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J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., +VanderPlas, J., Wanderman-Milne, S., & Zhang, Q. (2018). JAX: +Composable transformations of Python+NumPy programs (Version 0.3.13). +http://github.com/google/jax + + + DUCC: Distinctly useful code +collection + Reinecke + 2024 + Reinecke, M. (2024). DUCC: Distinctly +useful code collection (Version 0.33.0). +https://gitlab.mpcdf.mpg.de/mtr/ducc + + + Enzyme-JAX + Moses + 2024 + Moses, W. S., & Zinenko, O. +(2024). Enzyme-JAX (Version 0.0.6). +https://github.com/EnzymeAD/Enzyme-JAX + + + Instead of rewriting foreign code for machine +learning, automatically synthesize fast gradients + Moses + Advances in neural information processing +systems + 33 + 2020 + Moses, W., & Churavy, V. (2020). +Instead of rewriting foreign code for machine learning, automatically +synthesize fast gradients. In H. Larochelle, M. Ranzato, R. Hadsell, M. +F. Balcan, & H. Lin (Eds.), Advances in neural information +processing systems (Vol. 33, pp. 12472–12485). Curran Associates, Inc. +https://proceedings.neurips.cc/paper/2020/file/9332c513ef44b682e9347822c2e457ac-Paper.pdf + + + Reverse-mode automatic differentiation and +optimization of GPU kernels via enzyme + Moses + Proceedings of the international conference +for high performance computing, networking, storage and +analysis + 10.1145/3458817.3476165 + 9781450384421 + 2021 + Moses, W. S., Churavy, V., Paehler, +L., Hückelheim, J., Narayanan, S. H. K., Schanen, M., & Doerfert, J. +(2021). Reverse-mode automatic differentiation and optimization of GPU +kernels via enzyme. Proceedings of the International Conference for High +Performance Computing, Networking, Storage and Analysis. +https://doi.org/10.1145/3458817.3476165 + + + Scalable automatic differentiation of +multiple parallel paradigms through compiler +augmentation + Moses + Proceedings of the international conference +on high performance computing, networking, storage and +analysis + 10.1109/SC41404.2022.00065 + 9784665454445 + 2022 + Moses, W. S., Narayanan, S. H. K., +Paehler, L., Churavy, V., Schanen, M., Hückelheim, J., Doerfert, J., +& Hovland, P. (2022). Scalable automatic differentiation of multiple +parallel paradigms through compiler augmentation. Proceedings of the +International Conference on High Performance Computing, Networking, +Storage and Analysis. +https://doi.org/10.1109/SC41404.2022.00065 + + + RESOLVE + Arras + 2024 + Arras, P., Roth, J., Ding, S., +Reinecke, M., Fuchs, R., & Johnson, V. (2024). RESOLVE. +https://gitlab.mpcdf.mpg.de/ift/resolve + + + Re-envisioning numerical information field +theory (NIFTy.re): A library for Gaussian processes and variational +inference + Edenhofer + Journal of Open Source +Software + 98 + 9 + 10.21105/joss.06593 + 2024 + Edenhofer, G., Frank, P., Roth, J., +Leike, R. H., Guerdi, M., Scheel-Platz, L. I., Guardiani, M., Eberle, +V., Westerkamp, M., & Enßlin, T. A. (2024). Re-envisioning numerical +information field theory (NIFTy.re): A library for Gaussian processes +and variational inference. Journal of Open Source Software, 9(98), 6593. +https://doi.org/10.21105/joss.06593 + + + TensorFlow: Large-scale machine learning on +heterogeneous systems + Abadi + 2015 + Abadi, M., Agarwal, A., Barham, P., +Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., +Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, +M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., … Zheng, X. (2015). +TensorFlow: Large-scale machine learning on heterogeneous systems. +https://www.tensorflow.org/ + + + PyTorch 2: Faster machine learning through +dynamic Python bytecode transformation and graph +compilation + Ansel + 29th ACM international conference on +architectural support for programming languages and operating systems, +volume 2 (ASPLOS ’24) + 10.1145/3620665.3640366 + 2024 + Ansel, J., Yang, E., He, H., +Gimelshein, N., Jain, A., Voznesensky, M., Bao, B., Bell, P., Berard, +D., Burovski, E., Chauhan, G., Chourdia, A., Constable, W., Desmaison, +A., DeVito, Z., Ellison, E., Feng, W., Gong, J., Gschwind, M., … +Chintala, S. (2024, April). PyTorch 2: Faster machine learning through +dynamic Python bytecode transformation and graph compilation. 29th ACM +International Conference on Architectural Support for Programming +Languages and Operating Systems, Volume 2 (ASPLOS ’24). +https://doi.org/10.1145/3620665.3640366 + + + + + + From 4ddb0ab468965a445da9da53c69067f8e33058c5 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Fri, 21 Jun 2024 13:53:25 +0100 Subject: [PATCH 3/3] Creating 10.21105.joss.06532.jats --- .../paper.jats/10.21105.joss.06532.jats | 712 ++++++++++++++++++ 1 file changed, 712 insertions(+) create mode 100644 joss.06532/paper.jats/10.21105.joss.06532.jats diff --git a/joss.06532/paper.jats/10.21105.joss.06532.jats b/joss.06532/paper.jats/10.21105.joss.06532.jats new file mode 100644 index 0000000000..c787858b18 --- /dev/null +++ b/joss.06532/paper.jats/10.21105.joss.06532.jats @@ -0,0 +1,712 @@ + + +

+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6532 +10.21105/joss.06532 + +JAXbind: Bind any function to +JAX + + + +https://orcid.org/0000-0002-8873-8215 + +Roth +Jakob + + + + + + + +Reinecke +Martin + + + + +https://orcid.org/0000-0003-3122-4894 + +Edenhofer +Gordian + + + + + + + +Max Planck Institute for Astrophysics, +Karl-Schwarzschild-Str. 1, 85748 Garching, Germany + + + + +Ludwig Maximilian University of Munich, +Geschwister-Scholl-Platz 1, 80539 Munich, Germany + + + + +Technical University of Munich, Boltzmannstr. 3, 85748 +Garching, Germany + + + + +Department of Astrophysics, University of Vienna, +Türkenschanzstr. 17, A-1180 Vienna, Austria + + + + +22 +2 +2024 + +9 +98 +6532 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +Machine Learning +High Performance Computing + + + + + + Summary +

JAX is widely used in machine learning and scientific computing, + the latter of which often relies on existing high-performance code + that we would ideally like to incorporate into JAX. Reimplementing the + existing code in JAX is often impractical and the existing interface + in JAX for binding custom code either limits the user to a single + Jacobian product or requires deep knowledge of JAX and its C++ backend + for general Jacobian products. With JAXbind we + drastically reduce the effort required to bind custom functions + implemented in other programming languages with full support for + Jacobian-vector products and vector-Jacobian products to JAX. + Specifically, JAXbind provides an easy-to-use + Python interface for defining custom, so-called JAX primitives. Via + JAXbind, any function callable from Python can + be exposed as a JAX primitive. JAXbind allows a + user to interface the JAX function transformation engine with custom + derivatives and batching rules, enabling all JAX transformations for + the custom primitive.

+
+ + Statement of Need +

The use of JAX + (Bradbury + et al., 2018) is widespread in the natural sciences. Of + particular interest is JAX’s powerful transformation system. It + enables a user to retrieve arbitrary derivatives of functions, batch + computations, and just-in-time compile code for additional + performance. Its transformation system requires that all components of + the computation are written in JAX.

+

A plethora of high-performance code is not written in JAX and thus + not accessible from within JAX. Rewriting these codes is often + infeasible and/or inefficient. Ideally, we would like to mix existing + high-performance code with JAX code. However, connecting code to JAX + requires knowledge of the internals of JAX and its C++ backend.

+

In this paper, we present JAXbind, a package + for bridging any function to JAX without in-depth knowledge of JAX’s + transformation system. The interface is accessible from Python without + requiring any development in C++. The package is able to register any + function and its partial derivatives and their transpose functions as + a JAX native call, a so-called primitive.

+

We believe JAXbind to be highly useful in + scientific computing. We intend to use this package to connect the + Hartley transform and the spherical harmonic transform from DUCC + (Reinecke, + 2024) to the probabilistic programming package NIFTy + (Edenhofer + et al., 2024) as well as the radio interferometry response from + DUCC with the radio astronomy package resolve + (Arras + et al., 2024). Furthermore, we intend to connect the + non-uniform FFT from DUCC with JAX for applications in strong-lensing + astrophysics. We envision many further applications within and outside + of astrophysics.

+

The functionality of JAXbind extends the + external callback functionality in JAX. Currently, + JAXbind, akin to the external callback + functions in JAX, briefly requires Python’s global interpreter lock + (GIL) to call the user-specified Python function. In contrast to JAX’s + external callback functions, JAXbind allows for + both a custom Jacobian-vector product and vector-Jacobian product. To + the best of our knowledge no other code currently exists for easily + binding generic functions and both of their Jacobian products to JAX, + without the need for C++ or LLVM. The package that comes the closest + is Enzyme-JAX + (W. + S. Moses & Zinenko, 2024), which allows one to bind + arbitrary LLVM/MLIR, including C++, with automatically-generated + (W. + S. Moses et al., 2021, + 2022; + W. + Moses & Churavy, 2020) or manually-defined derivatives to + JAX.

+

PyTorch + (Ansel + et al., 2024) and TensorFlow + (Abadi + et al., 2015) also provide interfaces for custom extensions. + PyTorch has an extensively documented Python + interface1 for wrapping custom + Python functions as PyTorch functions. This interface connects the + custom function to PyTorch’s automatic differentiation engine, + allowing for custom Jacobian and Jacobian transposed applications, + similar to what is possible with JAXbind. Additionally, PyTorch allows + a user to interface its C++ backend with custom C++ or CUDA + extensions2. JAXbind, in + contrast, currently only supports functions executed on the CPU, + although the JAX built-in C++ interface also allows for custom GPU + kernels. TensorFlow includes a C++ + interface3 for custom functions + that can be executed on the CPU or GPU. Custom gradients can be added + to these functions.

+
+ + Automatic Differentiation and Code Example +

Automatic differentiation is a core feature of JAX and often one of + the main reasons for using it. Thus, it is essential that custom + functions registered with JAX support automatic differentiation. In + the following, we will outline which functions our package requires to + enable automatic differentiation via JAX. For simplicity, we assume + that we want to connect the nonlinear function + + + f(x1,x2)=x1x22 + to JAX. The JAXbind package expects the Python + function for + + f + to take three positional arguments. The first argument, + out, is a tuple into + which the results are written. The second argument is also a + tuple containing the input to the function, in + our case, + + x1 + and + + x2. + Via kwargs_dump, any keyword arguments given to + the registered JAX primitive can be forwarded to + + + f + in a serialized form.

+ import jaxbind + +def f(out, args, kwargs_dump): + kwargs = jaxbind.load_kwargs(kwargs_dump) + x1, x2 = args + out[0][()] = x1 * x2**2 +

JAX’s automatic differentiation engine can compute the + Jacobian-vector product jvp and vector-Jacobian + product vjp of JAX primitives. The + Jacobian-vector product in JAX is a function applying the Jacobian of + + + f + at a position + + x + to a tangent vector. In mathematical nomenclature this operation is + called the pushforward of + + f + and can be denoted as + + f(x):TxXTf(x)Y, + with + + TxX + and + + Tf(x)Y + being the tangent spaces of + + X + and + + Y + at the positions + + x + and + + f(x). + As the implementation of + + f + is not JAX native, JAX cannot automatically compute the + jvp. Instead, an implementation of the + pushforward has to be provided, which JAXbind + will register as the jvp of the JAX primitive + of + + f. + For our example, this Jacobian-vector-product function is given by + + + f(x1,x2)(dx1,dx2)=x22dx1+2x1x2dx2.

+ def f_jvp(out, args, kwargs_dump): + kwargs = jaxbind.load_kwargs(kwargs_dump) + x1, x2, dx1, dx2 = args + out[0][()] = x2**2 * dx1 + 2 * x1 * x2 * dx2 +

The vector-Jacobian product vjp in JAX is + the linear transpose of the Jacobian-vector product. In mathematical + nomenclature this is the pullback + + (f(x))T:Tf(x)YTxX + of + + f. + Analogously to the jvp, the user has to + implement this function as JAX cannot automatically construct it. For + our example function, the vector-Jacobian product is + + + (f(x1,x2))T(dy)=(x22dy,2x1x2dy).

+ def f_vjp(out, args, kwargs_dump): + kwargs = jaxbind.load_kwargs(kwargs_dump) + x1, x2, dy = args + out[0][()] = x2**2 * dy + out[1][()] = 2 * x1 * x2 * dy +

To just-in-time compile the function, JAX needs to abstractly + evaluate the code, i.e., it needs to be able to infer the shape and + dtype of the output of the function given only the shape and dtype of + the input. We have to provide these abstract evaluation functions + returning the output shape and dtype given an input shape and dtype + for + + f + as well as for the vjp application. The output + shape of the jvp is identical to the output + shape of + + f + itself and does not need to be specified again. The abstract + evaluation functions take normal positional and keyword arguments.

+ def f_abstract(*args, **kwargs): + assert args[0].shape == args[1].shape + return ((args[0].shape, args[0].dtype),) + +def f_abstract_T(*args, **kwargs): + return ( + (args[0].shape, args[0].dtype), + (args[0].shape, args[0].dtype), + ) +

We have now defined all ingredients necessary to register a JAX + primitive for our function + + f + using the JAXbind package.

+ f_jax = jaxbind.get_nonlinear_call( + f, (f_jvp, f_vjp), f_abstract, f_abstract_T +) +

f_jax is a JAX primitive registered via the + JAXbind package supporting all JAX + transformations. We can now compute the jvp and + vjp of the new JAX primitive and even + jit-compile and batch it.

+ import jax +import jax.numpy as jnp + +inp = (jnp.full((4,3), 4.), jnp.full((4,3), 2.)) +tan = (jnp.full((4,3), 1.), jnp.full((4,3), 1.)) +res, res_tan = jax.jvp(f_jax, inp, tan) + +cotan = [jnp.full((4,3), 6.)] +res, f_vjp = jax.vjp(f_jax, *inp) +res_cotan = f_vjp(cotan) + +f_jax_jit = jax.jit(f_jax) +res = f_jax_jit(*inp) +
+ + Higher Order Derivatives and Linear Functions +

JAX supports higher order derivatives and can differentiate a + jvp or vjp with respect + to the position at which the Jacobian was taken. Similar to first + derivatives, JAX can not automatically compute higher derivatives of a + general function + + f + that is not natively implemented in JAX. Higher order derivatives + would again need to be provided by the user. For many algorithms, + first derivatives are sufficient, and higher order derivatives are + often not implemented by high-performance codes. Therefore, the + current interface of JAXbind is, for + simplicity, restricted to first derivatives. In the future, the + interface could be easily expanded if specific use cases require + higher order derivatives.

+

In scientific computing, linear functions such as, e.g., spherical + harmonic transforms are widespread. If the function + + + f + is linear, differentiation becomes trivial. Specifically for a linear + function + + f, + the pushforward or jvp of + + + f + is identical to + + f + itself and independent of the position at which it is computed. + Expressed in formulas, + + f(x)(dx)=f(dx) + if + + f + is linear in + + x. + Analogously, the pullback or vjp becomes + independent of the initial position and is given by the linear + transpose of + + f, + thus + + (f(x))T(dy)=fT(dy). + Also, all higher order derivatives can be expressed in terms of + + + f + and its transpose. To make use of these simplifications, + JAXbind provides a special interface for linear + functions, supporting higher order derivatives, only requiring an + implementation of the function and its transpose.

+
+ + Platforms +

Currently, JAXbind only supports primitives + that act on CPU memory. In the future, GPU support could be added, + which should work analogously to the CPU support in most respects. The + automatic differentiation in JAX is backend agnostic and would thus + not require any additional bindings to work on the GPU.

+
+ + Acknowledgements +

We would like to thank Dan Foreman-Mackey for his detailed guide + (https://dfm.io/posts/extending-jax/) on connecting C++ code to JAX. + Jakob Roth acknowledges financial support from the German Federal + Ministry of Education and Research (BMBF) under grant 05A23WO1 + (Verbundprojekt D-MeerKAT III). Gordian Edenhofer acknowledges support + from the German Academic Scholarship Foundation in the form of a PhD + scholarship (“Promotionsstipendium der Studienstiftung des Deutschen + Volkes”).

+
+ + + + + + + + BradburyJames + FrostigRoy + HawkinsPeter + JohnsonMatthew James + LearyChris + MaclaurinDougal + NeculaGeorge + PaszkeAdam + VanderPlasJake + Wanderman-MilneSkye + ZhangQiao + + JAX: Composable transformations of Python+NumPy programs + 2018 + http://github.com/google/jax + + + + + + ReineckeMartin + + DUCC: Distinctly useful code collection + 2024 + https://gitlab.mpcdf.mpg.de/mtr/ducc + + + + + + MosesWilliam S. + ZinenkoOleksandr + + Enzyme-JAX + 2024 + https://github.com/EnzymeAD/Enzyme-JAX + + + + + + MosesWilliam + ChuravyValentin + + Instead of rewriting foreign code for machine learning, automatically synthesize fast gradients + Advances in neural information processing systems + + LarochelleH. + RanzatoM. + HadsellR. + BalcanM. F. + LinH. + + Curran Associates, Inc. + 2020 + 33 + https://proceedings.neurips.cc/paper/2020/file/9332c513ef44b682e9347822c2e457ac-Paper.pdf + 12472 + 12485 + + + + + + MosesWilliam S. + ChuravyValentin + PaehlerLudger + HückelheimJan + NarayananSri Hari Krishna + SchanenMichel + DoerfertJohannes + + Reverse-mode automatic differentiation and optimization of GPU kernels via enzyme + Proceedings of the international conference for high performance computing, networking, storage and analysis + Association for Computing Machinery + New York, NY, USA + 2021 + 9781450384421 + https://doi.org/10.1145/3458817.3476165 + 10.1145/3458817.3476165 + + + + + + MosesWilliam S. + NarayananSri Hari Krishna + PaehlerLudger + ChuravyValentin + SchanenMichel + HückelheimJan + DoerfertJohannes + HovlandPaul + + Scalable automatic differentiation of multiple parallel paradigms through compiler augmentation + Proceedings of the international conference on high performance computing, networking, storage and analysis + IEEE Press + Dallas, Texas + 2022 + 9784665454445 + 10.1109/SC41404.2022.00065 + + + + + + ArrasPhilipp + RothJakob + DingSimon + ReineckeMartin + FuchsRichard + JohnsonVishal + + RESOLVE + 2024 + https://gitlab.mpcdf.mpg.de/ift/resolve + + + + + + EdenhoferGordian + FrankPhilipp + RothJakob + LeikeReimar H. + GuerdiMassin + Scheel-PlatzLukas I. + GuardianiMatteo + EberleVincent + WesterkampMargret + EnßlinTorsten A. + + Re-envisioning numerical information field theory (NIFTy.re): A library for Gaussian processes and variational inference + Journal of Open Source Software + The Open Journal + 2024 + 9 + 98 + https://doi.org/10.21105/joss.06593 + 10.21105/joss.06593 + 6593 + + + + + + + AbadiMartín + AgarwalAshish + BarhamPaul + BrevdoEugene + ChenZhifeng + CitroCraig + CorradoGreg S. + DavisAndy + DeanJeffrey + DevinMatthieu + GhemawatSanjay + GoodfellowIan + HarpAndrew + IrvingGeoffrey + IsardMichael + JiaYangqing + JozefowiczRafal + KaiserLukasz + KudlurManjunath + LevenbergJosh + ManéDandelion + MongaRajat + MooreSherry + MurrayDerek + OlahChris + SchusterMike + ShlensJonathon + SteinerBenoit + SutskeverIlya + TalwarKunal + TuckerPaul + VanhouckeVincent + VasudevanVijay + ViégasFernanda + VinyalsOriol + WardenPete + WattenbergMartin + WickeMartin + YuYuan + ZhengXiaoqiang + + TensorFlow: Large-scale machine learning on heterogeneous systems + 2015 + https://www.tensorflow.org/ + + + + + + AnselJason + YangEdward + HeHorace + GimelsheinNatalia + JainAnimesh + VoznesenskyMichael + BaoBin + BellPeter + BerardDavid + BurovskiEvgeni + ChauhanGeeta + ChourdiaAnjali + ConstableWill + DesmaisonAlban + DeVitoZachary + EllisonElias + FengWill + GongJiong + GschwindMichael + HirshBrian + HuangSherlock + KalambarkarKshiteej + KirschLaurent + LazosMichael + LezcanoMario + LiangYanbo + LiangJason + LuYinghai + LukCK + MaherBert + PanYunjie + PuhrschChristian + ResoMatthias + SaroufimMark + SiraichiMarcos Yukio + SukHelen + SuoMichael + TilletPhil + WangEikan + WangXiaodong + WenWilliam + ZhangShunting + ZhaoXu + ZhouKeren + ZouRichard + MathewsAjit + ChananGregory + WuPeng + ChintalaSoumith + + PyTorch 2: Faster machine learning through dynamic Python bytecode transformation and graph compilation + 29th ACM international conference on architectural support for programming languages and operating systems, volume 2 (ASPLOS ’24) + ACM + 202404 + https://pytorch.org/assets/pytorch2-2.pdf + 10.1145/3620665.3640366 + + + + + +

https://pytorch.org/docs/stable/notes/extending.html

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https://pytorch.org/tutorials/advanced/cpp_extension.html

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+ +

https://www.tensorflow.org/guide/create_op

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