diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 4a9ee58..d279ac7 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -15,7 +15,7 @@ image: registry.heptapod.net:443/fluiddyn/transonic/ci/default:stable pixi:test: stage: pixi - image: registry.heptapod.net:443/fluiddyn/fluidsim/ci/default:pixi + image: registry.heptapod.net:443/fluiddyn/fluiddyn/ci/default:pixi script: - pixi info - pixi run install-editable diff --git a/.mdformat.toml b/.mdformat.toml new file mode 100644 index 0000000..972483a --- /dev/null +++ b/.mdformat.toml @@ -0,0 +1,3 @@ +wrap = 89 +number = true +end_of_line = "lf" diff --git a/Makefile b/Makefile index ce4ee35..3dbe4a4 100644 --- a/Makefile +++ b/Makefile @@ -4,6 +4,9 @@ COV=pytest --cov --cov-config=pyproject.toml develop: pdm sync --clean +lock: + pdm lock -G :all + black: black -l 82 src _transonic_testing data_tests tests diff --git a/README.md b/README.md index 2d18b61..bc0c41b 100644 --- a/README.md +++ b/README.md @@ -13,29 +13,27 @@ **Documentation**: -Transonic is a pure Python package (requiring Python >= 3.9) to easily -accelerate modern Python-Numpy code with different accelerators (currently -[Cython](https://cython.org/), [Pythran](https://github.com/serge-sans-paille/pythran) and [Numba](https://numba.pydata.org/), but potentially later [Cupy](https://cupy.chainer.org/), [PyTorch](https://pytorch.org/), [JAX](https://github.com/google/jax), [Weld](https://www.weld.rs/), [Pyccel](https://github.com/pyccel/pyccel), [Uarray](https://github.com/Quansight-Labs/uarray), etc...). - -**The accelerators are not hard dependencies of Transonic:** Python codes using -Transonic run fine without any accelerators installed (of course without -speedup)! - -> [!WARNING] -> Transonic is still in an active development stage (see our -> [roadmap](https://transonic.readthedocs.io/en/latest/roadmap.html)). -> Remarks and suggestions are very welcome. +Transonic is a pure Python package (requiring Python >= 3.9) to easily accelerate modern +Python-Numpy code with different accelerators (currently [Cython], [Pythran], [Numba] and +[JAX], but potentially later [Cupy], [PyTorch], [Weld], [Pyccel], etc...). + +**The accelerators are not hard dependencies of Transonic:** Python codes using Transonic +run fine without any accelerators installed (of course without speedup)! + +> [!WARNING] Transonic is still in an active development stage (see our +> [roadmap](https://transonic.readthedocs.io/en/latest/roadmap.html)). Remarks and +> suggestions are very welcome. > > However, Transonic is now really usable, useful and used "in production" in > [FluidSim](https://foss.heptapod.net/fluiddyn/fluidsim) and > [FluidFFT](https://foss.heptapod.net/fluiddyn/fluidfft) (see examples for -> [blocks](https://foss.heptapod.net/fluiddyn/fluidsim/src/default/fluidsim/base/time_stepping/pseudo_spect.py) and +> [blocks](https://foss.heptapod.net/fluiddyn/fluidsim/src/default/fluidsim/base/time_stepping/pseudo_spect.py) +> and > [@boost](https://foss.heptapod.net/fluiddyn/fluidfft/src/default/fluidfft/fft3d/operators.py)). ## The long-term project -> [!NOTE] -> The context of the creation of Transonic is presented in these documents: +> [!NOTE] The context of the creation of Transonic is presented in these documents: > > - [Transonic Vision](https://fluiddyn.netlify.app/transonic-vision.html) > - [Make your numerical Python code fly at transonic speed (EuroScipy 2019)](http://www.legi.grenoble-inp.fr/people/Pierre.Augier/docs/ipynbslides/20190904-euroscipy-transonic/pres.slides.html#/), @@ -45,63 +43,60 @@ Transonic targets Python end-users and library developers. It is based on the following principles: -- We'd like to write scientific / computing applications / libraries with - pythonic, readable, modern code (Python >= 3.6). +- We'd like to write scientific / computing applications / libraries with pythonic, + readable, modern code (Python >= 3.6). -- In some cases, Python-Numpy is too slow. However, there are tools to - accelerate such Python-Numpy code which lead to very good performances! +- In some cases, Python-Numpy is too slow. However, there are tools to accelerate such + Python-Numpy code which lead to very good performances! -- Let's try to write universal code which express what we want to compute and - not the special hacks we want to use to make it fast. We just need nice ways - to express that a function, a method or a block of code has to be accelerated - (and how it has to be accelerated). We'd like to be able to do this in a - pythonic way, with decorators and context managers. +- Let's try to write universal code which express what we want to compute and not the + special hacks we want to use to make it fast. We just need nice ways to express that a + function, a method or a block of code has to be accelerated (and how it has to be + accelerated). We'd like to be able to do this in a pythonic way, with decorators and + context managers. -- There are many tools to accelerate Python-Numpy code! Let's avoid writting - code specialized for only one of these tools. +- There are many tools to accelerate Python-Numpy code! Let's avoid writting code + specialized for only one of these tools. -- Let's try to keep the code as it would be written without acceleration. For - example, with Transonic, we are able to accelerate (simple) methods of - classes even though some accelerators don't support classes. +- Let's try to keep the code as it would be written without acceleration. For example, + with Transonic, we are able to accelerate (simple) methods of classes even though some + accelerators don't support classes. -- Let's accelerate/compile only what needs to be accelerated, i.e. only the - bottlenecks. Python and its interpreters are good for the rest. In most - cases, the benefice of writting big compiled extensions (with Cython or in - other languages) is negligible. +- Let's accelerate/compile only what needs to be accelerated, i.e. only the bottlenecks. + Python and its interpreters are good for the rest. In most cases, the benefice of + writting big compiled extensions (with Cython or in other languages) is negligible. -- Adding types is sometimes necessary. In modern Python, we have nice syntaxes - for type annotations! Let's use them. +- Adding types is sometimes necessary. In modern Python, we have nice syntaxes for type + annotations! Let's use them. -- Ahead-of-time (AOT) and just-in-time (JIT) compilation modes are both useful. - We'd like to have a nice, simple and unified API for these two modes. +- Ahead-of-time (AOT) and just-in-time (JIT) compilation modes are both useful. We'd like + to have a nice, simple and unified API for these two modes. - - AOT is useful to be able to distribute compiled packages and in some cases, - more optimizations can be applied. - - JIT is simpler to use (no need for type annotations) and optimizations can - be more hardware specific. + - AOT is useful to be able to distribute compiled packages and in some cases, more + optimizations can be applied. + - JIT is simpler to use (no need for type annotations) and optimizations can be more + hardware specific. - Note that with Transonic, AOT compilers (Pythran and Cython) can be used as - JIT compilers (with a cache mechanism). + Note that with Transonic, AOT compilers (Pythran and Cython) can be used as JIT + compilers (with a cache mechanism). To summarize, a **strategy to quickly develop a very efficient scientific application/library** with Python could be: -1. Use modern Python coding, standard Numpy/Scipy for the computations and all - the cool libraries you want. -2. Profile your applications on real cases, detect the bottlenecks and apply - standard optimizations with Numpy. +1. Use modern Python coding, standard Numpy/Scipy for the computations and all the cool + libraries you want. +2. Profile your applications on real cases, detect the bottlenecks and apply standard + optimizations with Numpy. 3. Add few lines of Transonic to compile the hot spots. ## What we have now -We start to have a good API to accelerate Python-Numpy code (functions, methods -and blocks of code). The default Transonic backend uses Pythran and works well. -[Here, we explain why Pythran is so great for Python users and why Transonic is -great for Pythran users](https://transonic.readthedocs.io/en/latest/backends/pythran.html). There are -also (more experimental) backends for Cython and Numba. +We start to have a good API to accelerate Python-Numpy code (functions, methods and +blocks of code). The default Transonic backend uses Pythran and works well. +[Here, we explain why Pythran is so great for Python users and why Transonic is great for Pythran users](https://transonic.readthedocs.io/en/latest/backends/pythran.html). +There are also (more experimental) backends for Cython and Numba. -> [!NOTE] -> Transonic can be used in libraries and applications using MPI (as +> [!NOTE] Transonic can be used in libraries and applications using MPI (as > [FluidSim](https://foss.heptapod.net/fluiddyn/fluidsim)). ## Installation and configuration @@ -112,22 +107,29 @@ pip install transonic Transonic is sensible to environment variables: -- `TRANSONIC_DIR` can be set to control where the cached files are - saved. +- `TRANSONIC_DIR` can be set to control where the cached files are saved. - `TRANSONIC_DEBUG` triggers a verbose mode. -- `TRANSONIC_COMPILE_AT_IMPORT` can be set to enable a mode for which - Transonic compiles at import time the Pythran file associated with the - imported module. This behavior can also be triggered programmatically - by using the function `set_compile_at_import`. -- `TRANSONIC_NO_REPLACE` can be set to disable all code replacements. - This is useful to compare execution times and when measuring code coverage. -- `TRANSONIC_COMPILE_JIT` can be set to false to disable the - compilation of jited functions. This can be useful for unittests. -- `TRANSONIC_BACKEND` to choose between the supported backends. The - default backend "pythran" is quite robust. There are now 3 other backends: - "cython", "numba" and "python" (prototypes). +- `TRANSONIC_COMPILE_AT_IMPORT` can be set to enable a mode for which Transonic compiles + at import time the Pythran file associated with the imported module. This behavior can + also be triggered programmatically by using the function `set_compile_at_import`. +- `TRANSONIC_NO_REPLACE` can be set to disable all code replacements. This is useful to + compare execution times and when measuring code coverage. +- `TRANSONIC_COMPILE_JIT` can be set to false to disable the compilation of jited + functions. This can be useful for unittests. +- `TRANSONIC_BACKEND` to choose between the supported backends. The default backend + "pythran" is quite robust. There are now 3 other backends: "cython", "numba" and + "python" (prototypes). - `TRANSONIC_MPI_TIMEOUT` sets the MPI timeout (default to 5 s). ## License Transonic is distributed under the BSD License. + +[cupy]: https://cupy.chainer.org/ +[cython]: https://cython.org/ +[jax]: https://github.com/google/jax +[numba]: https://numba.pydata.org/ +[pyccel]: https://github.com/pyccel/pyccel +[pythran]: https://github.com/serge-sans-paille/pythran +[pytorch]: https://pytorch.org/ +[weld]: https://github.com/weld-project/weld diff --git a/pdm.lock b/pdm.lock index d986998..148bc97 100644 --- a/pdm.lock +++ b/pdm.lock @@ -20,7 +20,7 @@ files = [ [[package]] name = "anyio" -version = "4.3.0" +version = "4.4.0" requires_python = ">=3.8" summary = "High level compatibility layer for multiple asynchronous event loop implementations" groups = ["doc"] @@ -31,8 +31,8 @@ dependencies = [ "typing-extensions>=4.1; python_version < \"3.11\"", ] files = [ - {file = "anyio-4.3.0-py3-none-any.whl", hash = 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