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Initial GPU acceleration support for LightGBM (#368) * add dummy gpu solver code * initial GPU code * fix crash bug * first working version * use asynchronous copy * use a better kernel for root * parallel read histogram * sparse features now works, but no acceleration, compute on CPU * compute sparse feature on CPU simultaneously * fix big bug; add gpu selection; add kernel selection * better debugging * clean up * add feature scatter * Add sparse_threshold control * fix a bug in feature scatter * clean up debug * temporarily add OpenCL kernels for k=64,256 * fix up CMakeList and definition USE_GPU * add OpenCL kernels as string literals * Add boost.compute as a submodule * add boost dependency into CMakeList * fix opencl pragma * use pinned memory for histogram * use pinned buffer for gradients and hessians * better debugging message * add double precision support on GPU * fix boost version in CMakeList * Add a README * reconstruct GPU initialization code for ResetTrainingData * move data to GPU in parallel * fix a bug during feature copy * update gpu kernels * update gpu code * initial port to LightGBM v2 * speedup GPU data loading process * Add 4-bit bin support to GPU * re-add sparse_threshold parameter * remove kMaxNumWorkgroups and allows an unlimited number of features * add feature mask support for skipping unused features * enable kernel cache * use GPU kernels withoug feature masks when all features are used * REAdme. * REAdme. * update README * fix typos (#349) * change compile to gcc on Apple as default * clean vscode related file * refine api of constructing from sampling data. * fix bug in the last commit. * more efficient algorithm to sample k from n. * fix bug in filter bin * change to boost from average output. * fix tests. * only stop training when all classes are finshed in multi-class. * limit the max tree output. change hessian in multi-class objective. * robust tree model loading. * fix test. * convert the probabilities to raw score in boost_from_average of classification. * fix the average label for binary classification. * Add boost_from_average to docs (#354) * don't use "ConvertToRawScore" for self-defined objective function. * boost_from_average seems doesn't work well in binary classification. remove it. * For a better jump link (#355) * Update Python-API.md * for a better jump in page A space is needed between `#` and the headers content according to Github's markdown format [guideline](https://guides.github.com/features/mastering-markdown/) After adding the spaces, we can jump to the exact position in page by click the link. * fixed something mentioned by @wxchan * Update Python-API.md * add FitByExistingTree. * adapt GPU tree learner for FitByExistingTree * avoid NaN output. * update boost.compute * fix typos (#361) * fix broken links (#359) * update README * disable GPU acceleration by default * fix image url * cleanup debug macro * remove old README * do not save sparse_threshold_ in FeatureGroup * add details for new GPU settings * ignore submodule when doing pep8 check * allocate workspace for at least one thread during builing Feature4 * move sparse_threshold to class Dataset * remove duplicated code in GPUTreeLearner::Split * Remove duplicated code in FindBestThresholds and BeforeFindBestSplit * do not rebuild ordered gradients and hessians for sparse features * support feature groups in GPUTreeLearner * Initial parallel learners with GPU support * add option device, cleanup code * clean up FindBestThresholds; add some omp parallel * constant hessian optimization for GPU * Fix GPUTreeLearner crash when there is zero feature * use np.testing.assert_almost_equal() to compare lists of floats in tests * travis for GPU
2017-04-09 16:53:14 +03:00
[submodule "include/boost/compute"]
Move compute and eigen libraries to external_libs folder (#3809) * move all submodules to external_libs folder * Update .Rbuildignore * Update MANIFEST.in * Update .appveyor.yml * Update CMakeLists.txt * Update build_r.R * Update test.sh * Update setup.py * Update CMakeLists.txt * Update test.sh * Update setup.py * Update conf.py * Update MANIFEST.in * Update LightGBM.vcxproj * continue * test * test * Update setup.py * hotfix * revert CI tests
2021-01-22 17:45:43 +03:00
path = external_libs/compute
Initial GPU acceleration support for LightGBM (#368) * add dummy gpu solver code * initial GPU code * fix crash bug * first working version * use asynchronous copy * use a better kernel for root * parallel read histogram * sparse features now works, but no acceleration, compute on CPU * compute sparse feature on CPU simultaneously * fix big bug; add gpu selection; add kernel selection * better debugging * clean up * add feature scatter * Add sparse_threshold control * fix a bug in feature scatter * clean up debug * temporarily add OpenCL kernels for k=64,256 * fix up CMakeList and definition USE_GPU * add OpenCL kernels as string literals * Add boost.compute as a submodule * add boost dependency into CMakeList * fix opencl pragma * use pinned memory for histogram * use pinned buffer for gradients and hessians * better debugging message * add double precision support on GPU * fix boost version in CMakeList * Add a README * reconstruct GPU initialization code for ResetTrainingData * move data to GPU in parallel * fix a bug during feature copy * update gpu kernels * update gpu code * initial port to LightGBM v2 * speedup GPU data loading process * Add 4-bit bin support to GPU * re-add sparse_threshold parameter * remove kMaxNumWorkgroups and allows an unlimited number of features * add feature mask support for skipping unused features * enable kernel cache * use GPU kernels withoug feature masks when all features are used * REAdme. * REAdme. * update README * fix typos (#349) * change compile to gcc on Apple as default * clean vscode related file * refine api of constructing from sampling data. * fix bug in the last commit. * more efficient algorithm to sample k from n. * fix bug in filter bin * change to boost from average output. * fix tests. * only stop training when all classes are finshed in multi-class. * limit the max tree output. change hessian in multi-class objective. * robust tree model loading. * fix test. * convert the probabilities to raw score in boost_from_average of classification. * fix the average label for binary classification. * Add boost_from_average to docs (#354) * don't use "ConvertToRawScore" for self-defined objective function. * boost_from_average seems doesn't work well in binary classification. remove it. * For a better jump link (#355) * Update Python-API.md * for a better jump in page A space is needed between `#` and the headers content according to Github's markdown format [guideline](https://guides.github.com/features/mastering-markdown/) After adding the spaces, we can jump to the exact position in page by click the link. * fixed something mentioned by @wxchan * Update Python-API.md * add FitByExistingTree. * adapt GPU tree learner for FitByExistingTree * avoid NaN output. * update boost.compute * fix typos (#361) * fix broken links (#359) * update README * disable GPU acceleration by default * fix image url * cleanup debug macro * remove old README * do not save sparse_threshold_ in FeatureGroup * add details for new GPU settings * ignore submodule when doing pep8 check * allocate workspace for at least one thread during builing Feature4 * move sparse_threshold to class Dataset * remove duplicated code in GPUTreeLearner::Split * Remove duplicated code in FindBestThresholds and BeforeFindBestSplit * do not rebuild ordered gradients and hessians for sparse features * support feature groups in GPUTreeLearner * Initial parallel learners with GPU support * add option device, cleanup code * clean up FindBestThresholds; add some omp parallel * constant hessian optimization for GPU * Fix GPUTreeLearner crash when there is zero feature * use np.testing.assert_almost_equal() to compare lists of floats in tests * travis for GPU
2017-04-09 16:53:14 +03:00
url = https://github.com/boostorg/compute
Trees with linear models at leaves (#3299) * Add Eigen library. * Working for simple test. * Apply changes to config params. * Handle nan data. * Update docs. * Add test. * Only load raw data if boosting=gbdt_linear * Remove unneeded code. * Minor updates. * Update to work with sk-learn interface. * Update to work with chunked datasets. * Throw error if we try to create a Booster with an already-constructed dataset having incompatible parameters. * Save raw data in binary dataset file. * Update docs and fix parameter checking. * Fix dataset loading. * Add test for regularization. * Fix bugs when saving and loading tree. * Add test for load/save linear model. * Remove unneeded code. * Fix case where not enough leaf data for linear model. * Simplify code. * Speed up code. * Speed up code. * Simplify code. * Speed up code. * Fix bugs. * Working version. * Store feature data column-wise (not fully working yet). * Fix bugs. * Speed up. * Speed up. * Remove unneeded code. * Small speedup. * Speed up. * Minor updates. * Remove unneeded code. * Fix bug. * Fix bug. * Speed up. * Speed up. * Simplify code. * Remove unneeded code. * Fix bug, add more tests. * Fix bug and add test. * Only store numerical features * Fix bug and speed up using templates. * Speed up prediction. * Fix bug with regularisation * Visual studio files. * Working version * Only check nans if necessary * Store coeff matrix as an array. * Align cache lines * Align cache lines * Preallocation coefficient calculation matrices * Small speedups * Small speedup * Reverse cache alignment changes * Change to dynamic schedule * Update docs. * Refactor so that linear tree learner is not a separate class. * Add refit capability. * Speed up * Small speedups. * Speed up add prediction to score. * Fix bug * Fix bug and speed up. * Speed up dataload. * Speed up dataload * Use vectors instead of pointers * Fix bug * Add OMP exception handling. * Change return type of LGBM_BoosterGetLinear to bool * Change return type of LGBM_BoosterGetLinear back to int, only parameter type needed to change * Remove unused internal_parent_ property of tree * Remove unused parameter to CreateTreeLearner * Remove reference to LinearTreeLearner * Minor style issues * Remove unneeded check * Reverse temporary testing change * Fix Visual Studio project files * Restore LightGBM.vcxproj.filters * Speed up * Speed up * Simplify code * Update docs * Simplify code * Initialise storage space for max num threads * Move Eigen to include directory and delete unused files * Remove old files. * Fix so it compiles with mingw * Fix gpu tree learner * Change AddPredictionToScore back to const * Fix python lint error * Fix C++ lint errors * Change eigen to a submodule * Update comment * Add the eigen folder * Try to fix build issues with eigen * Remove eigen files * Add eigen as submodule * Fix include paths * Exclude eigen files from Python linter * Ignore eigen folders for pydocstyle * Fix C++ linting errors * Fix docs * Fix docs * Exclude eigen directories from doxygen * Update manifest to include eigen * Update build_r to include eigen files * Fix compiler warnings * Store raw feature data as float * Use float for calculating linear coefficients * Remove eigen directory from GLOB * Don't compile linear model code when building R package * Fix doxygen issue * Fix lint issue * Fix lint issue * Remove uneeded code * Restore delected lines * Restore delected lines * Change return type of has_raw to bool * Update docs * Rename some variables and functions for readability * Make tree_learner parameter const in AddScore * Fix style issues * Pass vectors as const reference when setting tree properties * Make temporary storage of serial_tree_learner mutable so we can make the object's methods const * Remove get_raw_size, use num_numeric_features instead * Fix typo * Make contains_nan_ and any_nan_ properties immutable again * Remove data_has_nan_ property of tree * Remove temporary test code * Make linear_tree a dataset param * Fix lint error * Make LinearTreeLearner a separate class * Fix lint errors * Fix lint error * Add linear_tree_learner.o * Simulate omp_get_max_threads if openmp is not available * Update PushOneData to also store raw data. * Cast size to int * Fix bug in ReshapeRaw * Speed up code with multithreading * Use OMP_NUM_THREADS * Speed up with multithreading * Update to use ArrayToString * Fix tests * Fix test * Fix bug introduced in merge * Minor updates * Update docs
2020-12-24 09:01:23 +03:00
[submodule "eigen"]
Move compute and eigen libraries to external_libs folder (#3809) * move all submodules to external_libs folder * Update .Rbuildignore * Update MANIFEST.in * Update .appveyor.yml * Update CMakeLists.txt * Update build_r.R * Update test.sh * Update setup.py * Update CMakeLists.txt * Update test.sh * Update setup.py * Update conf.py * Update MANIFEST.in * Update LightGBM.vcxproj * continue * test * test * Update setup.py * hotfix * revert CI tests
2021-01-22 17:45:43 +03:00
path = external_libs/eigen
Trees with linear models at leaves (#3299) * Add Eigen library. * Working for simple test. * Apply changes to config params. * Handle nan data. * Update docs. * Add test. * Only load raw data if boosting=gbdt_linear * Remove unneeded code. * Minor updates. * Update to work with sk-learn interface. * Update to work with chunked datasets. * Throw error if we try to create a Booster with an already-constructed dataset having incompatible parameters. * Save raw data in binary dataset file. * Update docs and fix parameter checking. * Fix dataset loading. * Add test for regularization. * Fix bugs when saving and loading tree. * Add test for load/save linear model. * Remove unneeded code. * Fix case where not enough leaf data for linear model. * Simplify code. * Speed up code. * Speed up code. * Simplify code. * Speed up code. * Fix bugs. * Working version. * Store feature data column-wise (not fully working yet). * Fix bugs. * Speed up. * Speed up. * Remove unneeded code. * Small speedup. * Speed up. * Minor updates. * Remove unneeded code. * Fix bug. * Fix bug. * Speed up. * Speed up. * Simplify code. * Remove unneeded code. * Fix bug, add more tests. * Fix bug and add test. * Only store numerical features * Fix bug and speed up using templates. * Speed up prediction. * Fix bug with regularisation * Visual studio files. * Working version * Only check nans if necessary * Store coeff matrix as an array. * Align cache lines * Align cache lines * Preallocation coefficient calculation matrices * Small speedups * Small speedup * Reverse cache alignment changes * Change to dynamic schedule * Update docs. * Refactor so that linear tree learner is not a separate class. * Add refit capability. * Speed up * Small speedups. * Speed up add prediction to score. * Fix bug * Fix bug and speed up. * Speed up dataload. * Speed up dataload * Use vectors instead of pointers * Fix bug * Add OMP exception handling. * Change return type of LGBM_BoosterGetLinear to bool * Change return type of LGBM_BoosterGetLinear back to int, only parameter type needed to change * Remove unused internal_parent_ property of tree * Remove unused parameter to CreateTreeLearner * Remove reference to LinearTreeLearner * Minor style issues * Remove unneeded check * Reverse temporary testing change * Fix Visual Studio project files * Restore LightGBM.vcxproj.filters * Speed up * Speed up * Simplify code * Update docs * Simplify code * Initialise storage space for max num threads * Move Eigen to include directory and delete unused files * Remove old files. * Fix so it compiles with mingw * Fix gpu tree learner * Change AddPredictionToScore back to const * Fix python lint error * Fix C++ lint errors * Change eigen to a submodule * Update comment * Add the eigen folder * Try to fix build issues with eigen * Remove eigen files * Add eigen as submodule * Fix include paths * Exclude eigen files from Python linter * Ignore eigen folders for pydocstyle * Fix C++ linting errors * Fix docs * Fix docs * Exclude eigen directories from doxygen * Update manifest to include eigen * Update build_r to include eigen files * Fix compiler warnings * Store raw feature data as float * Use float for calculating linear coefficients * Remove eigen directory from GLOB * Don't compile linear model code when building R package * Fix doxygen issue * Fix lint issue * Fix lint issue * Remove uneeded code * Restore delected lines * Restore delected lines * Change return type of has_raw to bool * Update docs * Rename some variables and functions for readability * Make tree_learner parameter const in AddScore * Fix style issues * Pass vectors as const reference when setting tree properties * Make temporary storage of serial_tree_learner mutable so we can make the object's methods const * Remove get_raw_size, use num_numeric_features instead * Fix typo * Make contains_nan_ and any_nan_ properties immutable again * Remove data_has_nan_ property of tree * Remove temporary test code * Make linear_tree a dataset param * Fix lint error * Make LinearTreeLearner a separate class * Fix lint errors * Fix lint error * Add linear_tree_learner.o * Simulate omp_get_max_threads if openmp is not available * Update PushOneData to also store raw data. * Cast size to int * Fix bug in ReshapeRaw * Speed up code with multithreading * Use OMP_NUM_THREADS * Speed up with multithreading * Update to use ArrayToString * Fix tests * Fix test * Fix bug introduced in merge * Minor updates * Update docs
2020-12-24 09:01:23 +03:00
url = https://gitlab.com/libeigen/eigen.git
Fix model locale issue and improve model R/W performance. (#3405) * Fix LightGBM models locale sensitivity and improve R/W performance. When Java is used, the default C++ locale is broken. This is true for Java providers that use the C API or even Python models that require JEP. This patch solves that issue making the model reads/writes insensitive to such settings. To achieve it, within the model read/write codebase: - C++ streams are imbued with the classic locale - Calls to functions that are dependent on the locale are replaced - The default locale is not changed! This approach means: - The user's locale is never tampered with, avoiding issues such as https://github.com/microsoft/LightGBM/issues/2979 with the previous approach https://github.com/microsoft/LightGBM/pull/2891 - Datasets can still be read according the user's locale - The model file has a single format independent of locale Changes: - Add CommonC namespace which provides faster locale-independent versions of Common's methods - Model code makes conversions through CommonC - Cleanup unused Common methods - Performance improvements. Use fast libraries for locale-agnostic conversion: - value->string: https://github.com/fmtlib/fmt - string->double: https://github.com/lemire/fast_double_parser (10x faster double parsing according to their benchmark) Bugfixes: - https://github.com/microsoft/LightGBM/issues/2500 - https://github.com/microsoft/LightGBM/issues/2890 - https://github.com/ninia/jep/issues/205 (as it is related to LGBM as well) * Align CommonC namespace * Add new external_libs/ to python setup * Try fast_double_parser fix #1 Testing commit e09e5aad828bcb16bea7ed0ed8322e019112fdbe If it works it should fix more LGBM builds * CMake: Attempt to link fmt without explicit PUBLIC tag * Exclude external_libs from linting * Add exernal_libs to MANIFEST.in * Set dynamic linking option for fmt. * linting issues * Try to fix lint includes * Try to pass fPIC with static fmt lib * Try CMake P_I_C option with fmt library * [R-package] Add CMake support for R and CRAN * Cleanup CMakeLists * Try fmt hack to remove stdout * Switch to header-only mode * Add PRIVATE argument to target_link_libraries * use fmt in header-only mode * Remove CMakeLists comment * Change OpenMP to PUBLIC linking in Mac * Update fmt submodule to 7.1.2 * Use fmt in header-only-mode * Remove fmt from CMakeLists.txt * Upgrade fast_double_parser to v0.2.0 * Revert "Add PRIVATE argument to target_link_libraries" This reverts commit 3dd45dde7b92531b2530ab54522bb843c56227a7. * Address James Lamb's comments * Update R-package/.Rbuildignore Co-authored-by: James Lamb <jaylamb20@gmail.com> * Upgrade to fast_double_parser v0.3.0 - Solaris support * Use legacy code only in Solaris * Fix lint issues * Fix comment * Address StrikerRUS's comments (solaris ifdef). * Change header guards Co-authored-by: James Lamb <jaylamb20@gmail.com>
2020-12-08 16:36:24 +03:00
[submodule "external_libs/fmt"]
path = external_libs/fmt
url = https://github.com/fmtlib/fmt.git
[submodule "external_libs/fast_double_parser"]
path = external_libs/fast_double_parser
url = https://github.com/lemire/fast_double_parser.git