From f14c399b0f68a055b05b4e1f435a4b6b0ff24f2e Mon Sep 17 00:00:00 2001 From: Hyunsu Philip Cho Date: Fri, 5 May 2023 12:14:34 -0700 Subject: [PATCH 1/2] Deprecate C codegen --- docs/_static/deployment.png | Bin 183172 -> 64260 bytes docs/_static/deployment.svg | 4449 ++---------------- docs/conf.py | 1 + docs/index.rst | 161 +- docs/install.rst | 105 +- docs/knobs/compiler_param.rst | 11 - docs/knobs/index.rst | 1 - docs/treelite-runtime-api.rst | 8 - docs/tutorials/deploy.rst | 347 -- docs/tutorials/deploy_java.rst | 122 - docs/tutorials/first.rst | 143 - docs/tutorials/index.rst | 4 - docs/tutorials/optimize.rst | 376 -- python/treelite/annotator.py | 2 + python/treelite/contrib/__init__.py | 4 + python/treelite/frontend.py | 4 + python/treelite/warn.py | 9 + runtime/python/treelite_runtime/predictor.py | 3 + runtime/python/treelite_runtime/warn.py | 9 + 19 files changed, 584 insertions(+), 5175 deletions(-) delete mode 100644 docs/knobs/compiler_param.rst delete mode 100644 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("https://docs.scipy.org/doc/scipy/", None), "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None), "sklearn": ("https://scikit-learn.org/stable", None), + "tl2cgen": ("https://tl2cgen.readthedocs.io/en/latest/", None), } diff --git a/docs/index.rst b/docs/index.rst index 50b479c4..6f0d9027 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,9 +1,11 @@ -##################################################### +===================================================== Treelite : model compiler for decision tree ensembles -##################################################### +===================================================== -**Treelite** is a model compiler for decision tree ensembles, aimed at -efficient deployment. +**Treelite** is a universal model exchange and serialization format for +decision tree forests. Treelite aims to be a small library that enables +other C++ applications to exchange and store decision trees on the disk +as well as the network. .. raw:: html @@ -14,115 +16,84 @@ efficient deployment. data-size="large" data-show-count="true" aria-label="Watch dmlc/treelite on GitHub">Watch -************* +.. warning:: Tree compiler was migrated to TL2cgen + + If you are looking for a compiler to translate tree models into C code, + use :doc:`TL2cgen `. + To migrate existing code using Treelite 3.x, consult the page + :doc:`tl2cgen:treelite-migration`. + + Why Treelite? -************* - - -Use machine learning package of your choice -=========================================== -Treelite accommodates a wide range of decision tree ensemble models. In -particular, it handles both -`random forests `_ and -`gradient boosted trees `_. - -Treelite can read models produced by -`XGBoost `_, -`LightGBM `_, and -`scikit-learn `_. In cases where -you are using another package to train your model, you may use the -:doc:`flexible builder class `. - -Deploy with minimal dependencies -================================ -It is a great hassle to install machine learning packages (e.g. XGBoost, -LightGBM, scikit-learn, etc.) on every machine your tree model will run. This is -the case no longer: Treelite will export your model as a stand-alone -prediction library so that predictions will be made without any machine -learning package installed. +============= Universal, lightweight specification for all tree models -======================================================== -Are you designing an optimized prediction runtime software for tree models? +-------------------------------------------------------- +Are you designing a C++ application that needs to read and write tree models, +e.g. a prediction server? Do not be overwhelmed by the variety of tree models in the wild. Treelite -lets you convert many kinds of tree models into a **single, lightweight -exchange format**. You can serialize (save) any tree model into a byte -sequence or a file. Plus, Treelite is designed to be used as a component in -prediction runtimes. Currently, Treelite is used by -`Amazon SageMaker Neo `_ and -and `RAPIDS cuML `_. - -*********** -Quick start -*********** -Install Treelite from PyPI: - -.. code-block:: console +lets you convert many kinds of tree models into a **common specification**. +By using Treelite as a library, your application now only needs to deal +with one model specification instead of many. Treelite currently +supports: + +* `XGBoost `_ +* `LightGBM `_ +* `scikit-learn `_ +* :doc:`flexible builder class ` for users of other + tree libraries + +In addition, tree libraries can directly output trained trees using the +Treelite specification. For example, the random forest algoritm in +`RAPIDS cuML `_ stores the random forest +object using Treelite. - python3 -m pip install --user treelite treelite_runtime +.. raw:: html -Import your tree ensemble model into Treelite: +

+ +
+ (Click to enlarge) + +

-.. code-block:: python +A small library that's easy to embed in another C++ application +--------------------------------------------------------------- +Treelite has an up-to-date CMake build script. If your C++ +application uses CMake, it is easy to embed Treelite. +Treelite is currently used by the following applications: - import treelite - model = treelite.Model.load('my_model.model', model_format='xgboost') +* :doc:`tl2cgen:index` +* Forest Inference Library (FIL) in `RAPIDS cuML `_ +* `Triton Inference Server FIL Backend `_, + an optimized prediction runtime for CPUs and GPUs. -Deploy a source archive: +Quick start +=========== +Install Treelite: -.. code-block:: python +.. code-block:: console - # Produce a zipped source directory, containing all model information - # Run `make` on the target machine - model.export_srcpkg(platform='unix', toolchain='gcc', - pkgpath='./mymodel.zip', libname='mymodel.so', - verbose=True) + # From PyPI + pip install treelite + # From Conda + conda install -c conda-forge treelite -Deploy a shared library: +Import your tree ensemble model into Treelite: .. code-block:: python - # Like export_srcpkg, but generates a shared library immediately - # Use this only when the host and target machines are compatible - model.export_lib(toolchain='gcc', libpath='./mymodel.so', verbose=True) + import treelite + model = treelite.Model.load("my_model.model", model_format="xgboost") -Make predictions on the target machine: +Compute predictions using :doc:`treelite-gtil-api`: .. code-block:: python - import treelite_runtime - predictor = treelite_runtime.Predictor('./mymodel.so', verbose=True) - dmat = treelite_runtime.DMatrix(X) - out_pred = predictor.predict(dmat) - -Read :doc:`tutorials/first` for a more detailed example. See -:doc:`tutorials/deploy` for additional instructions on deployment. - -.. note:: A note on API compatibility - - Since Treelite is in early development, its API may change substantially - in the future. - -****************** -How Treelite works -****************** - -.. raw:: html - -

- -
- (Click to enlarge) - -

- -The workflow involves two distinct machines: **the host machine** that generates -prediction subroutine from a given tree model, and **the target machine** that -runs the subroutine. The two machines exchange a single C file that contains -all relevant information about the tree model. Only the host machine needs to -have Treelite installed; the target machine requires only a working C compiler. + X = ... # numpy array + treelite.gtil.predict(model, data=X) ******** Contents @@ -135,10 +106,8 @@ Contents install tutorials/index treelite-api - treelite-runtime-api treelite-gtil-api treelite-c-api - Treelite runtime Rust API knobs/index notes-on-serialization treelite-doxygen diff --git a/docs/install.rst b/docs/install.rst index 1dac6297..96b4eb7e 100644 --- a/docs/install.rst +++ b/docs/install.rst @@ -1,21 +1,20 @@ +============ Installation ============ You may choose one of two methods to install Treelite on your system: -* :ref:`install-pip` -* :ref:`install-conda` -* :ref:`install-source` - -.. _install-pip: +.. contents:: + :local: + :depth: 1 Download binary releases from PyPI (Recommended) ------------------------------------------------- +================================================ This is probably the most convenient method. Simply type .. code-block:: console - python3 -m pip install --user treelite treelite_runtime + pip install treelite to install the Treelite package. The command will locate the binary release that is compatible with your current platform. Check the installation by running @@ -23,14 +22,22 @@ your current platform. Check the installation by running .. code-block:: python import treelite - import treelite_runtime -in an interactive Python session. This method is available for only Windows, Mac OS X, and Linux. +in an interactive Python session. This method is available for only Windows, MacOS, and Linux. For other operating systems, see the next section. -.. note:: Installing OpenMP runtime on Mac OSX +.. note:: Windows users need to install Visual C++ Redistributable + + Treelite requires DLLs from `Visual C++ Redistributable + `_ + in order to function, so make sure to install it. Exception: If + you have Visual Studio installed, you already have access to + necessary libraries and thus don't need to install Visual C++ + Redistributable. + +.. note:: Installing OpenMP runtime on MacOS - Treelite requires the presence of OpenMP runtime. To install OpenMP runtime on a Mac OSX system, + Treelite requires the presence of OpenMP runtime. To install OpenMP runtime on a MacOS system, run the following command: .. code-block:: bash @@ -38,10 +45,8 @@ For other operating systems, see the next section. brew install libomp -.. _install-conda: - Download binary releases from Conda ------------------------------------------------- +=================================== Treelite is also available on Conda. .. code-block:: console @@ -54,7 +59,7 @@ available platforms. .. _install-source: Compile Treelite from the source --------------------------------- +================================ Installation consists of two steps: 1. Build the shared libraries from C++ code (See the note below for the list.) @@ -70,7 +75,7 @@ Installation consists of two steps: Operating System Main library Runtime library ================== ===================== ============================= Windows ``treelite.dll`` ``treelite_runtime.dll`` - Mac OS X ``libtreelite.dylib`` ``libtreelite_runtime.dylib`` + MacOS ``libtreelite.dylib`` ``libtreelite_runtime.dylib`` Linux / other UNIX ``libtreelite.so`` ``libtreelite_runtime.so`` ================== ===================== ============================= @@ -83,8 +88,8 @@ To get started, clone Treelite repo from GitHub. The next step is to build the shared libraries. -1-1. Compiling shared libraries on Linux and Mac OS X -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +1-1. Compiling shared libraries on Linux and MacOS +-------------------------------------------------- Here, we use CMake to generate a Makefile: .. code-block:: bash @@ -102,7 +107,7 @@ libraries. The compiled libraries will be under the ``build/`` directory. -.. note:: Compiling Treelite with multithreading on Mac OS X +.. note:: Compiling Treelite with multithreading on MacOS Treelite requires the presence of OpenMP runtime. To install OpenMP runtime on a Mac OSX system, run the following command: @@ -112,73 +117,29 @@ The compiled libraries will be under the ``build/`` directory. brew install libomp 1-2. Compiling shared libraries on Windows -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +------------------------------------------ We can use CMake to generate a Visual Studio project. The following snippet assumes that Visual -Studio 2017 is installed. Adjust the version depending on the copy that's installed on your system. +Studio 2022 is installed. Adjust the version depending on the copy that's installed on your system. .. code-block:: dosbatch mkdir build cd build - cmake .. -G"Visual Studio 15 2017 Win64" + cmake .. -G"Visual Studio 17 2022" -A x64 -.. note:: Visual Studio 2017 or newer is required +.. note:: Visual Studio 2019 or newer is required - Ensure that you have Visual Studio version 2017 or newer. + Treelite uses the C++17 standard. Ensure that you have Visual Studio version 2019 or newer. Once CMake finished running, open the generated solution file (``treelite.sln``) in Visual Studio. From the top menu, select **Build > Build Solution**. 2. Installing Python package -^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -The Python package is located at the ``python`` subdirectory. There are several -ways to install the package: - -**1. Install system-wide, which requires root permission** +---------------------------- +The Python package is located at the ``python`` subdirectory. Run Pip to install the Python +package. The Python package will re-use the native library built in Step 1. .. code-block:: bash - # Install treelite cd python - sudo python3 setup.py install - # Install treelite_runtime - cd ../runtime/python - sudo python3 setup.py install - -You will need Python `setuptools `_ -module for this to work. It is often part of the core Python installation. -Should it be necessary, the package can be installed using ``pip``: - -.. code-block:: bash - - pip install -U pip setuptools - -**2. Install for only current user** - -This is useful if you do not have the administrative rights. - -.. code-block:: bash - - # Install treelite - cd python - python3 setup.py install --user - # Install treelite_runtime - cd ../runtime/python - python3 setup.py install --user - -.. note:: Recompiling Treelite - - Every time the C++ portion of Treelite gets re-compiled, the Python - package must be re-installed for the new library to take effect. - -**3. Set the environment variable PYTHONPATH to locate Treelite package** - -Only set the environment variable ``PYTHONPATH`` to tell Python where to find -the Treelite package. This is useful for developers, as any changes made -to C++ code will be immediately visible to Python side without re-running ``setup.py``. - -.. code-block:: bash - - export PYTHONPATH=/path/to/treelite/python:/path/to/treelite/runtime/python - python3 # enter interactive session - + pip install . # will re-use libtreelite.so diff --git a/docs/knobs/compiler_param.rst b/docs/knobs/compiler_param.rst deleted file mode 100644 index 8cfc1ac3..00000000 --- a/docs/knobs/compiler_param.rst +++ /dev/null @@ -1,11 +0,0 @@ -Compiler Parameters -=================== - -Compiler parameters influence the way the prediction subroutine is generated -from a tree ensemble model. - ------------------------------ - -.. doxygengroup:: compiler_param - :project: treelite - :content-only: diff --git a/docs/knobs/index.rst b/docs/knobs/index.rst index 6a33ec88..e50b3c8d 100644 --- a/docs/knobs/index.rst +++ b/docs/knobs/index.rst @@ -5,5 +5,4 @@ Knobs and Parameters .. toctree:: :maxdepth: 1 - compiler_param model_param diff --git a/docs/treelite-runtime-api.rst b/docs/treelite-runtime-api.rst deleted file mode 100644 index 6eebf01a..00000000 --- a/docs/treelite-runtime-api.rst +++ /dev/null @@ -1,8 +0,0 @@ -==================== -Treelite runtime API -==================== - -Runtime API of Treelite Python package. - -.. automodule:: treelite_runtime - :members: diff --git a/docs/tutorials/deploy.rst b/docs/tutorials/deploy.rst deleted file mode 100644 index a30e594d..00000000 --- a/docs/tutorials/deploy.rst +++ /dev/null @@ -1,347 +0,0 @@ -Deploying models -================ - -After all the hard work you did to train your tree ensemble model, you now have -to **deploy** the model. Deployment refers to distributing your model to -other machines and devices so as to make predictions on them. To facilitate -the coming discussions, let us define a few terms. - -* **Host machine** : the machine running Treelite. -* **Target machine** : the machine on which predictions will be made. The host - machine may or may not be identical to the target machine. In cases where - it's infeasible to install Treelite on the target machine, the host and - target machines will be necessarily distinct. -* **Shared library** : a blob of executable subroutines that can be imported by - other native applications. Shared libraries will often have file extensions - .dll, .so, or .dylib. Going back to the particular context of tree deployment, - Treelite will produce a shared library containing the prediction subroutine - (compiled to native machine code). -* **Runtime package** : a :doc:`tiny fraction<../treelite-runtime-api>` of the - full Treelite package, consisting of a few helper functions that lets you - easily load shared libraries and make predictions. The runtime is good to - have, but on systems lacking Python we can do without it. - -In this document, we will document two options for deployment. We will present the programming -interface each deployment option presents, as well as its dependencies and requirements. - -.. contents:: Contents - :local: - :depth: 2 - -Option 1: Deploy prediction code with the runtime package ---------------------------------------------------------- -If feasible, this option is probably the most convenient. On the target machine, install the -Treelite runtime by running pip: - -.. code-block:: bash - - python3 -m pip install treelite_runtime --user - -Once the Treelite runtime is installed, it suffices to follow instructions in :doc:`first`. - -Option 2: Deploy prediciton code only -------------------------------------- - -With this option, neither Python nor a C++ compiler is required. You should be -able to adopt this option using any basic installation of UNIX-like operating systems. - -Dependencies and Requirements -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The target machine shall meet the following conditions: - -* A C compiler is available. -* The C compiler supports the following features of the - `C99 standard `_: inline functions; - declaration of loop variables inside ``for`` loop; the ``expf`` function in - ````; the ```` header. -* GNU Make or Microsoft NMake is installed. -* An archive utility exists that can open a .zip archive. - -Deployment instructions -^^^^^^^^^^^^^^^^^^^^^^^ -\1. On the host machine, install Treelite and import your tree ensemble model. -You should end up with the model object of type :py:class:`~treelite.Model`. - -.. code-block:: python - - ### Run this block on the **host** machine - - import treelite - model = treelite.Model.load('your_model.model', 'xgboost') - # You may also use `from_xgboost` method or the builder class - -\2. Export your model as a **source package** by calling the method -:py:meth:`~treelite.Model.export_srcpkg` of the :py:class:`~treelite.Model` -object. The source package will contain C code representation of the prediction -subroutine. - -.. code-block:: python - - ### Continued from the previous code block - - # Operating system of the target machine - platform = 'unix' - # C compiler to use to compile prediction code on the target machine - toolchain = 'gcc' - # Save the source package as a zip archive named mymodel.zip - # Later, we'll use this package to produce the library mymodel.so. - model.export_srcpkg(platform=platform, toolchain=toolchain, - pkgpath='./mymodel.zip', libname='mymodel.so', - verbose=True) - -.. note:: On the value of ``toolchain`` - - Treelite supports only three toolchain configurations ('msvc', 'gcc', 'clang') - for which it generates Makefiles. If you are using a compiler other than - these three, you will have to write your own Makefile. For now, just set - ``toolchain='gcc'`` and move on. - -After calling :py:meth:`~treelite.Model.export_srcpkg`, you should be able to -find the zip archive named ``mymodel.zip`` inside the current working directory. - -.. code-block:: console - - john.doe@host-machine:/home/john.doe/$ ls . - mymodel.zip your_model.model - -The content of ``mymodel.zip`` consists of the header and source files, as well -as the Makefile: - -.. code-block:: console - - john.doe@host-machine:/home/john.doe/$ unzip -l mymodel.zip - Archive: mymodel.zip - Length Date Time Name - --------- ---------- ----- ---- - 0 11-01-2017 23:11 mymodel/ - 167 11-01-2017 23:11 mymodel/Makefile - 4831036 11-01-2017 23:11 mymodel/mymodel.c - 311 11-01-2017 23:11 mymodel/mymodel.h - 109 11-01-2017 23:11 mymodel/recipe.json - --------- ------- - 4831623 5 files - -\3. Now you are ready to deploy the model to the target machine. Copy to the -target machine the archive ``mymodel.zip`` (source package). - -.. code-block:: console - - john.doe@host-machine:/home/john.doe/$ sftp john.doe@target-machine - Connected to target-machine. - sftp> put mymodel.zip - Uploading mymodel.zip to /home/john.doe/mymodel.zip - mymodel.zip 100% 410KB 618.2KB/s 00:00 - sftp> quit - -\4. It is time to move to the target machine. On the target machine, extract -the archive ``mymodel.zip``: - -.. code-block:: console - - john.doe@host-machine:/home/john.doe/$ ssh john.doe@target-machine - Last login: Tue Oct 31 00:43:36 2017 from host-machine - - john.doe@target-machine:/home/john.doe/$ unzip mymodel.zip - Archive: mymodel.zip - creating: mymodel/ - inflating: mymodel/Makefile - inflating: mymodel/mymodel.c - inflating: mymodel/mymodel.h - inflating: mymodel/recipe.json - -\5. Build the source package (using GNU Make or NMake). - -.. code-block:: console - - john.doe@target-machine:/home/john.doe/$ cd mymodel - john.doe@target-machine:/home/john.doe/mymodel/$ make - gcc -c -O3 -o mymodel.o mymodel.c -fPIC -std=c99 -flto -fopenmp - gcc -shared -O3 -o mymodel.so mymodel.o -std=c99 -flto -fopenmp - john.doe@target-machine:/home/john.doe/mymodel/$ ls - Makefile mymodel.c mymodel.so - mymodel.h mymodel.o recipe.json - -.. note:: Parallel compilation with GNU Make - - If you used ``parallel_comp`` option to split the model into multiple source - files, you can take advantage of parallel compilation. Simply replace ``make`` - with ``make -jN``, where ``N`` is replaced with the number of workers to - launch. Setting ``N`` too high may result into memory shortage. - -.. note:: Using other compilers - - If you are using a compiler other than gcc, clang, or Microsoft Visual C++, - you will need to compose your own Makefile. Open the ``Makefile`` and - make necessary changes. - -Prediction instructions -^^^^^^^^^^^^^^^^^^^^^^^ -The prediction library provides the function ``predict`` with the -following signature: - -.. code-block:: c - - float predict(union Entry* data, int pred_margin); - -Here, the argument ``data`` must be an array of length ``M``, where ``M`` is -the number of features used in the tree ensemble. The ``data`` array stores -all the feature values of a single row. To indicate presence or absence of -a feature value, we use the union type ``Entry``, which defined as - -.. code-block:: c - - union Entry { - int missing; - float fvalue; - }; - -For missing values, we set the ``missing`` field to -1. For non-missing ones, we -set the ``fvalue`` field to the feature value. The total number of features -is given by the function - -.. code-block:: c - - size_t get_num_feature(void); - -Let's look at an example. We'd start by initializing the array ``inst``, a dense -aray to hold feature values of a single data row: - -.. code-block:: c - - /* number of features */ - const size_t num_feature = get_num_feature(); - /* inst: dense vector storing feature values */ - union Entry* inst = malloc(sizeof(union Entry) * num_feature); - /* clear inst with all missing values */ - for (i = 0; i < num_feature; ++i) { - inst[i].missing = -1; - } - -Before calling the function ``predict``, the array ``inst`` needs to be -initialized with missing and present feature values. The following peudocode -illustrates the idea: - -.. code-block:: none - - For each data row rid: - inst[i].missing == -1 for every i, assuming all features lack values - - For each feature i for which the data row in fact has a feature value: - Set inst[i].fvalue = [feature value], to indicate presence - - Call predict(inst, 0) and get prediction for the data row rid - - For each feature i for which the row has a feature value: - Set inst[i].missing = -1, to prepare for next row (rid + 1) - -The task is not too difficult as long as the input data is given as a particular -form of sparse matrix: the `Compressed Sparse Row\ -`_ format. -The sparse matrix consists of three arrays: - -* ``val`` stores nonzero entries in - `row-major order `_. -* ``col_ind`` stores column indices of the entries in ``val``. The expression - ``col_ind[i]`` indicates the column index of the ``i`` th entry ``val[i]``. -* ``row_ptr`` stores the locations in ``val`` that start and end data rows. The - ``i`` th data row is given by the array slice ``val[row_ptr[i]:row_ptr[i+1]]``. - -.. code-block:: c - - /* nrow : number of data rows */ - for (rid = 0; rid < nrow; ++rid) { - ibegin = row_ptr[rid]; - iend = row_ptr[rid + 1]; - /* Fill nonzeros */ - for (i = ibegin; i < iend; ++i) { - inst[col_ind[i]].fvalue = val[i]; - } - out_pred[rid] = predict(inst, 0); - /* Drop nonzeros */ - for (i = ibegin; i < iend; ++i) { - inst[col_ind[i]].missing = -1; - } - } - -It only remains to create three arrays ``val``, ``col_ind``, and ``row_ptr``. -You may want to use a third-pary library here to read from -a SVMLight format. For now, we'll punt the issue of loading the input data -and write it out as constants in the program: - -.. code-block:: c - - #include - #include - #include "mymodel.h" - - int main(void) { - /* 5x13 "sparse" matrix, in CSR format - [[ 0. , 0. , 0.68, 0.99, 0. , 0.11, 0. , 0.82, 0. , - 0. , 0. , 0. , 0. ], - [ 0. , 0. , 0.99, 0. , 0. , 0. , 0. , 0. , 0. , - 0.61, 0. , 0. , 0. ], - [ 0.02, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , - 0. , 0. , 0. , 0. ], - [ 0. , 0. , 0.36, 0. , 0.82, 0. , 0. , 0.57, 0. , - 0. , 0. , 0. , 0.75], - [ 0.47, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , - 0. , 0. , 0.45, 0. ]] - */ - const float val[] = {0.68, 0.99, 0.11, 0.82, 0.99, 0.61, 0.02, 0.36, 0.82, - 0.57, 0.75, 0.47, 0.45}; - const size_t col_ind[] = {2, 3, 5, 7, 2, 9, 0, 2, 4, 7, 12, 0, 11}; - const size_t row_ptr[] = {0, 4, 6, 7, 11, 13}; - const size_t nrow = 5; - const size_t ncol = 13; - - /* number of features */ - const size_t num_feature = get_num_feature(); - /* inst: dense vector storing feature values */ - union Entry* inst = malloc(sizeof(union Entry) * num_feature); - float* out_pred = malloc(sizeof(float) * nrow); - size_t rid, ibegin, iend, i; - - /* clear inst with all missing */ - for (i = 0; i < num_feature; ++i) { - inst[i].missing = -1; - } - - for (rid = 0; rid < nrow; ++rid) { - ibegin = row_ptr[rid]; - iend = row_ptr[rid + 1]; - /* Fill nonzeros */ - for (i = ibegin; i < iend; ++i) { - inst[col_ind[i]].fvalue = val[i]; - } - out_pred[rid] = predict(inst, 0); - /* Drop nonzeros */ - for (i = ibegin; i < iend; ++i) { - inst[col_ind[i]].missing = -1; - } - printf("pred[%zu] = %f\n", rid, out_pred[rid]); - } - free(inst); - free(out_pred); - return 0; - } - -Save the program as a .c file and put it in the same directory ``mymodel/``. To -link the program against the prediction library ``mymodel.so``, simply run - -.. code-block:: bash - - gcc -o myprog myprog.c mymodel.so -I. -std=c99 -lm - -As long as the program ``myprog`` is in the same directory of the prediction -library ``mymodel.so``, we'll be good to go. - -A sample output: - -.. code-block:: none - - pred[0] = 44.880001 - pred[1] = 44.880001 - pred[2] = 44.880001 - pred[3] = 42.670002 - pred[4] = 44.880001 diff --git a/docs/tutorials/deploy_java.rst b/docs/tutorials/deploy_java.rst deleted file mode 100644 index 5db672e4..00000000 --- a/docs/tutorials/deploy_java.rst +++ /dev/null @@ -1,122 +0,0 @@ -Deploying models with Treelite4J -================================ - -Treelite4J is the Java runtime for Treelite. This tutorial will show how to use Treelite4J to deploy decision tree models to Java applications. - -Load compiled model -------------------- -Locate the compiled model (dll/so/dylib) in the local filesystem. We load the compiled model by creating a Predictor object: - -.. code-block:: java - - import ml.dmlc.treelite4j.Predictor; - - Predictor predictor = new Predictor("path/to/compiled_model.so", -1, true, true); - -The second argument is set to -1, to utilize all CPU cores available. See `here `_ for the meaning of third and fourth arguments. - -Query the model ---------------- -Once the compiled model is loaded, we can query it: - -.. code-block:: java - - // Get the input dimension, i.e. the number of feature values in the input vector - int num_feature = predictor.GetNumFeature(); - - // Get the number of classes. - // This number is 1 for tasks other than multi-class classification. - // For multi-class classification task, the number is equal to the number of classes. - int num_class = predictor.GetNumClass(); - -Predict with a single input (instance) --------------------------------------- -For predicting with a single input, we create an array of Entry objects, set their values, -and invoke the prediction function. - -.. code-block:: java - - import ml.dmlc.treelite4j.Entry; - - // Create an array of feature values for the input - int num_feature = predictor.GetNumFeature(); - Entry[] inst = new Entry[num_feature]; - - // Initialize all feature values as missing - for (int i = 0; i < num_feature; ++i) { - inst[i] = new Entry(); - inst[i].setMissing(); - } - - // Set feature values that are not missing - // In this example, we set feature 1, 3, and 7 - inst[1].setFValue(-0.5); - inst[3].setFValue(3.2); - inst[7].setFValue(-1.7); - - // Now run prediction - // (Put false in the second argument to get probability outputs) - float[] result = predictor.predict(inst, false); - // The result is either class probabilities (for multi-class classification) - // or a single number (for all other tasks, such as regression) - -Predict with a batch of inputs ------------------------------- -For predicting with a batch of inputs, we create a list of DataPoint objects. Each DataPoint object consists of feature values and corresponding feature indices. - -Let us look at an example. Consider the following 4-by-6 data matrix - -.. math:: - - \left[ - \begin{array}{cccccc} - 10 & 20 & \cdot & \cdot & \cdot & \cdot\\ - \cdot & 30 & \cdot & 40 & \cdot & \cdot\\ - \cdot & \cdot & 50 & 60 & 70 & \cdot\\ - \cdot & \cdot & \cdot & \cdot & \cdot & 80 - \end{array} - \right] - -where the dot (.) indicates the missing value. The matrix consists of 4 data points (instances), each with 6 feature values. -Since not all feature values are present, we need to store feature indices as well as feature values: - -.. code-block:: java - - import ml.dmlc.treelite4j.DataPoint; - - // Create a list consisting of 4 data points - List dmat = new ArrayList() { - { - // feature indices feature values - add(new DataPoint(new int[]{0, 1}, new float[]{10f, 20f})); - add(new DataPoint(new int[]{1, 3}, new float[]{30f, 40f})); - add(new DataPoint(new int[]{2, 3, 4}, new float[]{50f, 60f, 70f})); - add(new DataPoint(new int[]{5}, new float[]{80f})); - } - }; - -Once the list is created, we then convert it into a SparseBatch object. We use SparseBatch rather than DenseBatch because significant portion of the data matrix -consists of missing values. - -.. code-block:: java - - import ml.dmlc.treelite4j.BatchBuilder; - - // Convert data point list into SparseBatch object - SparseBatch batch = BatchBuilder.CreateSparseBatch(dmat); - -Now invoke the batch prediction function using the SparseBatch object: - -.. code-block:: java - - // verbose=true, pred_margin=false - float[][] result = predictor.predict(batch, true, false); - -The returned array is a two-dimensional array where the array ``result[i]`` represents the prediction for the ``i``-th data point. For most applications, each ``result[i]`` has length 1. Multi-class classification task is specical, in that for that task ``result[i]`` contains class probabilities, so the array is as long as the number of target classes. - -For your convenience, we also provide a convenience method to load a data text file in the LIBSVM format: - -.. code-block:: java - - List dmat = BatchBuilder.LoadDatasetFromLibSVM("path/to/my.data.libsvm"); - SparseBatch batch = BatchBuilder.CreateSparseBatch(dmat); diff --git a/docs/tutorials/first.rst b/docs/tutorials/first.rst deleted file mode 100644 index 2e7a0131..00000000 --- a/docs/tutorials/first.rst +++ /dev/null @@ -1,143 +0,0 @@ -First tutorial -============== - -This tutorial will demonstrate the basic workflow. - -.. code-block:: python - - import treelite - -Regression Example ------------------- - -In this tutorial, we will use a small regression example to describe the -full workflow. - -Load the Boston house prices dataset ------------------------------------- - -Let us use the Boston house prices dataset from scikit-learn -(:py:func:`sklearn.datasets.load_boston`). It consists of 506 houses -with 13 distinct features: - -.. code-block:: python - - from sklearn.datasets import load_boston - X, y = load_boston(return_X_y=True) - print(f'dimensions of X = {X.shape}') - print(f'dimensions of y = {y.shape}') - -Train a tree ensemble model using XGBoost ------------------------------------------ - -The first step is to train a tree ensemble model using XGBoost -(`dmlc/xgboost `_). - -Disclaimer: Treelite does NOT depend on the XGBoost package in any way. -XGBoost was used here only to provide a working example. - -.. code-block:: python - - import xgboost - dtrain = xgboost.DMatrix(X, label=y) - params = {'max_depth':3, 'eta':1, 'objective':'reg:squarederror', 'eval_metric':'rmse'} - bst = xgboost.train(params, dtrain, 20, [(dtrain, 'train')]) - -Pass XGBoost model into Treelite --------------------------------- - -Next, we feed the trained model into Treelite. If you used XGBoost to -train the model, it takes only one line of code: - -.. code-block:: python - - model = treelite.Model.from_xgboost(bst) - -.. note:: Using other packages to train decision trees - - With additional work, you can use models trained with other machine learning - packages. See :doc:`this page ` for instructions. - -Generate shared library ------------------------ - -Given a tree ensemble model, Treelite will produce a **prediction subroutine** -(internally represented as a C program). To use -the subroutine for prediction task, we package it as a `dynamic shared -library `_, -which exports the prediction subroutine for other programs to use. - -Before proceeding, you should decide which of the following compilers is -available on your system and set the variable ``toolchain`` -appropriately: - -- ``gcc`` -- ``clang`` -- ``msvc`` (Microsoft Visual C++) - -.. code-block:: python - - toolchain = 'gcc' # change this value as necessary - -The choice of toolchain will be used to compile the prediction -subroutine into native code. - -Now we are ready to generate the library. - -.. code-block:: python - - model.export_lib(toolchain=toolchain, libpath='./mymodel.so', verbose=True) - # ^^ - # set correct file extension here; see the following paragraph - -.. note:: File extension for shared library - - Make sure to use the correct file extension for the library, - depending on the operating system: - - - Windows: ``.dll`` - - Mac OS X: ``.dylib`` - - Linux / Other UNIX: ``.so`` - -.. note:: Want to deploy the model to another machine? - - This tutorial assumes that predictions will be made on the same machine that - is running Treelite. If you'd like to deploy your model to another machine - (that may not have Treelite installed), see the page :doc:`deploy`. - -.. note:: Reducing compilation time for large models - - For large models, :py:meth:`~treelite.Model.export_lib` may take a long time - to finish. To reduce compilation time, enable the ``parallel_comp`` option by - writing - - .. code-block:: python - - model.export_lib(toolchain=toolchain, libpath='./mymodel.so', - params={'parallel_comp': 32}, verbose=True) - - which splits the prediction subroutine into 32 source files that gets compiled - in parallel. Adjust this number according to the number of cores on your - machine. - -Use the shared library to make predictions ------------------------------------------- - -Once the shared library has been generated, we feed it into a separate -module (:py:mod:`treelite_runtime`) known as the runtime. The -optimized prediction subroutine is exposed through the -:py:class:`~treelite_runtime.Predictor` class: - -.. code-block:: python - - import treelite_runtime # runtime module - predictor = treelite_runtime.Predictor('./mymodel.so', verbose=True) - -We decide on which of the houses in ``X`` we should make predictions -for. Say, from 10th house to 20th: - -.. code-block:: python - - dmat = treelite_runtime.DMatrix(X[10:20]) - out_pred = predictor.predict(dmat) - print(out_pred) diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index 9d15b38c..c844c3c5 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -8,10 +8,6 @@ This page lists tutorials about Treelite. :maxdepth: 1 :caption: Contents: - first import - optimize - deploy - deploy_java builder json_import diff --git a/docs/tutorials/optimize.rst b/docs/tutorials/optimize.rst deleted file mode 100644 index 00d6ac3f..00000000 --- a/docs/tutorials/optimize.rst +++ /dev/null @@ -1,376 +0,0 @@ -Optimizing prediction subroutine -================================ - -Treelite offers system-level optimizations to boost prediction performance. -Notice that the model information is wholly preserved; the optimizations only -affect the manner at which prediction is performed. - -.. contents:: Contents - :local: - :depth: 2 - -Annotate conditional branches ------------------------------ - -This optimization analyzes and annotates every threshold conditions in the test -nodes to improve performance. - -How to use -~~~~~~~~~~ -The first step is to generate the **branch annotation record** for your ensemble -model. Make sure to have your training data ready. - -.. code-block:: python - - # model = your ensemble model (object of type treelite.Model) - # dmat = training data (object of type treelite.DMatrix) - - # Create annotator object - annotator = treelite.Annotator() - # Annotate branches by iterating over the training data - annotator.annotate_branch(model=model, dmat=dmat, verbose=True) - # Save the branch annotation record as a JSON file - annotator.save(path='mymodel-annotation.json') - -To utilize the branch annotation record, supply the compiler parameter -``annotate_in`` when exporting the model: - -.. code-block:: python - :emphasize-lines: 3, 8, 12 - - # Export a source directory - model.compile(dirpath='./mymodel', verbose=True, - params={'annotate_in': 'mymodel-annotation.json'}) - - # Export a source directory, packaged in a zip archive - model.export_srcpkg(platform='unix', toolchain='gcc', pkgpath='./mymodel.zip', - libname='mymodel.so', verbose=True, - params={'annotate_in': 'mymodel-annotation.json'}) - - # Export a shared library - model.export_lib(toolchain='gcc', libpath='./mymodel.so', verbose=True, - params={'annotate_in': 'mymodel-annotation.json'}) - -Technical details -~~~~~~~~~~~~~~~~~ - -Rationale -^^^^^^^^^ -Modern CPUs heavily rely on a technique known as -`branch prediction `_, in which -they "guess" the result of the conditional expression in each ``if``-``else`` -branch ahead of time. Given a program - -.. code-block:: c - - if ( [conditional expression] ) { - foo(); - } else { - bar(); - } - -the CPU will pre-fetch the instructions for the function ``foo()`` if the given -condition is likely to be true. On the other hand, if the condition is likely -to be false, the CPU will pre-fetch the instructions for the function ``bar()``. -It suffices to say that correctly predicting conditional branches has -great impact on performance. Each time the CPU predicts a branch correctly, it -can keep the instructions it had pre-fetched earlier. Each time the CPU fails to -predict, it must throw away the pre-fetched instructions and fetch anew another -set of instructions. If you'd like to learn more about the importance of branch -prediction, read -`this excellent introductory article -from Stanford\ -`_. - -The prediction subroutine for a decision tree ensemble is problematic, as it -is replete with conditional branches that will have to be guessed well: - -.. code-block:: c - - /* A slice of prediction subroutine */ - float predict_margin(const float* data) { - float sum = 0.0f; - if (!(data[0].missing != -1) || data[0].fvalue <= 9.5) { - if (!(data[0].missing != -1) || data[0].fvalue <= 3.5) { - if (!(data[10].missing != -1) || data[10].fvalue <= 0.74185) { - if (!(data[0].missing != -1) || data[0].fvalue <= 1.5) { - if (!(data[2].missing != -1) || data[2].fvalue <= 2.08671) { - if ( (data[4].missing != -1) && data[4].fvalue <= 2.02632) { - if (!(data[3].missing != -1) || data[3].fvalue <= 0.763339) { - sum += (float)0.00758165; - } else { - sum += (float)0.0060202; - } - } else { - if ( (data[1].missing != -1) && data[1].fvalue <= 0.0397456) { - sum += (float)0.00415399; - } else { - sum += (float)0.00821985; - } - } - /* and so forth... */ - -In fact, each threshold condition in the test nodes will need to be predicted. -While CPUs lack adequate information to make good guesses on these conditions, -we can help by providing that information. - -Mechanism for supplying the C compiler with branch information -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -We predict the likelihood of each condition by counting the number of data -points from the training data that satisfy that condition. See the diagram below -for an illustration. - -.. plot:: - :nofigs: - - from graphviz import Source - source = r""" - digraph branch_annotation { - graph [fontname = "helvetica"]; - node [fontname = "helvetica"]; - edge [fontname = "helvetica"]; - 0 [label="Feature 4224 < 2.0 ?", shape=box]; - 1 [label="Leaf: -0.0198"]; - 2 [label="Feature 13 < 0.848 ?", shape=box]; - 5 [label="Feature 12 < 0.878 ?", shape=box]; - 6 [label="Feature 5 < -0.384 ?", shape=box]; - 11 [label=" ⋮", margin=-0.5, shape=none, fontsize=20]; - 12 [label="Feature 8 < -0.231?", shape=box]; - 13 [label="Leaf: -0.0194"]; - 14 [label="Leaf: -0.0196"]; - 25 [label="Leaf: -0.0194"]; - 26 [label="Feature 1693 < 2 ?", shape=box]; - 53 [label="Leaf: 0"]; - 54 [label="Leaf: -0.0196"]; - 0 -> 1 [labeldistance=2.0, labelangle=45, - headlabel=3,092,211 >]; - 0 -> 2 [labeldistance=2.0, labelangle=-45, - headlabel=< No/Missing: 3,902,211>]; - 2 -> 5 [labeldistance=2.0, labelangle=45, - headlabel=3,342,535 >]; - 2 -> 6 [labeldistance=2.0, labelangle=-45, - headlabel=< No/Missing: 583,254>]; - 5 -> 11 [labeldistance=2.0, labelangle=45, - headlabel=2,878,952 >]; - 5 -> 12 [labeldistance=2.0, labelangle=-45, - headlabel=< No/Missing:
445,583>]; - 6 -> 13 [labeldistance=2.0, labelangle=45, - headlabel=266,188 >]; - 6 -> 14 [labeldistance=2.0, labelangle=-45, - headlabel=< No/Missing:
317,066>]; - 12 -> 25 [labeldistance=2.0, labelangle=45, - headlabel=257,828 >]; - 12 -> 26 [labeldistance=2.0, labelangle=-45, - headlabel=< No/Missing: 187,755>]; - 26 -> 53 [labeldistance=2.0, labelangle=45, - headlabel=4 >]; - 26 -> 54 [labeldistance=2.0, labelangle=-45, - headlabel=< No/Missing: 187,751>]; - } - """ - Source(source, format='png').render('../_static/branch_annotation', view=False) - Source(source, format='svg').render('../_static/branch_annotation', view=False) - -.. raw:: html - -

- -

- -If a condition is true at least 50% of the time (over the training data), the -condition is labeled as "expected to be true": - -.. code-block:: c - - /* expected to be true */ - if ( __builtin_expect( [condition], 1 ) ) { - ... - } else { - ... - } - -On the other hand, if a condition is false at least 50% of the time, the -condition is labeled as "expected to be false": - -.. code-block:: c - - /* expected to be false */ - if ( __builtin_expect( [condition], 0 ) ) { - ... - } else { - ... - } - -.. note:: On the expression ``__builtin_expect`` - - The ``__builtin_expect`` expression is a compiler intrinsic to supply the C - compiler with branch prediction information. Both - `gcc `_ - and `clang `_ - support it. Unfortunately, Microsoft Visual C++ does not. To take advantage - of branch annotation, make sure to use gcc or clang on the target machine. - -Use integer thresholds for conditions --------------------------------------- - -This optimization replaces all thresholds in the test nodes with integers so -that each threshold condition performs integer comparison instead of the usual -floating-point comparison. The thresholds are said to be **quantized** into -integer indices. - -BEFORE: - -.. code-block:: c - - if (data[3].fvalue < 1.5) { /* floating-point comparison */ - ... - } - -AFTER: - -.. code-block:: c - - if (data[3].qvalue < 3) { /* integer comparison */ - ... - } - -How to use -~~~~~~~~~~ -Simply add the compiler parameter ``quantize=1`` when exporting the model: - -.. code-block:: python - :emphasize-lines: 3, 8, 12 - - # Export a source directory - model.compile(dirpath='./mymodel', verbose=True, - params={'quantize': 1}) - - # Export a source directory, packaged in a zip archive - model.export_srcpkg(platform='unix', toolchain='gcc', pkgpath='./mymodel.zip', - libname='mymodel.so', verbose=True, - params={'quantize': 1}) - - # Export a shared library - model.export_lib(toolchain='gcc', libpath='./mymodel.so', verbose=True, - params={'quantize': 1}) - -Technical details -~~~~~~~~~~~~~~~~~ - -Rationale -^^^^^^^^^ -On some platforms such as x86-64, replacing floating-point thresholds with -integers helps improve performance by 1) **reducing executable code size** and 2) -**improving data locality**. This is so because on these platforms, integer -constants can be embedded as part of the comparison instruction, whereas -floating-point constants cannot. - -Let's look at x86-64 platform. The integer comparison - -.. code-block:: c - - a <= 4 - -produces one assembly instruction: - -.. code-block:: nasm - - cmpl $4, 8(%rsp) ; 8(%rsp) contains the variable a - -Since the integer constant ``4`` got embedded into the comparison instruction -`cmpl `_, we only -had to fetch the variable ``a`` from memory. - -On the other hand, the floating-point comparison - -.. code-block:: c - - b < 1.2f - -produces two assembly instructions: - -.. code-block:: nasm - - movss 250(%rip), %xmm0 ; 250(%rip) contains the constant 1.2f - ucomiss 12(%rsp), %xmm0 ; 12(%rsp) contains the variable b - -Notice that the floating-point constant ``1.2f`` did not get embedded into -the comparison instruction -`ucomiss `_. The -constant had to be fetched (with -`movss `_) into the -register ``xmm0`` before the comparsion could take place. To summarize, - -* a floating-point comparison takes twice as many instructions as an integer - comparsion, increasing the executable code size; -* a floating-point comparison involves an extra fetch instruction (``movss``), - potentially causing a - `cache miss `_. - -**Caveats**. As we'll see in the next section, using integer thresholds will -add overhead costs at prediction time. You should ensure that the benefits of -integer comparisons outweights the overhead costs. - -Mechanism for mapping features -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -When ``quantize`` option is enabled, Treelite will collect all thresholds -occuring in the tree ensemble model. For each feature, one list will be -generated that lists the thresholds in ascending order: - -.. code-block:: none - - /* example of how per-feature threshold list may look like */ - - Feature 0: [1.5, 6.5, 12.5] - Feature 3: [0.15, 0.35, 1.5] - Feature 6: [7, 9, 10, 135] - -Using these lists, we may convert any data point into integer indices via -simple look-ups. For feature 0 in the example above, values will be mapped -to integer indices as follows: - -.. code-block:: none - - Let x be the value of feature 0. - - Assign -1 if x < 1.5 - Assign 0 if x == 1.5 - Assign 1 if 1.5 < x < 6.5 - Assign 2 if x == 6.5 - Assign 3 if 6.5 < x < 12.5 - Assign 4 if x == 12.5 - Assign 5 if x > 12.5 - -Let's look at a specific example of how a floating-point vector gets translated -into a vector of integer indices: - -.. code-block:: none - - feature id 0 1 2 3 4 5 6 - [7, missing, missing, 0.2, missing, missing, 20 ] - => [3, missing, missing, 1, missing, missing, 5 ] - -Since the prediction subroutine still needs to accept floating-point features, -the features will be internally converted before actual prediction. If the -prediction subroutine looked like below without ``quantize`` option, - -.. code-block:: c - - float predict_margin(const Entry* data) { - /* ... Run through the trees to compute the leaf output score ... */ - - return score; - } - -it will now have an extra step of mapping the incoming data vector into integers: - -.. code-block:: c - :emphasize-lines: 2 - - float predict_margin(const Entry* data) { - /* ... Quantize feature values in data into integer indices ... */ - - /* ... Run through the trees to compute the leaf output score ... */ - return score; - } diff --git a/python/treelite/annotator.py b/python/treelite/annotator.py index 53674e3c..9c3e61fc 100644 --- a/python/treelite/annotator.py +++ b/python/treelite/annotator.py @@ -6,6 +6,7 @@ from .util import c_str, TreeliteError from .core import _LIB, _check_call from .frontend import Model +from .warn import deprecate class Annotator(): @@ -15,6 +16,7 @@ class Annotator(): """ def __init__(self): + deprecate("class Annotator") self.handle = None def annotate_branch(self, model, dmat, nthread=None, verbose=False): diff --git a/python/treelite/contrib/__init__.py b/python/treelite/contrib/__init__.py index efbf3eea..0388680f 100644 --- a/python/treelite/contrib/__init__.py +++ b/python/treelite/contrib/__init__.py @@ -10,6 +10,7 @@ import time import ctypes from ..util import TreeliteError, lineno, log_info +from ..warn import deprecate from .util import _libext, _toolchain_exist_check @@ -60,6 +61,7 @@ def generate_makefile(dirpath, platform, toolchain, options=None): # pylint: di optional Additional options to pass to toolchain """ + deprecate("Function treelite.generate_makefile()") if not os.path.isdir(dirpath): raise TreeliteError('Directory {} does not exist'.format(dirpath)) try: @@ -132,6 +134,7 @@ def generate_cmakelists(dirpath, options=None): optional Additional options to pass to toolchain """ + deprecate("Function treelite.generate_cmakelists()") if not os.path.isdir(dirpath): raise TreeliteError(f'Directory {dirpath} does not exist') try: @@ -232,6 +235,7 @@ def create_shared(toolchain, dirpath, *, nthread=None, verbose=False, options=No predictor = Predictor(libpath='./my/model/model.dll', verbose=True) """ + deprecate("Function treelite.create_shared()") # pylint: disable=R0912 if nthread is not None and nthread <= 0: diff --git a/python/treelite/frontend.py b/python/treelite/frontend.py index e9d67c7a..ecd6e63e 100644 --- a/python/treelite/frontend.py +++ b/python/treelite/frontend.py @@ -17,6 +17,7 @@ from .util import c_str, py_str, TreeliteError, type_info_to_ctypes_type, type_info_to_numpy_type from .core import _LIB, c_array, _check_call from .contrib import create_shared, generate_makefile, generate_cmakelists, _toolchain_exist_check +from .warn import deprecate def _isascii(string): @@ -250,6 +251,7 @@ def export_lib(self, toolchain, libpath, params=None, compiler='ast_native', # move the library out of the temporary directory shutil.move('/temporary/directory/mymodel.dll', './mymodel.dll') """ + deprecate("Method treelite.Model.export_lib()") _toolchain_exist_check(toolchain) _params = dict(params) if isinstance(params, list) else params @@ -321,6 +323,7 @@ def export_srcpkg(self, platform, toolchain, pkgpath, libname, params=None, root_dir='/temporary/directory', base_dir='mymodel/') """ + deprecate("Method treelite.Model.export_srcpkg()") # check for file extension fileext = os.path.splitext(pkgpath)[1] if fileext != '.zip': @@ -382,6 +385,7 @@ def compile(self, dirpath, params=None, compiler='ast_native', verbose=False): the value of ``parallel_comp``. Otherwise, there will be exactly two files: ``./model/header.h``, ``./my/model/main.c`` """ + deprecate("Method treelite.Model.compile()") compiler_handle = ctypes.c_void_p() _params = dict(params) if isinstance(params, list) else params if verbose and _params: diff --git a/python/treelite/warn.py b/python/treelite/warn.py new file mode 100644 index 00000000..dcc32186 --- /dev/null +++ b/python/treelite/warn.py @@ -0,0 +1,9 @@ +import warnings + +def deprecate(subject): + warnings.warn( + f"{subject} is deprecated and scheduled for removal in Treelite 4.0. " + "Please use TL2cgen instead. " + "Consult the migration guide at " + "https://tl2cgen.readthedocs.io/en/latest/treelite-migration.html." + ) diff --git a/runtime/python/treelite_runtime/predictor.py b/runtime/python/treelite_runtime/predictor.py index ec5bebb8..d75a797a 100644 --- a/runtime/python/treelite_runtime/predictor.py +++ b/runtime/python/treelite_runtime/predictor.py @@ -12,6 +12,7 @@ lib_extension_current_platform, type_info_to_ctypes_type, type_info_to_numpy_type, \ numpy_type_to_type_info from .libpath import TreeliteRuntimeLibraryNotFound, find_lib_path +from .warn import deprecate def _load_runtime_lib(): @@ -78,6 +79,7 @@ class Predictor: # pylint: disable=R0903 def __init__(self, libpath, nthread=None, verbose=False): + deprecate("class treelite_runtime.Predictor") if os.path.isdir(libpath): # libpath is a directory # directory is given; locate shared library inside it lib_found = False @@ -280,6 +282,7 @@ class DMatrix: def __init__(self, data, data_format=None, dtype=None, missing=None, feature_names=None, feature_types=None, verbose=False, nthread=None): + deprecate("class treelite_runtime.DMatrix") if data is None: raise TreeliteRuntimeError("'data' argument cannot be None") diff --git a/runtime/python/treelite_runtime/warn.py b/runtime/python/treelite_runtime/warn.py new file mode 100644 index 00000000..dcc32186 --- /dev/null +++ b/runtime/python/treelite_runtime/warn.py @@ -0,0 +1,9 @@ +import warnings + +def deprecate(subject): + warnings.warn( + f"{subject} is deprecated and scheduled for removal in Treelite 4.0. " + "Please use TL2cgen instead. " + "Consult the migration guide at " + "https://tl2cgen.readthedocs.io/en/latest/treelite-migration.html." + ) From b8b053e6af3442c37746fb823c84caa62d9f6366 Mon Sep 17 00:00:00 2001 From: Hyunsu Philip Cho Date: Fri, 5 May 2023 12:55:08 -0700 Subject: [PATCH 2/2] Fix formatting --- python/treelite/contrib/__init__.py | 2 +- python/treelite/warn.py | 2 ++ runtime/python/treelite_runtime/warn.py | 2 ++ 3 files changed, 5 insertions(+), 1 deletion(-) diff --git a/python/treelite/contrib/__init__.py b/python/treelite/contrib/__init__.py index 0388680f..fb0fb6c2 100644 --- a/python/treelite/contrib/__init__.py +++ b/python/treelite/contrib/__init__.py @@ -234,9 +234,9 @@ def create_shared(toolchain, dirpath, *, nthread=None, verbose=False, options=No predictor = Predictor(libpath='./my/model/model.dll', verbose=True) """ + # pylint: disable=R0912 deprecate("Function treelite.create_shared()") - # pylint: disable=R0912 if nthread is not None and nthread <= 0: raise TreeliteError('nthread must be positive integer') diff --git a/python/treelite/warn.py b/python/treelite/warn.py index dcc32186..5b620d94 100644 --- a/python/treelite/warn.py +++ b/python/treelite/warn.py @@ -1,6 +1,8 @@ +"""Warning for deprecated C codegen functionalities""" import warnings def deprecate(subject): + """Generate a warning with a subject""" warnings.warn( f"{subject} is deprecated and scheduled for removal in Treelite 4.0. " "Please use TL2cgen instead. " diff --git a/runtime/python/treelite_runtime/warn.py b/runtime/python/treelite_runtime/warn.py index dcc32186..5b620d94 100644 --- a/runtime/python/treelite_runtime/warn.py +++ b/runtime/python/treelite_runtime/warn.py @@ -1,6 +1,8 @@ +"""Warning for deprecated C codegen functionalities""" import warnings def deprecate(subject): + """Generate a warning with a subject""" warnings.warn( f"{subject} is deprecated and scheduled for removal in Treelite 4.0. " "Please use TL2cgen instead. "