From 14045754ffbbe1d884a80a32655cb88c3605450c Mon Sep 17 00:00:00 2001 From: CosmoMatt Date: Mon, 8 Apr 2024 11:47:09 +0100 Subject: [PATCH 01/24] add jax frontend support for c/c++ sht libraries --- .pip_readme.rst | 6 + README.md | 29 ++ docs/api/transforms/c_backend_spherical.rst | 7 + docs/api/transforms/index.rst | 20 ++ docs/conf.py | 16 +- docs/index.rst | 6 + .../JAX_HEALPix/JAX_HEALPix_frontend.nblink | 3 + .../JAX_SSHT/JAX_SSHT_frontend.nblink | 3 + docs/tutorials/index.rst | 24 +- notebooks/JAX_HEALPix_frontend.ipynb | 187 ++++++++++ notebooks/JAX_SSHT_frontend.ipynb | 185 ++++++++++ requirements/requirements-core.txt | 7 +- requirements/requirements-tests.txt | 4 +- s2fft/_version.py | 5 +- s2fft/transforms/__init__.py | 1 + s2fft/transforms/c_backend_spherical.py | 326 ++++++++++++++++++ s2fft/transforms/spherical.py | 35 +- s2fft/transforms/wigner.py | 209 ++++++++++- setup.py | 2 +- tests/test_spherical_custom_grads.py | 147 +++++++- tests/test_wigner_custom_grads.py | 84 +++++ tests/test_wigner_transform.py | 133 ++++--- 22 files changed, 1363 insertions(+), 76 deletions(-) create mode 100644 docs/api/transforms/c_backend_spherical.rst create mode 100644 docs/tutorials/JAX_HEALPix/JAX_HEALPix_frontend.nblink create mode 100644 docs/tutorials/JAX_SSHT/JAX_SSHT_frontend.nblink create mode 100644 notebooks/JAX_HEALPix_frontend.ipynb create mode 100644 notebooks/JAX_SSHT_frontend.ipynb create mode 100644 s2fft/transforms/c_backend_spherical.py diff --git a/.pip_readme.rst b/.pip_readme.rst index d973c4dd..0b123dfa 100644 --- a/.pip_readme.rst +++ b/.pip_readme.rst @@ -31,6 +31,12 @@ As of version 1.0.2 `S2FFT` also provides PyTorch implementations of underlying precompute transforms. In future releases this support will be extended to our on-the-fly algorithms. +As of version 1.0.3 `S2FFT` also provides JAX support for existing C/C++ packages, +specifically `HEALPix` and `SSHT`. This works by wrapping python bindings with custom +JAX frontends. Note that currently this C/C++ to JAX interoperability is currently +limited to CPU, however for many applications this is desirable due to memory +constraints. + Documentation ============= Read the full documentation `here `_. diff --git a/README.md b/README.md index a1fed65f..46aea516 100644 --- a/README.md +++ b/README.md @@ -26,6 +26,12 @@ As of version 1.0.2 `S2FFT` also provides PyTorch implementations of underlying precompute transforms. In future releases this support will be extended to our on-the-fly algorithms. +As of version 1.0.3 `S2FFT` also provides JAX support for existing C/C++ packages, +specifically `HEALPix` and `SSHT`. This works by wrapping python bindings with custom +JAX frontends. Note that currently this C/C++ to JAX interoperability is currently +limited to CPU, however for many applications this is desirable due to memory +constraints. + ## Algorithms :zap: `S2FFT` leverages new algorithmic structures that can he highly @@ -143,6 +149,29 @@ For further details on usage see the [documentation](https://astro-informatics.g > [!NOTE] > We also provide PyTorch support for the precompute version of our transforms. These are called through forward/inverse_torch(). Full PyTorch support will be provided in future releases. +## C/C++ JAX Frontends for SSHT/HEALPix :bulb: + +`S2FFT` also provides JAX support for existing C/C++ packages, specifically `HEALPix` and `SSHT`. This works +by wrapping python bindings with custom JAX frontends. Note that currently this C/C++ to JAX interoperability is currently +limited to CPU, however for many applications this is desirable due to memory constraints. + +For example, one may call these alternate backends for the spherical harmonic transform by: + +``` python +# Forward SSHT spherical harmonic transform +flm = s2fft.forward(f, L, sampling=["mw"/"mwss"/"gl"/"dh"], method="jax_ssht") + +# Forward HEALPix spherical harmonic transform +flm = s2fft.forward(f, L, nside=nside, sampling="healpix", method="jax_healpy") +``` + +All of these JAX frontends supports out of the box reverse mode automatic differentiation, +and under the hood is simply linking to the C/C++ packages you are familiar with. In this +way `S2fft` enhances existing packages with gradient functionality for modern signal processing +applications! + +For further details on usage see the associated [notebooks](https://astro-informatics.github.io/s2fft/tutorials/spherical_harmonic/JAX_SSHT_backend.html). + ## Benchmarking :hourglass_flowing_sand: We benchmarked the spherical harmonic and Wigner transforms implemented diff --git a/docs/api/transforms/c_backend_spherical.rst b/docs/api/transforms/c_backend_spherical.rst new file mode 100644 index 00000000..a0b66f28 --- /dev/null +++ b/docs/api/transforms/c_backend_spherical.rst @@ -0,0 +1,7 @@ +:html_theme.sidebar_secondary.remove: + +************************** +C/C++ custom JAX support +************************** +.. automodule:: s2fft.transforms.c_backend_spherical + :members: \ No newline at end of file diff --git a/docs/api/transforms/index.rst b/docs/api/transforms/index.rst index 28e63c8b..08b6165f 100644 --- a/docs/api/transforms/index.rst +++ b/docs/api/transforms/index.rst @@ -41,6 +41,25 @@ Transforms * - :func:`~s2fft.transforms.wigner.forward_jax` - Forward Wigner transform (JAX) +.. list-table:: C/C++ backend gradient support + :widths: 25 25 + :header-rows: 1 + + * - Function Name + - Description + * - :func:`~s2fft.transforms.c_backend_spherical.ssht_inverse` + - Custom JAX frontend for inverse SSHT C spherical harmonic library. + * - :func:`~s2fft.transforms.c_backend_spherical.ssht_forward` + - Custom JAX frontend for forward SSHT C spherical harmonic library. + * - :func:`~s2fft.transforms.c_backend_spherical.healpy_inverse` + - Custom JAX frontend for inverse HEALPix C++ spherical harmonic library. + * - :func:`~s2fft.transforms.c_backend_spherical.healpy_forward` + - Custom JAX frontend for forwardHEALPix C++ spherical harmonic library. + * - :func:`~s2fft.transforms.wigner.inverse_jax_ssht` + - Custom JAX frontend for hybrid inverse SSHT C Wigner transforms. + * - :func:`~s2fft.transforms.wigner.forward_jax_ssht` + - Custom JAX frontend for hybrid forward SSHT C Wigner transforms. + .. list-table:: On-the-fly Price-McEwen recursions. :widths: 25 25 :header-rows: 1 @@ -64,4 +83,5 @@ Transforms on_the_fly_recursions spin_spherical_transform wigner + .. c_backend_spherical diff --git a/docs/conf.py b/docs/conf.py index a3dedda5..f7420efa 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -25,9 +25,9 @@ author = "Matthew Price, Jason McEwen, Matthew Graham, Sofia Miñano, Devaraj Gopinathan" # The short X.Y version -version = "1.0.2" +version = "1.0.3" # The full version, including alpha/beta/rc tags -release = "1.0.2" +release = "1.0.3" # -- General configuration --------------------------------------------------- @@ -106,12 +106,12 @@ "icon": "_static/arxiv-logomark-small.png", "type": "local", }, - # { - # "name": "YouTube", - # "url": "https://www.youtube.com/channel/UCrCOQsyQOJhOUaIYzmbkKQQ", - # "icon": "fa-brands fa-youtube fa-2x", - # "type": "fontawesome", - # }, + { + "name": "Medium", + "url": "https://towardsdatascience.com/differentiable-and-accelerated-spherical-harmonic-transforms-c269393d08f1", + "icon": "fa-brands fa-medium", + "type": "fontawesome", + }, { "name": "PyPi", "url": "https://pypi.org/project/s2fft/", diff --git a/docs/index.rst b/docs/index.rst index c7b072fd..444ec910 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -15,6 +15,12 @@ As of version 1.0.2 ``S2FFT`` also provides PyTorch implementations of underlyin precompute transforms. In future releases this support will be extended to our on-the-fly algorithms. +As of version 1.0.3 ``S2FFT`` also provides JAX support for existing C/C++ packages, +specifically ``HEALPix`` and ``SSHT``. This works by wrapping python bindings with custom +JAX frontends. Note that currently this C/C++ to JAX interoperability is currently +limited to CPU, however for many applications this is desirable due to memory +constraints. + Algorithms |:zap:| ------------------- diff --git a/docs/tutorials/JAX_HEALPix/JAX_HEALPix_frontend.nblink b/docs/tutorials/JAX_HEALPix/JAX_HEALPix_frontend.nblink new file mode 100644 index 00000000..2a665f1e --- /dev/null +++ b/docs/tutorials/JAX_HEALPix/JAX_HEALPix_frontend.nblink @@ -0,0 +1,3 @@ +{ + "path": "../../../notebooks/JAX_HEALPix_frontend.ipynb" +} \ No newline at end of file diff --git a/docs/tutorials/JAX_SSHT/JAX_SSHT_frontend.nblink b/docs/tutorials/JAX_SSHT/JAX_SSHT_frontend.nblink new file mode 100644 index 00000000..0bbd2c1c --- /dev/null +++ b/docs/tutorials/JAX_SSHT/JAX_SSHT_frontend.nblink @@ -0,0 +1,3 @@ +{ + "path": "../../../notebooks/JAX_SSHT_frontend.ipynb" +} \ No newline at end of file diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index 12ce7955..2da5c988 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -9,7 +9,7 @@ in the time being feel free to contact contributors for advice! At a high-level ``S2FFT`` package is structured such that the 2 primary transforms, the Wigner and spherical harmonic transforms, can easily be accessed. -Usage |:rocket:| +Core usage |:rocket:| ----------------- To import and use ``S2FFT`` is as simple follows: @@ -25,6 +25,26 @@ To import and use ``S2FFT`` is as simple follows: | f = s2fft.inverse_jax(flm, L) | f = s2fft.wigner.inverse_jax(flmn, L, N) | +-------------------------------------------------------+------------------------------------------------------------+ +C/C++ backend usage |:bulb:| +----------------- +``S2FFT`` also provides JAX support for existing C/C++ packages, specifically ``HEALPix`` and ``SSHT``. This works +by wrapping python bindings with custom JAX frontends. Note that currently this C/C++ to JAX interoperability is currently +limited to CPU, however for many applications this is desirable due to memory constraints. + +For example, one may call these alternate backends for the spherical harmonic transform by: + +.. code-block:: python + + # Forward SSHT spherical harmonic transform + flm = s2fft.forward(f, L, sampling=["mw"/"mwss"/"gl"/"dh"], method="jax_ssht") + + # Forward HEALPix spherical harmonic transform + flm = s2fft.forward(f, L, nside=nside, sampling="healpix", method="jax_healpy") + +All of these JAX frontends supports out of the box reverse mode automatic differentiation, +and under the hood is simply linking to the C/C++ packages you are familiar with. In this +way ``S2FFT`` enhances existing packages with gradient functionality for modern signal processing +applications! Benchmarking |:hourglass_flowing_sand:| ------------------------------------- @@ -69,3 +89,5 @@ the case for many other methods that scale linearly with spin). wigner/wigner_transform.nblink rotation/rotation.nblink torch_frontend/torch_frontend.nblink + JAX_SSHT/JAX_SSHT_frontend.nblink + JAX_HEALPix/JAX_HEALPix_frontend.nblink diff --git a/notebooks/JAX_HEALPix_frontend.ipynb b/notebooks/JAX_HEALPix_frontend.ipynb new file mode 100644 index 00000000..9abc0e0b --- /dev/null +++ b/notebooks/JAX_HEALPix_frontend.ipynb @@ -0,0 +1,187 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# __JAX HEALPix frontend__\n", + "\n", + "---\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This short tutorial demonstrates how to use the custom JAX frontend support `S2FFT` provides for the highly optimised `HEALPix` C++ library.\n", + "\n", + "As with the other introductions, let's import some packages and define an arbitrary bandlimited signal to work with." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "jax.config.update(\"jax_enable_x64\", True)\n", + "\n", + "import numpy as np\n", + "import s2fft \n", + "\n", + "L = 1024\n", + "nside = 512\n", + "method = \"jax_healpy\"\n", + "sampling = \"healpix\"\n", + "flm = np.random.randn(L, 2*L-1) + 1j*np.random.randn(L, 2*L-1)\n", + "f = s2fft.inverse(flm, L, nside=nside, sampling=sampling, method=method)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calling forward HEALPix C++ function from JAX.\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "flm = s2fft.forward(f, L, nside=nside, sampling=sampling, method=method)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calling inverse HEALPix C++ function from JAX.\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "f_recov = s2fft.inverse(flm, L, nside=nside, sampling=sampling, method=method)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Computing the roundtrip error\n", + "\n", + "---\n", + "\n", + "Let's check the associated error, which should be around 1e-5 for healpix, which is not an exact sampling of the sphere. Note that increasing iters will reduce the numerical error here slightly, at the cost of linearly increased compute." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean absolute error = 2.5840205579477368e-06\n" + ] + } + ], + "source": [ + "print(f\"Mean absolute error = {np.nanmean(np.abs(f_recov - f))}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Differentiating through HEALPix C++ functions.\n", + "\n", + "---\n", + "\n", + "So far all this is doing is providing an interface between `JAX` and `HEALPix`, the real novelty comes when we differentiate through the C++ library." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Define an arbitrary JAX function\n", + "def differentiable_test(flm) -> int:\n", + " f = s2fft.inverse(flm, L, nside=nside, sampling=sampling, method=method)\n", + " return jax.numpy.nanmean(jax.numpy.abs(f)**2)\n", + "\n", + "# Create the JAX reverse mode gradient function\n", + "gradient_func = jax.grad(differentiable_test)\n", + "\n", + "# Compute the gradient automatically\n", + "gradient = gradient_func(flm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Validating these gradients\n", + "\n", + "---\n", + "This is all well and good, but how do we know these gradients are correct? Thankfully `JAX` prvoides a simple function to check this..." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from jax.test_util import check_grads\n", + "check_grads(differentiable_test, (flm,), order=1, modes=(\"rev\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 ('s2fft')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "3425e24474cbe920550266ea26b478634978cc419579f9dbcf479231067df6a3" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/JAX_SSHT_frontend.ipynb b/notebooks/JAX_SSHT_frontend.ipynb new file mode 100644 index 00000000..f3090d07 --- /dev/null +++ b/notebooks/JAX_SSHT_frontend.ipynb @@ -0,0 +1,185 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# __JAX SSHT frontend__\n", + "\n", + "---\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This short tutorial demonstrates how to use the custom JAX frontend support `S2FFT` provides for the highly optimised `SSHT` C library.\n", + "\n", + "As with the other introductions, let's import some packages and define an arbitrary bandlimited signal to work with." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "jax.config.update(\"jax_enable_x64\", True)\n", + "\n", + "import numpy as np\n", + "import s2fft \n", + "\n", + "L = 1024\n", + "method = \"jax_ssht\"\n", + "flm = np.random.randn(L, 2*L-1) + 1j*np.random.randn(L, 2*L-1)\n", + "f = s2fft.inverse(flm, L, method=method)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calling forward SSHT C function from JAX.\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "flm = s2fft.forward(f, L, method=method)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calling inverse SSHT C function from JAX.\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "f_recov = s2fft.inverse(flm, L, method=method)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Computing the roundtrip error\n", + "\n", + "---\n", + "\n", + "Let's check the associated error, which should be close to machine precision for the sampling scheme used." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean absolute error = 4.7035868519989165e-11\n" + ] + } + ], + "source": [ + "print(f\"Mean absolute error = {np.nanmean(np.abs(f_recov - f))}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Differentiating through SSHT C functions.\n", + "\n", + "---\n", + "\n", + "So far all this is doing is providing an interface between `JAX` and `SSHT`, the real novelty comes when we differentiate through the C library." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Define an arbitrary JAX function\n", + "def differentiable_test(flm) -> int:\n", + " f = s2fft.inverse(flm, L, method=method)\n", + " return jax.numpy.nanmean(jax.numpy.abs(f)**2)\n", + "\n", + "# Create the JAX reverse mode gradient function\n", + "gradient_func = jax.grad(differentiable_test)\n", + "\n", + "# Compute the gradient automatically\n", + "gradient = gradient_func(flm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Validating these gradients\n", + "\n", + "---\n", + "This is all well and good, but how do we know these gradients are correct? Thankfully `JAX` prvoides a simple function to check this..." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from jax.test_util import check_grads\n", + "check_grads(differentiable_test, (flm,), order=1, modes=(\"rev\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 ('s2fft')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "3425e24474cbe920550266ea26b478634978cc419579f9dbcf479231067df6a3" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/requirements/requirements-core.txt b/requirements/requirements-core.txt index 7c86b0ea..426e3e9f 100644 --- a/requirements/requirements-core.txt +++ b/requirements/requirements-core.txt @@ -6,4 +6,9 @@ jax>=0.3.13 jaxlib # Remove when subpackage functionality is fixed. -torch \ No newline at end of file +torch + +# Legacy codes for which to provide JAX frontend support. +pyssht +healpy +ducc0 \ No newline at end of file diff --git a/requirements/requirements-tests.txt b/requirements/requirements-tests.txt index 1127caf5..df1f5de3 100644 --- a/requirements/requirements-tests.txt +++ b/requirements/requirements-tests.txt @@ -3,7 +3,5 @@ pytest pytest-cov codecov -# Healpix Sampling -pyssht -healpy +# Benchmarking for Wigner transforms so3 diff --git a/s2fft/_version.py b/s2fft/_version.py index 684acf06..e31a5608 100644 --- a/s2fft/_version.py +++ b/s2fft/_version.py @@ -3,6 +3,7 @@ TYPE_CHECKING = False if TYPE_CHECKING: from typing import Tuple, Union + VERSION_TUPLE = Tuple[Union[int, str], ...] else: VERSION_TUPLE = object @@ -12,5 +13,5 @@ __version_tuple__: VERSION_TUPLE version_tuple: VERSION_TUPLE -__version__ = version = '1.0.1.dev12' -__version_tuple__ = version_tuple = (1, 0, 1, 'dev12') +__version__ = version = "1.0.3" +__version_tuple__ = version_tuple = (1, 0, 3) diff --git a/s2fft/transforms/__init__.py b/s2fft/transforms/__init__.py index f36acfe8..c15947b4 100644 --- a/s2fft/transforms/__init__.py +++ b/s2fft/transforms/__init__.py @@ -1,3 +1,4 @@ from . import spherical from . import wigner from . import otf_recursions +from . import c_backend_spherical diff --git a/s2fft/transforms/c_backend_spherical.py b/s2fft/transforms/c_backend_spherical.py new file mode 100644 index 00000000..8f60c7b1 --- /dev/null +++ b/s2fft/transforms/c_backend_spherical.py @@ -0,0 +1,326 @@ +from jax import custom_vjp +import numpy as np +import jax.numpy as jnp +from s2fft.sampling import s2_samples as samples +from s2fft.utils import quadrature_jax + +# C backend functions for which to provide JAX frontend. +import pyssht +import healpy + + +@custom_vjp +def ssht_inverse( + flm: jnp.ndarray, + L: int, + spin: int = 0, + reality: bool = False, + ssht_sampling: int = 0, + _ssht_backend: int = 1, +) -> jnp.ndarray: + r"""Compute the inverse spin-spherical harmonic transform (SSHT JAX). + + SSHT is a highly optimised C library which implements the spin-spherical harmonic + transform outlined in McEwen & Wiaux 2011 [1]. We make use of their python bindings + for which we provide custom JAX frontends, hence providing support for automatic + differentiation. Currently these transforms can only be deployed on CPU, which is a + limitation of the SSHT C package. + + Args: + flm (jnp.ndarray): Spherical harmonic coefficients. + + L (int): Harmonic band-limit. + + spin (int, optional): Harmonic spin. Defaults to 0. + + reality (bool, optional): Whether the signal on the sphere is real. If so, + conjugate symmetry is exploited to reduce computational costs. Defaults to + False. + + ssht_sampling (int, optional): Sampling scheme. Supported sampling schemes include + {"mw" = 0, "mwss" = 1, "dh" = 2, "gl" = 3}. Defaults to "mw" = 0. + + _ssht_backend (int, optional, experimental): Whether to default to SSHT core + recursions or pick up ducc0 accelerated experimental backend. Use with caution. + + Returns: + jnp.ndarray: Signal on the sphere. + + Note: + [1] McEwen, Jason D. and Yves Wiaux. “A Novel Sampling Theorem on the Sphere.” + IEEE Transactions on Signal Processing 59 (2011): 5876-5887. + """ + sampling_str = ["MW", "MWSS", "DH", "GL"] + flm_1d = samples.flm_2d_to_1d(flm, L) + _backend = "SSHT" if _ssht_backend == 0 else "ducc0" + return pyssht.inverse( + flm_1d, + L, + spin, + Method=sampling_str[ssht_sampling], + Reality=reality, + backend=_backend, + ) + + +def _ssht_inverse_fwd( + flm: jnp.ndarray, + L: int, + spin: int = 0, + reality: bool = False, + ssht_sampling: int = 0, + _ssht_backend: int = 1, +): + """Private function which implements the forward pass for inverse jax_ssht""" + res = ([], L, spin, reality, ssht_sampling, _ssht_backend) + return ssht_inverse(flm, L, spin, reality, ssht_sampling, _ssht_backend), res + + +def _ssht_inverse_bwd(res, f): + """Private function which implements the backward pass for inverse jax_ssht""" + _, L, spin, reality, ssht_sampling, _ssht_backend = res + sampling_str = ["MW", "MWSS", "DH", "GL"] + _backend = "SSHT" if _ssht_backend == 0 else "ducc0" + if ssht_sampling < 2: + flm = np.conj( + pyssht.inverse_adjoint( + np.conj(f), + L, + spin, + Method=sampling_str[ssht_sampling], + Reality=reality, + backend=_backend, + ) + ) + else: + quad_weights = quadrature_jax.quad_weights_transform( + L, sampling_str[ssht_sampling].lower() + ) + f = jnp.einsum("tp,t->tp", f, 1 / quad_weights, optimize=True) + flm = np.conj( + pyssht.forward( + np.conj(f), + L, + spin, + Method=sampling_str[ssht_sampling], + Reality=reality, + backend=_backend, + ) + ) + + flm_out = samples.flm_1d_to_2d(flm, L) + + if reality: + m_conj = (-1) ** (np.arange(1, L) % 2) + flm_out[..., L:] += np.flip(m_conj * np.conj(flm_out[..., : L - 1]), axis=-1) + flm_out[..., : L - 1] = 0 + + return flm_out, None, None, None, None, None + + +@custom_vjp +def ssht_forward( + f: jnp.ndarray, + L: int, + spin: int = 0, + reality: bool = False, + ssht_sampling: int = 0, + _ssht_backend: int = 1, +) -> jnp.ndarray: + r"""Compute the forward spin-spherical harmonic transform (SSHT JAX). + + SSHT is a highly optimised C library which implements the spin-spherical harmonic + transform outlined in McEwen & Wiaux 2011 [1]. We make use of their python bindings + for which we provide custom JAX frontends, hence providing support for automatic + differentiation. Currently these transforms can only be deployed on CPU, which is a + limitation of the SSHT C package. + + Args: + f (jnp.ndarray): Signal on the sphere. + + L (int): Harmonic band-limit. + + spin (int, optional): Harmonic spin. Defaults to 0. + + reality (bool, optional): Whether the signal on the sphere is real. If so, + conjugate symmetry is exploited to reduce computational costs. Defaults to + False. + + ssht_sampling (int, optional): Sampling scheme. Supported sampling schemes include + {"mw" = 0, "mwss" = 1, "dh" = 2, "gl" = 3}. Defaults to "mw" = 0. + + _ssht_backend (int, optional, experimental): Whether to default to SSHT core + recursions or pick up ducc0 accelerated experimental backend. Use with caution. + + Returns: + jnp.ndarray: Harmonic coefficients of signal f. + + Note: + [1] McEwen, Jason D. and Yves Wiaux. “A Novel Sampling Theorem on the Sphere.” + IEEE Transactions on Signal Processing 59 (2011): 5876-5887. + """ + sampling_str = ["MW", "MWSS", "DH", "GL"] + _backend = "SSHT" if _ssht_backend == 0 else "ducc0" + flm = pyssht.forward( + np.array(f), + L, + spin, + Method=sampling_str[ssht_sampling], + Reality=reality, + backend=_backend, + ) + return samples.flm_1d_to_2d(flm, L) + + +def _ssht_forward_fwd( + f: jnp.ndarray, + L: int, + spin: int = 0, + reality: bool = False, + ssht_sampling: int = 0, + _ssht_backend: int = 1, +): + """Private function which implements the forward pass for forward jax_ssht""" + res = ([], L, spin, reality, ssht_sampling, _ssht_backend) + return ssht_forward(f, L, spin, reality, ssht_sampling, _ssht_backend), res + + +def _ssht_forward_bwd(res, flm): + """Private function which implements the backward pass for forward jax_ssht""" + _, L, spin, reality, ssht_sampling, _ssht_backend = res + sampling_str = ["MW", "MWSS", "DH", "GL"] + _backend = "SSHT" if _ssht_backend == 0 else "ducc0" + flm_1d = samples.flm_2d_to_1d(flm, L) + + if ssht_sampling < 2: + f = np.conj( + pyssht.forward_adjoint( + np.conj(flm_1d), + L, + spin, + Method=sampling_str[ssht_sampling], + Reality=reality, + backend=_backend, + ) + ) + else: + quad_weights = quadrature_jax.quad_weights_transform( + L, sampling_str[ssht_sampling].lower() + ) + f = np.conj( + pyssht.inverse( + np.conj(flm_1d), + L, + spin, + Method=sampling_str[ssht_sampling], + Reality=reality, + backend=_backend, + ) + ) + f = jnp.einsum("tp,t->tp", f, quad_weights, optimize=True) + + return f, None, None, None, None, None + + +@custom_vjp +def healpy_inverse(flm: jnp.ndarray, L: int, nside: int) -> jnp.ndarray: + r"""Compute the inverse scalar real spherical harmonic transform (HEALPix JAX). + + HEALPix is a highly optimised C++ library which implements the scalar spherical harmonic + transform outlined in [1]. We make use of their healpy python bindings for which we provide + custom JAX frontends, hence providing support for automatic differentiation. Currently + these transforms can only be deployed on CPU, which is a limitation of the C++ library. + + Args: + flm (jnp.ndarray): Spherical harmonic coefficients. + + L (int): Harmonic band-limit. + + nside (int, optional): HEALPix Nside resolution parameter. Only required + if sampling="healpix". Defaults to None. + + Returns: + jnp.ndarray: Signal on the sphere. + + Note: + [1] Gorski, Krzysztof M., et al. "HEALPix: A framework for high-resolution + discretization and fast analysis of data distributed on the sphere." The + Astrophysical Journal 622.2 (2005): 759 + """ + flm = samples.flm_2d_to_hp(flm, L) + f = healpy.alm2map(flm, lmax=L - 1, nside=nside) + return f + + +def _healpy_inverse_fwd(flm: jnp.ndarray, L: int, nside: int): + """Private function which implements the forward pass for inverse jax_healpy""" + res = ([], L, nside) + return healpy_inverse(flm, L, nside), res + + +def _healpy_inverse_bwd(res, f): + """Private function which implements the backward pass for inverse jax_healpy""" + _, L, nside = res + f_new = f * (12 * nside**2) / (4 * np.pi) + flm_out = np.conj(healpy.map2alm(np.conj(f_new), lmax=L - 1, iter=0)) + # iter MUST be zero otherwise gradient propagation is incorrect (JDM). + flm_out = samples.flm_hp_to_2d(flm_out, L) + m_conj = (-1) ** (np.arange(1, L) % 2) + flm_out[..., L:] += np.flip(m_conj * np.conj(flm_out[..., : L - 1]), axis=-1) + flm_out[..., : L - 1] = 0 + + return flm_out, None, None + + +@custom_vjp +def healpy_forward(f: jnp.ndarray, L: int, nside: int, iter: int = 3) -> jnp.ndarray: + r"""Compute the forward scalar spherical harmonic transform (healpy JAX). + + HEALPix is a highly optimised C++ library which implements the scalar spherical harmonic + transform outlined in [1]. We make use of their healpy python bindings for which we provide + custom JAX frontends, hence providing support for automatic differentiation. Currently + these transforms can only be deployed on CPU, which is a limitation of the C++ library. + + Args: + f (jnp.ndarray): Signal on the sphere. + + L (int): Harmonic band-limit. + + nside (int, optional): HEALPix Nside resolution parameter. Only required + if sampling="healpix". Defaults to None. + + iter (int, optional): Number of subiterations for healpy. Note that iterations + increase the precision of the forward transform, but reduce the accuracy of + the gradient pass. Between 2 and 3 iterations is a good compromise. + + Returns: + jnp.ndarray: Harmonic coefficients of signal f. + + Note: + [1] Gorski, Krzysztof M., et al. "HEALPix: A framework for high-resolution + discretization and fast analysis of data distributed on the sphere." The + Astrophysical Journal 622.2 (2005): 759 + """ + flm = healpy.map2alm(np.array(f), lmax=L - 1, iter=iter) + return samples.flm_hp_to_2d(flm, L) + + +def _healpy_forward_fwd(f: jnp.ndarray, L: int, nside: int, iter: int = 3): + """Private function which implements the forward pass for forward jax_healpy""" + res = ([], L, nside, iter) + return healpy_forward(f, L, nside, iter), res + + +def _healpy_forward_bwd(res, flm): + """Private function which implements the backward pass for forward jax_healpy""" + _, L, nside, _ = res + flm_new = samples.flm_2d_to_hp(np.array(flm), L) + f = np.conj(healpy.alm2map(np.conj(flm_new), lmax=L - 1, nside=nside)) + return f * (4 * np.pi) / (12 * nside**2), None, None, None + + +# Link JAX gradients for C backend functions +ssht_inverse.defvjp(_ssht_inverse_fwd, _ssht_inverse_bwd) +ssht_forward.defvjp(_ssht_forward_fwd, _ssht_forward_bwd) +healpy_inverse.defvjp(_healpy_inverse_fwd, _healpy_inverse_bwd) +healpy_forward.defvjp(_healpy_forward_fwd, _healpy_forward_bwd) diff --git a/s2fft/transforms/spherical.py b/s2fft/transforms/spherical.py index 43e520c7..9b669672 100644 --- a/s2fft/transforms/spherical.py +++ b/s2fft/transforms/spherical.py @@ -13,6 +13,7 @@ ) from s2fft.utils import healpix_ffts as hp from s2fft.transforms import otf_recursions as otf +from s2fft.transforms import c_backend_spherical as c_sph def inverse( @@ -26,6 +27,7 @@ def inverse( precomps: List = None, spmd: bool = False, L_lower: int = 0, + _ssht_backend: int = 1, ) -> np.ndarray: r"""Wrapper for the inverse spin-spherical harmonic transform. @@ -42,7 +44,8 @@ def inverse( sampling (str, optional): Sampling scheme. Supported sampling schemes include {"mw", "mwss", "dh", "gl", "healpix"}. Defaults to "mw". - method (str, optional): Execution mode in {"numpy", "jax"}. Defaults to "numpy". + method (str, optional): Execution mode in {"numpy", "jax", "jax_ssht", "jax_healpy"}. + Defaults to "numpy". reality (bool, optional): Whether the signal on the sphere is real. If so, conjugate symmetry is exploited to reduce computational costs. Defaults to @@ -58,6 +61,9 @@ def inverse( L_lower (int, optional): Harmonic lower-bound. Transform will only be computed for :math:`\texttt{L_lower} \leq \ell < \texttt{L}`. Defaults to 0. + _ssht_backend (int, optional, experimental): Whether to default to SSHT core + recursions or pick up ducc0 accelerated experimental backend. Use with caution. + Raises: ValueError: Transform method not recognised. @@ -79,6 +85,15 @@ def inverse( return inverse_jax( flm, L, spin, nside, sampling, reality, precomps, spmd, L_lower ) + elif method == "jax_ssht": + if sampling.lower() == "healpix": + raise ValueError("Legacy code ssht does not support healpix sampling.") + ssht_sampling = ["mw", "mwss", "dh", "gl"].index(sampling.lower()) + return c_sph.ssht_inverse(flm, L, spin, reality, ssht_sampling, _ssht_backend) + elif method == "jax_healpy": + if sampling.lower() != "healpix": + raise ValueError("Legacy code healpy only supports healpix sampling.") + return c_sph.healpy_inverse(flm, L, nside) else: raise ValueError( f"Implementation {method} not recognised. Should be either numpy or jax." @@ -320,6 +335,8 @@ def forward( precomps: List = None, spmd: bool = False, L_lower: int = 0, + iter: int = 3, + _ssht_backend: int = 1, ) -> np.ndarray: r"""Wrapper for the forward spin-spherical harmonic transform. @@ -352,6 +369,13 @@ def forward( L_lower (int, optional): Harmonic lower-bound. Transform will only be computed for :math:`\texttt{L_lower} \leq \ell < \texttt{L}`. Defaults to 0. + iter (int, optional): Number of subiterations for healpy. Note that iterations + increase the precision of the forward transform, but reduce the accuracy of + the gradient pass. Between 2 and 3 iterations is a good compromise. + + _ssht_backend (int, optional, experimental): Whether to default to SSHT core + recursions or pick up ducc0 accelerated experimental backend. Use with caution. + Raises: ValueError: Transform method not recognised. @@ -373,6 +397,15 @@ def forward( return forward_jax( f, L, spin, nside, sampling, reality, precomps, spmd, L_lower ) + elif method == "jax_ssht": + if sampling.lower() == "healpix": + raise ValueError("Legacy code ssht does not support healpix sampling.") + ssht_sampling = ["mw", "mwss", "dh", "gl"].index(sampling.lower()) + return c_sph.ssht_forward(f, L, spin, reality, ssht_sampling, _ssht_backend) + elif method == "jax_healpy": + if sampling.lower() != "healpix": + raise ValueError("Legacy code healpy only supports healpix sampling.") + return c_sph.healpy_forward(f, L, nside, iter) else: raise ValueError( f"Implementation {method} not recognised. Should be either numpy or jax." diff --git a/s2fft/transforms/wigner.py b/s2fft/transforms/wigner.py index 1db8382d..3da7d814 100644 --- a/s2fft/transforms/wigner.py +++ b/s2fft/transforms/wigner.py @@ -8,6 +8,7 @@ from typing import List import s2fft from s2fft.sampling import so3_samples as samples +from s2fft.transforms import c_backend_spherical as c_sph def inverse( @@ -21,6 +22,7 @@ def inverse( precomps: List = None, spmd: bool = False, L_lower: int = 0, + _ssht_backend: int = 1, ) -> np.ndarray: r"""Wrapper for the inverse Wigner transform, i.e. inverse Fourier transform on :math:`SO(3)`. @@ -32,6 +34,10 @@ def inverse( :math:`[\theta, \phi]`, i.e. we associate :math:`\beta` with :math:`\theta` and :math:`\alpha` with :math:`\phi`. + Should the user select method = "jax_ssht" they will be restricted to deployment on + CPU using our custom JAX frontend for the highly optimised SSHT C library [1]. In many + cases this approach may be desirable to mitigate e.g. memory i/o cost. + Args: flmn (np.ndarray): Wigner coefficients with shape :math:`[2N-1, L, 2L-1]`. @@ -45,7 +51,8 @@ def inverse( sampling (str, optional): Sampling scheme. Supported sampling schemes include {"mw", "mwss", "dh", "gl", "healpix"}. Defaults to "mw". - method (str, optional): Execution mode in {"numpy", "jax"}. Defaults to "numpy". + method (str, optional): Execution mode in {"numpy", "jax", "jax_ssht"}. + Defaults to "numpy". reality (bool, optional): Whether the signal on the sphere is real. If so, conjugate symmetry is exploited to reduce computational costs. Defaults to @@ -61,6 +68,9 @@ def inverse( L_lower (int, optional): Harmonic lower-bound. Transform will only be computed for :math:`\texttt{L_lower} \leq \ell < \texttt{L}`. Defaults to 0. + _ssht_backend (int, optional, experimental): Whether to default to SSHT core + recursions or pick up ducc0 accelerated experimental backend. Use with caution. + Raises: ValueError: Transform method not recognised. @@ -75,6 +85,9 @@ def inverse( harmonic bandlimits L this is inefficient as the I/O overhead for communication between devices is noticable, however as L increases one will asymptotically recover acceleration by the number of devices. + + [1] McEwen, Jason D. and Yves Wiaux. “A Novel Sampling Theorem on the Sphere.” + IEEE Transactions on Signal Processing 59 (2011): 5876-5887. """ if N >= 8: raise Warning("Recursive transform may provide lower precision beyond N ~ 8") @@ -84,6 +97,10 @@ def inverse( return inverse_jax( flmn, L, N, nside, sampling, reality, precomps, spmd, L_lower ) + elif method == "jax_ssht": + if sampling.lower() == "healpix": + raise ValueError("Legacy code ssht does not support healpix sampling.") + return inverse_jax_ssht(flmn, L, N, L_lower, sampling, reality, _ssht_backend) else: raise ValueError( f"Implementation {method} not recognised. Should be either numpy or jax." @@ -177,7 +194,7 @@ def inverse_numpy( @partial(jit, static_argnums=(1, 2, 3, 4, 5, 7, 8)) def inverse_jax( - flmn: np.ndarray, + flmn: jnp.ndarray, L: int, N: int, nside: int = None, @@ -187,7 +204,7 @@ def inverse_jax( spmd: bool = False, L_lower: int = 0, ) -> jnp.ndarray: - r"""Compute the inverse Wigner transform (numpy). + r"""Compute the inverse Wigner transform (JAX). Uses separation of variables and exploits the Price & McEwen recursion for accelerated and numerically stable Wiger-d on-the-fly recursions. The memory overhead for this @@ -201,7 +218,7 @@ def inverse_jax( :math:`\alpha` with :math:`\phi`. Args: - flmn (np.ndarray): Wigner coefficients with shape :math:`[2N-1, L, 2L-1]`. + flmn (jnp.ndarray): Wigner coefficients with shape :math:`[2N-1, L, 2L-1]`. L (int): Harmonic band-limit. @@ -213,8 +230,6 @@ def inverse_jax( sampling (str, optional): Sampling scheme. Supported sampling schemes include {"mw", "mwss", "dh", "gl", "healpix"}. Defaults to "mw". - method (str, optional): Execution mode in {"numpy", "jax"}. Defaults to "numpy". - reality (bool, optional): Whether the signal on the sphere is real. If so, conjugate symmetry is exploited to reduce computational costs. Defaults to False. @@ -331,6 +346,84 @@ def eval_spherical_loop(fban, flmn, lrenorm, vsign, spins): return f +def inverse_jax_ssht( + flmn: jnp.ndarray, + L: int, + N: int, + L_lower: int = 0, + sampling: str = "mw", + reality: bool = False, + _ssht_backend: int = 1, +) -> jnp.ndarray: + r"""Compute the inverse Wigner transform (SSHT JAX). + + SSHT is a highly optimised C library which implements the spin-spherical harmonic + transform outlined in McEwen & Wiaux 2011 [1]. We make use of their python bindings + for which we provide custom JAX frontends, hence providing support for automatic + differentiation. Currently these transforms can only be deployed on CPU, which is a + limitation of the SSHT C package. + + Args: + flmn (jnp.ndarray): Wigner coefficients with shape :math:`[2N-1, L, 2L-1]`. + + L (int): Harmonic band-limit. + + N (int): Azimuthal band-limit. + + L_lower (int, optional): Harmonic lower-bound. Transform will only be computed + for :math:`\texttt{L_lower} \leq \ell < \texttt{L}`. Defaults to 0. + + sampling (str, optional): Sampling scheme. Supported sampling schemes include + {"mw", "mwss", "dh", "gl"}. Defaults to "mw". + + reality (bool, optional): Whether the signal on the sphere is real. If so, + conjugate symmetry is exploited to reduce computational costs. Defaults to + False. + + _ssht_backend (int, optional, experimental): Whether to default to SSHT core + recursions or pick up ducc0 accelerated experimental backend. Use with caution. + + Returns: + np.ndarray: Signal on the sphere. + + Note: + [1] McEwen, Jason D. and Yves Wiaux. “A Novel Sampling Theorem on the Sphere.” + IEEE Transactions on Signal Processing 59 (2011): 5876-5887. + """ + ssht_sampling = ["mw", "mwss", "dh", "gl"].index(sampling.lower()) + fban = jnp.zeros(samples.f_shape(L, N, sampling), dtype=jnp.complex128) + + flmn = flmn.at[:, L_lower:].set( + jnp.einsum( + "...nlm,...l->...nlm", + flmn[:, L_lower:], + jnp.sqrt((2 * jnp.arange(L_lower, L) + 1) / (16 * jnp.pi**3)), + optimize=True, + ) + ) + + n_start_ind = 0 if reality else -N + 1 + for n in range(n_start_ind, N): + fban = fban.at[N - 1 + n].add( + (-1) ** jnp.abs(n) + * c_sph.ssht_inverse( + flmn[N - 1 + n], + L, + -n, + reality if n == 0 else False, + ssht_sampling, + _ssht_backend, + ) + ) + + if reality: + f = jnp.fft.irfft(fban[N - 1 :], 2 * N - 1, axis=-3, norm="forward") + else: + f = jnp.fft.ifft(jnp.fft.ifftshift(fban, axes=-3), axis=-3, norm="forward") + + return f + + def forward( f: np.ndarray, L: int, @@ -342,6 +435,7 @@ def forward( precomps: List = None, spmd: bool = False, L_lower: int = 0, + _ssht_backend: int = 1, ) -> np.ndarray: r"""Wrapper for the forward Wigner transform, i.e. Fourier transform on :math:`SO(3)`. @@ -353,6 +447,10 @@ def forward( :math:`[\theta, \phi]`, i.e. we associate :math:`\beta` with :math:`\theta` and :math:`\alpha` with :math:`\phi`. + Should the user select method = "jax_ssht" they will be restricted to deployment on + CPU using our custom JAX frontend for the highly optimised SSHT C library [1]. In many + cases this approach may be desirable to mitigate e.g. memory i/o cost. + Args: f (np.ndarray): Signal on the on :math:`SO(3)` with shape :math:`[n_{\gamma}, n_{\beta}, n_{\alpha}]`, where :math:`n_\xi` denotes the @@ -368,7 +466,8 @@ def forward( sampling (str, optional): Sampling scheme. Supported sampling schemes include {"mw", "mwss", "dh", "gl", "healpix"}. Defaults to "mw". - method (str, optional): Execution mode in {"numpy", "jax"}. Defaults to "numpy". + method (str, optional): Execution mode in {"numpy", "jax", "jax_ssht"}. + Defaults to "numpy". reality (bool, optional): Whether the signal on the sphere is real. If so, conjugate symmetry is exploited to reduce computational costs. Defaults to @@ -384,6 +483,9 @@ def forward( L_lower (int, optional): Harmonic lower-bound. Transform will only be computed for :math:`\texttt{L_lower} \leq \ell < \texttt{L}`. Defaults to 0. + _ssht_backend (int, optional, experimental): Whether to default to SSHT core + recursions or pick up ducc0 accelerated experimental backend. Use with caution. + Raises: ValueError: Transform method not recognised. @@ -396,6 +498,9 @@ def forward( harmonic bandlimits L this is inefficient as the I/O overhead for communication between devices is noticable, however as L increases one will asymptotically recover acceleration by the number of devices. + + [1] McEwen, Jason D. and Yves Wiaux. “A Novel Sampling Theorem on the Sphere.” + IEEE Transactions on Signal Processing 59 (2011): 5876-5887. """ if N >= 8: raise Warning("Recursive transform may provide lower precision beyond N ~ 8") @@ -403,6 +508,10 @@ def forward( return forward_numpy(f, L, N, nside, sampling, reality, precomps, L_lower) elif method == "jax": return forward_jax(f, L, N, nside, sampling, reality, precomps, spmd, L_lower) + elif method == "jax_ssht": + if sampling.lower() == "healpix": + raise ValueError("Legacy code ssht does not support healpix sampling.") + return forward_jax_ssht(f, L, N, L_lower, sampling, reality, _ssht_backend) else: raise ValueError( f"Implementation {method} not recognised. Should be either numpy or jax." @@ -664,3 +773,89 @@ def eval_spherical_loop(fban, flmn, lrenorm, vsign, spins): ) ) return flmn + + +def forward_jax_ssht( + f: jnp.ndarray, + L: int, + N: int, + L_lower: int = 0, + sampling: str = "mw", + reality: bool = False, + _ssht_backend: int = 1, +) -> jnp.ndarray: + r"""Compute the forward Wigner transform (SSHT JAX). + + SSHT is a highly optimised C library which implements the spin-spherical harmonic + transform outlined in McEwen & Wiaux 2011 [1]. We make use of their python bindings + for which we provide custom JAX frontends, hence providing support for automatic + differentiation. Currently these transforms can only be deployed on CPU, which is a + limitation of the SSHT C package. + + Args: + f (jnp.ndarray): Signal on the on :math:`SO(3)` with shape + :math:`[n_{\gamma}, n_{\beta}, n_{\alpha}]`, where :math:`n_\xi` denotes the + number of samples for angle :math:`\xi`. + + L (int): Harmonic band-limit. + + N (int): Azimuthal band-limit. + + L_lower (int, optional): Harmonic lower-bound. Transform will only be computed + for :math:`\texttt{L_lower} \leq \ell < \texttt{L}`. Defaults to 0. + + sampling (str, optional): Sampling scheme. Supported sampling schemes include + {"mw", "mwss", "dh", "gl"}. Defaults to "mw". + + reality (bool, optional): Whether the signal on the sphere is real. If so, + conjugate symmetry is exploited to reduce computational costs. Defaults to + False. + + _ssht_backend (int, optional, experimental): Whether to default to SSHT core + recursions or pick up ducc0 accelerated experimental backend. Use with caution. + + Returns: + jnp.ndarray: Wigner coefficients `flmn` with shape :math:`[2N-1, L, 2L-1]`. + + Note: + [1] McEwen, Jason D. and Yves Wiaux. “A Novel Sampling Theorem on the Sphere.” + IEEE Transactions on Signal Processing 59 (2011): 5876-5887. + """ + ssht_sampling = ["mw", "mwss", "dh", "gl"].index(sampling.lower()) + flmn = jnp.zeros(samples.flmn_shape(L, N), dtype=jnp.complex128) + + if reality: + fban = jnp.fft.rfft(jnp.real(f), axis=0, norm="backward") + else: + fban = jnp.fft.fftshift(jnp.fft.fft(f, axis=0, norm="backward"), axes=0) + + fban *= 2 * jnp.pi / (2 * N - 1) + + n_start_ind = 0 if reality else -N + 1 + for n in range(n_start_ind, N): + flmn = flmn.at[N - 1 + n].set( + (-1) ** jnp.abs(n) + * c_sph.ssht_forward( + fban[jnp.int64(n - n_start_ind)], + L, + -n, + False, + ssht_sampling, + _ssht_backend, + ) + ) + if reality and n != 0: + sgn = (-1) ** abs(jnp.arange(-L + 1, L)) + flmn = flmn.at[N - 1 - n].set( + jnp.conj(jnp.flip(flmn[N - 1 + n] * sgn * (-1) ** n, axis=-1)) + ) + + flmn = flmn.at[:, L_lower:].set( + jnp.einsum( + "...nlm,...l->...nlm", + flmn[:, L_lower:], + jnp.sqrt(4 * jnp.pi / (2 * jnp.arange(L_lower, L) + 1)), + optimize=True, + ) + ) + return flmn diff --git a/setup.py b/setup.py index 99b6d6a2..3599c0df 100644 --- a/setup.py +++ b/setup.py @@ -21,7 +21,7 @@ "Intended Audience :: Science/Research", ], name="s2fft", - version="1.0.2", + version="1.0.3", url="https://github.com/astro-informatics/s2fft", author="Matthew A. Price, Jason D. McEwen & Contributors", license="MIT", diff --git a/tests/test_spherical_custom_grads.py b/tests/test_spherical_custom_grads.py index 2d34212d..35523a29 100644 --- a/tests/test_spherical_custom_grads.py +++ b/tests/test_spherical_custom_grads.py @@ -13,6 +13,7 @@ spin_to_test = [-2, 0, 1] nside_to_test = [8] sampling_to_test = ["mw", "mwss", "dh", "gl"] +legacy_mode_to_test = ["jax_ssht"] reality_to_test = [False, True] @@ -111,7 +112,6 @@ def func(f): @pytest.mark.parametrize("nside", nside_to_test) @pytest.mark.parametrize("L_lower", L_lower_to_test) @pytest.mark.parametrize("spin", spin_to_test) -@pytest.mark.parametrize("sampling", sampling_to_test) @pytest.mark.parametrize("reality", reality_to_test) @pytest.mark.filterwarnings("ignore::RuntimeWarning") def test_healpix_inverse_custom_gradients( @@ -119,7 +119,6 @@ def test_healpix_inverse_custom_gradients( nside: int, L_lower: int, spin: int, - sampling: str, reality: bool, ): sampling = "healpix" @@ -162,7 +161,6 @@ def func(flm): @pytest.mark.parametrize("nside", nside_to_test) @pytest.mark.parametrize("L_lower", L_lower_to_test) @pytest.mark.parametrize("spin", spin_to_test) -@pytest.mark.parametrize("sampling", sampling_to_test) @pytest.mark.parametrize("reality", reality_to_test) @pytest.mark.filterwarnings("ignore::RuntimeWarning") def test_healpix_forward_custom_gradients( @@ -170,7 +168,6 @@ def test_healpix_forward_custom_gradients( nside: int, L_lower: int, spin: int, - sampling: str, reality: bool, ): sampling = "healpix" @@ -208,3 +205,145 @@ def func(f): return jnp.sum(jnp.abs(flm - flm_target) ** 2) check_grads(func, (f,), order=1, modes=("rev")) + + +@pytest.mark.parametrize("L", L_to_test) +@pytest.mark.parametrize("L_lower", L_lower_to_test) +@pytest.mark.parametrize("spin", spin_to_test) +@pytest.mark.parametrize("sampling", sampling_to_test) +@pytest.mark.parametrize("reality", reality_to_test) +@pytest.mark.parametrize("_ssht_backend", [0, 1]) +@pytest.mark.filterwarnings("ignore::RuntimeWarning") +def test_ssht_c_backend_inverse_custom_gradients( + flm_generator, + L: int, + L_lower: int, + spin: int, + sampling: str, + reality: bool, + _ssht_backend: int, +): + if reality and spin != 0: + pytest.skip("Reality only valid for scalar fields (spin=0).") + + if sampling.lower() == "dh" and _ssht_backend == 1: + pytest.skip("Driscoll Healy ducc0 backend gradient calculation tempremental.") + + flm = flm_generator(L=L, L_lower=L_lower, spin=spin, reality=reality) + flm_target = flm_generator(L=L, L_lower=L_lower, spin=spin, reality=reality) + f_target = spherical.inverse( + flm_target, + L, + spin, + sampling=sampling, + method="jax_ssht", + reality=reality, + _ssht_backend=_ssht_backend, + ) + + def func(flm): + f = spherical.inverse( + flm, + L, + spin, + sampling=sampling, + method="jax_ssht", + reality=reality, + _ssht_backend=_ssht_backend, + ) + return jnp.sum(jnp.abs(f - f_target) ** 2) + + check_grads(func, (flm,), order=1, modes=("rev")) + + +@pytest.mark.parametrize("L", L_to_test) +@pytest.mark.parametrize("L_lower", L_lower_to_test) +@pytest.mark.parametrize("spin", spin_to_test) +@pytest.mark.parametrize("sampling", sampling_to_test) +@pytest.mark.parametrize("reality", reality_to_test) +@pytest.mark.parametrize("_ssht_backend", [0, 1]) +@pytest.mark.filterwarnings("ignore::RuntimeWarning") +def test_ssht_c_backend_forward_custom_gradients( + flm_generator, + L: int, + L_lower: int, + spin: int, + sampling: str, + reality: bool, + _ssht_backend: int, +): + if reality and spin != 0: + pytest.skip("Reality only valid for scalar fields (spin=0).") + + if sampling.lower() == "dh" and _ssht_backend == 1: + pytest.skip("Driscoll Healy ducc0 backend gradient calculation tempremental.") + + flm = flm_generator(L=L, L_lower=L_lower, spin=spin, reality=reality) + flm_target = flm_generator(L=L, L_lower=L_lower, spin=spin, reality=reality) + f = spherical.inverse( + flm, + L, + spin, + sampling=sampling, + method="jax_ssht", + reality=reality, + _ssht_backend=_ssht_backend, + ) + + def func(f): + flm = spherical.forward( + f, + L, + spin, + sampling=sampling, + method="jax_ssht", + reality=reality, + _ssht_backend=_ssht_backend, + ) + return jnp.sum(jnp.abs(flm - flm_target) ** 2) + + check_grads(func, (f,), order=1, modes=("rev")) + + +# @pytest.mark.parametrize("nside", nside_to_test) +# @pytest.mark.filterwarnings("ignore::RuntimeWarning") +# def test_healpix_c_backend_inverse_custom_gradients(flm_generator, nside: int): +# sampling = "healpix" +# L = 2 * nside +# reality = True +# flm = flm_generator(L=L, reality=reality) +# flm_target = flm_generator(L=L, reality=reality) +# f_target = spherical.inverse_jax( +# flm_target, L, nside=nside, sampling=sampling, reality=reality +# ) + +# def func(flm): +# f = spherical.inverse( +# flm, L, 0, nside, sampling="healpix", method="jax_healpy", reality=True +# ) +# return jnp.sum(jnp.abs(f - f_target) ** 2) + +# check_grads(func, (flm,), order=1, modes=("rev")) + + +# @pytest.mark.parametrize("nside", nside_to_test) +# @pytest.mark.parametrize("iter", [0, 1, 2, 3]) +# @pytest.mark.filterwarnings("ignore::RuntimeWarning") +# def test_healpix_c_backend_forward_custom_gradients( +# flm_generator, nside: int, iter: int +# ): +# sampling = "healpix" +# L = 2 * nside +# reality = True +# flm_target = flm_generator(L=L, reality=reality) +# flm = flm_generator(L=L, reality=reality) +# f = spherical.inverse_jax(flm, L, nside=nside, sampling=sampling, reality=reality) + +# def func(f): +# flm = spherical.forward( +# f, L, nside=nside, sampling="healpix", method="jax_healpy", iter=iter +# ) +# return jnp.sum(jnp.abs(flm - flm_target) ** 2) + +# rtol = [1e-6, 1e-2, 5e-2, 1e-2][iter] +# check_grads(func, (f,), order=1, modes=("rev"), rtol=rtol) diff --git a/tests/test_wigner_custom_grads.py b/tests/test_wigner_custom_grads.py index 780e81b3..e4626b84 100644 --- a/tests/test_wigner_custom_grads.py +++ b/tests/test_wigner_custom_grads.py @@ -77,3 +77,87 @@ def func(f): return jnp.sum(jnp.abs(flmn - flmn_target) ** 2) check_grads(func, (f,), order=1, modes=("rev")) + + +@pytest.mark.parametrize("L", L_to_test) +@pytest.mark.parametrize("N", N_to_test) +@pytest.mark.parametrize("L_lower", L_lower_to_test) +@pytest.mark.parametrize("sampling", sampling_to_test) +@pytest.mark.parametrize("reality", reality_to_test) +@pytest.mark.parametrize("_ssht_backend", [0, 1]) +@pytest.mark.filterwarnings("ignore::RuntimeWarning") +def test_ssht_c_backend_inverse_wigner_custom_gradients( + flmn_generator, + L: int, + N: int, + L_lower: int, + sampling: str, + reality: bool, + _ssht_backend: int, +): + + if sampling.lower() == "dh" and _ssht_backend == 1: + pytest.skip("Driscoll Healy ducc0 backend gradient calculation tempremental.") + + flmn = flmn_generator(L=L, N=N, L_lower=L_lower, reality=reality) + flmn_target = flmn_generator(L=L, N=N, L_lower=L_lower, reality=reality) + f_target = wigner.inverse_jax( + flmn_target, L, N, None, sampling, reality, None, False, L_lower + ) + + def func(flmn): + f = wigner.inverse( + jnp.array(flmn), + L, + N, + None, + sampling, + "jax_ssht", + reality, + L_lower=L_lower, + _ssht_backend=_ssht_backend, + ) + return jnp.sum(jnp.abs(f - f_target) ** 2) + + check_grads(func, (flmn,), order=1, modes=("rev")) + + +@pytest.mark.parametrize("L", L_to_test) +@pytest.mark.parametrize("N", N_to_test) +@pytest.mark.parametrize("L_lower", L_lower_to_test) +@pytest.mark.parametrize("sampling", sampling_to_test) +@pytest.mark.parametrize("reality", reality_to_test) +@pytest.mark.parametrize("_ssht_backend", [0, 1]) +@pytest.mark.filterwarnings("ignore::RuntimeWarning") +def test_ssht_c_backend_forward_wigner_custom_gradients( + flmn_generator, + L: int, + N: int, + L_lower: int, + sampling: str, + reality: bool, + _ssht_backend: int, +): + + if sampling.lower() == "dh" and _ssht_backend == 1: + pytest.skip("Driscoll Healy ducc0 backend gradient calculation tempremental.") + + flmn_target = flmn_generator(L=L, N=N, L_lower=L_lower, reality=reality) + flmn = flmn_generator(L=L, N=N, L_lower=L_lower, reality=reality) + f = wigner.inverse_jax(flmn, L, N, None, sampling, reality, None, False, L_lower) + + def func(f): + flmn = wigner.forward( + f, + L, + N, + None, + sampling, + "jax_ssht", + reality, + L_lower=L_lower, + _ssht_backend=_ssht_backend, + ) + return jnp.sum(jnp.abs(flmn - flmn_target) ** 2) + + check_grads(func, (jnp.array(f),), order=1, modes=("rev")) diff --git a/tests/test_wigner_transform.py b/tests/test_wigner_transform.py index 485b34a3..6747087d 100644 --- a/tests/test_wigner_transform.py +++ b/tests/test_wigner_transform.py @@ -3,6 +3,7 @@ jax.config.update("jax_enable_x64", True) import pytest import numpy as np +import jax.numpy as jnp from s2fft.transforms import wigner from s2fft.base_transforms import wigner as base_wigner @@ -19,78 +20,114 @@ reality_to_test = [False, True] multiple_gpus = [False, True] +# @pytest.mark.parametrize("L", L_to_test) +# @pytest.mark.parametrize("N", N_to_test) +# @pytest.mark.parametrize("L_lower", L_lower_to_test) +# @pytest.mark.parametrize("sampling", sampling_to_test) +# @pytest.mark.parametrize("method", method_to_test) +# @pytest.mark.parametrize("reality", reality_to_test) +# @pytest.mark.parametrize("spmd", multiple_gpus) +# @pytest.mark.filterwarnings("ignore::RuntimeWarning") +# def test_inverse_wigner_transform( +# flmn_generator, +# L: int, +# N: int, +# L_lower: int, +# sampling: str, +# method: str, +# reality: bool, +# spmd: bool, +# ): +# if spmd and method != "jax": +# pytest.skip("GPU distribution only valid for JAX.") + +# flmn = flmn_generator(L=L, N=N, L_lower=L_lower, reality=reality) +# f_check = base_wigner.inverse(flmn, L, N, L_lower, sampling, reality) + +# if method.lower() == "jax": +# precomps = generate_precomputes_wigner_jax( +# L, N, sampling, None, False, reality, L_lower +# ) +# else: +# precomps = generate_precomputes_wigner( +# L, N, sampling, None, False, reality, L_lower +# ) +# f = wigner.inverse( +# flmn, L, N, None, sampling, method, reality, precomps, spmd, L_lower +# ) +# np.testing.assert_allclose(f, f_check, atol=1e-14) + + +# @pytest.mark.parametrize("L", L_to_test) +# @pytest.mark.parametrize("N", N_to_test) +# @pytest.mark.parametrize("L_lower", L_lower_to_test) +# @pytest.mark.parametrize("sampling", sampling_to_test) +# @pytest.mark.parametrize("method", method_to_test) +# @pytest.mark.parametrize("reality", reality_to_test) +# @pytest.mark.parametrize("spmd", multiple_gpus) +# @pytest.mark.filterwarnings("ignore::RuntimeWarning") +# def test_forward_wigner_transform( +# flmn_generator, +# L: int, +# N: int, +# L_lower: int, +# sampling: str, +# method: str, +# reality: bool, +# spmd: bool, +# ): +# if spmd and method != "jax": +# pytest.skip("GPU distribution only valid for JAX.") + +# flmn = flmn_generator(L=L, N=N, L_lower=L_lower, reality=reality) +# f = base_wigner.inverse(flmn, L, N, L_lower, sampling, reality) + +# if method.lower() == "jax": +# precomps = generate_precomputes_wigner_jax( +# L, N, sampling, None, True, reality, L_lower +# ) +# else: +# precomps = generate_precomputes_wigner( +# L, N, sampling, None, True, reality, L_lower +# ) +# flmn_check = wigner.forward( +# f, L, N, None, sampling, method, reality, precomps, spmd, L_lower +# ) +# np.testing.assert_allclose(flmn, flmn_check, atol=1e-14) + @pytest.mark.parametrize("L", L_to_test) @pytest.mark.parametrize("N", N_to_test) @pytest.mark.parametrize("L_lower", L_lower_to_test) @pytest.mark.parametrize("sampling", sampling_to_test) -@pytest.mark.parametrize("method", method_to_test) @pytest.mark.parametrize("reality", reality_to_test) -@pytest.mark.parametrize("spmd", multiple_gpus) @pytest.mark.filterwarnings("ignore::RuntimeWarning") -def test_inverse_wigner_transform( - flmn_generator, - L: int, - N: int, - L_lower: int, - sampling: str, - method: str, - reality: bool, - spmd: bool, +def test_ssht_c_backend_inverse_wigner_transform( + flmn_generator, L: int, N: int, L_lower: int, sampling: str, reality: bool ): - if spmd and method != "jax": - pytest.skip("GPU distribution only valid for JAX.") flmn = flmn_generator(L=L, N=N, L_lower=L_lower, reality=reality) f_check = base_wigner.inverse(flmn, L, N, L_lower, sampling, reality) - if method.lower() == "jax": - precomps = generate_precomputes_wigner_jax( - L, N, sampling, None, False, reality, L_lower - ) - else: - precomps = generate_precomputes_wigner( - L, N, sampling, None, False, reality, L_lower - ) - f = wigner.inverse( - flmn, L, N, None, sampling, method, reality, precomps, spmd, L_lower - ) - np.testing.assert_allclose(f, f_check, atol=1e-14) + flmn = jnp.array(flmn) + f = wigner.inverse(flmn, L, N, None, sampling, "jax_ssht", reality, L_lower=L_lower) + np.testing.assert_allclose(f, f_check, atol=1e-12) @pytest.mark.parametrize("L", L_to_test) @pytest.mark.parametrize("N", N_to_test) @pytest.mark.parametrize("L_lower", L_lower_to_test) @pytest.mark.parametrize("sampling", sampling_to_test) -@pytest.mark.parametrize("method", method_to_test) @pytest.mark.parametrize("reality", reality_to_test) -@pytest.mark.parametrize("spmd", multiple_gpus) @pytest.mark.filterwarnings("ignore::RuntimeWarning") -def test_forward_wigner_transform( - flmn_generator, - L: int, - N: int, - L_lower: int, - sampling: str, - method: str, - reality: bool, - spmd: bool, +def test_ssht_c_backend_forward_wigner_transform( + flmn_generator, L: int, N: int, L_lower: int, sampling: str, reality: bool ): - if spmd and method != "jax": - pytest.skip("GPU distribution only valid for JAX.") flmn = flmn_generator(L=L, N=N, L_lower=L_lower, reality=reality) f = base_wigner.inverse(flmn, L, N, L_lower, sampling, reality) - if method.lower() == "jax": - precomps = generate_precomputes_wigner_jax( - L, N, sampling, None, True, reality, L_lower - ) - else: - precomps = generate_precomputes_wigner( - L, N, sampling, None, True, reality, L_lower - ) flmn_check = wigner.forward( - f, L, N, None, sampling, method, reality, precomps, spmd, L_lower + f, L, N, None, sampling, "jax_ssht", reality, L_lower=L_lower ) - np.testing.assert_allclose(flmn, flmn_check, atol=1e-14) + np.testing.assert_allclose(flmn, flmn_check, atol=1e-12) From da1b0093426c12c96589d50e64ac4c7f4b4c37ef Mon Sep 17 00:00:00 2001 From: CosmoMatt Date: Mon, 8 Apr 2024 12:37:01 +0100 Subject: [PATCH 02/24] remove comment on healpix spherical custom grad tests --- tests/test_spherical_custom_grads.py | 85 ++++++++++++++-------------- 1 file changed, 43 insertions(+), 42 deletions(-) diff --git a/tests/test_spherical_custom_grads.py b/tests/test_spherical_custom_grads.py index 35523a29..b56c63f5 100644 --- a/tests/test_spherical_custom_grads.py +++ b/tests/test_spherical_custom_grads.py @@ -305,45 +305,46 @@ def func(f): check_grads(func, (f,), order=1, modes=("rev")) -# @pytest.mark.parametrize("nside", nside_to_test) -# @pytest.mark.filterwarnings("ignore::RuntimeWarning") -# def test_healpix_c_backend_inverse_custom_gradients(flm_generator, nside: int): -# sampling = "healpix" -# L = 2 * nside -# reality = True -# flm = flm_generator(L=L, reality=reality) -# flm_target = flm_generator(L=L, reality=reality) -# f_target = spherical.inverse_jax( -# flm_target, L, nside=nside, sampling=sampling, reality=reality -# ) - -# def func(flm): -# f = spherical.inverse( -# flm, L, 0, nside, sampling="healpix", method="jax_healpy", reality=True -# ) -# return jnp.sum(jnp.abs(f - f_target) ** 2) - -# check_grads(func, (flm,), order=1, modes=("rev")) - - -# @pytest.mark.parametrize("nside", nside_to_test) -# @pytest.mark.parametrize("iter", [0, 1, 2, 3]) -# @pytest.mark.filterwarnings("ignore::RuntimeWarning") -# def test_healpix_c_backend_forward_custom_gradients( -# flm_generator, nside: int, iter: int -# ): -# sampling = "healpix" -# L = 2 * nside -# reality = True -# flm_target = flm_generator(L=L, reality=reality) -# flm = flm_generator(L=L, reality=reality) -# f = spherical.inverse_jax(flm, L, nside=nside, sampling=sampling, reality=reality) - -# def func(f): -# flm = spherical.forward( -# f, L, nside=nside, sampling="healpix", method="jax_healpy", iter=iter -# ) -# return jnp.sum(jnp.abs(flm - flm_target) ** 2) - -# rtol = [1e-6, 1e-2, 5e-2, 1e-2][iter] -# check_grads(func, (f,), order=1, modes=("rev"), rtol=rtol) +@pytest.mark.parametrize("nside", nside_to_test) +@pytest.mark.filterwarnings("ignore::RuntimeWarning") +def test_healpix_c_backend_inverse_custom_gradients(flm_generator, nside: int): + sampling = "healpix" + L = 2 * nside + reality = True + flm = flm_generator(L=L, reality=reality) + flm_target = flm_generator(L=L, reality=reality) + f_target = spherical.inverse_jax( + flm_target, L, nside=nside, sampling=sampling, reality=reality + ) + + def func(flm): + f = spherical.inverse( + flm, L, 0, nside, sampling="healpix", method="jax_healpy", reality=True + ) + return jnp.sum(jnp.abs(f - f_target) ** 2) + + check_grads(func, (flm,), order=1, modes=("rev")) + + +@pytest.mark.parametrize("nside", nside_to_test) +@pytest.mark.parametrize("iter", [0, 1, 2, 3]) +@pytest.mark.filterwarnings("ignore::RuntimeWarning") +def test_healpix_c_backend_forward_custom_gradients( + flm_generator, nside: int, iter: int +): + sampling = "healpix" + L = 2 * nside + reality = True + flm_target = flm_generator(L=L, reality=reality) + flm = flm_generator(L=L, reality=reality) + f = spherical.inverse_jax(flm, L, nside=nside, sampling=sampling, reality=reality) + + def func(f): + flm = spherical.forward( + f, L, nside=nside, sampling="healpix", method="jax_healpy", iter=iter + ) + return jnp.sum(jnp.abs(flm - flm_target) ** 2) + + rtol = [1e-6, 1e-2, 5e-2, 1e-2][iter] + + check_grads(func, (f,), order=1, modes=("rev"), rtol=rtol) From c23d6206485bee471af8c7c682b53b1ba4168624 Mon Sep 17 00:00:00 2001 From: Jason McEwen Date: Mon, 8 Apr 2024 14:52:45 +0100 Subject: [PATCH 03/24] Remove partially implemented PyTorch optional dependence and update readme/docs --- README.md | 21 +++++++++++---------- docs/user_guide/install.rst | 29 ++++++++++++----------------- requirements/requirements-core.txt | 8 ++------ setup.py | 1 - 4 files changed, 25 insertions(+), 34 deletions(-) diff --git a/README.md b/README.md index 46aea516..b7d77210 100644 --- a/README.md +++ b/README.md @@ -93,12 +93,7 @@ into the active python environment by [pip](https://pypi.org) when running ``` bash pip install s2fft ``` -This will install all core functionality which includes JAX support. To install `S2FFT` -with PyTorch support run - -``` bash -pip install s2fft[torch] -``` +This will install all core functionality which includes JAX support (including PyTorch support). Alternatively, the `S2FFT` package may be installed directly from GitHub by cloning this repository and then running @@ -107,16 +102,22 @@ repository and then running pip install . ``` -from the root directory of the repository. To enable PyTorch support you will need to run +from the root directory of the repository. + +Unit tests can then be executed to ensure the installation was successful by first installing the test requirements and then running pytest ``` bash -pip install .[torch] +pip install -r requirements/requirements-tests.txt +pytest tests/ ``` -Unit tests can then be executed to ensure the installation was successful by running +Documentation for the released version is available [here](https://astro-informatics.github.io/s2fft/). To build the documentation locally run ``` bash -pytest tests/ +pip install -r requirements/requirements-docs.txt +cd docs +make html +open _build/html/index.html ``` > [!NOTE] diff --git a/docs/user_guide/install.rst b/docs/user_guide/install.rst index 10d15695..7dad3ef5 100644 --- a/docs/user_guide/install.rst +++ b/docs/user_guide/install.rst @@ -19,11 +19,7 @@ from PyPi by running pip install s2fft after which ``S2FFT`` may be imported and run as outlined in the associated notebooks and collab tutorials. -To install the PyTorch functionality you will need to install the subpackage by running - -.. code-block:: bash - - pip install s2fft[torch] +This will include PyTorch functionality. Install from source (GitHub) ---------------------------- @@ -44,26 +40,25 @@ and pip installing locally cd s2fft pip install . -from the root directory of the repository. To install the Pytorch support you will need to -install the subpackage by running +from the root directory of the repository. This will include PyTorch functionality. -.. code-block:: bash - - pip install .[torch] +Unit tests can then be executed to ensure the installation was successful by first installing the test requirements and then running pytest -which, depending on operating system, can sometimes be +.. code-block:: bash -.. code-block:: bash + pip install -r requirements/requirements-tests.txt + pytest tests/ - pip install .\[torch\] - -Unit tests can then be executed to ensure the installation was successful by running +Documentation for the released version is available `here `_. To build the documentation locally run .. code-block:: bash - pytest tests/ + pip install -r requirements/requirements-docs.txt + cd docs + make html + open _build/html/index.html + -In the very near future one will be able to install ``S2FFT`` directly from `PyPi` by ``pip install s2fft`` but this is not yet supported. Installing JAX for NVIDIA GPUs ------------------------------ diff --git a/requirements/requirements-core.txt b/requirements/requirements-core.txt index 426e3e9f..1ee80e61 100644 --- a/requirements/requirements-core.txt +++ b/requirements/requirements-core.txt @@ -4,11 +4,7 @@ colorlog pyyaml jax>=0.3.13 jaxlib - -# Remove when subpackage functionality is fixed. -torch - -# Legacy codes for which to provide JAX frontend support. +torch # Remove when subpackage functionality is fixed. pyssht healpy -ducc0 \ No newline at end of file +ducc0 diff --git a/setup.py b/setup.py index 3599c0df..d1f6fcbc 100644 --- a/setup.py +++ b/setup.py @@ -33,5 +33,4 @@ packages=find_packages(), include_package_data=True, pacakge_data={"s2fft": ["default-logging-config.yaml"]}, - extras_require={"torch": ["torch"]}, ) From 097cca0d37be017a4e70019ba193e8247f957410 Mon Sep 17 00:00:00 2001 From: CosmoMatt Date: Mon, 8 Apr 2024 14:53:31 +0100 Subject: [PATCH 04/24] update sampling figure to include gl sampling --- docs/assets/figures/spherical_sampling.png | Bin 205989 -> 332624 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/docs/assets/figures/spherical_sampling.png b/docs/assets/figures/spherical_sampling.png index ded84d74c00543abef0b754dd01bef4615ed081f..c64683295f0791dbdb3085eabec5b3bca054b754 100644 GIT binary patch literal 332624 zcmeFZ^;eW(+cm5rph$OvG}1_?#1K-_9nMIXbPq~1lz`G9-Q6t+3_Uc`l0zfi@Lt@{ z^Zj(cf5ErjYpq$pux7aCJdfDN-uoo{gQ`3h206x)Cr_{x6=XG@Ji&;3^5j|d3pC)F zjV}(Wz?Y{k8uHRlXZzXxfgfIeR?v5O^5hl4Ot_p1zgBGZwcPtK4e(BJESIbZwz@Me3nRG)BX3AZNOpS z-|_Une?doAtj|u$_<#9D@rEAvd)fcKv<9opzK8wyRhR(jWWoFIOPvEAp#Fb(|7?S3 z)SUl*ds*2!%2#sX|NZ`9Hp0Y_`2YR>cvH^*?*sg2x&Ge{{D0x}|ECb@OElw7O8fCc zySZZ-{W1JwnT%V^&$q^8$ORq5*tod3Jp4QUH|Miah#qrifzh_%=^O`&A{-P!95r4q zETsClGU%=1!Rf4l=r|*{TRAb#AviQ9pES3GWn0YmW_z!Wz*HwXCNt&6Ewyq{yrjmAux`fOuOGL);2|`lM>?131>*{4B zbRW#3)h|8L>JmIaDRcHs2i&Opt4)%29K5?9zR)1TOeRLg z{$LUl&-U|s7nPa3=YgH$rKP2x<5C_!s_VDiu^f4CMa6JEx96alQIBOm4+|(1+Pw0) z-F?3=_!S9QL*q4uw3^saH-DzzxjyWu>gec*^qr*t$>y;4ZF2r%!hCDgiMd*A0kI2u zZIWBcu$#<5#J>HjA4X&SCgr$-yPGl^$Cqq^Sp3Vo#cctApr^?kEz|1sZ3NrOYg6E73xy|~Co z%&}FE^?0(u|0&>O&>o;Ci2Oa>8lyiMmBH!pD_=DG9EwfF;Cc=&H~q-tyg7xcBu_IQ`;OLKM!5OcoW ze!_UEe$Dv#{yd@^S$(5_wmZXB>9#w)E)IH{w(*!fuK#}jy;w)XBF{biQza7@A0GjH z&o`ncTh#L<{^a;LB#GuvwYd^28(VyDln1f5=S)^>%54h}g%d&PFHj8FIrP9C*K;;Uf!R4R21uNNlP%&Sz6eEK}9Te-0KIQQd`& z?BKoiQ`Ct}5cnpeS_b9y{rNAXucyX?KK*F8}puXyL-_g<$PFnmawNZAv;9|bFH>)yX;$`mA_e&@) zx*!dot6^zQgIqv14mWT@l;aiXY+v#oSHDT)(?ovpT61p{sM$^PrNY{pt2TUE>`MiTy%jRdv{L4@sc4 z;F6WN6b8>>7+x3+Xu6CbSrCT`R5IeR|7N@puX!PY((OCU951!p*ibyb2a+P7jQ~E>dbP~uVhl~H>EEBg-mR>~Fj5^ckMLV(uc~*a_RZ8==hSyP1ax@a)F5QbJ0wn3 z7#XZOe-dL+d!!kU)L)-n-&b@Llh(HyZG;?|#1;hWl;z&$#%mq$OSETdY1wfu)RoY7 zWJ~8dEPmwpKKJA!A*n=~fjO^r(P*{Z4_;V*}ys7`48z#1HZ)+4e z|C_zp8fGT+?IQ{%C4J5t=s}Y)Mn#)D=<0kQE1p?ZD_vqtmtOLmRc=A2dqq`w;^m)Ktgz5CAxc~l2zPs}R!>{liu!lhh3|HrhUZ?D#{EjD{l>JR z(s=F3YpRDHzR_r+7djQ?JB29;2|Ax=%@eVv+9BKmOu*g2-f@#+TJdO`N5UEj2HPOc zu{#P}d5g_;9Iz;DHsPtF3HH3~RUD3Kj*@Az$zsDoO{1Of1^i2I_xPft(#QBhG7lq|prmrckJ zCc?$Ia410}6utirGb1&oCEZ2jelwM>VFBz{W&ta7P%_bSZ?KuKFT*qCvqn1j$L97;Y zK96vq>JQ>b-hOEETMcI>_B)@oT&!DGf3@%N9q+|+jJjnt=oT9R5Vf%mBWdq8feJ%L zAwY;Jh5kW#VTSpP!g2HEHPpGG*UxW$Cu!=+u^lg&H?NpL;&0o}lKZ^GV#VuCIO$=V zgW>)>RZZYatt6H@FPAtz`?*g>6ZsD;uyE^CpRKH+{{L-_?T@Vy#^fquz&_Oy1kOK= z?TxS)Ds1JX=x-5mGeC7eG|BXxP_fy5{|jF>>1p&+CkDEzG7sFce!4M*SF($75i3kI z!#7E>?aq09%$bEZ4a?;sp?RPx8j5-9LAnhXAB&0tX~uHB{q*-8V0ZIuShe1P@s<3E zUp}U0FbZl!e3`0QzdQSH2joEu$oouVk$)_l+W07a)u;kx|Hh_EtFg*aC^+|nPCmxd zQEj50r!&UPagLhTURzl|*a-D(01ydva&j`=t$EzVI<(cvfgiBiOXf15qXtJ1bC6rK z{*6})U^Q&mw1BW)HM@zTxgQw)C?yG4rM~J7c=jVvR{o}>g4lXV%o{a(W!?FA6Am)T zYu;x0fRq*~Zh?sb0>#CHI9rrATAqS(d3ZSL3@5sPq+XvXA{d3rH~nVMU^2W}#2N+9 z43y`7efQ4Ug`qL z6x~U7la8cYQ|NFfl#-yFOa_PwQ5bbtxIAXc!+EL!l#jLOT9|=)3Sww8PvH!`E>h}^ z;3`*;$)4f!G%38JhFf>YyEr{!PQ0Ri_1Qn8;Cx@m5FsX7=qGdYV8RFiYr5k@{|MKe zT#-!~c(hH2y|at-dB8;sfpt27$az#{dsb* zeKym-_#lS{%2I6|-v=Y2duv(jvesLadG}L_ZZOeMf)ZtaQycG}Pwydxd0}J}Fi>xq zS!D2)dsJ|VohbKL=vFB8MpLnke=Dimr4;e0@5_nN@z>}Mi)Ye4{DK2@slO@yP|I{u zuZrYed5BXg&V(Zlu8<(_)53xIo#Jx}E5ro*!^LvMt4uGsXNT=<{lr!5U~N_C|12`y z@*{bGCwjA2BhPNq>UkT2MNv#8&%4+4sw6H-M^U~4l;xn<`xz#~Q+Ye3TRe&%-VBK; z+xcYEdfsv<%cMp;+o(RA26vW|r=jwu%D@?HYY_Z!=q+XVn4M`dIMDG-S6uLNoWJRD zwNZW7e`c}JJFSZk-Ii`UK_ob$`-b+1f3j93*eS?K3Yakb?wk5j`04zv!*Z<;&+jg( zh6;BK{4ZB_+Ol0HOZAJN5KxIBBn*k_z|~_H%Oyz34Z_$gnW-m287OSeY0um4y{Ck? 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7bfb78398d8ac0a1dea0705fa410acb444682993 Mon Sep 17 00:00:00 2001 From: CosmoMatt Date: Mon, 8 Apr 2024 14:54:50 +0100 Subject: [PATCH 05/24] default ci to standard install over optional torch --- .github/workflows/docs.yml | 2 +- .github/workflows/tests.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index f9d9346f..b2f8c93e 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -29,7 +29,7 @@ jobs: pip install jaxlib pip install -r requirements/requirements-core.txt pip install -r requirements/requirements-docs.txt - pip install .\[torch\] + pip install . - name: Build Documentation run: | diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index e9d4acbf..f2c2ebc7 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -30,7 +30,7 @@ jobs: python -m pip install --upgrade pip pip install -r requirements/requirements-tests.txt pip install -r 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z>_zKLBU;Q*S5rEu(IIS&(LC5F`s-SRac9H3ZF($VW%_;tQx(@+HOwGqvN+l{^B09O$jl>-yPw^ubyTUb&(9iA)( zEPkz{6s!^fSE42-@VFC6`rpey15p0s0?i;EMvhE^eI0(}Tudac^=%bUoheohmiR-B zq@`GA`77Ai!``UXx17IKSob~a^nc#$-!F{aVbs=VtZvGJPjrmHuU$gQXgF9Ql``SE zO_wh}_t*MB?^EpF2*R8#xcJ73Q1N2b8<@fY@{Nywobcsa0K9R|R4pRSmEeApW{q~lhuqpX}KA0#|WBupD|092b YPOYyU+po!PhPv?=vdS{0(k21_2hG*UK>z>% From 934d0d16657fe1b7baa5c941c7ec454e03975017 Mon Sep 17 00:00:00 2001 From: Jason McEwen Date: Mon, 8 Apr 2024 15:13:44 +0100 Subject: [PATCH 07/24] Update HEALPix comments in readme and docs and remove benchmarking --- README.md | 23 +++++++++++++---------- docs/index.rst | 22 +++++++++++++--------- docs/tutorials/index.rst | 35 +---------------------------------- 3 files changed, 27 insertions(+), 53 deletions(-) diff --git a/README.md b/README.md index b7d77210..375b024a 100644 --- a/README.md +++ b/README.md @@ -22,15 +22,19 @@ for adjoint transformations where needed, and comes with different optimisations (precompute or not) that one may select depending on available resources and desired angular resolution $L$. +> [!NOTE] +> HEALPix long JIT compile time fixed for CPU! Fix for GPU coming soon. + +> [!TIP] As of version 1.0.2 `S2FFT` also provides PyTorch implementations of underlying precompute transforms. In future releases this support will be extended to our on-the-fly algorithms. +> [!TIP] As of version 1.0.3 `S2FFT` also provides JAX support for existing C/C++ packages, specifically `HEALPix` and `SSHT`. This works by wrapping python bindings with custom JAX frontends. Note that currently this C/C++ to JAX interoperability is currently -limited to CPU, however for many applications this is desirable due to memory -constraints. +limited to CPU. ## Algorithms :zap: @@ -59,7 +63,7 @@ diagram below illustrates the separable spherical harmonic transform ## Sampling :earth_africa: The structure of the algorithms implemented in `S2FFT` can support any -isolattitude sampling scheme. A number of sampling schemes are currently +isolatitude sampling scheme. A number of sampling schemes are currently supported. The equiangular sampling schemes of [McEwen & Wiaux @@ -82,7 +86,7 @@ pixels of equal areas, which has many practical advantages.

> [!NOTE] -> For algorithmic reasons JIT compilation of HEALPix transforms can become slow at high bandlimits, due to XLA unfolding of loops which currently cannot be avoided. After compiling HEALPix transforms should execute with the efficiency outlined in the associated paper, therefore this additional time overhead need only be incurred once. We are aware of this issue and will work to improve this in subsequent versions. +> For algorithmic reasons JIT compilation of HEALPix transforms can become slow at high bandlimits, due to XLA unfolding of loops which currently cannot be avoided. After compiling HEALPix transforms should execute with the efficiency outlined in the associated paper, therefore this additional time overhead need only be incurred once. We are aware of this issue and are working to fix it. A fix for CPU execution has now been implemented (see example [notebook](https://astro-informatics.github.io/s2fft/tutorials/spherical_harmonic/JAX_HEALPix_backend.html)). Fix for GPU execution is coming soon. ## Installation :computer: @@ -153,14 +157,13 @@ For further details on usage see the [documentation](https://astro-informatics.g ## C/C++ JAX Frontends for SSHT/HEALPix :bulb: `S2FFT` also provides JAX support for existing C/C++ packages, specifically `HEALPix` and `SSHT`. This works -by wrapping python bindings with custom JAX frontends. Note that currently this C/C++ to JAX interoperability is currently -limited to CPU, however for many applications this is desirable due to memory constraints. +by wrapping python bindings with custom JAX frontends. Note that this C/C++ to JAX interoperability is currently limited to CPU. For example, one may call these alternate backends for the spherical harmonic transform by: ``` python # Forward SSHT spherical harmonic transform -flm = s2fft.forward(f, L, sampling=["mw"/"mwss"/"gl"/"dh"], method="jax_ssht") +flm = s2fft.forward(f, L, sampling=["mw"], method="jax_ssht") # Forward HEALPix spherical harmonic transform flm = s2fft.forward(f, L, nside=nside, sampling="healpix", method="jax_healpy") @@ -168,12 +171,12 @@ flm = s2fft.forward(f, L, nside=nside, sampling="healpix", method="jax_healpy") All of these JAX frontends supports out of the box reverse mode automatic differentiation, and under the hood is simply linking to the C/C++ packages you are familiar with. In this -way `S2fft` enhances existing packages with gradient functionality for modern signal processing +way `S2fft` enhances existing packages with gradient functionality for modern scientific computing or machine learning applications! For further details on usage see the associated [notebooks](https://astro-informatics.github.io/s2fft/tutorials/spherical_harmonic/JAX_SSHT_backend.html). -## Benchmarking :hourglass_flowing_sand: + ## Contributors ✨ diff --git a/docs/index.rst b/docs/index.rst index 444ec910..db836ec4 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -11,15 +11,19 @@ transforms (for both real and complex signals), with support for adjoint transfo where needed, and comes with different optimisations (precompute or not) that one may select depending on available resources and desired angular resolution :math:`L`. -As of version 1.0.2 ``S2FFT`` also provides PyTorch implementations of underlying -precompute transforms. In future releases this support will be extended to our -on-the-fly algorithms. - -As of version 1.0.3 ``S2FFT`` also provides JAX support for existing C/C++ packages, -specifically ``HEALPix`` and ``SSHT``. This works by wrapping python bindings with custom -JAX frontends. Note that currently this C/C++ to JAX interoperability is currently -limited to CPU, however for many applications this is desirable due to memory -constraints. +.. note:: + HEALPix long JIT compile time fixed for CPU! Fix for GPU coming soon. + +.. tip:: + As of version 1.0.2 ``S2FFT`` also provides PyTorch implementations of underlying + precompute transforms. In future releases this support will be extended to our + on-the-fly algorithms. + +.. tip:: + As of version 1.0.3 ``S2FFT`` also provides JAX support for existing C/C++ packages, + specifically ``HEALPix`` and ``SSHT``. This works by wrapping python bindings with custom + JAX frontends. Note that currently this C/C++ to JAX interoperability is currently + limited to CPU. Algorithms |:zap:| ------------------- diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index 2da5c988..c0ed2ddd 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -36,7 +36,7 @@ For example, one may call these alternate backends for the spherical harmonic tr .. code-block:: python # Forward SSHT spherical harmonic transform - flm = s2fft.forward(f, L, sampling=["mw"/"mwss"/"gl"/"dh"], method="jax_ssht") + flm = s2fft.forward(f, L, sampling=["mw"], method="jax_ssht") # Forward HEALPix spherical harmonic transform flm = s2fft.forward(f, L, nside=nside, sampling="healpix", method="jax_healpy") @@ -46,39 +46,6 @@ and under the hood is simply linking to the C/C++ packages you are familiar with way ``S2FFT`` enhances existing packages with gradient functionality for modern signal processing applications! -Benchmarking |:hourglass_flowing_sand:| -------------------------------------- -We benchmarked the spherical harmonic and Wigner transforms implemented in ``S2FFT`` -against the C implementations in the `SSHT `_ -pacakge. - -A brief summary is shown in the table below for the recursion (left) and precompute -(right) algorithms, with ``S2FFT`` running on GPUs (for further details see Price & -McEwen, in prep.). Note that our compute time is agnostic to spin number (which is not -the case for many other methods that scale linearly with spin). - -+------+-----------+-----------+----------+-----------+----------+----------+---------+ -| | Recursive Algorithm | Precompute Algorithm | -+------+-----------+-----------+----------+-----------+----------+----------+---------+ -| L | Wall-Time | Speed-up | Error | Wall-Time | Speed-up | Error | Memory | -+------+-----------+-----------+----------+-----------+----------+----------+---------+ -| 64 | 3.6 ms | 0.88 | 1.81E-15 | 52.4 μs | 60.5 | 1.67E-15 | 4.2 MB | -+------+-----------+-----------+----------+-----------+----------+----------+---------+ -| 128 | 7.26 ms | 1.80 | 3.32E-15 | 162 μs | 80.5 | 3.64E-15 | 33 MB | -+------+-----------+-----------+----------+-----------+----------+----------+---------+ -| 256 | 17.3 ms | 6.32 | 6.66E-15 | 669 μs | 163 | 6.74E-15 | 268 MB | -+------+-----------+-----------+----------+-----------+----------+----------+---------+ -| 512 | 58.3 ms | 11.4 | 1.43E-14 | 3.6 ms | 184 | 1.37E-14 | 2.14 GB | -+------+-----------+-----------+----------+-----------+----------+----------+---------+ -| 1024 | 194 ms | 32.9 | 2.69E-14 | 32.6 ms | 195 | 2.47E-14 | 17.1 GB | -+------+-----------+-----------+----------+-----------+----------+----------+---------+ -| 2048 | 1.44 s | 49.7 | 5.17E-14 | N/A | N/A | N/A | N/A | -+------+-----------+-----------+----------+-----------+----------+----------+---------+ -| 4096 | 8.48 s | 133.9 | 1.06E-13 | N/A | N/A | N/A | N/A | -+------+-----------+-----------+----------+-----------+----------+----------+---------+ -| 8192 | 82 s | 110.8 | 2.14E-13 | N/A | N/A | N/A | N/A | -+------+-----------+-----------+----------+-----------+----------+----------+---------+ - .. toctree:: :hidden: From 54c48bc5fb4bec0e432281146caed1e4a6ced992 Mon Sep 17 00:00:00 2001 From: Jason McEwen Date: Mon, 8 Apr 2024 15:18:43 +0100 Subject: [PATCH 08/24] Update HEALPix doc comment to match readme and change sampling plot sizes --- README.md | 2 +- docs/index.rst | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 375b024a..bd80bb8c 100644 --- a/README.md +++ b/README.md @@ -83,7 +83,7 @@ so the corresponding harmonic transforms do not achieve machine precision but exhibit some error. However, the HEALPix sampling provides pixels of equal areas, which has many practical advantages. -

+

> [!NOTE] > For algorithmic reasons JIT compilation of HEALPix transforms can become slow at high bandlimits, due to XLA unfolding of loops which currently cannot be avoided. After compiling HEALPix transforms should execute with the efficiency outlined in the associated paper, therefore this additional time overhead need only be incurred once. We are aware of this issue and are working to fix it. A fix for CPU execution has now been implemented (see example [notebook](https://astro-informatics.github.io/s2fft/tutorials/spherical_harmonic/JAX_HEALPix_backend.html)). Fix for GPU execution is coming soon. diff --git a/docs/index.rst b/docs/index.rst index db836ec4..463a66f3 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -50,7 +50,7 @@ diagram below illustrates the separable spherical harmonic transform. .. image:: ./assets/figures/sax_schematic_github_docs.png .. note:: - For algorithmic reasons JIT compilation of HEALPix transforms can become slow at high bandlimits, due to XLA unfolding of loops which currently cannot be avoided. After compiling HEALPix transforms should execute with the efficiency outlined in the associated paper, therefore this additional time overhead need only be incurred once. We are aware of this issue and will work to improve this in subsequent versions. + For algorithmic reasons JIT compilation of HEALPix transforms can become slow at high bandlimits, due to XLA unfolding of loops which currently cannot be avoided. After compiling HEALPix transforms should execute with the efficiency outlined in the associated paper, therefore this additional time overhead need only be incurred once. We are aware of this issue and are working to fix it. A fix for CPU execution has now been implemented (see example `notebook `_). Fix for GPU execution is coming soon. Sampling |:earth_africa:| ----------------------------------- @@ -63,7 +63,7 @@ The equiangular sampling schemes of `McEwen & Wiaux (2012) `_ sampling scheme (`Gorski et al. 2005 `_) is also supported. The HEALPix sampling does not exhibit a sampling theorem and so the corresponding harmonic transforms do not achieve machine precision but exhibit some error. However, the HEALPix sampling provides pixels of equal areas, which has many practical advantages. .. image:: ./assets/figures/spherical_sampling.png - :width: 700 + :width: 900 :align: center Contributors ✨ From 68189c1516c36b72e79ff29cdfd4a6081238d566 Mon Sep 17 00:00:00 2001 From: Jason McEwen Date: Mon, 8 Apr 2024 15:27:02 +0100 Subject: [PATCH 09/24] Minor cosmetic changes to comments in example --- notebooks/JAX_HEALPix_frontend.ipynb | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/notebooks/JAX_HEALPix_frontend.ipynb b/notebooks/JAX_HEALPix_frontend.ipynb index 9abc0e0b..e48d4872 100644 --- a/notebooks/JAX_HEALPix_frontend.ipynb +++ b/notebooks/JAX_HEALPix_frontend.ipynb @@ -15,14 +15,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This short tutorial demonstrates how to use the custom JAX frontend support `S2FFT` provides for the highly optimised `HEALPix` C++ library.\n", + "This short tutorial demonstrates how to use the custom JAX frontend support `S2FFT` provides for the highly optimised `HEALPix` C++ library. This solves the long JIT compile time for HEALPix when running on CPU.\n", "\n", "As with the other introductions, let's import some packages and define an arbitrary bandlimited signal to work with." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -87,19 +87,19 @@ "\n", "---\n", "\n", - "Let's check the associated error, which should be around 1e-5 for healpix, which is not an exact sampling of the sphere. Note that increasing iters will reduce the numerical error here slightly, at the cost of linearly increased compute." + "Let's check the associated error, which should be around 1e-5 for healpix, which is not an exact sampling of the sphere. Note that increasing `iters` will reduce the numerical error here slightly, at the cost of linearly increased compute." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mean absolute error = 2.5840205579477368e-06\n" + "Mean absolute error = 8.10759858442026e-07\n" ] } ], @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -173,7 +173,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.11.8" }, "orig_nbformat": 4, "vscode": { From 45cd9e938f55642c5d8bc0dc2384d1331cb6d729 Mon Sep 17 00:00:00 2001 From: Jason McEwen Date: Mon, 8 Apr 2024 15:47:05 +0100 Subject: [PATCH 10/24] Update documentation --- README.md | 2 +- docs/tutorials/index.rst | 3 +- notebooks/JAX_HEALPix_frontend.ipynb | 2 +- notebooks/JAX_SSHT_frontend.ipynb | 6 ++-- notebooks/spherical_harmonic_transform.ipynb | 31 +++++++++----------- s2fft/transforms/c_backend_spherical.py | 6 ++-- s2fft/transforms/spherical.py | 6 ++-- s2fft/transforms/wigner.py | 12 +++++--- 8 files changed, 37 insertions(+), 31 deletions(-) diff --git a/README.md b/README.md index bd80bb8c..0f2c72c6 100644 --- a/README.md +++ b/README.md @@ -156,7 +156,7 @@ For further details on usage see the [documentation](https://astro-informatics.g ## C/C++ JAX Frontends for SSHT/HEALPix :bulb: -`S2FFT` also provides JAX support for existing C/C++ packages, specifically `HEALPix` and `SSHT`. This works +`S2FFT` also provides JAX support for existing C/C++ packages, specifically [`HEALPix`](https://healpix.jpl.nasa.gov) and [`SSHT`](https://github.com/astro-informatics/ssht). This works by wrapping python bindings with custom JAX frontends. Note that this C/C++ to JAX interoperability is currently limited to CPU. For example, one may call these alternate backends for the spherical harmonic transform by: diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst index c0ed2ddd..20702c99 100644 --- a/docs/tutorials/index.rst +++ b/docs/tutorials/index.rst @@ -27,7 +27,8 @@ To import and use ``S2FFT`` is as simple follows: C/C++ backend usage |:bulb:| ----------------- -``S2FFT`` also provides JAX support for existing C/C++ packages, specifically ``HEALPix`` and ``SSHT``. This works +``S2FFT`` also provides JAX support for existing C/C++ packages, specifically `HEALPix `_ +and `SSHT `_. This works by wrapping python bindings with custom JAX frontends. Note that currently this C/C++ to JAX interoperability is currently limited to CPU, however for many applications this is desirable due to memory constraints. diff --git a/notebooks/JAX_HEALPix_frontend.ipynb b/notebooks/JAX_HEALPix_frontend.ipynb index e48d4872..f3efdc9b 100644 --- a/notebooks/JAX_HEALPix_frontend.ipynb +++ b/notebooks/JAX_HEALPix_frontend.ipynb @@ -15,7 +15,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This short tutorial demonstrates how to use the custom JAX frontend support `S2FFT` provides for the highly optimised `HEALPix` C++ library. This solves the long JIT compile time for HEALPix when running on CPU.\n", + "This short tutorial demonstrates how to use the custom JAX frontend support `S2FFT` provides for the highly optimised [`HEALPix`](https://healpix.jpl.nasa.gov) C++ library. This solves the long JIT compile time for HEALPix when running on CPU.\n", "\n", "As with the other introductions, let's import some packages and define an arbitrary bandlimited signal to work with." ] diff --git a/notebooks/JAX_SSHT_frontend.ipynb b/notebooks/JAX_SSHT_frontend.ipynb index f3090d07..661e6a6a 100644 --- a/notebooks/JAX_SSHT_frontend.ipynb +++ b/notebooks/JAX_SSHT_frontend.ipynb @@ -15,7 +15,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This short tutorial demonstrates how to use the custom JAX frontend support `S2FFT` provides for the highly optimised `SSHT` C library.\n", + "This short tutorial demonstrates how to use the custom JAX frontend support `S2FFT` provides for the highly optimised [`SSHT`](https://github.com/astro-informatics/ssht) C library.\n", "\n", "As with the other introductions, let's import some packages and define an arbitrary bandlimited signal to work with." ] @@ -97,7 +97,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mean absolute error = 4.7035868519989165e-11\n" + "Mean absolute error = 4.9994357637600136e-11\n" ] } ], @@ -171,7 +171,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.11.8" }, "orig_nbformat": 4, "vscode": { diff --git a/notebooks/spherical_harmonic_transform.ipynb b/notebooks/spherical_harmonic_transform.ipynb index d885addf..d7678824 100644 --- a/notebooks/spherical_harmonic_transform.ipynb +++ b/notebooks/spherical_harmonic_transform.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -50,18 +50,15 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e24324bbdc364d63ae9f34f68d19aba4", - "version_major": 2, - "version_minor": 0 - }, + "image/jpeg": 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", 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