microsoft
/
LightGBM
зеркало из https://github.com/microsoft/LightGBM.git
1
0
Форкнуть 0
Код Задачи Пакеты Проекты Релизы Вики Активность

[ci] prevent trailing whitespace, ensure files end with newline (#6373)

This commit is contained in:
James Lamb 2024-03-18 23:24:14 -05:00 коммит произвёл GitHub
Родитель 6a1ec44437
Коммит 631e0a2a7b
Не найден ключ, соответствующий данной подписи
Идентификатор ключа GPG: B5690EEEBB952194
42 изменённых файлов: 186 добавлений и 177 удалений
Показать статистику Скачать Patch файл Скачать Diff файл Показать все файлы Свернуть все файлы

2
.ci/install-clang-devel.sh
Просмотреть файл

@ -56,7 +56,7 @@ cp --remove-destination /usr/lib/llvm-${CLANG_VERSION}/bin/* /usr/bin/
# per https://www.stats.ox.ac.uk/pub/bdr/Rconfig/r-devel-linux-x86_64-fedora-clang # per https://www.stats.ox.ac.uk/pub/bdr/Rconfig/r-devel-linux-x86_64-fedora-clang
# #
# clang was built to use libc++: for a version built to default to libstdc++ # clang was built to use libc++: for a version built to default to libstdc++
# (as shipped by Fedora/Debian/Ubuntu), add -stdlib=libc++ to CXX # (as shipped by Fedora/Debian/Ubuntu), add -stdlib=libc++ to CXX
# and install the libcxx-devel/libc++-dev package. # and install the libcxx-devel/libc++-dev package.
mkdir -p "${HOME}/.R" mkdir -p "${HOME}/.R"

10
.pre-commit-config.yaml
Просмотреть файл

@ -1,3 +1,4 @@
# exclude files which are auto-generated by build tools
exclude: | exclude: |
(?x)^( (?x)^(
build| build|
@ -5,8 +6,17 @@ exclude: |
lightgbm-python| lightgbm-python|
lightgbm_r| lightgbm_r|
)$ )$
|R-package/configure$
|R-package/inst/Makevars$
|R-package/inst/Makevars.win$
|R-package/man/.*Rd$
repos: repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.5.0
hooks:
- id: end-of-file-fixer
- id: trailing-whitespace
- repo: https://github.com/pycqa/isort - repo: https://github.com/pycqa/isort
rev: 5.13.2 rev: 5.13.2
hooks: hooks:

2
R-package/LICENSE
Просмотреть файл

@ -1,2 +1,2 @@
YEAR: 2016 YEAR: 2016
COPYRIGHT HOLDER: Microsoft Corporation COPYRIGHT HOLDER: Microsoft Corporation

2
R-package/cran-comments.md
Просмотреть файл

@ -14,7 +14,7 @@ warning was not fixed within 14 days.
```text ```text
/usr/local/clang-trunk/bin/../include/c++/v1/__fwd/string_view.h:22:41: /usr/local/clang-trunk/bin/../include/c++/v1/__fwd/string_view.h:22:41:
warning: 'char_traits<fmt::detail::char8_type>' is deprecated: warning: 'char_traits<fmt::detail::char8_type>' is deprecated:
char_traits<T> for T not equal to char, wchar_t, char8_t, char16_t or char32_t is non-standard and is provided for a temporary period. char_traits<T> for T not equal to char, wchar_t, char8_t, char16_t or char32_t is non-standard and is provided for a temporary period.
It will be removed in LLVM 19, so please migrate off of it. [-Wdeprecated-declarations] It will be removed in LLVM 19, so please migrate off of it. [-Wdeprecated-declarations]
``` ```

2
SECURITY.md
Просмотреть файл

@ -14,7 +14,7 @@ Instead, please report them to the Microsoft Security Response Center (MSRC) at
If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/opensource/security/pgpkey). If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/opensource/security/pgpkey).
You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://aka.ms/opensource/security/msrc). You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://aka.ms/opensource/security/msrc).
Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue: Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:

2
build-cran-package.sh
Просмотреть файл

@ -4,7 +4,7 @@
# Prepare a source distribution of the R package # Prepare a source distribution of the R package
# to be submitted to CRAN. # to be submitted to CRAN.
# #
# [arguments] # [arguments]
# #
# --r-executable Customize the R executable used by `R CMD build`. # --r-executable Customize the R executable used by `R CMD build`.
# Useful if building the R package in an environment with # Useful if building the R package in an environment with

8
docs/Advanced-Topics.rst
Просмотреть файл

@ -113,8 +113,8 @@ Unlike a categorical feature, however, ``positions`` are used to adjust the targ
The position file corresponds with training data file line by line, and has one position per line. And if the name of training data file is ``train.txt``, the position file should be named as ``train.txt.position`` and placed in the same folder as the data file. The position file corresponds with training data file line by line, and has one position per line. And if the name of training data file is ``train.txt``, the position file should be named as ``train.txt.position`` and placed in the same folder as the data file.
In this case, LightGBM will load the position file automatically if it exists. The positions can also be specified through the ``Dataset`` constructor when using Python API. If the positions are specified in both approaches, the ``.position`` file will be ignored. In this case, LightGBM will load the position file automatically if it exists. The positions can also be specified through the ``Dataset`` constructor when using Python API. If the positions are specified in both approaches, the ``.position`` file will be ignored.
Currently, implemented is an approach to model position bias by using an idea of Generalized Additive Models (`GAM <https://en.wikipedia.org/wiki/Generalized_additive_model>`_) to linearly decompose the document score ``s`` into the sum of a relevance component ``f`` and a positional component ``g``: ``s(x, pos) = f(x) + g(pos)`` where the former component depends on the original query-document features and the latter depends on the position of an item. Currently, implemented is an approach to model position bias by using an idea of Generalized Additive Models (`GAM <https://en.wikipedia.org/wiki/Generalized_additive_model>`_) to linearly decompose the document score ``s`` into the sum of a relevance component ``f`` and a positional component ``g``: ``s(x, pos) = f(x) + g(pos)`` where the former component depends on the original query-document features and the latter depends on the position of an item.
During the training, the compound scoring function ``s(x, pos)`` is fit with a standard ranking algorithm (e.g., LambdaMART) which boils down to jointly learning the relevance component ``f(x)`` (it is later returned as an unbiased model) and the position factors ``g(pos)`` that help better explain the observed (biased) labels. During the training, the compound scoring function ``s(x, pos)`` is fit with a standard ranking algorithm (e.g., LambdaMART) which boils down to jointly learning the relevance component ``f(x)`` (it is later returned as an unbiased model) and the position factors ``g(pos)`` that help better explain the observed (biased) labels.
Similar score decomposition ideas have previously been applied for classification & pointwise ranking tasks with assumptions of binary labels and binary relevance (a.k.a. "two-tower" models, refer to the papers: `Towards Disentangling Relevance and Bias in Unbiased Learning to Rank <https://arxiv.org/abs/2212.13937>`_, `PAL: a position-bias aware learning framework for CTR prediction in live recommender systems <https://dl.acm.org/doi/10.1145/3298689.3347033>`_, `A General Framework for Debiasing in CTR Prediction <https://arxiv.org/abs/2112.02767>`_). Similar score decomposition ideas have previously been applied for classification & pointwise ranking tasks with assumptions of binary labels and binary relevance (a.k.a. "two-tower" models, refer to the papers: `Towards Disentangling Relevance and Bias in Unbiased Learning to Rank <https://arxiv.org/abs/2212.13937>`_, `PAL: a position-bias aware learning framework for CTR prediction in live recommender systems <https://dl.acm.org/doi/10.1145/3298689.3347033>`_, `A General Framework for Debiasing in CTR Prediction <https://arxiv.org/abs/2112.02767>`_).
In LightGBM, we adapt this idea to general pairwise Lerarning-to-Rank with arbitrary ordinal relevance labels. In LightGBM, we adapt this idea to general pairwise Lerarning-to-Rank with arbitrary ordinal relevance labels.
Besides, GAMs have been used in the context of explainable ML (`Accurate Intelligible Models with Pairwise Interactions <https://www.cs.cornell.edu/~yinlou/papers/lou-kdd13.pdf>`_) to linearly decompose the contribution of each feature (and possibly their pairwise interactions) to the overall score, for subsequent analysis and interpretation of their effects in the trained models. Besides, GAMs have been used in the context of explainable ML (`Accurate Intelligible Models with Pairwise Interactions <https://www.cs.cornell.edu/~yinlou/papers/lou-kdd13.pdf>`_) to linearly decompose the contribution of each feature (and possibly their pairwise interactions) to the overall score, for subsequent analysis and interpretation of their effects in the trained models.

2
docs/Features.rst
Просмотреть файл

@ -21,7 +21,7 @@ LightGBM uses histogram-based algorithms\ `[4, 5, 6] <#references>`__, which buc
- To get one leaf's histograms in a binary tree, use the histogram subtraction of its parent and its neighbor - To get one leaf's histograms in a binary tree, use the histogram subtraction of its parent and its neighbor
- So it needs to construct histograms for only one leaf (with smaller ``#data`` than its neighbor). It then can get histograms of its neighbor by histogram subtraction with small cost (``O(#bins)``) - So it needs to construct histograms for only one leaf (with smaller ``#data`` than its neighbor). It then can get histograms of its neighbor by histogram subtraction with small cost (``O(#bins)``)
- **Reduce memory usage** - **Reduce memory usage**
- Replaces continuous values with discrete bins. If ``#bins`` is small, can use small data type, e.g. uint8\_t, to store training data - Replaces continuous values with discrete bins. If ``#bins`` is small, can use small data type, e.g. uint8\_t, to store training data

6
docs/GPU-Targets.rst
Просмотреть файл

@ -107,7 +107,7 @@ Example of using GPU (``gpu_platform_id = 0`` and ``gpu_device_id = 0`` in our s
[LightGBM] [Info] 40 dense feature groups (0.12 MB) transferred to GPU in 0.004211 secs. 76 sparse feature groups. [LightGBM] [Info] 40 dense feature groups (0.12 MB) transferred to GPU in 0.004211 secs. 76 sparse feature groups.
[LightGBM] [Info] No further splits with positive gain, best gain: -inf [LightGBM] [Info] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Trained a tree with leaves=16 and depth=8 [LightGBM] [Info] Trained a tree with leaves=16 and depth=8
[1]: test's rmse:1.10643e-17 [1]: test's rmse:1.10643e-17
[LightGBM] [Info] No further splits with positive gain, best gain: -inf [LightGBM] [Info] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Trained a tree with leaves=7 and depth=5 [LightGBM] [Info] Trained a tree with leaves=7 and depth=5
[2]: test's rmse:0 [2]: test's rmse:0
@ -145,11 +145,11 @@ Example of using CPU (``gpu_platform_id = 0``, ``gpu_device_id = 1``). The GPU d
[LightGBM] [Info] 40 dense feature groups (0.12 MB) transferred to GPU in 0.004540 secs. 76 sparse feature groups. [LightGBM] [Info] 40 dense feature groups (0.12 MB) transferred to GPU in 0.004540 secs. 76 sparse feature groups.
[LightGBM] [Info] No further splits with positive gain, best gain: -inf [LightGBM] [Info] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Trained a tree with leaves=16 and depth=8 [LightGBM] [Info] Trained a tree with leaves=16 and depth=8
[1]: test's rmse:1.10643e-17 [1]: test's rmse:1.10643e-17
[LightGBM] [Info] No further splits with positive gain, best gain: -inf [LightGBM] [Info] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Trained a tree with leaves=7 and depth=5 [LightGBM] [Info] Trained a tree with leaves=7 and depth=5
[2]: test's rmse:0 [2]: test's rmse:0
Known issues: Known issues:

2
docs/GPU-Tutorial.rst
Просмотреть файл

@ -61,7 +61,7 @@ Now we are ready to checkout LightGBM and compile it with GPU support:
cd LightGBM cd LightGBM
mkdir build mkdir build
cd build cd build
cmake -DUSE_GPU=1 .. cmake -DUSE_GPU=1 ..
  # if you have installed NVIDIA CUDA to a customized location, you should specify paths to OpenCL headers and library like the following:   # if you have installed NVIDIA CUDA to a customized location, you should specify paths to OpenCL headers and library like the following:
# cmake -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ .. # cmake -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ ..
make -j$(nproc) make -j$(nproc)

2
docs/Key-Events.md
Просмотреть файл

@ -75,7 +75,7 @@ The list includes the commits where the major feature added is considered workin
* 22/06/2017 [Microsoft/LightGBM@d862b3e](https://github.com/microsoft/LightGBM/pull/642): CIntegration: Travis OSX Support (Pull Request 642) * 22/06/2017 [Microsoft/LightGBM@d862b3e](https://github.com/microsoft/LightGBM/pull/642): CIntegration: Travis OSX Support (Pull Request 642)
* 20/06/2017 [Microsoft/LightGBM@80c641c](https://github.com/microsoft/LightGBM/pull/635): Release: Python pip package (Pull Request 635) * 20/06/2017 [Microsoft/LightGBM@80c641c](https://github.com/microsoft/LightGBM/pull/635): Release: Python pip package (Pull Request 635)
* 18/06/2017 [Microsoft/LightGBM@4d2aa84](https://github.com/microsoft/LightGBM/pull/634): CIntegration: AppVeyor Support (Pull Request 634) * 18/06/2017 [Microsoft/LightGBM@4d2aa84](https://github.com/microsoft/LightGBM/pull/634): CIntegration: AppVeyor Support (Pull Request 634)
* 06/06/2017 [Microsoft/LightGBM@2c9ce59](https://github.com/microsoft/LightGBM/pull/592): Release: R-package version 0.2 (Pull Request 592) * 06/06/2017 [Microsoft/LightGBM@2c9ce59](https://github.com/microsoft/LightGBM/pull/592): Release: R-package version 0.2 (Pull Request 592)
* 05/06/2017 [Microsoft/LightGBM@f98d75f](https://github.com/microsoft/LightGBM/pull/584): Feature: Use custom compiler for R-package (Pull Request 584) * 05/06/2017 [Microsoft/LightGBM@f98d75f](https://github.com/microsoft/LightGBM/pull/584): Feature: Use custom compiler for R-package (Pull Request 584)
* 29/05/2017 [Microsoft/LightGBM@993bbd5](https://github.com/microsoft/LightGBM/pull/559): Parameter: Early Stopping for predictions (Pull Request 559) * 29/05/2017 [Microsoft/LightGBM@993bbd5](https://github.com/microsoft/LightGBM/pull/559): Parameter: Early Stopping for predictions (Pull Request 559)
* 26/05/2017 [Microsoft/LightGBM@3abff37](https://github.com/microsoft/LightGBM/commit/3abff370bb353293e4a03e516111dd02785fbd97): Feature: Parameter to disable missing values (Commit) * 26/05/2017 [Microsoft/LightGBM@3abff37](https://github.com/microsoft/LightGBM/commit/3abff370bb353293e4a03e516111dd02785fbd97): Feature: Parameter to disable missing values (Commit)

2
docs/Makefile
Просмотреть файл

@ -17,4 +17,4 @@ help:
# Catch-all target: route all unknown targets to Sphinx using the new # Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile %: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

2
docs/_static/images/artifacts-download.svg поставляемый
Просмотреть файл

@ -1 +1 @@
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="118" height="20"><linearGradient id="b" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="a"><rect width="118" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#a)"><path fill="#555" d="M0 0h55v20H0z"/><path fill="#4c1" d="M55 0h63v20H55z"/><path fill="url(#b)" d="M0 0h118v20H0z"/></g><g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="110"> <text x="285" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="450">artifacts</text><text x="285" y="140" transform="scale(.1)" textLength="450">artifacts</text><text x="855" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="530">download</text><text x="855" y="140" transform="scale(.1)" textLength="530">download</text></g> </svg> <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="118" height="20"><linearGradient id="b" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="a"><rect width="118" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#a)"><path fill="#555" d="M0 0h55v20H0z"/><path fill="#4c1" d="M55 0h63v20H55z"/><path fill="url(#b)" d="M0 0h118v20H0z"/></g><g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="110"> <text x="285" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="450">artifacts</text><text x="285" y="140" transform="scale(.1)" textLength="450">artifacts</text><text x="855" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="530">download</text><text x="855" y="140" transform="scale(.1)" textLength="530">download</text></g> </svg>

Режим "рядом" Свайп Оверлей

До

Ширина:  |  Высота:  |  Размер: 964 B

После

Ширина:  |  Высота:  |  Размер: 965 B

2
docs/_static/images/artifacts-fetching.svg поставляемый
Просмотреть файл

@ -1 +1 @@
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="122" height="20"><linearGradient id="b" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="a"><rect width="122" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#a)"><path fill="#555" d="M0 0h55v20H0z"/><path fill="#9f9f9f" d="M55 0h67v20H55z"/><path fill="url(#b)" d="M0 0h122v20H0z"/></g><g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="110"> <text x="285" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="450">artifacts</text><text x="285" y="140" transform="scale(.1)" textLength="450">artifacts</text><text x="875" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="570">fetching...</text><text x="875" y="140" transform="scale(.1)" textLength="570">fetching...</text></g> </svg> <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="122" height="20"><linearGradient id="b" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="a"><rect width="122" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#a)"><path fill="#555" d="M0 0h55v20H0z"/><path fill="#9f9f9f" d="M55 0h67v20H55z"/><path fill="url(#b)" d="M0 0h122v20H0z"/></g><g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="110"> <text x="285" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="450">artifacts</text><text x="285" y="140" transform="scale(.1)" textLength="450">artifacts</text><text x="875" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="570">fetching...</text><text x="875" y="140" transform="scale(.1)" textLength="570">fetching...</text></g> </svg>

Режим "рядом" Свайп Оверлей

До

Ширина:  |  Высота:  |  Размер: 973 B

После

Ширина:  |  Высота:  |  Размер: 974 B

2
docs/_static/images/artifacts-not-available.svg поставляемый
Просмотреть файл

@ -1 +1 @@
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="302" height="20"><linearGradient id="b" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="a"><rect width="302" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#a)"><path fill="#555" d="M0 0h55v20H0z"/><path fill="#9f9f9f" d="M55 0h247v20H55z"/><path fill="url(#b)" d="M0 0h302v20H0z"/></g><g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="110"> <text x="285" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="450">artifacts</text><text x="285" y="140" transform="scale(.1)" textLength="450">artifacts</text><text x="1775" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="2370">link is available only on Read the Docs site</text><text x="1775" y="140" transform="scale(.1)" textLength="2370">link is available only on Read the Docs site</text></g> </svg> <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="302" height="20"><linearGradient id="b" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="a"><rect width="302" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#a)"><path fill="#555" d="M0 0h55v20H0z"/><path fill="#9f9f9f" d="M55 0h247v20H55z"/><path fill="url(#b)" d="M0 0h302v20H0z"/></g><g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="110"> <text x="285" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="450">artifacts</text><text x="285" y="140" transform="scale(.1)" textLength="450">artifacts</text><text x="1775" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="2370">link is available only on Read the Docs site</text><text x="1775" y="140" transform="scale(.1)" textLength="2370">link is available only on Read the Docs site</text></g> </svg>

Режим "рядом" Свайп Оверлей

До

Ширина:  |  Высота:  |  Размер: 1.0 KiB

После

Ширина:  |  Высота:  |  Размер: 1.0 KiB

2
docs/_static/images/dask-concat.svg поставляемый
Просмотреть файл

Различия файлов скрыты, потому что одна или несколько строк слишком длинны
Режим "рядом" Свайп Оверлей

До

Ширина:  |  Высота:  |  Размер: 87 KiB

После

Ширина:  |  Высота:  |  Размер: 87 KiB

2
docs/_static/images/dask-initial-setup.svg поставляемый
Просмотреть файл

Различия файлов скрыты, потому что одна или несколько строк слишком длинны
Режим "рядом" Свайп Оверлей

До

Ширина:  |  Высота:  |  Размер: 59 KiB

После

Ширина:  |  Высота:  |  Размер: 59 KiB

8
examples/README.md
Просмотреть файл

@ -34,7 +34,7 @@ Machine Learning Challenge Winning Solutions
| 1st | [Google Analytics Customer Revenue Prediction](https://www.kaggle.com/c/ga-customer-revenue-prediction) | [link](https://www.kaggle.com/c/ga-customer-revenue-prediction/discussion/82614#latest-482575) | 2019.3 | | 1st | [Google Analytics Customer Revenue Prediction](https://www.kaggle.com/c/ga-customer-revenue-prediction) | [link](https://www.kaggle.com/c/ga-customer-revenue-prediction/discussion/82614#latest-482575) | 2019.3 |
| 1st | [VSB Power Line Fault Detection](https://www.kaggle.com/c/vsb-power-line-fault-detection) | [link](https://www.kaggle.com/c/vsb-power-line-fault-detection/discussion/87038#latest-521846) | 2019.3 | | 1st | [VSB Power Line Fault Detection](https://www.kaggle.com/c/vsb-power-line-fault-detection) | [link](https://www.kaggle.com/c/vsb-power-line-fault-detection/discussion/87038#latest-521846) | 2019.3 |
| 5th | [Elo Merchant Category Recommendation](https://www.kaggle.com/c/elo-merchant-category-recommendation) | [link](https://www.kaggle.com/c/elo-merchant-category-recommendation/discussion/82314#latest-525737) | 2019.2 | | 5th | [Elo Merchant Category Recommendation](https://www.kaggle.com/c/elo-merchant-category-recommendation) | [link](https://www.kaggle.com/c/elo-merchant-category-recommendation/discussion/82314#latest-525737) | 2019.2 |
| 2nd | [PLAsTiCC Astronomical Classification](https://www.kaggle.com/c/PLAsTiCC-2018) | [link](https://www.kaggle.com/c/PLAsTiCC-2018/discussion/75059#latest-462457) | 2018.12 | | 2nd | [PLAsTiCC Astronomical Classification](https://www.kaggle.com/c/PLAsTiCC-2018) | [link](https://www.kaggle.com/c/PLAsTiCC-2018/discussion/75059#latest-462457) | 2018.12 |
| 1st | [Google Research Doodle Recognition Challenge](https://www.kaggle.com/c/quickdraw-doodle-recognition) | [link](https://www.kaggle.com/c/quickdraw-doodle-recognition/discussion/73738#latest-550028) | 2018.12 | | 1st | [Google Research Doodle Recognition Challenge](https://www.kaggle.com/c/quickdraw-doodle-recognition) | [link](https://www.kaggle.com/c/quickdraw-doodle-recognition/discussion/73738#latest-550028) | 2018.12 |
| 1st | [Home Credit Group Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk) | [link](https://www.kaggle.com/c/home-credit-default-risk/discussion/64480#latest-514514) | 2018.8 | | 1st | [Home Credit Group Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk) | [link](https://www.kaggle.com/c/home-credit-default-risk/discussion/64480#latest-514514) | 2018.8 |
| 2nd | [Home Credit Group Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk) | [link](https://www.kaggle.com/c/home-credit-default-risk/discussion/64722#latest-394948) | 2018.8 | | 2nd | [Home Credit Group Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk) | [link](https://www.kaggle.com/c/home-credit-default-risk/discussion/64722#latest-394948) | 2018.8 |
@ -45,11 +45,11 @@ Machine Learning Challenge Winning Solutions
| 2nd | [Avito Demand Prediction Challenge](https://www.kaggle.com/c/avito-demand-prediction) | [link](https://www.kaggle.com/c/avito-demand-prediction/discussion/59871#latest-470807) | 2018.6 | | 2nd | [Avito Demand Prediction Challenge](https://www.kaggle.com/c/avito-demand-prediction) | [link](https://www.kaggle.com/c/avito-demand-prediction/discussion/59871#latest-470807) | 2018.6 |
| 3rd | [Avito Demand Prediction Challenge](https://www.kaggle.com/c/avito-demand-prediction) | [link](https://www.kaggle.com/c/avito-demand-prediction/discussion/59885#latest-364403) | 2018.6 | | 3rd | [Avito Demand Prediction Challenge](https://www.kaggle.com/c/avito-demand-prediction) | [link](https://www.kaggle.com/c/avito-demand-prediction/discussion/59885#latest-364403) | 2018.6 |
| 1st | [TalkingData AdTracking Fraud Detection Challenge](https://www.kaggle.com/c/talkingdata-adtracking-fraud-detection) | [link](https://www.kaggle.com/c/talkingdata-adtracking-fraud-detection/discussion/56475)| 2018.5 | | 1st | [TalkingData AdTracking Fraud Detection Challenge](https://www.kaggle.com/c/talkingdata-adtracking-fraud-detection) | [link](https://www.kaggle.com/c/talkingdata-adtracking-fraud-detection/discussion/56475)| 2018.5 |
| 1st | [DonorsChoose.org Application Screening](https://www.kaggle.com/c/donorschoose-application-screening)| [link](https://www.kaggle.com/shadowwarrior/1st-place-solution/notebook) | 2018.4 | | 1st | [DonorsChoose.org Application Screening](https://www.kaggle.com/c/donorschoose-application-screening)| [link](https://www.kaggle.com/shadowwarrior/1st-place-solution/notebook) | 2018.4 |
| 1st | [Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)| [link](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/discussion/52557) | 2018.3 | | 1st | [Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)| [link](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/discussion/52557) | 2018.3 |
| 1st | [Mercari Price Suggestion Challenge](https://www.kaggle.com/c/mercari-price-suggestion-challenge) | [link](https://www.kaggle.com/c/mercari-price-suggestion-challenge/discussion/50256) | 2018.2 | | 1st | [Mercari Price Suggestion Challenge](https://www.kaggle.com/c/mercari-price-suggestion-challenge) | [link](https://www.kaggle.com/c/mercari-price-suggestion-challenge/discussion/50256) | 2018.2 |
| 1st | [IEEE's Signal Processing Society, Camera Model Identification](https://www.kaggle.com/c/sp-society-camera-model-identification)| [link](https://www.kaggle.com/c/sp-society-camera-model-identification/discussion/49367) | 2018.2 | | 1st | [IEEE's Signal Processing Society, Camera Model Identification](https://www.kaggle.com/c/sp-society-camera-model-identification)| [link](https://www.kaggle.com/c/sp-society-camera-model-identification/discussion/49367) | 2018.2 |
| 1st | [Recruit Restaurant Visitor Forecasting](https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting) | [link](https://www.kaggle.com/pureheart/1st-place-lgb-model-public-0-470-private-0-502/comments) | 2018.2| | 1st | [Recruit Restaurant Visitor Forecasting](https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting) | [link](https://www.kaggle.com/pureheart/1st-place-lgb-model-public-0-470-private-0-502/comments) | 2018.2|
| 1st | [WSDM CUP 2018 - KKBox's Music Recommendation Challenge](https://www.kaggle.com/c/kkbox-music-recommendation-challenge) | [link](https://www.kaggle.com/c/kkbox-music-recommendation-challenge/discussion/45942) | 2017.12 | | 1st | [WSDM CUP 2018 - KKBox's Music Recommendation Challenge](https://www.kaggle.com/c/kkbox-music-recommendation-challenge) | [link](https://www.kaggle.com/c/kkbox-music-recommendation-challenge/discussion/45942) | 2017.12 |
| 1st | [Porto Seguro’s Safe Driver Prediction](https://www.kaggle.com/c/porto-seguro-safe-driver-prediction) | [link](https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/discussion/44629) |2017.11 | | 1st | [Porto Seguro’s Safe Driver Prediction](https://www.kaggle.com/c/porto-seguro-safe-driver-prediction) | [link](https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/discussion/44629) |2017.11 |
| 1st | [Quora Question Pairs](https://www.kaggle.com/c/quora-question-pairs) | [link](https://www.kaggle.com/c/quora-question-pairs/discussion/34355) | 2017.6 | | 1st | [Quora Question Pairs](https://www.kaggle.com/c/quora-question-pairs) | [link](https://www.kaggle.com/c/quora-question-pairs/discussion/34355) | 2017.6 |

12
examples/binary_classification/train.conf
Просмотреть файл

@ -12,10 +12,10 @@ boosting_type = gbdt
objective = binary objective = binary
# eval metrics, support multi metric, delimited by ',' , support following metrics # eval metrics, support multi metric, delimited by ',' , support following metrics
# l1 # l1
# l2 , default metric for regression # l2 , default metric for regression
# ndcg , default metric for lambdarank # ndcg , default metric for lambdarank
# auc # auc
# binary_logloss , default metric for binary # binary_logloss , default metric for binary
# binary_error # binary_error
metric = binary_logloss,auc metric = binary_logloss,auc
@ -29,7 +29,7 @@ is_training_metric = true
# column in data to use as label # column in data to use as label
label_column = 0 label_column = 0
# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.
max_bin = 255 max_bin = 255
# training data # training data
@ -39,7 +39,7 @@ data = binary.train
# validation data, support multi validation data, separated by ',' # validation data, support multi validation data, separated by ','
# if existing weight file, should name to "binary.test.weight" # if existing weight file, should name to "binary.test.weight"
# alias: valid, test, test_data, # alias: valid, test, test_data,
valid_data = binary.test valid_data = binary.test
# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds # number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds
@ -59,10 +59,10 @@ num_leaves = 63
# alias: tree # alias: tree
tree_learner = serial tree_learner = serial
# number of threads for multi-threading. One thread will use each CPU. The default is the CPU count. # number of threads for multi-threading. One thread will use each CPU. The default is the CPU count.
# num_threads = 8 # num_threads = 8
# feature sub-sample, will random select 80% feature to train on each iteration # feature sub-sample, will random select 80% feature to train on each iteration
# alias: sub_feature # alias: sub_feature
feature_fraction = 0.8 feature_fraction = 0.8

12
examples/binary_classification/train_linear.conf
Просмотреть файл

@ -14,10 +14,10 @@ objective = binary
linear_tree = true linear_tree = true
# eval metrics, support multi metric, delimited by ',' , support following metrics # eval metrics, support multi metric, delimited by ',' , support following metrics
# l1 # l1
# l2 , default metric for regression # l2 , default metric for regression
# ndcg , default metric for lambdarank # ndcg , default metric for lambdarank
# auc # auc
# binary_logloss , default metric for binary # binary_logloss , default metric for binary
# binary_error # binary_error
metric = binary_logloss,auc metric = binary_logloss,auc
@ -28,7 +28,7 @@ metric_freq = 1
# true if need output metric for training data, alias: tranining_metric, train_metric # true if need output metric for training data, alias: tranining_metric, train_metric
is_training_metric = true is_training_metric = true
# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.
max_bin = 255 max_bin = 255
# training data # training data
@ -38,7 +38,7 @@ data = binary.train
# validation data, support multi validation data, separated by ',' # validation data, support multi validation data, separated by ','
# if existing weight file, should name to "binary.test.weight" # if existing weight file, should name to "binary.test.weight"
# alias: valid, test, test_data, # alias: valid, test, test_data,
valid_data = binary.test valid_data = binary.test
# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds # number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds
@ -58,10 +58,10 @@ num_leaves = 63
# alias: tree # alias: tree
tree_learner = serial tree_learner = serial
# number of threads for multi-threading. One thread will use each CPU. The default is set to CPU count. # number of threads for multi-threading. One thread will use each CPU. The default is set to CPU count.
# num_threads = 8 # num_threads = 8
# feature sub-sample, will random select 80% feature to train on each iteration # feature sub-sample, will random select 80% feature to train on each iteration
# alias: sub_feature # alias: sub_feature
feature_fraction = 0.8 feature_fraction = 0.8

2
examples/lambdarank/README.md
Просмотреть файл

@ -29,5 +29,5 @@ Run the following command in this folder:
Data Format Data Format
----------- -----------
To learn more about the query format used in this example, check out the To learn more about the query format used in this example, check out the
[query data format](https://lightgbm.readthedocs.io/en/latest/Parameters.html#query-data). [query data format](https://lightgbm.readthedocs.io/en/latest/Parameters.html#query-data).

12
examples/lambdarank/train.conf
Просмотреть файл

@ -12,10 +12,10 @@ boosting_type = gbdt
objective = lambdarank objective = lambdarank
# eval metrics, support multi metric, delimited by ',' , support following metrics # eval metrics, support multi metric, delimited by ',' , support following metrics
# l1 # l1
# l2 , default metric for regression # l2 , default metric for regression
# ndcg , default metric for lambdarank # ndcg , default metric for lambdarank
# auc # auc
# binary_logloss , default metric for binary # binary_logloss , default metric for binary
# binary_error # binary_error
metric = ndcg metric = ndcg
@ -32,7 +32,7 @@ is_training_metric = true
# column in data to use as label # column in data to use as label
label_column = 0 label_column = 0
# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.
max_bin = 255 max_bin = 255
# training data # training data
@ -44,7 +44,7 @@ data = rank.train
# validation data, support multi validation data, separated by ',' # validation data, support multi validation data, separated by ','
# if existing weight file, should name to "rank.test.weight" # if existing weight file, should name to "rank.test.weight"
# if existing query file, should name to "rank.test.query" # if existing query file, should name to "rank.test.query"
# alias: valid, test, test_data, # alias: valid, test, test_data,
valid_data = rank.test valid_data = rank.test
# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds # number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds
@ -64,10 +64,10 @@ num_leaves = 31
# alias: tree # alias: tree
tree_learner = serial tree_learner = serial
# number of threads for multi-threading. One thread will use one CPU, defalut is setted to #cpu. # number of threads for multi-threading. One thread will use one CPU, defalut is setted to #cpu.
# num_threads = 8 # num_threads = 8
# feature sub-sample, will random select 80% feature to train on each iteration # feature sub-sample, will random select 80% feature to train on each iteration
# alias: sub_feature # alias: sub_feature
feature_fraction = 1.0 feature_fraction = 1.0

6
examples/multiclass_classification/train.conf
Просмотреть файл

@ -13,10 +13,10 @@ boosting_type = gbdt
objective = multiclass objective = multiclass
# eval metrics, support multi metric, delimited by ',' , support following metrics # eval metrics, support multi metric, delimited by ',' , support following metrics
# l1 # l1
# l2 , default metric for regression # l2 , default metric for regression
# ndcg , default metric for lambdarank # ndcg , default metric for lambdarank
# auc # auc
# binary_logloss , default metric for binary # binary_logloss , default metric for binary
# binary_error # binary_error
# multi_logloss # multi_logloss
@ -44,7 +44,7 @@ is_training_metric = true
# column in data to use as label # column in data to use as label
label_column = 0 label_column = 0
# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.
max_bin = 255 max_bin = 255
# training data # training data

10
examples/parallel_learning/train.conf
Просмотреть файл

@ -12,10 +12,10 @@ boosting_type = gbdt
objective = binary objective = binary
# eval metrics, support multi metric, delimite by ',' , support following metrics # eval metrics, support multi metric, delimite by ',' , support following metrics
# l1 # l1
# l2 , default metric for regression # l2 , default metric for regression
# ndcg , default metric for lambdarank # ndcg , default metric for lambdarank
# auc # auc
# binary_logloss , default metric for binary # binary_logloss , default metric for binary
# binary_error # binary_error
metric = binary_logloss,auc metric = binary_logloss,auc
@ -29,7 +29,7 @@ is_training_metric = true
# column in data to use as label # column in data to use as label
label_column = 0 label_column = 0
# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.
max_bin = 255 max_bin = 255
# training data # training data
@ -39,7 +39,7 @@ data = binary.train
# validation data, support multi validation data, separated by ',' # validation data, support multi validation data, separated by ','
# if existing weight file, should name to "binary.test.weight" # if existing weight file, should name to "binary.test.weight"
# alias: valid, test, test_data, # alias: valid, test, test_data,
valid_data = binary.test valid_data = binary.test
# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds # number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds
@ -62,7 +62,7 @@ tree_learner = feature
# number of threads for multi-threading. One thread will use each CPU. The default is the CPU count. # number of threads for multi-threading. One thread will use each CPU. The default is the CPU count.
# num_threads = 8 # num_threads = 8
# feature sub-sample, will random select 80% feature to train on each iteration # feature sub-sample, will random select 80% feature to train on each iteration
# alias: sub_feature # alias: sub_feature
feature_fraction = 0.8 feature_fraction = 0.8

4
examples/python-guide/README.md
Просмотреть файл

@ -23,11 +23,11 @@ Examples include:
- [simple_example.py](https://github.com/microsoft/LightGBM/blob/master/examples/python-guide/simple_example.py) - [simple_example.py](https://github.com/microsoft/LightGBM/blob/master/examples/python-guide/simple_example.py)
- Construct Dataset - Construct Dataset
- Basic train and predict - Basic train and predict
- Eval during training - Eval during training
- Early stopping - Early stopping
- Save model to file - Save model to file
- [sklearn_example.py](https://github.com/microsoft/LightGBM/blob/master/examples/python-guide/sklearn_example.py) - [sklearn_example.py](https://github.com/microsoft/LightGBM/blob/master/examples/python-guide/sklearn_example.py)
- Create data for learning with sklearn interface - Create data for learning with sklearn interface
- Basic train and predict with sklearn interface - Basic train and predict with sklearn interface
- Feature importances with sklearn interface - Feature importances with sklearn interface
- Self-defined eval metric with sklearn interface - Self-defined eval metric with sklearn interface

12
examples/regression/train.conf
Просмотреть файл

@ -12,10 +12,10 @@ boosting_type = gbdt
objective = regression objective = regression
# eval metrics, support multi metric, delimite by ',' , support following metrics # eval metrics, support multi metric, delimite by ',' , support following metrics
# l1 # l1
# l2 , default metric for regression # l2 , default metric for regression
# ndcg , default metric for lambdarank # ndcg , default metric for lambdarank
# auc # auc
# binary_logloss , default metric for binary # binary_logloss , default metric for binary
# binary_error # binary_error
metric = l2 metric = l2
@ -29,7 +29,7 @@ is_training_metric = true
# column in data to use as label # column in data to use as label
label_column = 0 label_column = 0
# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.
max_bin = 255 max_bin = 255
# forced bin thresholds # forced bin thresholds
@ -42,7 +42,7 @@ data = regression.train
# validation data, support multi validation data, separated by ',' # validation data, support multi validation data, separated by ','
# if exsting weight file, should name to "regression.test.weight" # if exsting weight file, should name to "regression.test.weight"
# alias: valid, test, test_data, # alias: valid, test, test_data,
valid_data = regression.test valid_data = regression.test
# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds # number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds
@ -62,10 +62,10 @@ num_leaves = 31
# alias: tree # alias: tree
tree_learner = serial tree_learner = serial
# number of threads for multi-threading. One thread will use one CPU, default is setted to #cpu. # number of threads for multi-threading. One thread will use one CPU, default is setted to #cpu.
# num_threads = 8 # num_threads = 8
# feature sub-sample, will random select 80% feature to train on each iteration # feature sub-sample, will random select 80% feature to train on each iteration
# alias: sub_feature # alias: sub_feature
feature_fraction = 0.9 feature_fraction = 0.9

2
examples/xendcg/README.md
Просмотреть файл

@ -29,5 +29,5 @@ Run the following command in this folder:
Data Format Data Format
----------- -----------
To learn more about the query format used in this example, check out the To learn more about the query format used in this example, check out the
[query data format](https://lightgbm.readthedocs.io/en/latest/Parameters.html#query-data). [query data format](https://lightgbm.readthedocs.io/en/latest/Parameters.html#query-data).

10
examples/xendcg/train.conf
Просмотреть файл

@ -12,10 +12,10 @@ boosting_type = gbdt
objective = rank_xendcg objective = rank_xendcg
# eval metrics, support multi metric, delimite by ',' , support following metrics # eval metrics, support multi metric, delimite by ',' , support following metrics
# l1 # l1
# l2 , default metric for regression # l2 , default metric for regression
# ndcg , default metric for lambdarank # ndcg , default metric for lambdarank
# auc # auc
# binary_logloss , default metric for binary # binary_logloss , default metric for binary
# binary_error # binary_error
metric = ndcg metric = ndcg
@ -32,7 +32,7 @@ is_training_metric = true
# column in data to use as label # column in data to use as label
label_column = 0 label_column = 0
# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. # number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.
max_bin = 255 max_bin = 255
# training data # training data
@ -44,7 +44,7 @@ data = rank.train
# validation data, support multi validation data, separated by ',' # validation data, support multi validation data, separated by ','
# if existing weight file, should name to "rank.test.weight" # if existing weight file, should name to "rank.test.weight"
# if existing query file, should name to "rank.test.query" # if existing query file, should name to "rank.test.query"
# alias: valid, test, test_data, # alias: valid, test, test_data,
valid_data = rank.test valid_data = rank.test
# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds # number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds
@ -68,7 +68,7 @@ tree_learner = serial
num_threads = 1 num_threads = 1
objective_seed = 1025 objective_seed = 1025
# feature sub-sample, will random select 80% feature to train on each iteration # feature sub-sample, will random select 80% feature to train on each iteration
# alias: sub_feature # alias: sub_feature
feature_fraction = 1.0 feature_fraction = 1.0

2
include/LightGBM/bin.h
Просмотреть файл

@ -144,7 +144,7 @@ class BinMapper {
/*! /*!
* \brief Maximum categorical value * \brief Maximum categorical value
* \return Maximum categorical value for categorical features, 0 for numerical features * \return Maximum categorical value for categorical features, 0 for numerical features
*/ */
inline int MaxCatValue() const { inline int MaxCatValue() const {
if (bin_2_categorical_.size() == 0) { if (bin_2_categorical_.size() == 0) {

6
include/LightGBM/network.h
Просмотреть файл

@ -128,7 +128,7 @@ class Network {
const ReduceFunction& reducer); const ReduceFunction& reducer);
/*! /*!
* \brief Performing all_gather by using Bruck algorithm. * \brief Performing all_gather by using Bruck algorithm.
Communication times is O(log(n)), and communication cost is O(send_size * number_machine) Communication times is O(log(n)), and communication cost is O(send_size * number_machine)
* It can be used when all nodes have same input size. * It can be used when all nodes have same input size.
* \param input Input data * \param input Input data
@ -138,7 +138,7 @@ class Network {
static void Allgather(char* input, comm_size_t send_size, char* output); static void Allgather(char* input, comm_size_t send_size, char* output);
/*! /*!
* \brief Performing all_gather by using Bruck algorithm. * \brief Performing all_gather by using Bruck algorithm.
Communication times is O(log(n)), and communication cost is O(all_size) Communication times is O(log(n)), and communication cost is O(all_size)
* It can be used when nodes have different input size. * It can be used when nodes have different input size.
* \param input Input data * \param input Input data
@ -150,7 +150,7 @@ class Network {
static void Allgather(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size); static void Allgather(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size);
/*! /*!
* \brief Perform reduce scatter by using recursive halving algorithm. * \brief Perform reduce scatter by using recursive halving algorithm.
Communication times is O(log(n)), and communication cost is O(input_size) Communication times is O(log(n)), and communication cost is O(input_size)
* \param input Input data * \param input Input data
* \param input_size The size of input data * \param input_size The size of input data

2
include/LightGBM/utils/common.h
Просмотреть файл

@ -1232,7 +1232,7 @@ struct __TToStringHelper<T, true, true> {
* Converts an array to a string with with values separated by the space character. * Converts an array to a string with with values separated by the space character.
* This method replaces Common's ``ArrayToString`` and ``ArrayToStringFast`` functionality * This method replaces Common's ``ArrayToString`` and ``ArrayToStringFast`` functionality
* and is locale-independent. * and is locale-independent.
* *
* \note If ``high_precision_output`` is set to true, * \note If ``high_precision_output`` is set to true,
* floating point values are output with more digits of precision. * floating point values are output with more digits of precision.
*/ */

2
pmml/README.md
Просмотреть файл

@ -1,4 +1,4 @@
PMML Generator PMML Generator
============== ==============
The old Python convert script is removed due to it cannot support the new format of categorical features. The old Python convert script is removed due to it cannot support the new format of categorical features.

2
src/c_api.cpp
Просмотреть файл

@ -107,7 +107,7 @@ class SingleRowPredictorInner {
/*! /*!
* \brief Object to store resources meant for single-row Fast Predict methods. * \brief Object to store resources meant for single-row Fast Predict methods.
* *
* For legacy reasons this is called `FastConfig` in the public C API. * For legacy reasons this is called `FastConfig` in the public C API.
* *
* Meant to be used by the *Fast* predict methods only. * Meant to be used by the *Fast* predict methods only.

21
src/treelearner/kernels/histogram_16_64_256.hu
Просмотреть файл

@ -25,36 +25,36 @@ typedef unsigned char uchar;
template<typename T> template<typename T>
__device__ double as_double(const T t) { __device__ double as_double(const T t) {
static_assert(sizeof(T) == sizeof(double), "size mismatch"); static_assert(sizeof(T) == sizeof(double), "size mismatch");
double d; double d;
memcpy(&d, &t, sizeof(T)); memcpy(&d, &t, sizeof(T));
return d; return d;
} }
template<typename T> template<typename T>
__device__ unsigned long long as_ulong_ulong(const T t) { __device__ unsigned long long as_ulong_ulong(const T t) {
static_assert(sizeof(T) == sizeof(unsigned long long), "size mismatch"); static_assert(sizeof(T) == sizeof(unsigned long long), "size mismatch");
unsigned long long u; unsigned long long u;
memcpy(&u, &t, sizeof(T)); memcpy(&u, &t, sizeof(T));
return u; return u;
} }
template<typename T> template<typename T>
__device__ float as_float(const T t) { __device__ float as_float(const T t) {
static_assert(sizeof(T) == sizeof(float), "size mismatch"); static_assert(sizeof(T) == sizeof(float), "size mismatch");
float f; float f;
memcpy(&f, &t, sizeof(T)); memcpy(&f, &t, sizeof(T));
return f; return f;
} }
template<typename T> template<typename T>
__device__ unsigned int as_uint(const T t) { __device__ unsigned int as_uint(const T t) {
static_assert(sizeof(T) == sizeof(unsigned int), "size_mismatch"); static_assert(sizeof(T) == sizeof(unsigned int), "size_mismatch");
unsigned int u; unsigned int u;
memcpy(&u, &t, sizeof(T)); memcpy(&u, &t, sizeof(T));
return u; return u;
} }
template<typename T> template<typename T>
__device__ uchar4 as_uchar4(const T t) { __device__ uchar4 as_uchar4(const T t) {
static_assert(sizeof(T) == sizeof(uchar4), "size mismatch"); static_assert(sizeof(T) == sizeof(uchar4), "size mismatch");
uchar4 u; uchar4 u;
memcpy(&u, &t, sizeof(T)); memcpy(&u, &t, sizeof(T));
return u; return u;
} }
@ -158,4 +158,3 @@ DECLARE(histogram256);
} // namespace LightGBM } // namespace LightGBM
#endif // LIGHTGBM_TREELEARNER_KERNELS_HISTOGRAM_16_64_256_HU_ #endif // LIGHTGBM_TREELEARNER_KERNELS_HISTOGRAM_16_64_256_HU_

4
src/treelearner/leaf_splits.hpp
Просмотреть файл

@ -38,7 +38,7 @@ class LeafSplits {
} }
/*! /*!
* \brief Init split on current leaf on partial data. * \brief Init split on current leaf on partial data.
* \param leaf Index of current leaf * \param leaf Index of current leaf
* \param data_partition current data partition * \param data_partition current data partition
* \param sum_gradients * \param sum_gradients
@ -54,7 +54,7 @@ class LeafSplits {
} }
/*! /*!
* \brief Init split on current leaf on partial data. * \brief Init split on current leaf on partial data.
* \param leaf Index of current leaf * \param leaf Index of current leaf
* \param data_partition current data partition * \param data_partition current data partition
* \param sum_gradients * \param sum_gradients

82
src/treelearner/ocl/histogram16.cl
Просмотреть файл

@ -73,12 +73,12 @@ typedef uint acc_int_type;
// local memory size in bytes // local memory size in bytes
#define LOCAL_MEM_SIZE (DWORD_FEATURES * (sizeof(uint) + 2 * sizeof(acc_type)) * NUM_BINS * NUM_BANKS) #define LOCAL_MEM_SIZE (DWORD_FEATURES * (sizeof(uint) + 2 * sizeof(acc_type)) * NUM_BINS * NUM_BANKS)
// unroll the atomic operation for a few times. Takes more code space, // unroll the atomic operation for a few times. Takes more code space,
// but compiler can generate better code for faster atomics. // but compiler can generate better code for faster atomics.
#define UNROLL_ATOMIC 1 #define UNROLL_ATOMIC 1
// Options passed by compiler at run time: // Options passed by compiler at run time:
// IGNORE_INDICES will be set when the kernel does not // IGNORE_INDICES will be set when the kernel does not
// #define IGNORE_INDICES // #define IGNORE_INDICES
// #define POWER_FEATURE_WORKGROUPS 10 // #define POWER_FEATURE_WORKGROUPS 10
@ -161,7 +161,7 @@ R""()
// this function will be called by histogram16 // this function will be called by histogram16
// we have one sub-histogram of one feature in registers, and need to read others // we have one sub-histogram of one feature in registers, and need to read others
void within_kernel_reduction16x8(uchar8 feature_mask, void within_kernel_reduction16x8(uchar8 feature_mask,
__global const acc_type* restrict feature4_sub_hist, __global const acc_type* restrict feature4_sub_hist,
const uint skip_id, const uint skip_id,
acc_type stat_val, acc_type stat_val,
const ushort num_sub_hist, const ushort num_sub_hist,
@ -173,7 +173,7 @@ void within_kernel_reduction16x8(uchar8 feature_mask,
uchar is_hessian_first = (ltid >> LOG2_DWORD_FEATURES) & 1; // hessian or gradient uchar is_hessian_first = (ltid >> LOG2_DWORD_FEATURES) & 1; // hessian or gradient
ushort bin_id = ltid >> (LOG2_DWORD_FEATURES + 1); // range 0 - 16 ushort bin_id = ltid >> (LOG2_DWORD_FEATURES + 1); // range 0 - 16
ushort i; ushort i;
#if POWER_FEATURE_WORKGROUPS != 0 #if POWER_FEATURE_WORKGROUPS != 0
// if there is only 1 work group, no need to do the reduction // if there is only 1 work group, no need to do the reduction
// add all sub-histograms for 4 features // add all sub-histograms for 4 features
__global const acc_type* restrict p = feature4_sub_hist + ltid; __global const acc_type* restrict p = feature4_sub_hist + ltid;
@ -185,7 +185,7 @@ void within_kernel_reduction16x8(uchar8 feature_mask,
// skip the counters we already have // skip the counters we already have
p += 2 * DWORD_FEATURES * NUM_BINS; p += 2 * DWORD_FEATURES * NUM_BINS;
for (i = i + 1; i < num_sub_hist; ++i) { for (i = i + 1; i < num_sub_hist; ++i) {
stat_val += *p; stat_val += *p;
p += NUM_BINS * DWORD_FEATURES * 2; p += NUM_BINS * DWORD_FEATURES * 2;
} }
#endif #endif
@ -208,12 +208,12 @@ R""()
__attribute__((reqd_work_group_size(LOCAL_SIZE_0, 1, 1))) __attribute__((reqd_work_group_size(LOCAL_SIZE_0, 1, 1)))
#if USE_CONSTANT_BUF == 1 #if USE_CONSTANT_BUF == 1
__kernel void histogram16(__global const uchar4* restrict feature_data_base, __kernel void histogram16(__global const uchar4* restrict feature_data_base,
__constant const uchar8* restrict feature_masks __attribute__((max_constant_size(65536))), __constant const uchar8* restrict feature_masks __attribute__((max_constant_size(65536))),
const data_size_t feature_size, const data_size_t feature_size,
__constant const data_size_t* restrict data_indices __attribute__((max_constant_size(65536))), __constant const data_size_t* restrict data_indices __attribute__((max_constant_size(65536))),
const data_size_t num_data, const data_size_t num_data,
__constant const score_t* restrict ordered_gradients __attribute__((max_constant_size(65536))), __constant const score_t* restrict ordered_gradients __attribute__((max_constant_size(65536))),
#if CONST_HESSIAN == 0 #if CONST_HESSIAN == 0
__constant const score_t* restrict ordered_hessians __attribute__((max_constant_size(65536))), __constant const score_t* restrict ordered_hessians __attribute__((max_constant_size(65536))),
#else #else
@ -223,18 +223,18 @@ __kernel void histogram16(__global const uchar4* restrict feature_data_base,
__global volatile int * sync_counters, __global volatile int * sync_counters,
__global acc_type* restrict hist_buf_base) { __global acc_type* restrict hist_buf_base) {
#else #else
__kernel void histogram16(__global const uchar4* feature_data_base, __kernel void histogram16(__global const uchar4* feature_data_base,
__constant const uchar8* restrict feature_masks __attribute__((max_constant_size(65536))), __constant const uchar8* restrict feature_masks __attribute__((max_constant_size(65536))),
const data_size_t feature_size, const data_size_t feature_size,
__global const data_size_t* data_indices, __global const data_size_t* data_indices,
const data_size_t num_data, const data_size_t num_data,
__global const score_t* ordered_gradients, __global const score_t* ordered_gradients,
#if CONST_HESSIAN == 0 #if CONST_HESSIAN == 0
__global const score_t* ordered_hessians, __global const score_t* ordered_hessians,
#else #else
const score_t const_hessian, const score_t const_hessian,
#endif #endif
__global char* restrict output_buf, __global char* restrict output_buf,
__global volatile int * sync_counters, __global volatile int * sync_counters,
__global acc_type* restrict hist_buf_base) { __global acc_type* restrict hist_buf_base) {
#endif #endif
@ -260,38 +260,38 @@ __kernel void histogram16(__global const uchar4* feature_data_base,
// there are 8 banks (sub-histograms) used by 256 threads total 8 KB // there are 8 banks (sub-histograms) used by 256 threads total 8 KB
/* memory layout of gh_hist: /* memory layout of gh_hist:
----------------------------------------------------------------------------------------------- -----------------------------------------------------------------------------------------------
bk0_g_f0_bin0 bk0_g_f1_bin0 bk0_g_f2_bin0 bk0_g_f3_bin0 bk0_g_f4_bin0 bk0_g_f5_bin0 bk0_g_f6_bin0 bk0_g_f7_bin0 bk0_g_f0_bin0 bk0_g_f1_bin0 bk0_g_f2_bin0 bk0_g_f3_bin0 bk0_g_f4_bin0 bk0_g_f5_bin0 bk0_g_f6_bin0 bk0_g_f7_bin0
bk0_h_f0_bin0 bk0_h_f1_bin0 bk0_h_f2_bin0 bk0_h_f3_bin0 bk0_h_f4_bin0 bk0_h_f5_bin0 bk0_h_f6_bin0 bk0_h_f7_bin0 bk0_h_f0_bin0 bk0_h_f1_bin0 bk0_h_f2_bin0 bk0_h_f3_bin0 bk0_h_f4_bin0 bk0_h_f5_bin0 bk0_h_f6_bin0 bk0_h_f7_bin0
bk1_g_f0_bin0 bk1_g_f1_bin0 bk1_g_f2_bin0 bk1_g_f3_bin0 bk1_g_f4_bin0 bk1_g_f5_bin0 bk1_g_f6_bin0 bk1_g_f7_bin0 bk1_g_f0_bin0 bk1_g_f1_bin0 bk1_g_f2_bin0 bk1_g_f3_bin0 bk1_g_f4_bin0 bk1_g_f5_bin0 bk1_g_f6_bin0 bk1_g_f7_bin0
bk1_h_f0_bin0 bk1_h_f1_bin0 bk1_h_f2_bin0 bk1_h_f3_bin0 bk1_h_f4_bin0 bk1_h_f5_bin0 bk1_h_f6_bin0 bk1_h_f7_bin0 bk1_h_f0_bin0 bk1_h_f1_bin0 bk1_h_f2_bin0 bk1_h_f3_bin0 bk1_h_f4_bin0 bk1_h_f5_bin0 bk1_h_f6_bin0 bk1_h_f7_bin0
bk2_g_f0_bin0 bk2_g_f1_bin0 bk2_g_f2_bin0 bk2_g_f3_bin0 bk2_g_f4_bin0 bk2_g_f5_bin0 bk2_g_f6_bin0 bk2_g_f7_bin0 bk2_g_f0_bin0 bk2_g_f1_bin0 bk2_g_f2_bin0 bk2_g_f3_bin0 bk2_g_f4_bin0 bk2_g_f5_bin0 bk2_g_f6_bin0 bk2_g_f7_bin0
bk2_h_f0_bin0 bk2_h_f1_bin0 bk2_h_f2_bin0 bk2_h_f3_bin0 bk2_h_f4_bin0 bk2_h_f5_bin0 bk2_h_f6_bin0 bk2_h_f7_bin0 bk2_h_f0_bin0 bk2_h_f1_bin0 bk2_h_f2_bin0 bk2_h_f3_bin0 bk2_h_f4_bin0 bk2_h_f5_bin0 bk2_h_f6_bin0 bk2_h_f7_bin0
bk3_g_f0_bin0 bk3_g_f1_bin0 bk3_g_f2_bin0 bk3_g_f3_bin0 bk3_g_f4_bin0 bk3_g_f5_bin0 bk3_g_f6_bin0 bk3_g_f7_bin0 bk3_g_f0_bin0 bk3_g_f1_bin0 bk3_g_f2_bin0 bk3_g_f3_bin0 bk3_g_f4_bin0 bk3_g_f5_bin0 bk3_g_f6_bin0 bk3_g_f7_bin0
bk3_h_f0_bin0 bk3_h_f1_bin0 bk3_h_f2_bin0 bk3_h_f3_bin0 bk3_h_f4_bin0 bk3_h_f5_bin0 bk3_h_f6_bin0 bk3_h_f7_bin0 bk3_h_f0_bin0 bk3_h_f1_bin0 bk3_h_f2_bin0 bk3_h_f3_bin0 bk3_h_f4_bin0 bk3_h_f5_bin0 bk3_h_f6_bin0 bk3_h_f7_bin0
bk4_g_f0_bin0 bk4_g_f1_bin0 bk4_g_f2_bin0 bk4_g_f3_bin0 bk4_g_f4_bin0 bk4_g_f5_bin0 bk4_g_f6_bin0 bk4_g_f7_bin0 bk4_g_f0_bin0 bk4_g_f1_bin0 bk4_g_f2_bin0 bk4_g_f3_bin0 bk4_g_f4_bin0 bk4_g_f5_bin0 bk4_g_f6_bin0 bk4_g_f7_bin0
bk4_h_f0_bin0 bk4_h_f1_bin0 bk4_h_f2_bin0 bk4_h_f3_bin0 bk4_h_f4_bin0 bk4_h_f5_bin0 bk4_h_f6_bin0 bk4_h_f7_bin0 bk4_h_f0_bin0 bk4_h_f1_bin0 bk4_h_f2_bin0 bk4_h_f3_bin0 bk4_h_f4_bin0 bk4_h_f5_bin0 bk4_h_f6_bin0 bk4_h_f7_bin0
bk5_g_f0_bin0 bk5_g_f1_bin0 bk5_g_f2_bin0 bk5_g_f3_bin0 bk5_g_f4_bin0 bk5_g_f5_bin0 bk5_g_f6_bin0 bk5_g_f7_bin0 bk5_g_f0_bin0 bk5_g_f1_bin0 bk5_g_f2_bin0 bk5_g_f3_bin0 bk5_g_f4_bin0 bk5_g_f5_bin0 bk5_g_f6_bin0 bk5_g_f7_bin0
bk5_h_f0_bin0 bk5_h_f1_bin0 bk5_h_f2_bin0 bk5_h_f3_bin0 bk5_h_f4_bin0 bk5_h_f5_bin0 bk5_h_f6_bin0 bk5_h_f7_bin0 bk5_h_f0_bin0 bk5_h_f1_bin0 bk5_h_f2_bin0 bk5_h_f3_bin0 bk5_h_f4_bin0 bk5_h_f5_bin0 bk5_h_f6_bin0 bk5_h_f7_bin0
bk6_g_f0_bin0 bk6_g_f1_bin0 bk6_g_f2_bin0 bk6_g_f3_bin0 bk6_g_f4_bin0 bk6_g_f5_bin0 bk6_g_f6_bin0 bk6_g_f7_bin0 bk6_g_f0_bin0 bk6_g_f1_bin0 bk6_g_f2_bin0 bk6_g_f3_bin0 bk6_g_f4_bin0 bk6_g_f5_bin0 bk6_g_f6_bin0 bk6_g_f7_bin0
bk6_h_f0_bin0 bk6_h_f1_bin0 bk6_h_f2_bin0 bk6_h_f3_bin0 bk6_h_f4_bin0 bk6_h_f5_bin0 bk6_h_f6_bin0 bk6_h_f7_bin0 bk6_h_f0_bin0 bk6_h_f1_bin0 bk6_h_f2_bin0 bk6_h_f3_bin0 bk6_h_f4_bin0 bk6_h_f5_bin0 bk6_h_f6_bin0 bk6_h_f7_bin0
bk7_g_f0_bin0 bk7_g_f1_bin0 bk7_g_f2_bin0 bk7_g_f3_bin0 bk7_g_f4_bin0 bk7_g_f5_bin0 bk7_g_f6_bin0 bk7_g_f7_bin0 bk7_g_f0_bin0 bk7_g_f1_bin0 bk7_g_f2_bin0 bk7_g_f3_bin0 bk7_g_f4_bin0 bk7_g_f5_bin0 bk7_g_f6_bin0 bk7_g_f7_bin0
bk7_h_f0_bin0 bk7_h_f1_bin0 bk7_h_f2_bin0 bk7_h_f3_bin0 bk7_h_f4_bin0 bk7_h_f5_bin0 bk7_h_f6_bin0 bk7_h_f7_bin0 bk7_h_f0_bin0 bk7_h_f1_bin0 bk7_h_f2_bin0 bk7_h_f3_bin0 bk7_h_f4_bin0 bk7_h_f5_bin0 bk7_h_f6_bin0 bk7_h_f7_bin0
... ...
bk0_g_f0_bin16 bk0_g_f1_bin16 bk0_g_f2_bin16 bk0_g_f3_bin16 bk0_g_f4_bin16 bk0_g_f5_bin16 bk0_g_f6_bin16 bk0_g_f7_bin16 bk0_g_f0_bin16 bk0_g_f1_bin16 bk0_g_f2_bin16 bk0_g_f3_bin16 bk0_g_f4_bin16 bk0_g_f5_bin16 bk0_g_f6_bin16 bk0_g_f7_bin16
bk0_h_f0_bin16 bk0_h_f1_bin16 bk0_h_f2_bin16 bk0_h_f3_bin16 bk0_h_f4_bin16 bk0_h_f5_bin16 bk0_h_f6_bin16 bk0_h_f7_bin16 bk0_h_f0_bin16 bk0_h_f1_bin16 bk0_h_f2_bin16 bk0_h_f3_bin16 bk0_h_f4_bin16 bk0_h_f5_bin16 bk0_h_f6_bin16 bk0_h_f7_bin16
bk1_g_f0_bin16 bk1_g_f1_bin16 bk1_g_f2_bin16 bk1_g_f3_bin16 bk1_g_f4_bin16 bk1_g_f5_bin16 bk1_g_f6_bin16 bk1_g_f7_bin16 bk1_g_f0_bin16 bk1_g_f1_bin16 bk1_g_f2_bin16 bk1_g_f3_bin16 bk1_g_f4_bin16 bk1_g_f5_bin16 bk1_g_f6_bin16 bk1_g_f7_bin16
bk1_h_f0_bin16 bk1_h_f1_bin16 bk1_h_f2_bin16 bk1_h_f3_bin16 bk1_h_f4_bin16 bk1_h_f5_bin16 bk1_h_f6_bin16 bk1_h_f7_bin16 bk1_h_f0_bin16 bk1_h_f1_bin16 bk1_h_f2_bin16 bk1_h_f3_bin16 bk1_h_f4_bin16 bk1_h_f5_bin16 bk1_h_f6_bin16 bk1_h_f7_bin16
bk2_g_f0_bin16 bk2_g_f1_bin16 bk2_g_f2_bin16 bk2_g_f3_bin16 bk2_g_f4_bin16 bk2_g_f5_bin16 bk2_g_f6_bin16 bk2_g_f7_bin16 bk2_g_f0_bin16 bk2_g_f1_bin16 bk2_g_f2_bin16 bk2_g_f3_bin16 bk2_g_f4_bin16 bk2_g_f5_bin16 bk2_g_f6_bin16 bk2_g_f7_bin16
bk2_h_f0_bin16 bk2_h_f1_bin16 bk2_h_f2_bin16 bk2_h_f3_bin16 bk2_h_f4_bin16 bk2_h_f5_bin16 bk2_h_f6_bin16 bk2_h_f7_bin16 bk2_h_f0_bin16 bk2_h_f1_bin16 bk2_h_f2_bin16 bk2_h_f3_bin16 bk2_h_f4_bin16 bk2_h_f5_bin16 bk2_h_f6_bin16 bk2_h_f7_bin16
bk3_g_f0_bin16 bk3_g_f1_bin16 bk3_g_f2_bin16 bk3_g_f3_bin16 bk3_g_f4_bin16 bk3_g_f5_bin16 bk3_g_f6_bin16 bk3_g_f7_bin16 bk3_g_f0_bin16 bk3_g_f1_bin16 bk3_g_f2_bin16 bk3_g_f3_bin16 bk3_g_f4_bin16 bk3_g_f5_bin16 bk3_g_f6_bin16 bk3_g_f7_bin16
bk3_h_f0_bin16 bk3_h_f1_bin16 bk3_h_f2_bin16 bk3_h_f3_bin16 bk3_h_f4_bin16 bk3_h_f5_bin16 bk3_h_f6_bin16 bk3_h_f7_bin16 bk3_h_f0_bin16 bk3_h_f1_bin16 bk3_h_f2_bin16 bk3_h_f3_bin16 bk3_h_f4_bin16 bk3_h_f5_bin16 bk3_h_f6_bin16 bk3_h_f7_bin16
bk4_g_f0_bin16 bk4_g_f1_bin16 bk4_g_f2_bin16 bk4_g_f3_bin16 bk4_g_f4_bin16 bk4_g_f5_bin16 bk4_g_f6_bin16 bk4_g_f7_bin16 bk4_g_f0_bin16 bk4_g_f1_bin16 bk4_g_f2_bin16 bk4_g_f3_bin16 bk4_g_f4_bin16 bk4_g_f5_bin16 bk4_g_f6_bin16 bk4_g_f7_bin16
bk4_h_f0_bin16 bk4_h_f1_bin16 bk4_h_f2_bin16 bk4_h_f3_bin16 bk4_h_f4_bin16 bk4_h_f5_bin16 bk4_h_f6_bin16 bk4_h_f7_bin16 bk4_h_f0_bin16 bk4_h_f1_bin16 bk4_h_f2_bin16 bk4_h_f3_bin16 bk4_h_f4_bin16 bk4_h_f5_bin16 bk4_h_f6_bin16 bk4_h_f7_bin16
bk5_g_f0_bin16 bk5_g_f1_bin16 bk5_g_f2_bin16 bk5_g_f3_bin16 bk5_g_f4_bin16 bk5_g_f5_bin16 bk5_g_f6_bin16 bk5_g_f7_bin16 bk5_g_f0_bin16 bk5_g_f1_bin16 bk5_g_f2_bin16 bk5_g_f3_bin16 bk5_g_f4_bin16 bk5_g_f5_bin16 bk5_g_f6_bin16 bk5_g_f7_bin16
bk5_h_f0_bin16 bk5_h_f1_bin16 bk5_h_f2_bin16 bk5_h_f3_bin16 bk5_h_f4_bin16 bk5_h_f5_bin16 bk5_h_f6_bin16 bk5_h_f7_bin16 bk5_h_f0_bin16 bk5_h_f1_bin16 bk5_h_f2_bin16 bk5_h_f3_bin16 bk5_h_f4_bin16 bk5_h_f5_bin16 bk5_h_f6_bin16 bk5_h_f7_bin16
bk6_g_f0_bin16 bk6_g_f1_bin16 bk6_g_f2_bin16 bk6_g_f3_bin16 bk6_g_f4_bin16 bk6_g_f5_bin16 bk6_g_f6_bin16 bk6_g_f7_bin16 bk6_g_f0_bin16 bk6_g_f1_bin16 bk6_g_f2_bin16 bk6_g_f3_bin16 bk6_g_f4_bin16 bk6_g_f5_bin16 bk6_g_f6_bin16 bk6_g_f7_bin16
bk6_h_f0_bin16 bk6_h_f1_bin16 bk6_h_f2_bin16 bk6_h_f3_bin16 bk6_h_f4_bin16 bk6_h_f5_bin16 bk6_h_f6_bin16 bk6_h_f7_bin16 bk6_h_f0_bin16 bk6_h_f1_bin16 bk6_h_f2_bin16 bk6_h_f3_bin16 bk6_h_f4_bin16 bk6_h_f5_bin16 bk6_h_f6_bin16 bk6_h_f7_bin16
bk7_g_f0_bin16 bk7_g_f1_bin16 bk7_g_f2_bin16 bk7_g_f3_bin16 bk7_g_f4_bin16 bk7_g_f5_bin16 bk7_g_f6_bin16 bk7_g_f7_bin16 bk7_g_f0_bin16 bk7_g_f1_bin16 bk7_g_f2_bin16 bk7_g_f3_bin16 bk7_g_f4_bin16 bk7_g_f5_bin16 bk7_g_f6_bin16 bk7_g_f7_bin16
bk7_h_f0_bin16 bk7_h_f1_bin16 bk7_h_f2_bin16 bk7_h_f3_bin16 bk7_h_f4_bin16 bk7_h_f5_bin16 bk7_h_f6_bin16 bk7_h_f7_bin16 bk7_h_f0_bin16 bk7_h_f1_bin16 bk7_h_f2_bin16 bk7_h_f3_bin16 bk7_h_f4_bin16 bk7_h_f5_bin16 bk7_h_f6_bin16 bk7_h_f7_bin16
----------------------------------------------------------------------------------------------- -----------------------------------------------------------------------------------------------
*/ */
@ -333,7 +333,7 @@ __kernel void histogram16(__global const uchar4* feature_data_base,
uchar is_hessian_first = (ltid >> LOG2_DWORD_FEATURES) & 1; uchar is_hessian_first = (ltid >> LOG2_DWORD_FEATURES) & 1;
// thread 0-15 write result to bank0, 16-31 to bank1, 32-47 to bank2, 48-63 to bank3, etc // thread 0-15 write result to bank0, 16-31 to bank1, 32-47 to bank2, 48-63 to bank3, etc
ushort bank = (ltid >> (LOG2_DWORD_FEATURES + 1)) & BANK_MASK; ushort bank = (ltid >> (LOG2_DWORD_FEATURES + 1)) & BANK_MASK;
ushort group_feature = group_id >> POWER_FEATURE_WORKGROUPS; ushort group_feature = group_id >> POWER_FEATURE_WORKGROUPS;
// each 2^POWER_FEATURE_WORKGROUPS workgroups process on one feature (compile-time constant) // each 2^POWER_FEATURE_WORKGROUPS workgroups process on one feature (compile-time constant)
// feature_size is the number of examples per feature // feature_size is the number of examples per feature
@ -615,12 +615,12 @@ R""()
)"" )""
R""() R""()
*/ */
#if ENABLE_ALL_FEATURES == 0 #if ENABLE_ALL_FEATURES == 0
// restore feature_mask // restore feature_mask
feature_mask = feature_masks[group_feature]; feature_mask = feature_masks[group_feature];
#endif #endif
// now reduce the 4 banks of subhistograms into 1 // now reduce the 4 banks of subhistograms into 1
acc_type stat_val = 0.0f; acc_type stat_val = 0.0f;
uint cnt_val = 0; uint cnt_val = 0;
@ -644,7 +644,7 @@ R""()
} }
} }
#endif #endif
// now thread 0 - 7 holds feature 0 - 7's gradient for bin 0 and counter bin 0 // now thread 0 - 7 holds feature 0 - 7's gradient for bin 0 and counter bin 0
// now thread 8 - 15 holds feature 0 - 7's hessian for bin 0 and counter bin 1 // now thread 8 - 15 holds feature 0 - 7's hessian for bin 0 and counter bin 1
// now thread 16- 23 holds feature 0 - 7's gradient for bin 1 and counter bin 2 // now thread 16- 23 holds feature 0 - 7's gradient for bin 1 and counter bin 2
@ -664,7 +664,7 @@ R""()
// thread 8 - 15 read counters stored by thread 0 - 7 // thread 8 - 15 read counters stored by thread 0 - 7
// thread 24- 31 read counters stored by thread 8 - 15 // thread 24- 31 read counters stored by thread 8 - 15
// thread 40- 47 read counters stored by thread 16- 23, etc // thread 40- 47 read counters stored by thread 16- 23, etc
stat_val = const_hessian * stat_val = const_hessian *
cnt_hist[((ltid - DWORD_FEATURES) >> (LOG2_DWORD_FEATURES + 1)) * DWORD_FEATURES + (ltid & DWORD_FEATURES_MASK)]; cnt_hist[((ltid - DWORD_FEATURES) >> (LOG2_DWORD_FEATURES + 1)) * DWORD_FEATURES + (ltid & DWORD_FEATURES_MASK)];
} }
else { else {
@ -688,12 +688,12 @@ R""()
h_f0_bin1 h_f1_bin1 h_f2_bin1 h_f3_bin1 h_f4_bin1 h_f5_bin1 h_f6_bin1 h_f7_bin1 h_f0_bin1 h_f1_bin1 h_f2_bin1 h_f3_bin1 h_f4_bin1 h_f5_bin1 h_f6_bin1 h_f7_bin1
... ...
... ...
g_f0_bin16 g_f1_bin16 g_f2_bin16 g_f3_bin16 g_f4_bin16 g_f5_bin16 g_f6_bin16 g_f7_bin16 g_f0_bin16 g_f1_bin16 g_f2_bin16 g_f3_bin16 g_f4_bin16 g_f5_bin16 g_f6_bin16 g_f7_bin16
h_f0_bin16 h_f1_bin16 h_f2_bin16 h_f3_bin16 h_f4_bin16 h_f5_bin16 h_f6_bin16 h_f7_bin16 h_f0_bin16 h_f1_bin16 h_f2_bin16 h_f3_bin16 h_f4_bin16 h_f5_bin16 h_f6_bin16 h_f7_bin16
c_f0_bin0 c_f1_bin0 c_f2_bin0 c_f3_bin0 c_f4_bin0 c_f5_bin0 c_f6_bin0 c_f7_bin0 c_f0_bin0 c_f1_bin0 c_f2_bin0 c_f3_bin0 c_f4_bin0 c_f5_bin0 c_f6_bin0 c_f7_bin0
c_f0_bin1 c_f1_bin1 c_f2_bin1 c_f3_bin1 c_f4_bin1 c_f5_bin1 c_f6_bin1 c_f7_bin1 c_f0_bin1 c_f1_bin1 c_f2_bin1 c_f3_bin1 c_f4_bin1 c_f5_bin1 c_f6_bin1 c_f7_bin1
... ...
c_f0_bin16 c_f1_bin16 c_f2_bin16 c_f3_bin16 c_f4_bin16 c_f5_bin16 c_f6_bin16 c_f7_bin16 c_f0_bin16 c_f1_bin16 c_f2_bin16 c_f3_bin16 c_f4_bin16 c_f5_bin16 c_f6_bin16 c_f7_bin16
*/ */
// if there is only one workgroup processing this feature4, don't even need to write // if there is only one workgroup processing this feature4, don't even need to write
uint feature4_id = (group_id >> POWER_FEATURE_WORKGROUPS); uint feature4_id = (group_id >> POWER_FEATURE_WORKGROUPS);
@ -704,7 +704,7 @@ R""()
output[0 * DWORD_FEATURES * NUM_BINS + ltid] = stat_val; output[0 * DWORD_FEATURES * NUM_BINS + ltid] = stat_val;
barrier(CLK_LOCAL_MEM_FENCE | CLK_GLOBAL_MEM_FENCE); barrier(CLK_LOCAL_MEM_FENCE | CLK_GLOBAL_MEM_FENCE);
mem_fence(CLK_GLOBAL_MEM_FENCE); mem_fence(CLK_GLOBAL_MEM_FENCE);
// To avoid the cost of an extra reducing kernel, we have to deal with some // To avoid the cost of an extra reducing kernel, we have to deal with some
// gray area in OpenCL. We want the last work group that process this feature to // gray area in OpenCL. We want the last work group that process this feature to
// make the final reduction, and other threads will just quit. // make the final reduction, and other threads will just quit.
// This requires that the results written by other workgroups available to the // This requires that the results written by other workgroups available to the
@ -750,13 +750,13 @@ R""()
#endif #endif
// locate our feature4's block in output memory // locate our feature4's block in output memory
uint output_offset = (feature4_id << POWER_FEATURE_WORKGROUPS); uint output_offset = (feature4_id << POWER_FEATURE_WORKGROUPS);
__global acc_type const * restrict feature4_subhists = __global acc_type const * restrict feature4_subhists =
(__global acc_type *)output_buf + output_offset * DWORD_FEATURES * 2 * NUM_BINS; (__global acc_type *)output_buf + output_offset * DWORD_FEATURES * 2 * NUM_BINS;
// skip reading the data already in local memory // skip reading the data already in local memory
uint skip_id = group_id ^ output_offset; uint skip_id = group_id ^ output_offset;
// locate output histogram location for this feature4 // locate output histogram location for this feature4
__global acc_type* restrict hist_buf = hist_buf_base + feature4_id * DWORD_FEATURES * 2 * NUM_BINS; __global acc_type* restrict hist_buf = hist_buf_base + feature4_id * DWORD_FEATURES * 2 * NUM_BINS;
within_kernel_reduction16x8(feature_mask, feature4_subhists, skip_id, stat_val, within_kernel_reduction16x8(feature_mask, feature4_subhists, skip_id, stat_val,
1 << POWER_FEATURE_WORKGROUPS, hist_buf, (__local acc_type *)shared_array); 1 << POWER_FEATURE_WORKGROUPS, hist_buf, (__local acc_type *)shared_array);
} }
} }

38
src/treelearner/ocl/histogram256.cl
Просмотреть файл

@ -47,12 +47,12 @@ typedef uint acc_int_type;
#endif #endif
#define LOCAL_MEM_SIZE (4 * (sizeof(uint) + 2 * sizeof(acc_type)) * NUM_BINS) #define LOCAL_MEM_SIZE (4 * (sizeof(uint) + 2 * sizeof(acc_type)) * NUM_BINS)
// unroll the atomic operation for a few times. Takes more code space, // unroll the atomic operation for a few times. Takes more code space,
// but compiler can generate better code for faster atomics. // but compiler can generate better code for faster atomics.
#define UNROLL_ATOMIC 1 #define UNROLL_ATOMIC 1
// Options passed by compiler at run time: // Options passed by compiler at run time:
// IGNORE_INDICES will be set when the kernel does not // IGNORE_INDICES will be set when the kernel does not
// #define IGNORE_INDICES // #define IGNORE_INDICES
// #define POWER_FEATURE_WORKGROUPS 10 // #define POWER_FEATURE_WORKGROUPS 10
@ -137,7 +137,7 @@ R""()
// this function will be called by histogram256 // this function will be called by histogram256
// we have one sub-histogram of one feature in local memory, and need to read others // we have one sub-histogram of one feature in local memory, and need to read others
void within_kernel_reduction256x4(uchar4 feature_mask, void within_kernel_reduction256x4(uchar4 feature_mask,
__global const acc_type* restrict feature4_sub_hist, __global const acc_type* restrict feature4_sub_hist,
const uint skip_id, const uint skip_id,
const uint old_val_f0_cont_bin0, const uint old_val_f0_cont_bin0,
const ushort num_sub_hist, const ushort num_sub_hist,
@ -314,12 +314,12 @@ R""()
*/ */
__attribute__((reqd_work_group_size(LOCAL_SIZE_0, 1, 1))) __attribute__((reqd_work_group_size(LOCAL_SIZE_0, 1, 1)))
#if USE_CONSTANT_BUF == 1 #if USE_CONSTANT_BUF == 1
__kernel void histogram256(__global const uchar4* restrict feature_data_base, __kernel void histogram256(__global const uchar4* restrict feature_data_base,
__constant const uchar4* restrict feature_masks __attribute__((max_constant_size(65536))), __constant const uchar4* restrict feature_masks __attribute__((max_constant_size(65536))),
const data_size_t feature_size, const data_size_t feature_size,
__constant const data_size_t* restrict data_indices __attribute__((max_constant_size(65536))), __constant const data_size_t* restrict data_indices __attribute__((max_constant_size(65536))),
const data_size_t num_data, const data_size_t num_data,
__constant const score_t* restrict ordered_gradients __attribute__((max_constant_size(65536))), __constant const score_t* restrict ordered_gradients __attribute__((max_constant_size(65536))),
#if CONST_HESSIAN == 0 #if CONST_HESSIAN == 0
__constant const score_t* restrict ordered_hessians __attribute__((max_constant_size(65536))), __constant const score_t* restrict ordered_hessians __attribute__((max_constant_size(65536))),
#else #else
@ -329,18 +329,18 @@ __kernel void histogram256(__global const uchar4* restrict feature_data_base,
__global volatile int * sync_counters, __global volatile int * sync_counters,
__global acc_type* restrict hist_buf_base) { __global acc_type* restrict hist_buf_base) {
#else #else
__kernel void histogram256(__global const uchar4* feature_data_base, __kernel void histogram256(__global const uchar4* feature_data_base,
__constant const uchar4* restrict feature_masks __attribute__((max_constant_size(65536))), __constant const uchar4* restrict feature_masks __attribute__((max_constant_size(65536))),
const data_size_t feature_size, const data_size_t feature_size,
__global const data_size_t* data_indices, __global const data_size_t* data_indices,
const data_size_t num_data, const data_size_t num_data,
__global const score_t* ordered_gradients, __global const score_t* ordered_gradients,
#if CONST_HESSIAN == 0 #if CONST_HESSIAN == 0
__global const score_t* ordered_hessians, __global const score_t* ordered_hessians,
#else #else
const score_t const_hessian, const score_t const_hessian,
#endif #endif
__global char* restrict output_buf, __global char* restrict output_buf,
__global volatile int * sync_counters, __global volatile int * sync_counters,
__global acc_type* restrict hist_buf_base) { __global acc_type* restrict hist_buf_base) {
#endif #endif
@ -363,20 +363,20 @@ __kernel void histogram256(__global const uchar4* feature_data_base,
// gradient/hessian histograms // gradient/hessian histograms
// assume this starts at 32 * 4 = 128-byte boundary // assume this starts at 32 * 4 = 128-byte boundary
// total size: 2 * 4 * 256 * size_of(float) = 8 KB // total size: 2 * 4 * 256 * size_of(float) = 8 KB
// organization: each feature/grad/hessian is at a different bank, // organization: each feature/grad/hessian is at a different bank,
// as independent of the feature value as possible // as independent of the feature value as possible
__local acc_type * gh_hist = (__local acc_type *)shared_array; __local acc_type * gh_hist = (__local acc_type *)shared_array;
// counter histogram // counter histogram
// total size: 4 * 256 * size_of(uint) = 4 KB // total size: 4 * 256 * size_of(uint) = 4 KB
#if CONST_HESSIAN == 1 #if CONST_HESSIAN == 1
__local uint * cnt_hist = (__local uint *)(gh_hist + 2 * 4 * NUM_BINS); __local uint * cnt_hist = (__local uint *)(gh_hist + 2 * 4 * NUM_BINS);
#endif #endif
// thread 0, 1, 2, 3 compute histograms for gradients first // thread 0, 1, 2, 3 compute histograms for gradients first
// thread 4, 5, 6, 7 compute histograms for Hessians first // thread 4, 5, 6, 7 compute histograms for Hessians first
// etc. // etc.
uchar is_hessian_first = (ltid >> 2) & 1; uchar is_hessian_first = (ltid >> 2) & 1;
ushort group_feature = group_id >> POWER_FEATURE_WORKGROUPS; ushort group_feature = group_id >> POWER_FEATURE_WORKGROUPS;
// each 2^POWER_FEATURE_WORKGROUPS workgroups process on one feature (compile-time constant) // each 2^POWER_FEATURE_WORKGROUPS workgroups process on one feature (compile-time constant)
// feature_size is the number of examples per feature // feature_size is the number of examples per feature
@ -725,7 +725,7 @@ R""()
} }
barrier(CLK_LOCAL_MEM_FENCE | CLK_GLOBAL_MEM_FENCE); barrier(CLK_LOCAL_MEM_FENCE | CLK_GLOBAL_MEM_FENCE);
mem_fence(CLK_GLOBAL_MEM_FENCE); mem_fence(CLK_GLOBAL_MEM_FENCE);
// To avoid the cost of an extra reducing kernel, we have to deal with some // To avoid the cost of an extra reducing kernel, we have to deal with some
// gray area in OpenCL. We want the last work group that process this feature to // gray area in OpenCL. We want the last work group that process this feature to
// make the final reduction, and other threads will just quit. // make the final reduction, and other threads will just quit.
// This requires that the results written by other workgroups available to the // This requires that the results written by other workgroups available to the
@ -773,15 +773,15 @@ R""()
#endif #endif
// locate our feature4's block in output memory // locate our feature4's block in output memory
uint output_offset = (feature4_id << POWER_FEATURE_WORKGROUPS); uint output_offset = (feature4_id << POWER_FEATURE_WORKGROUPS);
__global acc_type const * restrict feature4_subhists = __global acc_type const * restrict feature4_subhists =
(__global acc_type *)output_buf + output_offset * 4 * 2 * NUM_BINS; (__global acc_type *)output_buf + output_offset * 4 * 2 * NUM_BINS;
// skip reading the data already in local memory // skip reading the data already in local memory
uint skip_id = group_id ^ output_offset; uint skip_id = group_id ^ output_offset;
// locate output histogram location for this feature4 // locate output histogram location for this feature4
__global acc_type* restrict hist_buf = hist_buf_base + feature4_id * 4 * 2 * NUM_BINS; __global acc_type* restrict hist_buf = hist_buf_base + feature4_id * 4 * 2 * NUM_BINS;
within_kernel_reduction256x4(feature_mask, feature4_subhists, skip_id, old_val, 1 << POWER_FEATURE_WORKGROUPS, within_kernel_reduction256x4(feature_mask, feature4_subhists, skip_id, old_val, 1 << POWER_FEATURE_WORKGROUPS,
hist_buf, (__local acc_type *)shared_array); hist_buf, (__local acc_type *)shared_array);
// if (ltid == 0) // if (ltid == 0)
// printf("workgroup %d reduction done, %g %g %g %g %g %g %g %g\n", group_id, hist_buf[0], hist_buf[3*NUM_BINS], hist_buf[2*3*NUM_BINS], hist_buf[3*3*NUM_BINS], hist_buf[1], hist_buf[3*NUM_BINS+1], hist_buf[2*3*NUM_BINS+1], hist_buf[3*3*NUM_BINS+1]); // printf("workgroup %d reduction done, %g %g %g %g %g %g %g %g\n", group_id, hist_buf[0], hist_buf[3*NUM_BINS], hist_buf[2*3*NUM_BINS], hist_buf[3*3*NUM_BINS], hist_buf[1], hist_buf[3*NUM_BINS+1], hist_buf[2*3*NUM_BINS+1], hist_buf[3*3*NUM_BINS+1]);
} }
} }

38
src/treelearner/ocl/histogram64.cl
Просмотреть файл

@ -65,12 +65,12 @@ typedef uint acc_int_type;
// local memory size in bytes // local memory size in bytes
#define LOCAL_MEM_SIZE (4 * (sizeof(uint) + 2 * sizeof(acc_type)) * NUM_BINS * NUM_BANKS) #define LOCAL_MEM_SIZE (4 * (sizeof(uint) + 2 * sizeof(acc_type)) * NUM_BINS * NUM_BANKS)
// unroll the atomic operation for a few times. Takes more code space, // unroll the atomic operation for a few times. Takes more code space,
// but compiler can generate better code for faster atomics. // but compiler can generate better code for faster atomics.
#define UNROLL_ATOMIC 1 #define UNROLL_ATOMIC 1
// Options passed by compiler at run time: // Options passed by compiler at run time:
// IGNORE_INDICES will be set when the kernel does not // IGNORE_INDICES will be set when the kernel does not
// #define IGNORE_INDICES // #define IGNORE_INDICES
// #define POWER_FEATURE_WORKGROUPS 10 // #define POWER_FEATURE_WORKGROUPS 10
@ -155,7 +155,7 @@ R""()
// this function will be called by histogram64 // this function will be called by histogram64
// we have one sub-histogram of one feature in registers, and need to read others // we have one sub-histogram of one feature in registers, and need to read others
void within_kernel_reduction64x4(uchar4 feature_mask, void within_kernel_reduction64x4(uchar4 feature_mask,
__global const acc_type* restrict feature4_sub_hist, __global const acc_type* restrict feature4_sub_hist,
const uint skip_id, const uint skip_id,
acc_type g_val, acc_type h_val, acc_type g_val, acc_type h_val,
const ushort num_sub_hist, const ushort num_sub_hist,
@ -166,7 +166,7 @@ void within_kernel_reduction64x4(uchar4 feature_mask,
ushort feature_id = ltid & 3; // range 0 - 4 ushort feature_id = ltid & 3; // range 0 - 4
const ushort bin_id = ltid >> 2; // range 0 - 63W const ushort bin_id = ltid >> 2; // range 0 - 63W
ushort i; ushort i;
#if POWER_FEATURE_WORKGROUPS != 0 #if POWER_FEATURE_WORKGROUPS != 0
// if there is only 1 work group, no need to do the reduction // if there is only 1 work group, no need to do the reduction
// add all sub-histograms for 4 features // add all sub-histograms for 4 features
__global const acc_type* restrict p = feature4_sub_hist + ltid; __global const acc_type* restrict p = feature4_sub_hist + ltid;
@ -212,12 +212,12 @@ R""()
*/ */
__attribute__((reqd_work_group_size(LOCAL_SIZE_0, 1, 1))) __attribute__((reqd_work_group_size(LOCAL_SIZE_0, 1, 1)))
#if USE_CONSTANT_BUF == 1 #if USE_CONSTANT_BUF == 1
__kernel void histogram64(__global const uchar4* restrict feature_data_base, __kernel void histogram64(__global const uchar4* restrict feature_data_base,
__constant const uchar4* restrict feature_masks __attribute__((max_constant_size(65536))), __constant const uchar4* restrict feature_masks __attribute__((max_constant_size(65536))),
const data_size_t feature_size, const data_size_t feature_size,
__constant const data_size_t* restrict data_indices __attribute__((max_constant_size(65536))), __constant const data_size_t* restrict data_indices __attribute__((max_constant_size(65536))),
const data_size_t num_data, const data_size_t num_data,
__constant const score_t* restrict ordered_gradients __attribute__((max_constant_size(65536))), __constant const score_t* restrict ordered_gradients __attribute__((max_constant_size(65536))),
#if CONST_HESSIAN == 0 #if CONST_HESSIAN == 0
__constant const score_t* restrict ordered_hessians __attribute__((max_constant_size(65536))), __constant const score_t* restrict ordered_hessians __attribute__((max_constant_size(65536))),
#else #else
@ -227,18 +227,18 @@ __kernel void histogram64(__global const uchar4* restrict feature_data_base,
__global volatile int * sync_counters, __global volatile int * sync_counters,
__global acc_type* restrict hist_buf_base) { __global acc_type* restrict hist_buf_base) {
#else #else
__kernel void histogram64(__global const uchar4* feature_data_base, __kernel void histogram64(__global const uchar4* feature_data_base,
__constant const uchar4* restrict feature_masks __attribute__((max_constant_size(65536))), __constant const uchar4* restrict feature_masks __attribute__((max_constant_size(65536))),
const data_size_t feature_size, const data_size_t feature_size,
__global const data_size_t* data_indices, __global const data_size_t* data_indices,
const data_size_t num_data, const data_size_t num_data,
__global const score_t* ordered_gradients, __global const score_t* ordered_gradients,
#if CONST_HESSIAN == 0 #if CONST_HESSIAN == 0
__global const score_t* ordered_hessians, __global const score_t* ordered_hessians,
#else #else
const score_t const_hessian, const score_t const_hessian,
#endif #endif
__global char* restrict output_buf, __global char* restrict output_buf,
__global volatile int * sync_counters, __global volatile int * sync_counters,
__global acc_type* restrict hist_buf_base) { __global acc_type* restrict hist_buf_base) {
#endif #endif
@ -313,7 +313,7 @@ __kernel void histogram64(__global const uchar4* feature_data_base,
uchar is_hessian_first = (ltid >> 2) & 1; uchar is_hessian_first = (ltid >> 2) & 1;
// thread 0-7 write result to bank0, 8-15 to bank1, 16-23 to bank2, 24-31 to bank3 // thread 0-7 write result to bank0, 8-15 to bank1, 16-23 to bank2, 24-31 to bank3
ushort bank = (ltid >> 3) & BANK_MASK; ushort bank = (ltid >> 3) & BANK_MASK;
ushort group_feature = group_id >> POWER_FEATURE_WORKGROUPS; ushort group_feature = group_id >> POWER_FEATURE_WORKGROUPS;
// each 2^POWER_FEATURE_WORKGROUPS workgroups process on one feature (compile-time constant) // each 2^POWER_FEATURE_WORKGROUPS workgroups process on one feature (compile-time constant)
// feature_size is the number of examples per feature // feature_size is the number of examples per feature
@ -582,7 +582,7 @@ R""()
atomic_local_add_f(gh_hist + addr2, s0_stat2); atomic_local_add_f(gh_hist + addr2, s0_stat2);
#endif #endif
barrier(CLK_LOCAL_MEM_FENCE); barrier(CLK_LOCAL_MEM_FENCE);
/* Makes MSVC happy with long string literal /* Makes MSVC happy with long string literal
)"" )""
R""() R""()
@ -591,7 +591,7 @@ R""()
// restore feature_mask // restore feature_mask
feature_mask = feature_masks[group_feature]; feature_mask = feature_masks[group_feature];
#endif #endif
// now reduce the 4 banks of subhistograms into 1 // now reduce the 4 banks of subhistograms into 1
/* memory layout of gh_hist: /* memory layout of gh_hist:
----------------------------------------------------------------------------------------------- -----------------------------------------------------------------------------------------------
@ -680,7 +680,7 @@ R""()
output[1 * 4 * NUM_BINS + ltid] = h_val; output[1 * 4 * NUM_BINS + ltid] = h_val;
barrier(CLK_LOCAL_MEM_FENCE | CLK_GLOBAL_MEM_FENCE); barrier(CLK_LOCAL_MEM_FENCE | CLK_GLOBAL_MEM_FENCE);
mem_fence(CLK_GLOBAL_MEM_FENCE); mem_fence(CLK_GLOBAL_MEM_FENCE);
// To avoid the cost of an extra reducing kernel, we have to deal with some // To avoid the cost of an extra reducing kernel, we have to deal with some
// gray area in OpenCL. We want the last work group that process this feature to // gray area in OpenCL. We want the last work group that process this feature to
// make the final reduction, and other threads will just quit. // make the final reduction, and other threads will just quit.
// This requires that the results written by other workgroups available to the // This requires that the results written by other workgroups available to the
@ -726,13 +726,13 @@ R""()
#endif #endif
// locate our feature4's block in output memory // locate our feature4's block in output memory
uint output_offset = (feature4_id << POWER_FEATURE_WORKGROUPS); uint output_offset = (feature4_id << POWER_FEATURE_WORKGROUPS);
__global acc_type const * restrict feature4_subhists = __global acc_type const * restrict feature4_subhists =
(__global acc_type *)output_buf + output_offset * 4 * 2 * NUM_BINS; (__global acc_type *)output_buf + output_offset * 4 * 2 * NUM_BINS;
// skip reading the data already in local memory // skip reading the data already in local memory
uint skip_id = group_id ^ output_offset; uint skip_id = group_id ^ output_offset;
// locate output histogram location for this feature4 // locate output histogram location for this feature4
__global acc_type* restrict hist_buf = hist_buf_base + feature4_id * 4 * 2 * NUM_BINS; __global acc_type* restrict hist_buf = hist_buf_base + feature4_id * 4 * 2 * NUM_BINS;
within_kernel_reduction64x4(feature_mask, feature4_subhists, skip_id, g_val, h_val, within_kernel_reduction64x4(feature_mask, feature4_subhists, skip_id, g_val, h_val,
1 << POWER_FEATURE_WORKGROUPS, hist_buf, (__local acc_type *)shared_array); 1 << POWER_FEATURE_WORKGROUPS, hist_buf, (__local acc_type *)shared_array);
} }
} }

14
swig/StringArray.i
Просмотреть файл

@ -6,7 +6,7 @@
*/ */
/** /**
* This wraps the StringArray.hpp class for SWIG usage, * This wraps the StringArray.hpp class for SWIG usage,
* adding the basic C-style wrappers needed to make it * adding the basic C-style wrappers needed to make it
* usable for the users of the low-level lightgbmJNI API. * usable for the users of the low-level lightgbmJNI API.
*/ */
@ -23,7 +23,7 @@
/** /**
* @brief Creates a new StringArray and returns its handle. * @brief Creates a new StringArray and returns its handle.
* *
* @param num_strings number of strings to store. * @param num_strings number of strings to store.
* @param string_size the maximum number of characters that can be stored in each string. * @param string_size the maximum number of characters that can be stored in each string.
* @return StringArrayHandle or nullptr in case of allocation failure. * @return StringArrayHandle or nullptr in case of allocation failure.
@ -38,7 +38,7 @@
/** /**
* @brief Free the StringArray object. * @brief Free the StringArray object.
* *
* @param handle StringArray handle. * @param handle StringArray handle.
*/ */
void StringArrayHandle_free(StringArrayHandle handle) void StringArrayHandle_free(StringArrayHandle handle)
@ -49,7 +49,7 @@
/** /**
* @brief Return the raw pointer to the array of strings. * @brief Return the raw pointer to the array of strings.
* Wrapped in Java into String[] automatically. * Wrapped in Java into String[] automatically.
* *
* @param handle StringArray handle. * @param handle StringArray handle.
* @return Raw pointer to the string array which `various.i` maps to String[]. * @return Raw pointer to the string array which `various.i` maps to String[].
*/ */
@ -60,7 +60,7 @@
/** /**
* For the end user to extract a specific string from the StringArray object. * For the end user to extract a specific string from the StringArray object.
* *
* @param handle StringArray handle. * @param handle StringArray handle.
* @param index index of the string to retrieve from the array. * @param index index of the string to retrieve from the array.
* @return raw pointer to string at index, or nullptr if out of bounds. * @return raw pointer to string at index, or nullptr if out of bounds.
@ -72,7 +72,7 @@
/** /**
* @brief Replaces one string of the array at index with the new content. * @brief Replaces one string of the array at index with the new content.
* *
* @param handle StringArray handle. * @param handle StringArray handle.
* @param index Index of the string to replace * @param index Index of the string to replace
* @param new_content The content to replace * @param new_content The content to replace
@ -85,7 +85,7 @@
/** /**
* @brief Retrieve the number of strings in the StringArray. * @brief Retrieve the number of strings in the StringArray.
* *
* @param handle StringArray handle. * @param handle StringArray handle.
* @return number of strings that the array stores. * @return number of strings that the array stores.
*/ */

6
swig/pointer_manipulation.i
Просмотреть файл

@ -6,11 +6,11 @@
* This SWIG interface extension provides support to * This SWIG interface extension provides support to
* the pointer manipulation methods present in the standard * the pointer manipulation methods present in the standard
* SWIG wrappers, but with support for larger arrays. * SWIG wrappers, but with support for larger arrays.
* *
* SWIG provides this in https://github.com/swig/swig/blob/master/Lib/carrays.i * SWIG provides this in https://github.com/swig/swig/blob/master/Lib/carrays.i
* but the standard methods only provide arrays with up to * but the standard methods only provide arrays with up to
* max(int32_t) elements. * max(int32_t) elements.
* *
* The `long_array_functions` wrappers extend this * The `long_array_functions` wrappers extend this
* to arrays of size max(int64_t) instead of max(int32_t). * to arrays of size max(int64_t) instead of max(int32_t).
*/ */
@ -103,7 +103,7 @@ void delete_##NAME(TYPE *ary);
TYPE NAME##_getitem(TYPE *ary, int64_t index); TYPE NAME##_getitem(TYPE *ary, int64_t index);
void NAME##_setitem(TYPE *ary, int64_t index, TYPE value); void NAME##_setitem(TYPE *ary, int64_t index, TYPE value);
%enddef %enddef
/* Custom template for arrays of pointers */ /* Custom template for arrays of pointers */
%define %ptr_array_functions(TYPE,NAME) %define %ptr_array_functions(TYPE,NAME)

2
tests/cpp_tests/testutils.cpp
Просмотреть файл

@ -277,7 +277,7 @@ namespace LightGBM {
/*! /*!
* Pushes data from 1 thread into a Dataset based on thread_id and nrows. * Pushes data from 1 thread into a Dataset based on thread_id and nrows.
* e.g. with 100 rows, thread 0 will push rows 0-49, and thread 2 will push rows 50-99. * e.g. with 100 rows, thread 0 will push rows 0-49, and thread 2 will push rows 50-99.
* Note that rows are still pushed in microbatches within their range. * Note that rows are still pushed in microbatches within their range.
*/ */
void TestUtils::PushSparseBatch(DatasetHandle dataset_handle, void TestUtils::PushSparseBatch(DatasetHandle dataset_handle,
int32_t nrows, int32_t nrows,

2
windows/LightGBM.vcxproj.filters
Просмотреть файл

@ -351,4 +351,4 @@
<Filter>src\utils</Filter> <Filter>src\utils</Filter>
</ClCompile> </ClCompile>
</ItemGroup> </ItemGroup>
</Project> </Project>