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* Disable new/delete test for HIPGravatar Antonio Sanchez2021-02-25
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* Fix CUDA device new and delete, and add test.Gravatar Antonio Sanchez2021-02-24
| | | | HIP does not support new/delete on device, so test is skipped.
* Eliminate CMake FindPackageHandleStandardArgs warnings.Gravatar Antonio Sanchez2021-02-24
| | | | | | | | | | | | | | | | | CMake complains that the package name does not match when the case differs, e.g.: ``` CMake Warning (dev) at /usr/share/cmake-3.18/Modules/FindPackageHandleStandardArgs.cmake:273 (message): The package name passed to `find_package_handle_standard_args` (UMFPACK) does not match the name of the calling package (Umfpack). This can lead to problems in calling code that expects `find_package` result variables (e.g., `_FOUND`) to follow a certain pattern. Call Stack (most recent call first): cmake/FindUmfpack.cmake:50 (find_package_handle_standard_args) bench/spbench/CMakeLists.txt:24 (find_package) This warning is for project developers. Use -Wno-dev to suppress it. ``` Here we rename the libraries to match their true cases.
* Fixed sparse conservativeResize() when both num cols and rows decreased.Gravatar Adam Shapiro2021-02-23
| | | | | The previous implementation caused a buffer overflow trying to calculate non- zero counts for columns that no longer exist.
* Missing change regarding #1910Gravatar Christoph Hertzberg2021-02-19
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* Bug #1910: Make SparseCholesky work for RowMajor matricesGravatar Christoph Hertzberg2021-02-19
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* New accurate algorithm for pow(x,y). This version is accurate to 1.4 ulps ↵Gravatar Rasmus Munk Larsen2021-02-17
| | | | for float, while still being 10x faster than std::pow for AVX512. A future change will introduce a specialization for double.
* Updated pfrexp implementation.Gravatar Antonio Sanchez2021-02-17
| | | | | | The original implementation fails for 0, denormals, inf, and NaN. See #2150
* Fix ldexp implementations.Gravatar Antonio Sanchez2021-02-10
| | | | | | | | | | | | | | | | | The previous implementations produced garbage values if the exponent did not fit within the exponent bits. See #2131 for a complete discussion, and !375 for other possible implementations. Here we implement the 4-factor version. See `pldexp_impl` in `GenericPacketMathFunctions.h` for a full description. The SSE `pcmp*` methods were moved down since `pcmp_le<Packet4i>` requires `por`. Left as a "TODO" is to delegate to a faster version if we know the exponent does fit within the exponent bits. Fixes #2131.
* add specialization of check_sparse_solving() for SuperLU solver, in order to ↵Gravatar Ralf Hannemann-Tamas2021-02-08
| | | | test adjoint and transpose solves
* Add more tests for pow and fix a corner case for huge exponent where the ↵Gravatar Rasmus Munk Larsen2021-02-05
| | | | result is always zero or infinite unless x is one.
* Allow for negative strides.Gravatar Gael Guennebaud2021-01-27
| | | | | | | Note that using a stride of -1 is still not possible because it would clash with the definition of Eigen::Dynamic. This fixes #747.
* Replace language_support module with builtin CheckLanguageGravatar Samir Benmendil2021-01-27
| | | | | | | | | | | | The workaround_9220 function was introduced a long time ago to workaround a CMake issue with enable_language(OPTIONAL). Since then CMake has clarified that the OPTIONAL keywords has not been implemented[0]. A CheckLanguage module is now provided with CMake to check if a language can be enabled. Use that instead. [0] https://cmake.org/cmake/help/v3.18/command/enable_language.html
* Eliminate implicit conversion warning in test/array_cwise.cppGravatar Antonio Sanchez2021-01-23
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* Replace std::isnan with numext::isnan for c++03Gravatar Antonio Sanchez2021-01-23
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* Fix pow and other cwise ops for half/bfloat16.Gravatar Antonio Sanchez2021-01-22
| | | | | | | | | | | | | The new `generic_pow` implementation was failing for half/bfloat16 since their construction from int/float is not `constexpr`. Modified in `GenericPacketMathFunctions` to remove `constexpr`. While adding tests for half/bfloat16, found other issues related to implicit conversions. Also needed to implement `numext::arg` for non-integer, non-complex, non-float/double/long double types. These seem to be implicitly converted to `std::complex<T>`, which then fails for half/bfloat16.
* Specialize std::complex operators for use on GPU device.Gravatar Antonio Sanchez2021-01-22
| | | | | | | | | | | | | | | | | | NVCC and older versions of clang do not fully support `std::complex` on device, leading to either compile errors (Cannot call `__host__` function) or worse, runtime errors (Illegal instruction). For most functions, we can implement specialized `numext` versions. Here we specialize the standard operators (with the exception of stream operators and member function operators with a scalar that are already specialized in `<complex>`) so they can be used in device code as well. To import these operators into the current scope, use `EIGEN_USING_STD_COMPLEX_OPERATORS`. By default, these are imported into the `Eigen`, `Eigen:internal`, and `Eigen::numext` namespaces. This allow us to remove specializations of the sum/difference/product/quotient ops, and allow us to treat complex numbers like most other scalars (e.g. in tests).
* Fix pfrexp/pldexp for half.Gravatar Antonio Sanchez2021-01-21
| | | | | | | | | | The recent addition of vectorized pow (!330) relies on `pfrexp` and `pldexp`. This was missing for `Eigen::half` and `Eigen::bfloat16`. Adding tests for these packet ops also exposed an issue with handling negative values in `pfrexp`, returning an incorrect exponent. Added the missing implementations, corrected the exponent in `pfrexp1`, and added `packetmath` tests.
* Fix stable_norm_1 test.Gravatar Antonio Sanchez2021-01-21
| | | | | | | | Test enters an infinite loop if size is 1x1 when choosing to select unique indices for adding `inf` and `NaN` to the input. Here we revert to non-unique indices, and split the `hypotNorm` check into two cases: one where both `inf` and `NaN` are added, and one where only `NaN` is added.
* Vectorize `pow(x, y)`. This closes ↵Gravatar Rasmus Munk Larsen2021-01-18
| | | | | | | | | | | | | | | | | | | | | | https://gitlab.com/libeigen/eigen/-/issues/2085, which also contains a description of the algorithm. I ran some testing (comparing to `std::pow(double(x), double(y)))` for `x` in the set of all (positive) floats in the interval `[std::sqrt(std::numeric_limits<float>::min()), std::sqrt(std::numeric_limits<float>::max())]`, and `y` in `{2, sqrt(2), -sqrt(2)}` I get the following error statistics: ``` max_rel_error = 8.34405e-07 rms_rel_error = 2.76654e-07 ``` If I widen the range to all normal float I see lower accuracy for arguments where the result is subnormal, e.g. for `y = sqrt(2)`: ``` max_rel_error = 0.666667 rms = 6.8727e-05 count = 1335165689 argmax = 2.56049e-32, 2.10195e-45 != 1.4013e-45 ``` which seems reasonable, since these results are subnormals with only couple of significant bits left.
* Improved std::complex sqrt and rsqrt.Gravatar Antonio Sanchez2021-01-17
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Replaces `std::sqrt` with `complex_sqrt` for all platforms (previously `complex_sqrt` was only used for CUDA and MSVC), and implements custom `complex_rsqrt`. Also introduces `numext::rsqrt` to simplify implementation, and modified `numext::hypot` to adhere to IEEE IEC 6059 for special cases. The `complex_sqrt` and `complex_rsqrt` implementations were found to be significantly faster than `std::sqrt<std::complex<T>>` and `1/numext::sqrt<std::complex<T>>`. Benchmark file attached. ``` GCC 10, Intel Xeon, x86_64: --------------------------------------------------------------------------- Benchmark Time CPU Iterations --------------------------------------------------------------------------- BM_Sqrt<std::complex<float>> 9.21 ns 9.21 ns 73225448 BM_StdSqrt<std::complex<float>> 17.1 ns 17.1 ns 40966545 BM_Sqrt<std::complex<double>> 8.53 ns 8.53 ns 81111062 BM_StdSqrt<std::complex<double>> 21.5 ns 21.5 ns 32757248 BM_Rsqrt<std::complex<float>> 10.3 ns 10.3 ns 68047474 BM_DivSqrt<std::complex<float>> 16.3 ns 16.3 ns 42770127 BM_Rsqrt<std::complex<double>> 11.3 ns 11.3 ns 61322028 BM_DivSqrt<std::complex<double>> 16.5 ns 16.5 ns 42200711 Clang 11, Intel Xeon, x86_64: --------------------------------------------------------------------------- Benchmark Time CPU Iterations --------------------------------------------------------------------------- BM_Sqrt<std::complex<float>> 7.46 ns 7.45 ns 90742042 BM_StdSqrt<std::complex<float>> 16.6 ns 16.6 ns 42369878 BM_Sqrt<std::complex<double>> 8.49 ns 8.49 ns 81629030 BM_StdSqrt<std::complex<double>> 21.8 ns 21.7 ns 31809588 BM_Rsqrt<std::complex<float>> 8.39 ns 8.39 ns 82933666 BM_DivSqrt<std::complex<float>> 14.4 ns 14.4 ns 48638676 BM_Rsqrt<std::complex<double>> 9.83 ns 9.82 ns 70068956 BM_DivSqrt<std::complex<double>> 15.7 ns 15.7 ns 44487798 Clang 9, Pixel 2, aarch64: --------------------------------------------------------------------------- Benchmark Time CPU Iterations --------------------------------------------------------------------------- BM_Sqrt<std::complex<float>> 24.2 ns 24.1 ns 28616031 BM_StdSqrt<std::complex<float>> 104 ns 103 ns 6826926 BM_Sqrt<std::complex<double>> 31.8 ns 31.8 ns 22157591 BM_StdSqrt<std::complex<double>> 128 ns 128 ns 5437375 BM_Rsqrt<std::complex<float>> 31.9 ns 31.8 ns 22384383 BM_DivSqrt<std::complex<float>> 99.2 ns 98.9 ns 7250438 BM_Rsqrt<std::complex<double>> 46.0 ns 45.8 ns 15338689 BM_DivSqrt<std::complex<double>> 119 ns 119 ns 5898944 ```
* Fix MSVC complex sqrt and packetmath test.Gravatar Antonio Sanchez2021-01-08
| | | | | | | | | MSVC incorrectly handles `inf` cases for `std::sqrt<std::complex<T>>`. Here we replace it with a custom version (currently used on GPU). Also fixed the `packetmath` test, which previously skipped several corner cases since `CHECK_CWISE1` only tests the first `PacketSize` elements.
* Fix rand test for MSVC.Gravatar Antonio Sanchez2021-01-07
| | | | | | | | MSVC's uniform random number generator is not quite as uniform as others, requiring a slightly wider threshold on the histogram test. After inspecting histograms for several runs, there's no obvious bias -- just some bins end up having slightly more less elements (often > 2% but less than 2.5%).
* Make Transform::computeRotationScaling(0,&S) continuousGravatar Essex Edwards2021-01-07
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* Fix Ref Stride checks.Gravatar Antonio Sanchez2021-01-05
| | | | | | | | | | | | | | | | | | | | The existing `Ref` class failed to consider cases where the Ref's `Stride` setting *could* match the underlying referred object's stride, but **didn't** at runtime. This led to trying to set invalid stride values, causing runtime failures in some cases, and garbage due to mismatched strides in others. Here we add the missing runtime checks. This involves computing the strides necessary to align with the referred object's storage, and verifying we can actually set those strides at runtime. In the `const` case, if it *may* be possible to refer to the original storage at compile-time but fails at runtime, then we defer to the `construct(...)` method that makes a copy. Added more tests to check these cases. Fixes #2093.
* Eliminate boolean product warnings by factoring out aGravatar Christoph Hertzberg2021-01-05
| | | `combine_scalar_factors` helper function.
* Add CUDA complex sqrt.Gravatar Antonio Sanchez2020-12-22
| | | | | | | | | | | | | | | This is to support scalar `sqrt` of complex numbers `std::complex<T>` on device, requested by Tensorflow folks. Technically `std::complex` is not supported by NVCC on device (though it is by clang), so the default `sqrt(std::complex<T>)` function only works on the host. Here we create an overload to add back the functionality. Also modified the CMake file to add `--relaxed-constexpr` (or equivalent) flag for NVCC to allow calling constexpr functions from device functions, and added support for specifying compute architecture for NVCC (was already available for clang).
* Replace M_LOG2E and M_LN2 with custom macros.Gravatar Antonio Sanchez2020-12-11
| | | | | | | | | | For these to exist we would need to define `_USE_MATH_DEFINES` before `cmath` or `math.h` is first included. However, we don't control the include order for projects outside Eigen, so even defining the macro in `Eigen/Core` does not fix the issue for projects that end up including `<cmath>` before Eigen does (explicitly or transitively). To fix this, we define `EIGEN_LOG2E` and `EIGEN_LN2` ourselves.
* Implement vectorized complex square root.Gravatar Rasmus Munk Larsen2020-12-08
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Closes #1905 Measured speedup for sqrt of `complex<float>` on Skylake: SSE: ``` name old time/op new time/op delta BM_eigen_sqrt_ctype/1 49.4ns ± 0% 54.3ns ± 0% +10.01% BM_eigen_sqrt_ctype/8 332ns ± 0% 50ns ± 1% -84.97% BM_eigen_sqrt_ctype/64 2.81µs ± 1% 0.38µs ± 0% -86.49% BM_eigen_sqrt_ctype/512 23.8µs ± 0% 3.0µs ± 0% -87.32% BM_eigen_sqrt_ctype/4k 202µs ± 0% 24µs ± 2% -88.03% BM_eigen_sqrt_ctype/32k 1.63ms ± 0% 0.19ms ± 0% -88.18% BM_eigen_sqrt_ctype/256k 13.0ms ± 0% 1.5ms ± 1% -88.20% BM_eigen_sqrt_ctype/1M 52.1ms ± 0% 6.2ms ± 0% -88.18% ``` AVX2: ``` name old cpu/op new cpu/op delta BM_eigen_sqrt_ctype/1 53.6ns ± 0% 55.6ns ± 0% +3.71% BM_eigen_sqrt_ctype/8 334ns ± 0% 27ns ± 0% -91.86% BM_eigen_sqrt_ctype/64 2.79µs ± 0% 0.22µs ± 2% -92.28% BM_eigen_sqrt_ctype/512 23.8µs ± 1% 1.7µs ± 1% -92.81% BM_eigen_sqrt_ctype/4k 201µs ± 0% 14µs ± 1% -93.24% BM_eigen_sqrt_ctype/32k 1.62ms ± 0% 0.11ms ± 1% -93.29% BM_eigen_sqrt_ctype/256k 13.0ms ± 0% 0.9ms ± 1% -93.31% BM_eigen_sqrt_ctype/1M 52.0ms ± 0% 3.5ms ± 1% -93.31% ``` AVX512: ``` name old cpu/op new cpu/op delta BM_eigen_sqrt_ctype/1 53.7ns ± 0% 56.2ns ± 1% +4.75% BM_eigen_sqrt_ctype/8 334ns ± 0% 18ns ± 2% -94.63% BM_eigen_sqrt_ctype/64 2.79µs ± 0% 0.12µs ± 1% -95.54% BM_eigen_sqrt_ctype/512 23.9µs ± 1% 1.0µs ± 1% -95.89% BM_eigen_sqrt_ctype/4k 202µs ± 0% 8µs ± 1% -96.13% BM_eigen_sqrt_ctype/32k 1.63ms ± 0% 0.06ms ± 1% -96.15% BM_eigen_sqrt_ctype/256k 13.0ms ± 0% 0.5ms ± 4% -96.11% BM_eigen_sqrt_ctype/1M 52.1ms ± 0% 2.0ms ± 1% -96.13% ```
* Add log2() to Eigen.Gravatar Rasmus Munk Larsen2020-12-04
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* Revert "Add log2() operator to Eigen"Gravatar Rasmus Munk Larsen2020-12-03
| | | | This reverts commit 4d91519a9be061da5d300079fca17dd0b9328050.
* Add log2() operator to EigenGravatar Rasmus Munk Larsen2020-12-03
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* Include chrono in main for c++11.Gravatar Antonio Sanchez2020-12-03
| | | | Hack to fix tensor tests, since min/max are overridden by `main.h`.
* AVX512 missing ops.Gravatar Antonio Sanchez2020-11-30
| | | | | | | | | | This allows the `packetmath` tests to pass for AVX512 on skylake. Made `half` and `bfloat16` consistent in terms of ops they support. Note the `log` tests are currently disabled for `bfloat16` since they fail due to poor precision (they were previously disabled for `Packet8bf` via test function specialization -- I just removed that specialization and disabled it in the generic test).
* Make inclusion of doc sub-directory optional by adjusting options.Gravatar Bowie Owens2020-11-27
| | | | | | | | | | Allows exclusion of doc and related targets to help when using eigen via add_subdirectory(). Requested by: https://gitlab.com/libeigen/eigen/-/issues/1842 Also required making EIGEN_TEST_BUILD_DOCUMENTATION a dependent option on EIGEN_BUILD_DOC. This ensures documentation targets are properly defined when EIGEN_TEST_BUILD_DOCUMENTATION is ON.
* Revert "Fix Half NaN definition and test."Gravatar Rasmus Munk Larsen2020-11-24
| | | | This reverts commit c770746d709686ef2b8b652616d9232f9b028e78.
* Fix Half NaN definition and test.Gravatar Rasmus Munk Larsen2020-11-24
| | | | | | | | | | | | | The `half_float` test was failing with `-mcpu=cortex-a55` (native `__fp16`) due to a bad NaN bit-pattern comparison (in the case of casting a float to `__fp16`, the signaling `NaN` is quieted). There was also an inconsistency between `numeric_limits<half>::quiet_NaN()` and `NumTraits::quiet_NaN()`. Here we correct the inconsistency and compare NaNs according to the IEEE 754 definition. Also modified the `bfloat16_float` test to match. Tested with `cortex-a53` and `cortex-a55`.
* Implement missing AVX half ops.Gravatar Antonio Sanchez2020-11-24
| | | | | | | | Minimal implementation of AVX `Eigen::half` ops to bring in line with `bfloat16`. Allows `packetmath_13` to pass. Also adjusted `bfloat16` packet traits to match the supported set of ops (e.g. Bessel is not actually implemented).
* Fix Half NaN definition and test.Gravatar Antonio Sanchez2020-11-23
| | | | | | | | | | | | | The `half_float` test was failing with `-mcpu=cortex-a55` (native `__fp16`) due to a bad NaN bit-pattern comparison (in the case of casting a float to `__fp16`, the signaling `NaN` is quieted). There was also an inconsistency between `numeric_limits<half>::quiet_NaN()` and `NumTraits::quiet_NaN()`. Here we correct the inconsistency and compare NaNs according to the IEEE 754 definition. Also modified the `bfloat16_float` test to match. Tested with `cortex-a53` and `cortex-a55`.
* Update AVX half packets, disable test.Gravatar Antonio Sanchez2020-11-21
| | | | | | | | The AVX half implementation is incomplete, causing the `packetmath_13` test to fail. This disables the test. Also refactored the existing AVX implementation to use `bit_cast` instead of direct access to `.x`.
* Fix sparse_extra_3, disable counting temporaries for testing ↵Gravatar Antonio Sanchez2020-11-18
| | | | | | | | | | | | | | | | | | | | | | | DynamicSparseMatrix. Multiplication of column-major `DynamicSparseMatrix`es involves three temporaries: - two for transposing twice to sort the coefficients (`ConservativeSparseSparseProduct.h`, L160-161) - one for a final copy assignment (`SparseAssign.h`, L108) The latter is avoided in an optimization for `SparseMatrix`. Since `DynamicSparseMatrix` is deprecated in favor of `SparseMatrix`, it's not worth the effort to optimize further, so I simply disabled counting temporaries via a macro. Note that due to the inclusion of `sparse_product.cpp`, the `sparse_extra` tests actually re-run all the original `sparse_product` tests as well. We may want to simply drop the `DynamicSparseMatrix` tests altogether, which would eliminate the test duplication. Related to #2048
* Re-enable Arm Neon Eigen::half packets of size 8Gravatar David Tellenbach2020-11-18
| | | | | | - Add predux_half_dowto4 - Remove explicit casts in Half.h to match the behaviour of BFloat16.h - Enable more packetmath tests for Eigen::half
* Add bit_cast for half/bfloat to/from uint16_t, fix TensorRandomGravatar Antonio Sanchez2020-11-18
| | | | | | | | | | 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.
* Initialize primitives to fix -Wuninitialized-const-reference.Gravatar Antonio Sanchez2020-11-18
| | | | | | | | | | | | | The `meta` test generates warnings with the latest version of clang due to passing uninitialized variables as const reference arguments. ``` test/meta.cpp:102:45: error: variable 'f' is uninitialized when passed as a const reference argument here [-Werror,-Wuninitialized-const-reference] VERIFY(( check_is_convertible(a.dot(b), f) )); ``` We don't actually use the variables, but initializing them eliminates the new warning. Fixes #2067.
* Eliminate double-promotion warnings.Gravatar Antonio Sanchez2020-11-16
| | | | | | | | | | | | | Clang currently complains about implicit conversions, e.g. ``` test/packetmath.cpp:680:59: warning: implicit conversion increases floating-point precision: 'typename Eigen::internal::random_retval<typename Eigen::internal::global_math_functions_filtering_base<double>::type>::type' (aka 'double') to 'long double' [-Wdouble-promotion] data1[0] = Scalar((2 * k + k1) * EIGEN_PI / 2 * internal::random<double>(0.8, 1.2)); ~ ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ test/packetmath.cpp:681:40: warning: implicit conversion increases floating-point precision: 'float' to 'long double' [-Wdouble-promotion] data1[1] = Scalar((2 * k + 2 + k1) * EIGEN_PI / 2 * internal::random<double>(0.8, 1.2)); ``` Modified to explicitly cast to double.
* Explicit casts of S -> std::complex<T>Gravatar Antonio Sanchez2020-11-14
| | | | | | | | | | | | | | | | When calling `internal::cast<S, std::complex<T>>(x)`, clang often generates an implicit conversion warning due to an implicit cast from type `S` to `T`. This currently affects the following tests: - `basicstuff` - `bfloat16_float` - `cxx11_tensor_casts` The implicit cast leads to widening/narrowing float conversions. Widening warnings only seem to be generated by clang (`-Wdouble-promotion`). To eliminate the warning, we explicitly cast the real-component first from `S` to `T`. We also adjust tests to use `internal::cast` instead of `static_cast` when a complex type may be involved.
* Suppress ignored-attributes warning (same as in vectorization_logic). Remove ↵Gravatar Christoph Hertzberg2020-11-13
| | | | redundant include and using namespace.
* Fix erroneous forward declaration of boost nvp.Gravatar Everton Constantino2020-11-10
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* CMakefile update for ROCm 4.0Gravatar Deven Desai2020-10-29
| | | | 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.
* Add support for Armv8.2-a __fp16Gravatar David Tellenbach2020-10-28
| | | | | | | | | | | | | | | Armv8.2-a provides a native half-precision floating point (__fp16 aka. float16_t). This patch introduces * __fp16 as underlying type of Eigen::half if this type is available * the packet types Packet4hf and Packet8hf representing float16x4_t and float16x8_t respectively * packet-math for the above packets with corresponding scalar type Eigen::half The packet-math functionality has been implemented by Ashutosh Sharma <ashutosh.sharma@amperecomputing.com>. This closes #1940.
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