen::Tensor<int, 2, 1, long>, 16, MakePointer> >, Eigen::Gpu\
Device>::data' requested here
    return m_rightImpl.evalSubExprsIfNeeded(m_leftImpl.data());

```

Adding the EIGEN_DEVICE_FUNC attribute resolves those errors
 2019-07-02 20:02:46 +00:00
Gael Guennebaud
ef8aca6a89 Merged in codeplaysoftware/eigen (pull request PR-667) 
[SYCL] :

Approved-by: Gael Guennebaud <g.gael@free.fr>
Approved-by: Rasmus Larsen <rmlarsen@google.com>
 2019-07-02 12:45:23 +00:00
Eugene Zhulenev
4ac93f8edc Allocate non-const scalar buffer for block evaluation with DefaultDevice 2019-07-01 10:55:19 -07:00
Mehdi Goli
9ea490c82c [SYCL] : 
* Modifying TensorDeviceSYCL to use `EIGEN_THROW_X`.
  * Modifying TensorMacro to use `EIGEN_TRY/CATCH(X)` macro.
  * Modifying TensorReverse.h to use `EIGEN_DEVICE_REF` instead of `&`.
  * Fixing the SYCL device macro in SpecialFunctionsImpl.h.
 2019-07-01 16:27:28 +01:00
Eugene Zhulenev
81a03bec75 Fix TensorReverse on GPU with m_stride[i]==0 2019-06-28 15:50:39 -07:00
Rasmus Munk Larsen
74a9dd1102 Fix preprocessor condition to only generate a warning when calling eigen::GpuDevice::synchronize() from device code, but not when calling from a non-GPU compilation unit. 2019-06-28 11:56:21 -07:00
Rasmus Munk Larsen
70d4020ad9 Remove comma causing warning in c++03 mode. 2019-06-28 11:39:45 -07:00
Eugene Zhulenev
6e7c76481a Merge with Eigen head 2019-06-28 11:22:46 -07:00
Eugene Zhulenev
878845cb25 Add block access to TensorReverseOp and make sure that TensorForcedEval uses block access when preferred 2019-06-28 11:13:44 -07:00
Mehdi Goli
7d08fa805a [SYCL] This PR adds the minimum modifications to the Eigen unsupported module required to run it on devices supporting SYCL. 
* Abstracting the pointer type so that both SYCL memory and pointer can be captured.
* Converting SYCL virtual pointer to SYCL device memory in Eigen evaluator class.
* Binding SYCL placeholder accessor to command group handler by using bind method in Eigen evaluator node.
* Adding SYCL macro for controlling loop unrolling.
* Modifying the TensorDeviceSycl.h and SYCL executor method to adopt the above changes.
 2019-06-28 10:08:23 +01:00
Christoph Hertzberg
adec097c61 Remove extra comma (causes warnings in C++03) 2019-06-26 16:14:28 +02:00
Eugene Zhulenev
229db81572 Optimize evaluation strategy for TensorSlicingOp and TensorChippingOp 2019-06-25 15:41:37 -07:00
Rasmus Larsen
c1b0aea653 Merged in Artem-B/eigen (pull request PR-654) 
Minor build improvements

Approved-by: Rasmus Larsen <rmlarsen@google.com>
 2019-05-31 22:27:04 +00:00
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