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* Eigen moved the `scanLauncehr` function inside the internal namespace.Gravatar mehdi-goli2020-05-11
| | | | | | | 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.
* Add parallelization of TensorScanOp for types without packet ops.Gravatar Rasmus Munk Larsen2020-05-06
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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)
* Fix accidental copy of loop variable.Gravatar Rasmus Munk Larsen2020-05-05
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* Vectorize and parallelize TensorScanOp.Gravatar Rasmus Munk Larsen2020-05-05
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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%
* Extend support for Packet16b:Gravatar Rasmus Munk Larsen2020-04-28
| | | | | | | | | | | | | | | | | * 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)
* Add async evaluation support to TensorSlicingOp.Gravatar Eugene Zhulenev2020-04-22
| | | Device::memcpy is not async-safe and might lead to deadlocks. Always evaluate slice expression in async mode.
* Add partial vectorization for matrices and tensors of bool. This speeds up ↵Gravatar Rasmus Munk Larsen2020-04-20
| | | | | | | | | | | | | | | | | | | | | | | | | 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%
* Fix a bug in TensorIndexList.hGravatar Changming Sun2020-04-13
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* Resolve C4346 when building eigen on windowsGravatar jangsoopark2020-04-08
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* Make file formatting comply with POSIX and Unix standardsGravatar Aaron Franke2020-03-23
| | | | UTF-8, LF, no BOM, and newlines at the end of files
* Fixing HIP breakage caused by the recent commit that introduces Packet4h2 as ↵Gravatar Deven Desai2020-03-12
| | | | the Eigen::Half packet type
* remove duplicate pset1 for half and add some comments about why we need ↵Gravatar Sami Kama2020-03-10
| | | | expose pmul/add/div/min/max on host
* Update MarketIO.hGravatar Cédric Hubert2020-02-28
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* Avoid a division in NonBlockingThreadPool::Steal.Gravatar Ilya Tokar2020-02-14
| | | | | | | 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/
* Fail at compile time if default executor tries to use non-default deviceGravatar Eugene Zhulenev2020-02-06
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* Remove dead code from TensorReduction.hGravatar Eugene Zhulenev2020-01-29
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* fix hip-clang compilation due to new HIP scalar accessorGravatar Jeff Daily2020-01-20
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* Fix for HIP breakage - 200115. Adding a missing EIGEN_DEVICE_FUNC attrGravatar Deven Desai2020-01-16
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* Ensure Igamma does not NaN or Inf for large values.Gravatar Srinivas Vasudevan2020-01-14
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* Convert StridedLinearBufferCopy::Kind to enum classGravatar Eugene Zhulenev2020-01-13
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* Added special_packetmath test and tweaked bounds on tests.Gravatar Srinivas Vasudevan2020-01-11
| | | | | Refactor shared packetmath code to header file. (Squashed from PR !38)
* Properly initialize b vector in SplineFittingGravatar Matthew Powelson2020-01-09
| | | InterpolateWithDerivative does not initialize the be vector correctly. This issue is discussed In stackoverflow question 48382939.
* Bug #1785: Introduce numext::rint.Gravatar Ilya Tokar2020-01-07
| | | | | | 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).
* [SYCL Backend]Gravatar mehdi-goli2020-01-07
| | | | | | | * 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.
* Fix for HIP breakage - 191220Gravatar Deven Desai2019-12-20
| | | | | | | | | | | | | | | | | | | | | | | | | | | The breakage was introduced by the following commit : https://gitlab.com/libeigen/eigen/commit/ae07801dd8d295657f28b006e1e4999edf835052 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:100: /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
* Bug #1796: Make matrix squareroot usable for Map and Ref typesGravatar Christoph Hertzberg2019-12-20
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* Reduce code duplication and avoid confusing DoxygenGravatar Christoph Hertzberg2019-12-19
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* Hide recursive meta templates from DoxygenGravatar Christoph Hertzberg2019-12-19
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* Use double-braces initialization (as everywhere else in the test-suite).Gravatar Christoph Hertzberg2019-12-19
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* Fix trivial shadow warningGravatar Christoph Hertzberg2019-12-19
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* Fix TensorPadding bug in squeezed reads from inner dimension Gravatar Eugene Zhulenev2019-12-19
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* Return const data pointer from TensorRef evaluator.data()Gravatar Eugene Zhulenev2019-12-18
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* Tensor block evaluation cost modelGravatar Eugene Zhulenev2019-12-18
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* fix compilation due to new HIP scalar accessorGravatar Jeff Daily2019-12-17
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* Reduce block evaluation overhead for small tensor expressionsGravatar Eugene Zhulenev2019-12-17
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* Initialize non-trivially constructible types when allocating a temp buffer.Gravatar Eugene Zhulenev2019-12-12
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* Squeeze reads from two inner dimensions in TensorPaddingGravatar Eugene Zhulenev2019-12-11
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* Add back accidentally deleted default constructor to ↵Gravatar Eugene Zhulenev2019-12-11
| | | | TensorExecutorTilingContext.
* Remove block memory allocation required by removed block evaluation APIGravatar Eugene Zhulenev2019-12-10
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* Remove V2 suffix from TensorBlockGravatar Eugene Zhulenev2019-12-10
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* Remove TensorBlock.h and old TensorBlock/BlockMapperGravatar Eugene Zhulenev2019-12-10
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* Fix for HIP breakage detected on 191210Gravatar Deven Desai2019-12-10
| | | | | | | | The following commit introduces compile errors when running eigen with hipcc https://gitlab.com/libeigen/eigen/commit/2918f85ba976dbfbf72f7d4c1961a577f5850148 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
* Do not use std::vector in getResourceRequirementsGravatar Eugene Zhulenev2019-12-09
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* Undo the block size change.Gravatar Artem Belevich2019-12-09
| | | | .z *is* used by the EigenContractionKernelInternal().
* Add async evaluation support to TensorSelectOpGravatar Eugene Zhulenev2019-12-09
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* fix AlignedVector3 inconsisent interface with other Vector classes, default ↵Gravatar Janek Kozicki2019-12-06
| | | | constructor and operator- were missing.
* Add recursive work splitting to EvalShardedByInnerDimContextGravatar Eugene Zhulenev2019-12-05
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* Improve performance of contraction kernelsGravatar Artem Belevich2019-12-05
| | | | | | | | | | * 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).
* Add missing initialization in cxx11_tensor_trace.cpp.Gravatar Rasmus Munk Larsen2019-12-04
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* Capture TensorMap by value inside tensor expression ASTGravatar Eugene Zhulenev2019-12-03
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