From 2e4a78558c41163002fb4e3ac59c950f46999bc8 Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Mon, 16 Dec 2024 23:02:27 -0500 Subject: [PATCH 01/22] [BLOG] Add libsf_error_state: SciPy's first shared library --- apps/labs/posts/libsf-error-state.md | 702 ++++++++++++++++++ .../posts/libsf-error-state/featured.png | Bin 0 -> 473511 bytes .../public/posts/libsf-error-state/hero.png | Bin 0 -> 699483 bytes .../libsf-error-state/n_sphere_volume.png | Bin 0 -> 22158 bytes 4 files changed, 702 insertions(+) create mode 100644 apps/labs/posts/libsf-error-state.md create mode 100644 apps/labs/public/posts/libsf-error-state/featured.png create mode 100644 apps/labs/public/posts/libsf-error-state/hero.png create mode 100644 apps/labs/public/posts/libsf-error-state/n_sphere_volume.png diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md new file mode 100644 index 000000000..7f8859a7b --- /dev/null +++ b/apps/labs/posts/libsf-error-state.md @@ -0,0 +1,702 @@ +--- +title: "libsf_error_state: SciPy's first shared library" +authors: [albert-steppi] +published: December 15, 2024 +description: The story of the first shared library to make it into the world of low level code that lies beneath SciPy's surface. +category: [PyData ecosystem] +featuredImage: + src: /posts/libsf-error-state/featured.png + alt: The text "SciPy" along with the SciPy logo superimposed over a computer generated image of a circuitboard. +hero: + imageSrc: /posts/libsf-error-state/hero.png + imageAlt: The text "SciPy" along with the SciPy logo superimposed over a computer generated image of a circuitboard. +--- + +## Introduction + +This is the story of the first[^1] shared library to be shipped within SciPy. It offers a glimpse at some of the +complexity SciPy tries to hide from users, while previewing some exciting developments in +[`scipy.special`](https://docs.scipy.org/doc/scipy/reference/special.html). + +Python has become wildly popular as a scripting language for scientific and other data intensive applications. It owes +much of its success to an exemplary execution of the "compiled core, interpreted shell" principle. One can orchestrate +simulation pipelines, preprocess and munge data, set up and use explanatory or predictive statistical models, make plots +and tables, and more, in an expressive high level language—while delegating computationally intensive tasks to compiled +libraries. + +The default CPython interpreter was designed specifically to make it easy to extend Python programs with efficient +native code. The Python C API is well thought out and straightforward to work with. The controversial global interpreter +lock or GIL, a barrier to free threading within Python, has the benefit of making it much easier to call out to native +code, since one need not worry about the thread-safety of wrapped compiled libraries. + +By wrapping, filling in gaps, and providing user friendly Python APIs to a wide range of battle tested, permissively +licensed and public domain scientific software tools—SciPy's founding developers [] were able to kickstart the growth of +the Scientific Python ecosystem. There are now worlds of scientific and data intensive software available in +Python. Users who spend their time in the interpreted shell may not be aware of the complexity that lies +underneath. Journeying deeper into the stack, it can be surprising to see the level of work that can go into making even +a relatively simple feature work smoothly. + +## NumPy Universal functions + +A NumPy [`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) represents an arbitrary +dimensional array of elements of the same data type, stored in a continguous buffer at the machine level. A [universal +function](https://numpy.org/doc/stable/reference/ufuncs.html) or ufunc for short is a Python level function which +applies a transform to each element of an `ndarray` by looping and operating elementwise over the underlying contiguous +buffer in efficient native code. + +Here's the ufunc [`np.sqrt`](https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html) in action + +``` +In [1]: import numpy as np + +In [2]: A = np.arange(1, 11, dtype=float) + +In [3]: A +Out [3]: array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]) + +In [4]: np.sqrt(A) +Out [4]: +array([1. , 1.41421356, 1.73205081, 2. , 2.23606798, + 2.44948974, 2.64575131, 2.82842712, 3. , 3.16227766]) + +``` + +For large arrays, the speedup from by applying `np.sqrt` over an `ndarray` rather than +[`math.sqrt`](https://docs.python.org/3/library/math.html#math.sqrt) over each element of a list is substantial. On my +laptop I witnessed: + +``` +In [1]: import numpy as np + +In [2]: A = np.arange(1, 1000000, dtype=float) + +In [3]: %timeit np.sqrt(A) +1.35 ms ± 49 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each) + +In [4]: B = [float(t) for t in range(1, 1000000)] + +In [5]: %timeit [math.sqrt(t) for t in B] +103 ms ± 1.18 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) +``` + +## Error handling + +What should happen if one gives invalid input to a ufunc? If we pass a negative +[`float`](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex) to `math.sqrt` a +[`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) is raised []. + +``` +In [1]: import math + +In [2]: math.sqrt(-1.0) +ValueError Traceback (most recent call last) +Cell In[2], line 1p +----> 1 math.sqrt(-1.0) + +ValueError: math domain error +``` + +What if one applies a ufunc over a large array and only a small number of inputs are invalid? Would it be reasonable to +raise a `ValueError` even though almost every calculation succeeded and produced a useful result? Fortunately, that's +not what happens: + +``` +In [1]: import numpy as np + +In [2]: A = np.arange(-1, 1000, dtype=float) + +In [3]: np.sqrt(A) +:1: RuntimeWarning: invalid value encountered in sqrt + np.sqrt(A) +Out[3]: +array([ nan, 0. , 1. , ..., 31.57530681, + 31.591138 , 31.60696126]) +``` + +Instead a warning is raised and `-1.0` is mapped to [`NaN`](https://en.wikipedia.org/wiki/NaN), a special floating point +number representing an undefined result. `NaN`s propagate sanely through most calculations [], making them useful in +such situations. + +What if we want to suppress the warning? Perhaps we're applying a ufunc within an inner loop and getting buried in +unhelpful warning output. For such situations, NumPy provides an API for controlling [floating point error +handling](https://numpy.org/doc/stable/reference/routines.err.html). Here's the +[`np.errstate`](https://numpy.org/doc/stable/reference/generated/numpy.errstate.html#numpy.errstate) context manager in +action: + +``` +In [1]: import numpy as np + +In [2]: A = np.arange(-1, 1000, dtype=float) + +In [3]: with np.errstate(invalid="ignore"): + ...: B = np.sqrt(A) + ...: + +In [4]: B + +Out [5]: +array([ nan, 0. , 1. , ..., 31.57530681, + 31.591138 , 31.60696126] +``` + +What if we genuinely want to raise if any kind of floating point error occurs? Perhaps negative inputs imply a sensor +failure in a latency insensitive [] robot which will ping its handlers upon an exception and hibernate until it can be +physically recovered. + +`np.errstate` has us covered here too: + +``` +In [1]: import numpy as np + +In [2]: A = np.arange(-1, 1000, dtype=float) + +In [3]: with np.errstate(all="raise"): + ...: B = np.sqrt(A) + ...: +--------------------------------------------------------------------------- +FloatingPointError Traceback (most recent call last) +Cell In[3], line 2 + 1 with np.errstate(all="raise"): +----> 2 B = np.sqrt(A) + +FloatingPointError: invalid value encountered in sqrt + +``` + +## scipy.special + +NumPy has over 60 [ufuncs available](https://numpy.org/doc/stable/reference/ufuncs.html#available-ufuncs) for a range of +mathematical functions and operations, but more specialized functions useful in the sciences and engineering are out of +scope. Ufuncs for over 230 such functions can be found instead in +[`scipy.special`](https://docs.scipy.org/doc/scipy/reference/special.html) []. + +Just for fun, let's use +[`scipy.special.gamma`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gamma.html) to reproduce a +plot from the Wikipedia article ["_Volume of an n-ball_"](https://en.wikipedia.org/wiki/Volume_of_an_n-ball). The plot +shows how the volume of a solid multi-dimensional sphere depends on the dimension `n` when the radius `R` is one of 0.9, +1.0, or 1.1. + +```python +import matplotlib.pyplot as plt +import numpy as np +import scipy.special as special + +N = np.arange(0, 26) # dimension +R = np.array([0.9, 1.0, 1.1]) # radius +# meshgrid makes it convenient to evaluate formula over the grid N x R +N, R = np.meshgrid(N, R) + +# Formula for volume of an n-ball in terms of radius R and dimension N. +V = np.pi**(N/2)/special.gamma(N/2 + 1) * R**N + +fig, ax = plt.subplots(1, 1) +ax.scatter(N[0], V[0], color='cadetblue', alpha=0.6, label="$R = 0.9$") +ax.scatter(N[1], V[1], color='indianred', alpha=0.6, label="$R = 1.0$") +ax.scatter(N[2], V[2], color='darkseagreen', alpha=0.6, label="$R = 1.1$") +ax.set_xlim(0, 25) +ax.set_ylim(0, 10) +ax.set_xticks([5, 10, 15, 20, 25]) +ax.set_yticks([2, 4, 6, 8, 10]) +ax.grid() +ax.legend() +plt.show() +``` + +which outputs: + +

+ Plot showing how the volume of an n-ball depends on the + dimension n for radius R = 0.9, 1.0, and 1.1 +

+ +## scipy.special error handling + +What if one of the ufuncs in `scipy.special` recieves an array with some invalid elements? The [Gamma +function](https://en.wikipedia.org/wiki/Gamma_function) used above has singularities at non-positive +integers. + +``` +In [1]: import numpy as np + +In [2]: import scipy.special as special + +In [3]: x = np.linspace(-4, 4, 5) + +In [4]: special.gamma(x) + +Out [4]: array([nan, nan, inf, 1., 6.]) + +In [5]: with np.errstate(all="raise"): + ...: _ = special.gamma(x) + ...: + +In [6]: + +``` + +`np.errstate` had no impact on error handling, there were no warnings, but we do see `NaN`s in the output []. What's +going on? Well, NumPy's ufuncs are provided in a C [extension +module](https://docs.python.org/3/extending/extending.html) that's part of NumPy. There's some C code in this extension +module for managing the state for error handling policies. The ufuncs in `scipy.special` come from a different extension +module in SciPy. Extension modules from separate projects are like separate worlds, and cannot communicate with one +another except through their Python interfaces []. SciPy instead has its own context manager: +[`scipy.special.errstate`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.errstate.html) that +mirrors `np.errstate`. + +``` +In [1]: import numpy as np + +In [2]: import scipy.special as special + +In [3]: x = np.linspace(-4, 4, 5) + +In [4]: with special.errstate(all="raise"): + ...: _ = special.gamma(x) + ...: +--------------------------------------------------------------------------- +SpecialFunctionError Traceback (most recent call last) +Cell In[4], line 2 + 1 with special.errstate(all="raise"): +----> 2 _ = special.gamma(x) + +SpecialFunctionError: scipy.special/Gamma: singularity +``` + +## Some exciting developments + +To create a ufunc for a mathematical function, one needs a scalar implementation of this function written in a compiled +language, known as a scalar kernel. Until recently, `scipy.special` had scalar kernels written in all of C, C++, Fortran +77, and Cython. In August of 2023, Irwin Zaid (izaid) at Oxford proposed rewriting all of the +scalar kernels in C++ header files in such a way that they could be included in both C++ and CUDA programs. This would +allow these scalar kernels to also be used in GPU-aware array libraries like CuPy and PyTorch, improving special +function support across array library backends. I've been supported by the 2020 NASA ROSES grant, _Reinforcing the +Foundations of Scientific Python_ (with travel and compute costs covered by the 2023 NumFocus SDG _Streamlined Special +Function Development in SciPy_), to work together with Irwin to put this plan into action. With additional help from +SciPy maintainer Ilhan Polat (ilayn), who made a heroic effort to translate over twenty thousand lines of Fortran scalar +kernel code to C, and contributions from other volunteers, substantial progress has been made, and we are in the process +of splitting these headed into a separate library called [xsf](https://github.com/scipy/xsf/). This is a story for +another time. Until then, for more info, see [xsf/#1](https://github.com/scipy/xsf/issues/1). + +First of all, while working on this project, it became apparent to everyone involved that the infrastructure used in +SciPy for creating ufuncs was greatly complicated by the need to work with scalar kernels from so many +languages. Standardizing on C++ offered a chance to simplify things considerably. + +In the Spring of 2024, things moved quickly because Irwin was able to put in some time working on SciPy to help get +things off the ground. He found that the existing ufunc infrastructure was not flexible enough for work he had planned +involving [Generalized universal functions](https://numpy.org/doc/stable/reference/c-api/generalized-ufuncs.html) +(gufuncs for short) so he wrote new machinery from scratch. Ufuncs and gufuncs created with the new machinery live in a +separate extension module from those created with the old machinery []. It will be a great win in terms of simplifying +`scipy.special`'s build process when all ufuncs can be moved over to the new infrastructure, but in the short term there +was a problem. + +## Error handling redux + +After splitting them between multiple extension modules, the error policy state could no longer be easily shared +between ufuncs. We noticed something was wrong due to a failure in a doctest for +[`special.seterr`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.seterr.html#scipy.special.seterr). + +``` + Examples + -------- + >>> import scipy.special as sc + >>> from pytest import raises + >>> sc.gammaln(0) + inf + >>> olderr = sc.seterr(singular='raise') + >>> with raises(sc.SpecialFunctionError): + ... sc.gammaln(0) + ... + >>> _ = sc.seterr(**olderr) +``` + +[`gammaln`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gammaln.html), for the log of the +absolute value of the Gamma function, was one of a handful of ufuncs moved to the new extension module. + +Both extension modules contained a separate copy of the state for managing error handling policies, but it appeared that +the user facing `special.errstate` could only see and change the state from the first extension module. We saw three +options: + +1. Have the Python interface for modifying the error handling state (`special.errstate` and + [`special.seterr`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.seterr.html)) update the state + in each extension module, taking care that the state remains synchronized. + +2. Extract the error handling state and primitives for managing it into a shared library that would be linked with + each extension module. + +3. Keep the error handling state in one of the extension modules and have it retrieved from there by the other. + +We chose to create a shared library because it seemed like the more principled option []. As the saying goes: we do +things not because they are easy, but because we think they will be easy []. Quansight Labs Co-director Ralf Gommers +(rgommers) knew from long experience that this may be difficult to get right, but trusted that we'd be able to figure +it out. + +## Static vs dynamic linking + +Before we continue, let's review some background information. Feel free to skim or skip this section if you know +this stuff already. + +Consider the structure of a C program. It must have one and only one file with an [entry +point](https://en.wikipedia.org/wiki/Entry_point) function `main` where execution begins. This file may refer to +functions, global variables, and datatypes from other files. For a C program with multiple files, each file is compiled +separately into an object file of machine instructions, specific to a particular platform, giving explicit commands +directly to the CPU. A program called a linker is responsible for combining the generated object files into a single +program. A library is a collection of code containing functions and datatypes which can be used in programs, but which +itself doesn't have a `main` function. + +There are two ways library code can be linked into a program. The simplest way is [_static +linking_](https://en.wikipedia.org/wiki/Static_library), where the library code is bundled directly into the +program. Two programs statically linked with the same library will each have their own separate copy of the +library. Special function error handling broke because the code responsible for it was statically linked into each +separate extension module. By contrast, when library code is [_dynamically +linked_](https://en.wikipedia.org/wiki/Dynamic_linker), it is not included in the executable binary file for the program +at compile time. It instead lives in a separate binary file, called a _shared library_, which can be loaded into the +program at runtime. + +In addition to executable machine instructions, each object file contains a [_symbol +table_](https://en.wikipedia.org/wiki/Symbol_table) mapping each identifier used in the original source file (e.g. a +function name) to either the position in the executable code where the identifier is defined (e.g. the function's +definition) or a placeholder if there's no definition in the source file. When combining object files into a single +program through static linking, the linker fills these placeholders by searching the symbol tables of the other object +files being linked into the program. + +Object files from a statically linked library are treated no differently from object files generated from the program's +source code. By contrast: for dynamic linking, at compile time, the linker only checks the shared library's symbol table +for entries that could fill placeholders, but does not link the shared library into the program. Shared libraries are +instead loaded by programs at runtime. Function names, variable names, and other identifiers a shared library makes +available to programs are referred to as _symbols exported by the library_. + +Some benefits of shared libraries are that: + +- Separate programs can load and use the same library, rather than having separate copies bundled into each program, + reducing code duplication. +- Updates can be made to a shared library without requiring dependent programs to be recompiled, so long as the library's + interface doesn't change. + +There are tradeoffs. A small amount of overhead may be added to function calls, since the process of function +lookups is more involved, and the need to locate and load the shared libray into memory at runtime can incur a fixed +amount of startup overhead. + +## Sharing global state + +Shared libraries cannot share global state between programs running in separate processes. Each process has its own +separate address space of memory it can access, and interprocess communication requires special protocols. Nevertheless, +a shared library _can_ be used to share global state between separate Python extension modules that were dynamically +loaded by a Python interpreter running in the same process, since only a single copy of the library is loaded into +memory. + +Note that the title of this article isn't entirely accurate. A Python extension module is itself a shared library which +is loaded by the Python interpreter at runtime. Thus SciPy actually ships many shared libaries and has done so from its +earliest days. What we mean is the first regular shared library that's not a Python extension module. Also, although +we've been talking in terms of linking programs with a shared library, in this case we are linking Python extension +modules with a shared library. It's perfectly valid to link one shared library with one or more others. + +## The relevant code + +Having decided on the shared library approach, we looked into code `scipy.special` used for managing error handling state. +In essence, things were very simple. Let's walk through the core lines. + +There is an [`enum` type](https://learn.microsoft.com/en-us/cpp/c-language/c-enumeration-declarations?view=msvc-170) +`sf_action_t` giving human readable names to the different error handling policies: + +```c +enum sf_action_t { + SF_ERROR_IGNORE = 0; + SF_ERROR_WARN; + SF_ERROR_RAISE; +} +``` + +And then an array `sf_error_actions` storing the error handling policy associated to each special function error +handling type. We see that the default is to ignore errors all errors, matching what we observed in the example with +`special.gamma`. + +```c +static sf_action_t sf_error_actions[] = { + SF_ERROR_IGNORE, /* SF_ERROR_OK */ + SF_ERROR_IGNORE, /* SF_ERROR_SINGULAR */ + SF_ERROR_IGNORE, /* SF_ERROR_UNDERFLOW */ + SF_ERROR_IGNORE, /* SF_ERROR_OVERFLOW */ + SF_ERROR_IGNORE, /* SF_ERROR_SLOW */ + SF_ERROR_IGNORE, /* SF_ERROR_LOSS */ + SF_ERROR_IGNORE, /* SF_ERROR_NO_RESULT */ + SF_ERROR_IGNORE, /* SF_ERROR_DOMAIN */ + SF_ERROR_IGNORE, /* SF_ERROR_ARG */ + SF_ERROR_IGNORE, /* SF_ERROR_OTHER */ + SF_ERROR_IGNORE /* SF_ERROR__LAST */ +}; +``` + +To make the correspondence between entries in the array and special function error handling types +clear, there is another `enum` type, `sf_error_t`, which associates the name for each error type with +the corresponding index into `sf_error_actions`. + +```c +typedef enum { + SF_ERROR_OK = 0, /* no error */ + SF_ERROR_SINGULAR, /* singularity encountered */ + SF_ERROR_UNDERFLOW, /* floating point underflow */ + SF_ERROR_OVERFLOW, /* floating point overflow */ + SF_ERROR_SLOW, /* too many iterations required */ + SF_ERROR_LOSS, /* loss of precision */ + SF_ERROR_NO_RESULT, /* no result obtained */ + SF_ERROR_DOMAIN, /* out of domain */ + SF_ERROR_ARG, /* invalid input parameter */ + SF_ERROR_OTHER, /* unclassified error */ + SF_ERROR__LAST +} sf_error_t; +``` + +`sf_action_t` is declared static to make using it directly local to only this file. Instead there are are two functions, +`scipy_sf_error_set_action` for updating and `scipy_sf_error_get_action` for querying the the error handling policy +associated to a given error type. + +```c +void scipy_sf_error_set_action(sf_error_t code, sf_action_t action) +{ + sf_error_actions[(int)code] = action; +} + +sf_action_t scipy_sf_error_get_action(sf_error_t code) +{ + return sf_error_actions[(int)code]; +} +``` + +If a user runs the following in an IPython session + +```python +In [1]: import scipy.special as special + +In [2]: special.seterr(loss="warn") +``` + +then somewhere down in the stack, the following call will occur to update the error state. + +```c +scipy_sf_error_set_action(SF_ERROR_LOSS, SF_ERROR_WARN) +``` + +The idea then, was to split off this code into a shared library, which ended up being called `libsf_error_state` []. + +## The many battles to actually ship it + +`lib_sf_error_state`'s contents are fairly simple, but how does one actually ship a shared library within a Python +package like SciPy? The process for creating and using shared libraries depends on the operating system and compiler +toolchain. SciPy supports a wide range of platforms in aim of its goal to democratize access to scientific computing +tools and the greatest challenge turned out to be getting things to work on each of them. Several times, just as we +thought everything was finally working, another quirk would pop up that needed to be addressed. + +### Path troubles + +The initial challenge was finding the right invocations to give to the [meson build system](https://mesonbuild.com/) +used by SciPy []. Extension modules are configured in the `meson.build` files spread across SciPy's source tree +and we needed to figure out how to set up a shared library and link it into each of the special function ufunc +extension modules. Irwin and I begin working on this independently, comparing notes as we went. + +The first hiccup is that the following invocations were working on Irwin's Mac. + +Setting up the shared library like this. + +```meson +sf_error_state_lib = shared_library('sf_error_state', # Name of the library + # Implementation files contained it. + ['sf_error_state.c'], + # Additional places the preprocessor can look for header files + include_directories: ['../_lib', '../_build_utils/src'], + install: true +) +``` + +and adding a `link_with` entry to the creation of each extension module: + +``` +py3.extension_module('_special_ufuncs', + ['_special_ufuncs.cpp', '_special_ufuncs_docs.cpp', 'sf_error.cc'], + include_directories: ['../_lib', '../_build_utils/src'], + dependencies: [np_dep], + link_with: [sf_error_state_lib], # The new line + link_args: version_link_args, + install: true, + cpp_args: ['-DSP_SPECFUN_ERROR'], + subdir: 'scipy/special' +) +``` + +However, on my Linux machine SciPy would build without issue, but attempting to import `scipy.special` would +result in the error: + +``` +ImportError: libsf_error_state.so: cannot open shared object file: No such file or directory +``` + +After a period of head scratching in which I pondered every possible explanation except the correct one, I showed Irwin +what I tried and the error message I was getting. It turned out that the difference in operating systems was a red +herring. The issue was that of the two methods for building SciPy from source for development recommended in SciPy's +[contributor +documentation](https://docs.scipy.org/doc/scipy/building/index.html#building-from-source-for-scipy-development), I was +using the `"python dev.py build"` workflow and he was using an editable install: `"pip install -e . --no-build-isolation"`. +For the editable install, SciPy is installed directly in its own project folder, and the shared library and the relevant +extension modules were all being installed next to each other in `~/scipy/scipy/special`. For the `dev.py` workflow, +SciPy is installed elsewhere. Since I didn't specify where to install the shared library, it got put in the wrong +place. It turned out what I had to do was configure the `install_dir` in `meson` like this: + +``` +sf_error_state_lib = shared_library('sf_error_state', # Name of the library + # Implementation files contained it. + ['sf_error_state.c'], + # Additional places the preprocessor can look for header files + include_directories: ['../_lib', '../_build_utils/src'], + install: true + # Tell meson where to install the shared library + install_dir: py3.get_install_dir() / 'scipy/special', +) +``` + +But even after this, I was still receiving the same error. After consulting `meson`'s excellent documentation and +looking at some related issues, it turns out that the `pip` takes care to set the +[`RPATH`](https://en.wikipedia.org/wiki/Rpath) for each extension module, which tells the dynamic linker where to look +for shared libraries. + +To get things to work with `dev.py`, I needed to explicitly set the `RPATH` in `meson` by adding +`install_rpath: '$ORIGIN'` to each extension module. `'$ORIGIN'` means to search in the same folder as the extension +module. + +### Building Wheels on Windows + +After setting `install_dir` and `install_rpath` correctly, all but one of SciPy's CI jobs were passing. The sole failing +job involved building a wheel on Windows. A +[wheel](https://packaging.python.org/en/latest/specifications/binary-distribution-format/) can be thought of as a +precompiled binary for a Python package. The underlying issue was that Windows does not have support for something like +`RPATH`, following a less configurable set of +[rules](https://learn.microsoft.com/en-us/windows/win32/dlls/dynamic-link-library-search-order#standard-search-order-for-packaged-apps) +for determining the search path for shared libraries []. + +It took us a day of work to get to this point. Since things were not as straightforward as expected; I took it from here. + +At the time I didn't really have any experience developing for Windows and didn't even have a Windows machine available +at home to use []. I looked up how to run Windows in a VM and got to work. + +I found a solution using [delvewheel](https://github.com/adang1345/delvewheel) that worked but wasn't viable for +production. Fortunately, Ralf had seen this problem before and had a ready made solution: manually loading the shared +library from within `scipy/special/__init__.py` so it would be available when needed: + +```python +def _load_libsf_error_state(): + """Load libsf_error_state.dll shared library on Windows + libsf_error_state manages shared state used by + ``scipy.special.seterr`` and ``scipy.special.geterr`` so that these + can work consistently between special functions provided by different + extension modules. This shared library is installed in scipy/special + alongside this __init__.py file. Due to lack of rpath support, Windows + cannot find shared libraries installed within wheels. To circumvent this, + we pre-load ``lib_sf_error_state.dll`` when on Windows. + The logic for this function was borrowed from the function ``make_init`` + in `scipy/tools/openblas_support.py`: + https://github.com/scipy/scipy/blob/bb92c8014e21052e7dde67a76b28214dd1dcb94a/tools/openblas_support.py#L239-L274 + """ # noqa: E501 + if os.name == "nt": + try: + from ctypes import WinDLL + basedir = os.path.dirname(__file__) + except: # noqa: E722 + pass + else: + dll_path = os.path.join(basedir, "libsf_error_state.dll") + if os.path.exists(dll_path): + WinDLL(dll_path) + + +_load_libsf_error_state() +``` + +One week in, all CI jobs were passing and the [PR creating +`libsf_error_state`](https://github.com/scipy/scipy/pull/20321) was merged. We did it! We fixed the bug we'd introduced +in `special.errstate` with months to go before the next SciPy release—or so we thought. + +### Breaking SciPy for MSVC builds + +On May 30th, SciPy maintainer and Conda-Forge core member Axel Obermeir (h-vetinari) posted a comment on his open PR, +[scipy-feedstock/gh-277](https://github.com/conda-forge/scipy-feedstock/pull/277). The first release candidate for +SciPy 1.14.0 had just come out, he was starting the process to add version 1.14.0 to Conda-Forge, and noticed that +Windows builds for SciPy 1.14.0 were failing with the following error: + +``` +lld-link: error: undefined symbol: scipy_sf_error_get_action +``` + +How could this be? SciPy's own CI jobs were passing on Windows, but here, symbols from `lib_sf_error_state` were clearly +not being found. The thing is, at that point in time there was still a key gap in SciPy's CI coverage. Although there +were Windows builds in CI, they all used the [MinGW](https://en.wikipedia.org/wiki/MinGW) compiler toolchain, with no +jobs using [MSVC](https://visualstudio.microsoft.com/vs/features/cplusplus/). We had run into another platform specific +difference. + +Fortunately, Axel knew what the problem was. On Linux, Mac OS (and Windows while using MinGW), symbols from shared +libraries are exported by default, but with MSVC they must be explicitly exported from shared libraries and explicitly +imported into consumers by annotating source code with special compiler directives: +[\_\_declspec(dllexport)](https://learn.microsoft.com/en-us/cpp/build/exporting-from-a-dll-using-declspec-dllexport) for +exports and and +[\_\_declspec(dllimport)](https://learn.microsoft.com/en-us/cpp/build/importing-into-an-application-using-declspec-dllimport) +for imports. + +He had a recipe ready to use: defining and using macros which conditionally compiled to the right thing depending on their context. + +```C++ +// SCIPY_DLL +// inspired by https://github.com/abseil/abseil-cpp/blob/20240116.2/absl/base/config.h#L736-L753 +// +// When building sf_error_state as a DLL, this macro expands to `__declspec(dllexport)` +// so we can annotate symbols appropriately as being exported. When used in +// headers consuming a DLL, this macro expands to `__declspec(dllimport)` so +// that consumers know the symbol is defined inside the DLL. In all other cases, +// the macro expands to nothing. +// Note: SCIPY_DLL_{EX,IM}PORTS are set in scipy/special/meson.build +#if defined(SCIPY_DLL_EXPORTS) + #define SCIPY_DLL __declspec(dllexport) +#elif defined(SCIPY_DLL_IMPORTS) + #define SCIPY_DLL __declspec(dllimport) +#else + #define SCIPY_DLL +#endif +``` + +As soon as I had a chance, I fired up my Windows VM again and put together [a +PR](https://github.com/scipy/scipy/pull/20937) implementing Axel's solution. After a couple misteps, MSVC builds were +working again. There would be no need to push back the release date. A couple weeks later, fellow Quansight Labs member +and LFortran/LPython core developer Gagandeep Singh (czgdp1807) submitted [a +PR](https://github.com/scipy/scipy/pull/20985) to add an MSVC CI job, plugging the gap in SciPy's coverage. + +### Thread safety + +In the introduction I'd mentioned that CPython's global interpreter lock (GIL) makes it easier to extend Python with C +or other compiled languages since one doesn't need to worry about the thread safety of wrapped code. Still, being able +to have only one thread in a running Python process execute Python code at a time is greatly limiting for multicore +applications. In October 2023, a Python Enhancement Proposal (PEP) was accepted to make the GIL optional. This proposal, +[PEP 703](https://peps.python.org/pep-0703/), is well worth reading for its thoughtful summary of the surrounding +issues. CPython 3.13 launched with an optional [free-threaded +mode](https://docs.python.org/3.13/whatsnew/3.13.html#free-threaded-cpython) which supports running with the GIL +disabled. Free-threading in Python offers great promise, but to take advantage of it, extensions need to be rewritten +with thread safety in mind. + +`lib_sf_error_state` is obviously not thread safe. There is a global array carrying the current state of special +function error handling policies with nothing to stop competing threads from trying to access the same memory +location. Simultaneous modifications could even leave an entry in some corrupt and indeterminate state. This situation +is known as a [data race](https://en.wikipedia.org/wiki/Race_condition#Data_race), and leads to [undefined +behavior](https://en.wikipedia.org/wiki/Undefined_behavior) in C and C++. Weird things can happen when there is a data +race. + +The latest tale in the saga of `lib_sf_error_state` is [a PR](https://github.com/scipy/scipy/pull/21956) from Edgar +Margffoy (andfoy)—a member of Quansight Labs' [free-threaded Python +team](https://labs.quansight.org/blog/free-threaded-python-rollout_)—to ensure thread safety by declaring the array +`sf_error_actions` [thread local](https://en.wikipedia.org/wiki/Thread-local_storage). This eliminates the data-race by +making it so each thread gets its own separate copy of the array. Edgar and the others on the free-threaded Python team +have been doing great work improving support for free-threaded Python across the ecosystem for much of this past year. + +In a curious reversal, it is now the `win32` flavor of MinGW that is causing trouble due to [lack of proper threading +support](https://github.com/scipy/scipy/pull/21956#pullrequestreview-2476680430). + +## Reflections + +[^1]: The first regular shared library, not including Python extension modules. diff --git a/apps/labs/public/posts/libsf-error-state/featured.png b/apps/labs/public/posts/libsf-error-state/featured.png new file mode 100644 index 0000000000000000000000000000000000000000..de01c6d812450efa79319ef5868fa56a5d083633 GIT binary patch literal 473511 zcmV)QK(xP!P)A7Z?^EAR#d`Hcd`Y6Ir87Pgn@R?kCc>;j*F3)gOaL+lBAc8myDCGikY&Lm#mGM zte%ygkC~>Hm!_MSm6(^Il9`~Imz$QKt&*X-o1e9np|qZ#ub`i)nxUzZqpzf&o}-?x zouQ!b@o20RqqL%%nya^(s>7?Mx1*@OovOX1sjR1|rlzR3psKg1 zsIsD}va6`9t*EA_u)eFW#iFyorL(!Evc;{jw63tfu&}zau(Y_YwzROWtFpSKw9c!v z!o#qszp}Hlx6QD*$Ev%>vbn;ny3MV-!L_-(wz$Z+xWd4;y}G!(ytuZ$xUjas&cD9M z!M?(|z{s@1*15pk!@kkJz|qLQ#k0iFwZzKKy~V!5#l*qC!^6$H#nZ;a$G*kQ(8I&R z$et!o z;n&{Y*Xr8Yf_+#+v4io;^g1q^4a6^>fh$< z-{9up^3Lh$=i=ep=i%bz;o0c#<>l|+=kMs{>Eh?-6U9?(^vH_2}u}?)B^I z_2KXD+0+6>hSII=P_44uj^zruL_x1en@a**b@$~TU_4oMm^6mKY z`1Sho_xkMl`Stkq_4@t%`Tqa<`1$<#`2GL+{{R2~|2omSs{jBX07*naRCwBKypK~G zSDN>`|H3<|N!iZKV4Mjd*rABQBp`bkJ8Nvc0@fh1_E5OWGsYG|OA>>`Fw_o_2ZT~c zOcf+cgcQvnKt#W0s%(gUM3U_*50au*Cvg=;r0!N{mn<~h?!8u1QY4IS)qPImEv9`wVrEjlVAV0Ah2sKfU+ihjSq^HZpqm z;lqb+ckJ%m!or**6eqcSp2kVs(o)gT&>$W5lB^)`L>wc8VlJ2C_*`ypZ?6RZ<@kIF zz9{YSOgK(cbPlrzDT>>J_jd_mnT1sRGd6sYyJb%r6fcGp*3Yk}3J zrPWpY>e|Zc%KD?#Raeli*V|o2&BUU4=Hrh){tVjc(#NPR!po08nlR2RJ|KcK6GqpH zYjJUAV#c*Hqfu#86J}R5YBmOg_A%vvQmNFNgHczIpfLN`(9n=leltK2t7bbKU!xxz zQ`-r?nB%c%WVrG~lWf_0jvOCU9>} zWsMw;@$#X=jk?-e3{zEU_&uI?wU!@}=v{E=)~a&-M%4a#S-IR5476SuX!=oYAh7mP z>kFsGTojMl15{ykX+yk^C1`u7p?g~_mG<{h@A5gGq4*Mf&ht!AV~;qUrW>aYJ^%dk zKRZ%!s^PVY=bt}v`1vD0mrmZ9Zan<_;mR6m|6DBQjwNw)n+{q&U(CbDVo_kDw&Z5Y zXNzzg8D928d=UoMbj^}+a<)(2w@jvEmd<);l}*F`WI7embvD)3)=L_tYDYYsj5+#h zB=t2deIA@hC2Sp)m1R`}B*)>p&QoQ_Pn=n%$v94O%d-TREo8T|&F@0o+04SS?JJW zDLyzj+27kg7vl0lUchJH?J#ZT7y`p0No{ApHtF@m$*2t{L$RcD*nkT~)^BkqW7d(8 zxin`TjuneVHXf(l1_+3F91r6$|736friH^OwfQtud}tVp-Cyow`4* z9qg#CZuGM$t73Yxzo|*89o62MQcUmc#3Fa*MknX2H13IWg)A5Mgt@{#O|m8UBQ(c( z*kUo8-Ony=KnD~FpEKeQ@8xw;xEnr`!bA%kOAGnPjmoM{I2)Q0SUNS`*l@kNLYoNM zsZEpgl~-PAyO*5qR}6OC>S=Fpn@V##j?NYxS)g%zs<+pY%&^5+=djhL>+`cL+z5;g z4|iGwuhQ)wxH22(aBn<0JmsWI0=%LH+8d?;Hv6SKt~-7D+B*y5N$5ss9LI?tzWXl! 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zqXBsM+rfjYqR)e7t${PQe?GL^yJ(2aioSjwIPw-6dhy=9K46S-f4^UEe+Yb};0Mqo zCdkXjWe~Awn zvb4LB;_Lr@{kG7#eU9!f*ST-wrg*9Tc9#D&;qKkLxz&5)s_*V9{RTWxE_c2~;h~qC zqW#uZGaUGL@B2RU?}kr{ZvMOT+%CPmtn60Z{`>#R|G&F`6PQWPiQZjwu2dhGW7Fb+ z^Xg?5GTTI5H=5_%Ncewp{-4gdr=QNzt!V{qa?_Ty04)(c5gEe_s|3Jx0u^`v4Kkzt a%c~2oT*kfJmI-(^EQ6=3pUXO@geCy$%jcT_ literal 0 HcmV?d00001 From 40155ccd4f6772783fd883da9ea8a86e3e4e9311 Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Mon, 16 Dec 2024 23:15:52 -0500 Subject: [PATCH 02/22] Fix link to n_sphere volume plot --- apps/labs/posts/libsf-error-state.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index 7f8859a7b..339614307 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -206,7 +206,7 @@ which outputs:

Plot showing how the volume of an n-ball depends on the dimension n for radius R = 0.9, 1.0, and 1.1 From 5d9914e238b3853996c5006cccfba5152b0bc382 Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Wed, 18 Dec 2024 17:14:13 -0500 Subject: [PATCH 03/22] Add reflections section --- apps/labs/posts/libsf-error-state.md | 232 ++++++++++++++++----------- 1 file changed, 138 insertions(+), 94 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index 339614307..691b5e930 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -113,7 +113,7 @@ array([ nan, 0. , 1. , ..., 31.57530681, 31.591138 , 31.60696126]) ``` -Instead a warning is raised and `-1.0` is mapped to [`NaN`](https://en.wikipedia.org/wiki/NaN), a special floating point +Instead a warning is raised and `-1.0` is mapped to `NaN`, a special floating point number representing an undefined result. `NaN`s propagate sanely through most calculations [], making them useful in such situations. @@ -165,16 +165,15 @@ FloatingPointError: invalid value encountered in sqrt ## scipy.special -NumPy has over 60 [ufuncs available](https://numpy.org/doc/stable/reference/ufuncs.html#available-ufuncs) for a range of -mathematical functions and operations, but more specialized functions useful in the sciences and engineering are out of -scope. Ufuncs for over 230 such functions can be found instead in -[`scipy.special`](https://docs.scipy.org/doc/scipy/reference/special.html) []. +NumPy has over 60 ufuncs available [] for a range of mathematical functions and operations, but more specialized +functions useful in the sciences and engineering are out of scope. Ufuncs for over 230 such functions can be found +instead in [`scipy.special`](https://docs.scipy.org/doc/scipy/reference/special.html) []. Just for fun, let's use -[`scipy.special.gamma`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gamma.html) to reproduce a -plot from the Wikipedia article ["_Volume of an n-ball_"](https://en.wikipedia.org/wiki/Volume_of_an_n-ball). The plot -shows how the volume of a solid multi-dimensional sphere depends on the dimension `n` when the radius `R` is one of 0.9, -1.0, or 1.1. +[`scipy.special.gamma`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gamma.html), a continuous +extension of the factorial function, to reproduce a plot from the Wikipedia article ["_Volume of an +n-ball_"](https://en.wikipedia.org/wiki/Volume_of_an_n-ball). The plot shows how the volume of a solid multi-dimensional +sphere depends on the dimension `n` when the radius `R` is one of 0.9, 1.0, or 1.1. ```python import matplotlib.pyplot as plt @@ -214,9 +213,8 @@ which outputs: ## scipy.special error handling -What if one of the ufuncs in `scipy.special` recieves an array with some invalid elements? The [Gamma -function](https://en.wikipedia.org/wiki/Gamma_function) used above has singularities at non-positive -integers. +What if one of the ufuncs in `scipy.special` recieves an array with some invalid elements? The Gamma function has +singularities at non-positive integers, similar to how `1 / x` has a singularity at 0. ``` In [1]: import numpy as np @@ -269,16 +267,16 @@ SpecialFunctionError: scipy.special/Gamma: singularity To create a ufunc for a mathematical function, one needs a scalar implementation of this function written in a compiled language, known as a scalar kernel. Until recently, `scipy.special` had scalar kernels written in all of C, C++, Fortran -77, and Cython. In August of 2023, Irwin Zaid (izaid) at Oxford proposed rewriting all of the -scalar kernels in C++ header files in such a way that they could be included in both C++ and CUDA programs. This would -allow these scalar kernels to also be used in GPU-aware array libraries like CuPy and PyTorch, improving special -function support across array library backends. I've been supported by the 2020 NASA ROSES grant, _Reinforcing the -Foundations of Scientific Python_ (with travel and compute costs covered by the 2023 NumFocus SDG _Streamlined Special -Function Development in SciPy_), to work together with Irwin to put this plan into action. With additional help from -SciPy maintainer Ilhan Polat (ilayn), who made a heroic effort to translate over twenty thousand lines of Fortran scalar -kernel code to C, and contributions from other volunteers, substantial progress has been made, and we are in the process -of splitting these headed into a separate library called [xsf](https://github.com/scipy/xsf/). This is a story for -another time. Until then, for more info, see [xsf/#1](https://github.com/scipy/xsf/issues/1). +77, and Cython. In August of 2023, Irwin Zaid (izaid) at Oxford proposed rewriting all of the scalar kernels in C++ +header files in such a way that they could be included in both C++ and CUDA programs. This would allow these scalar +kernels to also be used in GPU-aware array libraries like CuPy and PyTorch, improving special function support across +array library backends. I've been supported by the 2020 NASA ROSES grant, _Reinforcing the Foundations of Scientific +Python_ (with travel and compute costs covered by the 2023 NumFocus SDG _Streamlined Special Function Development in +SciPy_), to work together with Irwin to put this plan into action. With additional help from SciPy maintainer Ilhan +Polat (ilayn), who made a heroic effort to translate over twenty thousand lines of Fortran scalar kernel code to C, and +contributions from other volunteers, substantial progress has been made, and we are in the process of splitting these +headed into a separate library called xsf. This is a story for another time. Until then, see [the first +issue in the xsf repo.](https://github.com/scipy/xsf/issues/1) for more info. First of all, while working on this project, it became apparent to everyone involved that the infrastructure used in SciPy for creating ufuncs was greatly complicated by the need to work with scalar kernels from so many @@ -316,8 +314,13 @@ between ufuncs. We noticed something was wrong due to a failure in a doctest for absolute value of the Gamma function, was one of a handful of ufuncs moved to the new extension module. Both extension modules contained a separate copy of the state for managing error handling policies, but it appeared that -the user facing `special.errstate` could only see and change the state from the first extension module. We saw three -options: +the user facing `special.errstate` could only see and change the state from the first extension module. While +investigating, we also found that, for some reason, there was a separate extension module just for the function +[`ellip_harm_2`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.ellip_harm_2.html#scipy.special.ellip_harm_2). +As expected, `special.errstate` did not and had never worked for `ellip_harm_2` either, but since there were no relevant +tests, no one knew. + +We saw three options: 1. Have the Python interface for modifying the error handling state (`special.errstate` and [`special.seterr`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.seterr.html)) update the state @@ -326,7 +329,7 @@ options: 2. Extract the error handling state and primitives for managing it into a shared library that would be linked with each extension module. -3. Keep the error handling state in one of the extension modules and have it retrieved from there by the other. +3. Keep the error handling state in one of the extension modules and have it retrieved from there by the others. We chose to create a shared library because it seemed like the more principled option []. As the saying goes: we do things not because they are easy, but because we think they will be easy []. Quansight Labs Co-director Ralf Gommers @@ -338,29 +341,25 @@ it out. Before we continue, let's review some background information. Feel free to skim or skip this section if you know this stuff already. -Consider the structure of a C program. It must have one and only one file with an [entry -point](https://en.wikipedia.org/wiki/Entry_point) function `main` where execution begins. This file may refer to -functions, global variables, and datatypes from other files. For a C program with multiple files, each file is compiled -separately into an object file of machine instructions, specific to a particular platform, giving explicit commands -directly to the CPU. A program called a linker is responsible for combining the generated object files into a single -program. A library is a collection of code containing functions and datatypes which can be used in programs, but which -itself doesn't have a `main` function. - -There are two ways library code can be linked into a program. The simplest way is [_static -linking_](https://en.wikipedia.org/wiki/Static_library), where the library code is bundled directly into the -program. Two programs statically linked with the same library will each have their own separate copy of the -library. Special function error handling broke because the code responsible for it was statically linked into each -separate extension module. By contrast, when library code is [_dynamically -linked_](https://en.wikipedia.org/wiki/Dynamic_linker), it is not included in the executable binary file for the program -at compile time. It instead lives in a separate binary file, called a _shared library_, which can be loaded into the -program at runtime. - -In addition to executable machine instructions, each object file contains a [_symbol -table_](https://en.wikipedia.org/wiki/Symbol_table) mapping each identifier used in the original source file (e.g. a -function name) to either the position in the executable code where the identifier is defined (e.g. the function's -definition) or a placeholder if there's no definition in the source file. When combining object files into a single -program through static linking, the linker fills these placeholders by searching the symbol tables of the other object -files being linked into the program. +Consider the structure of a C program. It must have one and only one file with an entry point function `main` where +execution begins. This file may refer to functions, global variables, and datatypes from other files. For a C program +with multiple files, each file is compiled separately into an object file of machine instructions, specific to a +particular platform, giving explicit commands directly to the CPU. A program called a linker is responsible for +combining the generated object files into a single program. A library is a collection of code containing functions and +datatypes which can be used in programs, but which itself doesn't have a `main` function. + +There are two ways library code can be linked into a program. The simplest way is _static linking_, where the library +code is bundled directly into the program. Two programs statically linked with the same library will each have their own +separate copy of the library. Special function error handling broke because the code responsible for it was statically +linked into each separate extension module. By contrast, when library code is _dynamically linked_, it is not included +in the executable binary file for the program at compile time. It instead lives in a separate binary file, called a +_shared library_, which can be loaded into the program at runtime. + +In addition to executable machine instructions, each object file contains a _symbol table_ mapping each identifier used +in the original source file (e.g. a function name) to either the position in the executable code where the identifier is +defined (e.g. the function's definition) or a placeholder if there's no definition in the source file. When combining +object files into a single program through static linking, the linker fills these placeholders by searching the symbol +tables of the other object files being linked into the program. Object files from a statically linked library are treated no differently from object files generated from the program's source code. By contrast: for dynamic linking, at compile time, the linker only checks the shared library's symbol table @@ -398,8 +397,7 @@ modules with a shared library. It's perfectly valid to link one shared library w Having decided on the shared library approach, we looked into code `scipy.special` used for managing error handling state. In essence, things were very simple. Let's walk through the core lines. -There is an [`enum` type](https://learn.microsoft.com/en-us/cpp/c-language/c-enumeration-declarations?view=msvc-170) -`sf_action_t` giving human readable names to the different error handling policies: +There is an enumeration type `sf_action_t` giving human readable names to the different error handling policies: ```c enum sf_action_t { @@ -429,9 +427,9 @@ static sf_action_t sf_error_actions[] = { }; ``` -To make the correspondence between entries in the array and special function error handling types -clear, there is another `enum` type, `sf_error_t`, which associates the name for each error type with -the corresponding index into `sf_error_actions`. +To make the correspondence between entries in the array and special function error handling types clear, there is +another enumeration type, `sf_error_t`, which associates the name for each error type with the corresponding index into +`sf_error_actions`. ```c typedef enum { @@ -449,9 +447,9 @@ typedef enum { } sf_error_t; ``` -`sf_action_t` is declared static to make using it directly local to only this file. Instead there are are two functions, -`scipy_sf_error_set_action` for updating and `scipy_sf_error_get_action` for querying the the error handling policy -associated to a given error type. +`sf_action_t` is declared static to make use of its name local only to the file where it's defined. Instead there are +are two functions, `scipy_sf_error_set_action` for updating and `scipy_sf_error_get_action` for querying the error +handling policy associated to a given error type. ```c void scipy_sf_error_set_action(sf_error_t code, sf_action_t action) @@ -483,7 +481,7 @@ The idea then, was to split off this code into a shared library, which ended up ## The many battles to actually ship it -`lib_sf_error_state`'s contents are fairly simple, but how does one actually ship a shared library within a Python +`libsf_error_state`'s contents are fairly simple, but how does one actually ship a shared library within a Python package like SciPy? The process for creating and using shared libraries depends on the operating system and compiler toolchain. SciPy supports a wide range of platforms in aim of its goal to democratize access to scientific computing tools and the greatest challenge turned out to be getting things to work on each of them. Several times, just as we @@ -491,10 +489,10 @@ thought everything was finally working, another quirk would pop up that needed t ### Path troubles -The initial challenge was finding the right invocations to give to the [meson build system](https://mesonbuild.com/) -used by SciPy []. Extension modules are configured in the `meson.build` files spread across SciPy's source tree -and we needed to figure out how to set up a shared library and link it into each of the special function ufunc -extension modules. Irwin and I begin working on this independently, comparing notes as we went. +The initial challenge was finding the right invocations to give to the meson build system, used by SciPy []. Extension +modules are configured in the `meson.build` files spread across SciPy's source tree and we needed to figure out how to +set up a shared library and link it into each of the special function ufunc extension modules. Irwin and I begin working +on this independently, comparing notes as we went. The first hiccup is that the following invocations were working on Irwin's Mac. @@ -557,7 +555,7 @@ sf_error_state_lib = shared_library('sf_error_state', # Name of the library But even after this, I was still receiving the same error. After consulting `meson`'s excellent documentation and looking at some related issues, it turns out that the `pip` takes care to set the -[`RPATH`](https://en.wikipedia.org/wiki/Rpath) for each extension module, which tells the dynamic linker where to look +`RPATH` [] for each extension module, which tells the dynamic linker where to look for shared libraries. To get things to work with `dev.py`, I needed to explicitly set the `RPATH` in `meson` by adding @@ -568,20 +566,18 @@ module. After setting `install_dir` and `install_rpath` correctly, all but one of SciPy's CI jobs were passing. The sole failing job involved building a wheel on Windows. A -[wheel](https://packaging.python.org/en/latest/specifications/binary-distribution-format/) can be thought of as a +wheel can be thought of as a precompiled binary for a Python package. The underlying issue was that Windows does not have support for something like -`RPATH`, following a less configurable set of -[rules](https://learn.microsoft.com/en-us/windows/win32/dlls/dynamic-link-library-search-order#standard-search-order-for-packaged-apps) -for determining the search path for shared libraries []. +`RPATH`, following a less configurable set of rules for determining the search path for shared libraries []. It took us a day of work to get to this point. Since things were not as straightforward as expected; I took it from here. At the time I didn't really have any experience developing for Windows and didn't even have a Windows machine available at home to use []. I looked up how to run Windows in a VM and got to work. -I found a solution using [delvewheel](https://github.com/adang1345/delvewheel) that worked but wasn't viable for -production. Fortunately, Ralf had seen this problem before and had a ready made solution: manually loading the shared -library from within `scipy/special/__init__.py` so it would be available when needed: +I found a solution using delvewheel, a tool for bundling shared libraries into wheels on Windows []. It worked on my +machine but wasn't viable for production []. Fortunately, Ralf had seen this problem before and had a ready made solution: +manually loading the shared library from within `scipy/special/__init__.py` so it would be available when needed: ```python def _load_libsf_error_state(): @@ -627,19 +623,16 @@ Windows builds for SciPy 1.14.0 were failing with the following error: lld-link: error: undefined symbol: scipy_sf_error_get_action ``` -How could this be? SciPy's own CI jobs were passing on Windows, but here, symbols from `lib_sf_error_state` were clearly -not being found. The thing is, at that point in time there was still a key gap in SciPy's CI coverage. Although there -were Windows builds in CI, they all used the [MinGW](https://en.wikipedia.org/wiki/MinGW) compiler toolchain, with no -jobs using [MSVC](https://visualstudio.microsoft.com/vs/features/cplusplus/). We had run into another platform specific -difference. + How could this be? SciPy's own CI jobs were passing on Windows, but here, symbols from `lib_sf_error_state` were + +clearly not being found. The thing is, at that point in time there was still a key gap in SciPy's CI coverage. Although +there were Windows builds in CI, they all used the MinGW compiler toolchain [], with no jobs using MSVC (Microsoft +Visual C++) []. We had run into another platform specific difference. Fortunately, Axel knew what the problem was. On Linux, Mac OS (and Windows while using MinGW), symbols from shared libraries are exported by default, but with MSVC they must be explicitly exported from shared libraries and explicitly -imported into consumers by annotating source code with special compiler directives: -[\_\_declspec(dllexport)](https://learn.microsoft.com/en-us/cpp/build/exporting-from-a-dll-using-declspec-dllexport) for -exports and and -[\_\_declspec(dllimport)](https://learn.microsoft.com/en-us/cpp/build/importing-into-an-application-using-declspec-dllimport) -for imports. +imported into consumers by annotating source code with special compiler directives: `__declspec(dllexport)` for exports +and and `__declspec(dllimport)` for imports. He had a recipe ready to use: defining and using macros which conditionally compiled to the right thing depending on their context. @@ -662,11 +655,11 @@ He had a recipe ready to use: defining and using macros which conditionally comp #endif ``` -As soon as I had a chance, I fired up my Windows VM again and put together [a -PR](https://github.com/scipy/scipy/pull/20937) implementing Axel's solution. After a couple misteps, MSVC builds were -working again. There would be no need to push back the release date. A couple weeks later, fellow Quansight Labs member -and LFortran/LPython core developer Gagandeep Singh (czgdp1807) submitted [a -PR](https://github.com/scipy/scipy/pull/20985) to add an MSVC CI job, plugging the gap in SciPy's coverage. +As soon as I had a chance, I fired up a Windows VM again and put together [a PR implementing Axel's +solution](https://github.com/scipy/scipy/pull/20937). After a couple misteps, MSVC builds were working again. There +would be no need to push back the release date. A couple weeks later, fellow Quansight Labs member and LFortran/LPython +core developer Gagandeep Singh (czgdp1807) submitted [a PR to add an MSVC CI +job](https://github.com/scipy/scipy/pull/20985), plugging the gap in SciPy's coverage. ### Thread safety @@ -683,20 +676,71 @@ with thread safety in mind. `lib_sf_error_state` is obviously not thread safe. There is a global array carrying the current state of special function error handling policies with nothing to stop competing threads from trying to access the same memory location. Simultaneous modifications could even leave an entry in some corrupt and indeterminate state. This situation -is known as a [data race](https://en.wikipedia.org/wiki/Race_condition#Data_race), and leads to [undefined -behavior](https://en.wikipedia.org/wiki/Undefined_behavior) in C and C++. Weird things can happen when there is a data -race. +is known as a data race and leads to undefined behavior in C and C++ []. Weird things can happen when there is a data race. The latest tale in the saga of `lib_sf_error_state` is [a PR](https://github.com/scipy/scipy/pull/21956) from Edgar -Margffoy (andfoy)—a member of Quansight Labs' [free-threaded Python -team](https://labs.quansight.org/blog/free-threaded-python-rollout_)—to ensure thread safety by declaring the array -`sf_error_actions` [thread local](https://en.wikipedia.org/wiki/Thread-local_storage). This eliminates the data-race by -making it so each thread gets its own separate copy of the array. Edgar and the others on the free-threaded Python team -have been doing great work improving support for free-threaded Python across the ecosystem for much of this past year. +Margffoy (andfoy)—a member of Quansight Labs' free-threaded Python team )—to ensure thread safety by declaring the array +`sf_error_actions` thread local. This eliminates the data-race by making it so each thread gets its own separate copy of +the array. Edgar and the others on the free-threaded Python team have been doing great work improving support for +free-threaded Python across the ecosystem for much of this past year. -In a curious reversal, it is now the `win32` flavor of MinGW that is causing trouble due to [lack of proper threading -support](https://github.com/scipy/scipy/pull/21956#pullrequestreview-2476680430). +In a curious reversal, it is now (the `win32` flavor of) MinGW that is causing trouble due to lack of proper threading +support []. ## Reflections +Now that the dust has mostly settled, it's valuable to look back and try to judge whether we made the right choice. Over +the entire timeframe, the primary goal for Irwin and I was always to make as much of SciPy special available on GPUs as +possible—with secondary goals of simplifying SciPy special's build process and improving the scalar kernel codebase. The +story of `libsf_error_state` is that of a side quest—a story of needing to fix something we broke in pursuit of bigger +things—and the challenges we faced and brought to others because we underestimated the difficulty of our chosen +solution. Having a single array for managing shared state between the extension modules appeals to a desire to have a +single source of truth [], but looking back, it would have taken much less time to have each extension module carry its +own copy of the array, and to always update them all together. + +While conducting research for this blog post, I discovered another wrinkle. Just beneath our noses, there was another +extension module, [`_ufuncs_cxx`](https://github.com/scipy/scipy/blob/main/scipy/special/meson.build#L187-L199) for +scalar kernels written in C++. Through the byzantine machinations of SciPy special's build system, brought about by +the need to handle scalar kernels written in 4 languages, the actual ufuncs from `_ufuncs_cxx` are defined in the +`_ufuncs` extension module discussed earlier. Because `special.errstate` worked without issue for these ufuncs, and +they appeared to be coming from the `_ufuncs` extension module, I never looked more deeply into `_ufuncs_cxx`. It +turns out that `_ufuncs_cxx` has its own copy of `sf_error_actions` and it is updated in synchrony when setting the +error state from Python. + +From [github.com/scipy/.../\_ufuncs_extra_code.pxi](https://github.com/scipy/scipy/blob/b98e79e4b326bf3beb4d861a744101ee7f438471/scipy/special/_ufuncs_extra_code.pxi#L158-L162): + +```python + for error, action in kwargs.items(): + action = _sf_error_action_map[action] + code = _sf_error_code_map[error] + _set_action(code, action) + scipy.special._ufuncs_cxx._set_action(code, action) +``` + +I also found [an October 2016 comment on GitHub](https://github.com/scipy/scipy/pull/6722#issuecomment-256539142) from +currently inactive SciPy maintainer Josh Wilson (person142), the original author of SciPy special's error handling +features, which shows he was acutely aware of the problems we ended up facing. He was trying to make special function +error handling work correctly in +[`scipy.special.cython_special`](https://docs.scipy.org/doc/scipy/reference/special.cython_special.html), a module for +calling scalar versions of special function from Cython. To quote: + +_"To make the necessary modifications to cython_special error handling we need to instead share a single global variable +between the two extension modules. On gcc we could do this with -rpath, but coming up with a portable solution seems +messy. Is there a nice way out of this?"_ + +A nice way? I think it's safe to answer with a definitive no. He wisely chose to instead disable error handling entirely +in `cython_special`. Perhaps now that we have a shared global variable, error handling in `cython_special` could be +revisited. + +Looking back at old comment threads, I get the impression that the earlier maintainers of `scipy.special` had a solid +understanding of issues I've only started to get grip on in three years as a maintainer. In [another GitHub comment from +2016](https://github.com/scipy/scipy/issues/6681#issuecomment-254192679), currently inactive SciPy BDFL Pauli Virtanen +suggested to _"make `sf_error` error handling thread-local (which probably should be done in any case)_", which is only +now happening. Open source maintainers come and go. Developers who have the time and will to contribute in one stage of +life may not in another. Looking to the future, I think it's important to strive to keep institutional memory from being +lost [^20]. The only solution I can think of is to write. + [^1]: The first regular shared library, not including Python extension modules. +[^20]: + See the article [Institutional Memory in Open Source](https://recompilermag.com/issues/extras/institutional-memory-in-open-source/) + by Thursday Bram for a thoughtful discussion of the problem of institutional memory in open source. From 4be742166281daea02078bfd274ca41969d2a49c Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Thu, 19 Dec 2024 00:46:04 -0500 Subject: [PATCH 04/22] More updates --- apps/labs/posts/libsf-error-state.md | 148 +++++++++++++-------------- 1 file changed, 72 insertions(+), 76 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index 691b5e930..dc0ca7338 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -61,8 +61,8 @@ array([1. , 1.41421356, 1.73205081, 2. , 2.23606798, ``` -For large arrays, the speedup from by applying `np.sqrt` over an `ndarray` rather than -[`math.sqrt`](https://docs.python.org/3/library/math.html#math.sqrt) over each element of a list is substantial. On my +For large arrays, the speedup from applying `np.sqrt` over an `ndarray` rather than +[`math.sqrt`](https://docs.python.org/3/library/math.html#math.sqrt) over each element of a list is significant. On my laptop I witnessed: ``` @@ -170,10 +170,10 @@ functions useful in the sciences and engineering are out of scope. Ufuncs for ov instead in [`scipy.special`](https://docs.scipy.org/doc/scipy/reference/special.html) []. Just for fun, let's use -[`scipy.special.gamma`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gamma.html), a continuous -extension of the factorial function, to reproduce a plot from the Wikipedia article ["_Volume of an -n-ball_"](https://en.wikipedia.org/wiki/Volume_of_an_n-ball). The plot shows how the volume of a solid multi-dimensional -sphere depends on the dimension `n` when the radius `R` is one of 0.9, 1.0, or 1.1. +[`scipy.special.gamma`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gamma.html), which implements a +continuous extension of the factorial function called the Gamma function, to reproduce a plot from the Wikipedia article +["_Volume of an n-ball_"](https://en.wikipedia.org/wiki/Volume_of_an_n-ball). The plot shows how the volume of a solid +multi-dimensional sphere depends on the dimension `n` when the radius `R` is one of 0.9, 1.0, or 1.1. ```python import matplotlib.pyplot as plt @@ -265,29 +265,31 @@ SpecialFunctionError: scipy.special/Gamma: singularity ## Some exciting developments -To create a ufunc for a mathematical function, one needs a scalar implementation of this function written in a compiled -language, known as a scalar kernel. Until recently, `scipy.special` had scalar kernels written in all of C, C++, Fortran -77, and Cython. In August of 2023, Irwin Zaid (izaid) at Oxford proposed rewriting all of the scalar kernels in C++ -header files in such a way that they could be included in both C++ and CUDA programs. This would allow these scalar -kernels to also be used in GPU-aware array libraries like CuPy and PyTorch, improving special function support across -array library backends. I've been supported by the 2020 NASA ROSES grant, _Reinforcing the Foundations of Scientific -Python_ (with travel and compute costs covered by the 2023 NumFocus SDG _Streamlined Special Function Development in -SciPy_), to work together with Irwin to put this plan into action. With additional help from SciPy maintainer Ilhan -Polat (ilayn), who made a heroic effort to translate over twenty thousand lines of Fortran scalar kernel code to C, and -contributions from other volunteers, substantial progress has been made, and we are in the process of splitting these -headed into a separate library called xsf. This is a story for another time. Until then, see [the first -issue in the xsf repo.](https://github.com/scipy/xsf/issues/1) for more info. - -First of all, while working on this project, it became apparent to everyone involved that the infrastructure used in -SciPy for creating ufuncs was greatly complicated by the need to work with scalar kernels from so many -languages. Standardizing on C++ offered a chance to simplify things considerably. - -In the Spring of 2024, things moved quickly because Irwin was able to put in some time working on SciPy to help get +To create a ufunc for a mathematical function, one needs a scalar implementation of this function, known as a scalar +kernel, that's written in a compiled language. Until recently, `scipy.special` had scalar kernels written in all of C, +C++, Fortran 77, and Cython. In August of 2023, Irwin Zaid (izaid) at Oxford proposed rewriting all of the scalar +kernels in C++ header files in such a way that they could be included in both C++ and CUDA programs. This would allow +these scalar kernels to also be used in GPU-aware array libraries like CuPy and PyTorch, improving special function +support across array library backends. I've been supported by the 2020 NASA ROSES grant, _Reinforcing the Foundations of +Scientific Python_ (with travel and compute costs partially covered by the 2023 NumFocus SDG _Streamlined Special +Function Development in SciPy_), to work together with Irwin to put this plan into action. With additional help from +SciPy maintainer Ilhan Polat (ilayn), who made a heroic effort to translate over twenty thousand lines of Fortran scalar +kernel code to C, and contributions from other volunteers, substantial progress has been made. We're now in the process +of splitting these headers into a separate library called xsf. This is a story deserving of its own post. Until then, +see [the first issue in the xsf repo](https://github.com/scipy/xsf/issues/1) for more info. + +At the outset of this project, `scipy.special` had been under-maintained for several years. I'm not sure if there were +any active maintainers left who fully understood the details of the infrastucture `scipy.special` has been using for +creating ufuncs. However, it was to clear to everyone involved that this infrastructure was greatly complicated by the +need to work with scalar kernels from so many languages. Standardizing on C++ offered a chance to simplify things +considerably. + +In the Spring of 2024, things moved fairly quickly because Irwin was able to put in time working on SciPy to help get things off the ground. He found that the existing ufunc infrastructure was not flexible enough for work he had planned involving [Generalized universal functions](https://numpy.org/doc/stable/reference/c-api/generalized-ufuncs.html) (gufuncs for short) so he wrote new machinery from scratch. Ufuncs and gufuncs created with the new machinery live in a separate extension module from those created with the old machinery []. It will be a great win in terms of simplifying -`scipy.special`'s build process when all ufuncs can be moved over to the new infrastructure, but in the short term there +`scipy.special`'s build process when all ufuncs can be moved over to the new infrastructure but in the short term there was a problem. ## Error handling redux @@ -295,6 +297,7 @@ was a problem. After splitting them between multiple extension modules, the error policy state could no longer be easily shared between ufuncs. We noticed something was wrong due to a failure in a doctest for [`special.seterr`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.seterr.html#scipy.special.seterr). +The expected error was not being raised. ``` Examples @@ -310,12 +313,12 @@ between ufuncs. We noticed something was wrong due to a failure in a doctest for >>> _ = sc.seterr(**olderr) ``` -[`gammaln`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gammaln.html), for the log of the -absolute value of the Gamma function, was one of a handful of ufuncs moved to the new extension module. +[`gammaln`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gammaln.html) was one of a handful of +ufuncs moved to the new extension module. -Both extension modules contained a separate copy of the state for managing error handling policies, but it appeared that -the user facing `special.errstate` could only see and change the state from the first extension module. While -investigating, we also found that, for some reason, there was a separate extension module just for the function +Both extension modules contained a separate copy of the state for managing error handling policies, but the user facing +`special.errstate` could only see and change the state from the first extension module. While investigating, we also +found that, for some reason, there was a separate extension module just for the function [`ellip_harm_2`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.ellip_harm_2.html#scipy.special.ellip_harm_2). As expected, `special.errstate` did not and had never worked for `ellip_harm_2` either, but since there were no relevant tests, no one knew. @@ -447,9 +450,9 @@ typedef enum { } sf_error_t; ``` -`sf_action_t` is declared static to make use of its name local only to the file where it's defined. Instead there are -are two functions, `scipy_sf_error_set_action` for updating and `scipy_sf_error_get_action` for querying the error -handling policy associated to a given error type. +`sf_action_t` is declared static to make use of its name local only to the file where it's defined. To work with this +array externally, there are two functions, `scipy_sf_error_set_action` for updating and `scipy_sf_error_get_action` for +querying the error handling policy associated to a given error type. ```c void scipy_sf_error_set_action(sf_error_t code, sf_action_t action) @@ -477,19 +480,21 @@ then somewhere down in the stack, the following call will occur to update the er scipy_sf_error_set_action(SF_ERROR_LOSS, SF_ERROR_WARN) ``` -The idea then, was to split off this code into a shared library, which ended up being called `libsf_error_state` []. +If `special.gamma` is evaluated at an array containing a negative integer, then when the scalar kernel is evaluating the +offending entry, it will signal `SF_ERROR_SINGULAR` and `sf_error_actions` will be consulted to figure out what action +to take. ## The many battles to actually ship it -`libsf_error_state`'s contents are fairly simple, but how does one actually ship a shared library within a Python -package like SciPy? The process for creating and using shared libraries depends on the operating system and compiler -toolchain. SciPy supports a wide range of platforms in aim of its goal to democratize access to scientific computing -tools and the greatest challenge turned out to be getting things to work on each of them. Several times, just as we -thought everything was finally working, another quirk would pop up that needed to be addressed. +The shared library `libsf_error_state`'s contents are fairly simple, but how does one actually ship a shared library +within a Python package like SciPy? The process for creating and using shared libraries depends on the operating system +and compiler toolchain. SciPy supports a wide range of platforms in aim of its goal to democratize access to scientific +computing tools and the greatest challenge turned out to be getting things to work on each of them. Several times, just +as we thought everything was finally working, another quirk would pop up that needed to be addressed. ### Path troubles -The initial challenge was finding the right invocations to give to the meson build system, used by SciPy []. Extension +The initial challenge was finding the right invocations to give to the meson build system used by SciPy []. Extension modules are configured in the `meson.build` files spread across SciPy's source tree and we needed to figure out how to set up a shared library and link it into each of the special function ufunc extension modules. Irwin and I begin working on this independently, comparing notes as we went. @@ -608,9 +613,9 @@ def _load_libsf_error_state(): _load_libsf_error_state() ``` -One week in, all CI jobs were passing and the [PR creating -`libsf_error_state`](https://github.com/scipy/scipy/pull/20321) was merged. We did it! We fixed the bug we'd introduced -in `special.errstate` with months to go before the next SciPy release—or so we thought. +One week in, all CI jobs were passing and the [PR](https://github.com/scipy/scipy/pull/20321) creating +`libsf_error_state` was merged. We did it! We fixed the bug we'd introduced in `special.errstate` with months to go +before the next SciPy release—or so we thought. ### Breaking SciPy for MSVC builds @@ -623,11 +628,10 @@ Windows builds for SciPy 1.14.0 were failing with the following error: lld-link: error: undefined symbol: scipy_sf_error_get_action ``` - How could this be? SciPy's own CI jobs were passing on Windows, but here, symbols from `lib_sf_error_state` were - -clearly not being found. The thing is, at that point in time there was still a key gap in SciPy's CI coverage. Although -there were Windows builds in CI, they all used the MinGW compiler toolchain [], with no jobs using MSVC (Microsoft -Visual C++) []. We had run into another platform specific difference. +How could this be? SciPy's own CI jobs were passing on Windows, but here, symbols from `lib_sf_error_state` were clearly +not being found. The thing is, at that point in time there was still a key gap in SciPy's CI coverage. Although there +were Windows builds in CI, they all used the MinGW compiler toolchain [], with no jobs using MSVC (Microsoft Visual C++) +[]. We had run into another platform specific difference. Fortunately, Axel knew what the problem was. On Linux, Mac OS (and Windows while using MinGW), symbols from shared libraries are exported by default, but with MSVC they must be explicitly exported from shared libraries and explicitly @@ -655,11 +659,11 @@ He had a recipe ready to use: defining and using macros which conditionally comp #endif ``` -As soon as I had a chance, I fired up a Windows VM again and put together [a PR implementing Axel's -solution](https://github.com/scipy/scipy/pull/20937). After a couple misteps, MSVC builds were working again. There -would be no need to push back the release date. A couple weeks later, fellow Quansight Labs member and LFortran/LPython -core developer Gagandeep Singh (czgdp1807) submitted [a PR to add an MSVC CI -job](https://github.com/scipy/scipy/pull/20985), plugging the gap in SciPy's coverage. +As soon as I had a chance, I fired up a Windows VM again and put together [a PR](https://github.com/scipy/scipy/pull/20937) +implementing Axel's solution. After a couple misteps, MSVC builds were working again. There would be no need to push +back the release date. A couple weeks later, fellow Quansight Labs member and LFortran/LPython core developer Gagandeep +Singh (czgdp1807) submitted [a PR](https://github.com/scipy/scipy/pull/20985) to add an MSVC CI job, plugging the gap in +SciPy's coverage. ### Thread safety @@ -667,19 +671,19 @@ In the introduction I'd mentioned that CPython's global interpreter lock (GIL) m or other compiled languages since one doesn't need to worry about the thread safety of wrapped code. Still, being able to have only one thread in a running Python process execute Python code at a time is greatly limiting for multicore applications. In October 2023, a Python Enhancement Proposal (PEP) was accepted to make the GIL optional. This proposal, -[PEP 703](https://peps.python.org/pep-0703/), is well worth reading for its thoughtful summary of the surrounding -issues. CPython 3.13 launched with an optional [free-threaded -mode](https://docs.python.org/3.13/whatsnew/3.13.html#free-threaded-cpython) which supports running with the GIL -disabled. Free-threading in Python offers great promise, but to take advantage of it, extensions need to be rewritten -with thread safety in mind. +[PEP 703](https://peps.python.org/pep-0703/), is well worth reading for its in-depth summary of the surrounding +issues. CPython 3.13 launched with an optional +[free-threaded mode](https://docs.python.org/3.13/whatsnew/3.13.html#free-threaded-cpython) +which supports running with the GIL disabled. Free-threading in Python offers great promise, but to take advantage of +it, extensions need to be rewritten with thread safety in mind. -`lib_sf_error_state` is obviously not thread safe. There is a global array carrying the current state of special +`libsf_error_state` is obviously not thread safe. There is a global array carrying the current state of special function error handling policies with nothing to stop competing threads from trying to access the same memory location. Simultaneous modifications could even leave an entry in some corrupt and indeterminate state. This situation is known as a data race and leads to undefined behavior in C and C++ []. Weird things can happen when there is a data race. -The latest tale in the saga of `lib_sf_error_state` is [a PR](https://github.com/scipy/scipy/pull/21956) from Edgar -Margffoy (andfoy)—a member of Quansight Labs' free-threaded Python team )—to ensure thread safety by declaring the array +The latest tale in the saga of `libsf_error_state` is [a PR](https://github.com/scipy/scipy/pull/21956) from Edgar +Margffoy (andfoy)—a member of Quansight Labs' free-threaded Python team—to ensure thread safety by declaring the array `sf_error_actions` thread local. This eliminates the data-race by making it so each thread gets its own separate copy of the array. Edgar and the others on the free-threaded Python team have been doing great work improving support for free-threaded Python across the ecosystem for much of this past year. @@ -719,28 +723,20 @@ From [github.com/scipy/.../\_ufuncs_extra_code.pxi](https://github.com/scipy/sci I also found [an October 2016 comment on GitHub](https://github.com/scipy/scipy/pull/6722#issuecomment-256539142) from currently inactive SciPy maintainer Josh Wilson (person142), the original author of SciPy special's error handling -features, which shows he was acutely aware of the problems we ended up facing. He was trying to make special function +features, which shows he was already aware of the problem we ended up facing. He was trying to make special function error handling work correctly in [`scipy.special.cython_special`](https://docs.scipy.org/doc/scipy/reference/special.cython_special.html), a module for calling scalar versions of special function from Cython. To quote: -_"To make the necessary modifications to cython_special error handling we need to instead share a single global variable +"To make the necessary modifications to cython_special error handling we need to instead share a single global variable between the two extension modules. On gcc we could do this with -rpath, but coming up with a portable solution seems -messy. Is there a nice way out of this?"_ +messy. Is there a nice way out of this?" -A nice way? I think it's safe to answer with a definitive no. He wisely chose to instead disable error handling entirely -in `cython_special`. Perhaps now that we have a shared global variable, error handling in `cython_special` could be -revisited. +A _nice_ way? Perhaps not. He wisely chose instead to disable error handling in `cython_special` entirely. -Looking back at old comment threads, I get the impression that the earlier maintainers of `scipy.special` had a solid -understanding of issues I've only started to get grip on in three years as a maintainer. In [another GitHub comment from +Before we finish, one more gem: in [another GitHub comment from 2016](https://github.com/scipy/scipy/issues/6681#issuecomment-254192679), currently inactive SciPy BDFL Pauli Virtanen -suggested to _"make `sf_error` error handling thread-local (which probably should be done in any case)_", which is only -now happening. Open source maintainers come and go. Developers who have the time and will to contribute in one stage of -life may not in another. Looking to the future, I think it's important to strive to keep institutional memory from being -lost [^20]. The only solution I can think of is to write. +suggested to "make `sf_error` error handling thread-local (which probably should be done in any case)". Over eight years +later this is finally happening. [^1]: The first regular shared library, not including Python extension modules. -[^20]: - See the article [Institutional Memory in Open Source](https://recompilermag.com/issues/extras/institutional-memory-in-open-source/) - by Thursday Bram for a thoughtful discussion of the problem of institutional memory in open source. From d6f6a0bc1f9653faa053a362422ea0bc51c73e04 Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Thu, 19 Dec 2024 00:50:07 -0500 Subject: [PATCH 05/22] One more update --- apps/labs/posts/libsf-error-state.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index dc0ca7338..7590a82e9 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -732,11 +732,13 @@ calling scalar versions of special function from Cython. To quote: between the two extension modules. On gcc we could do this with -rpath, but coming up with a portable solution seems messy. Is there a nice way out of this?" -A _nice_ way? Perhaps not. He wisely chose instead to disable error handling in `cython_special` entirely. +A _nice_ way? Perhaps not. He wisely chose instead to disable error handling in `cython_special` entirely. Now that +we know _a_ portable way to share global state between extension modules, maybe more possibilities will open up +though. Before we finish, one more gem: in [another GitHub comment from 2016](https://github.com/scipy/scipy/issues/6681#issuecomment-254192679), currently inactive SciPy BDFL Pauli Virtanen suggested to "make `sf_error` error handling thread-local (which probably should be done in any case)". Over eight years -later this is finally happening. +later this is finally happening. I should probably go back and read old comment threads more often. [^1]: The first regular shared library, not including Python extension modules. From ebfc521b2601c0c4f5e45895bdb6b81d5e96edac Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Thu, 19 Dec 2024 11:48:59 -0500 Subject: [PATCH 06/22] Add footnotes --- apps/labs/posts/libsf-error-state.md | 138 ++++++++++++++++++--------- 1 file changed, 91 insertions(+), 47 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index 7590a82e9..92d5e2a71 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -16,7 +16,7 @@ hero: This is the story of the first[^1] shared library to be shipped within SciPy. It offers a glimpse at some of the complexity SciPy tries to hide from users, while previewing some exciting developments in -[`scipy.special`](https://docs.scipy.org/doc/scipy/reference/special.html). +[scipy.special](https://docs.scipy.org/doc/scipy/reference/special.html). Python has become wildly popular as a scripting language for scientific and other data intensive applications. It owes much of its success to an exemplary execution of the "compiled core, interpreted shell" principle. One can orchestrate @@ -30,7 +30,7 @@ lock or GIL, a barrier to free threading within Python, has the benefit of makin code, since one need not worry about the thread-safety of wrapped compiled libraries. By wrapping, filling in gaps, and providing user friendly Python APIs to a wide range of battle tested, permissively -licensed and public domain scientific software tools—SciPy's founding developers [] were able to kickstart the growth of +licensed and public domain scientific software tools—SciPy's founding developers[^2] were able to kickstart the growth of the Scientific Python ecosystem. There are now worlds of scientific and data intensive software available in Python. Users who spend their time in the interpreted shell may not be aware of the complexity that lies underneath. Journeying deeper into the stack, it can be surprising to see the level of work that can go into making even @@ -38,13 +38,13 @@ a relatively simple feature work smoothly. ## NumPy Universal functions -A NumPy [`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) represents an arbitrary +A NumPy [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) represents an arbitrary dimensional array of elements of the same data type, stored in a continguous buffer at the machine level. A [universal function](https://numpy.org/doc/stable/reference/ufuncs.html) or ufunc for short is a Python level function which applies a transform to each element of an `ndarray` by looping and operating elementwise over the underlying contiguous buffer in efficient native code. -Here's the ufunc [`np.sqrt`](https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html) in action +Here's the ufunc [np.sqrt](https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html) in action ``` In [1]: import numpy as np @@ -62,7 +62,7 @@ array([1. , 1.41421356, 1.73205081, 2. , 2.23606798, ``` For large arrays, the speedup from applying `np.sqrt` over an `ndarray` rather than -[`math.sqrt`](https://docs.python.org/3/library/math.html#math.sqrt) over each element of a list is significant. On my +[math.sqrt](https://docs.python.org/3/library/math.html#math.sqrt) over each element of a list is significant. On my laptop I witnessed: ``` @@ -82,8 +82,8 @@ In [5]: %timeit [math.sqrt(t) for t in B] ## Error handling What should happen if one gives invalid input to a ufunc? If we pass a negative -[`float`](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex) to `math.sqrt` a -[`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) is raised []. +[float](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex) to `math.sqrt` a +[ValueError](https://docs.python.org/3/library/exceptions.html#ValueError) is raised.[^3] ``` In [1]: import math @@ -114,7 +114,7 @@ array([ nan, 0. , 1. , ..., 31.57530681, ``` Instead a warning is raised and `-1.0` is mapped to `NaN`, a special floating point -number representing an undefined result. `NaN`s propagate sanely through most calculations [], making them useful in +number representing an undefined result. `NaN`s propagate sanely through most calculations, making them useful in such situations. What if we want to suppress the warning? Perhaps we're applying a ufunc within an inner loop and getting buried in @@ -140,7 +140,7 @@ array([ nan, 0. , 1. , ..., 31.57530681, ``` What if we genuinely want to raise if any kind of floating point error occurs? Perhaps negative inputs imply a sensor -failure in a latency insensitive [] robot which will ping its handlers upon an exception and hibernate until it can be +failure in a latency insensitive robot which will ping its handlers upon an exception and hibernate until it can be physically recovered. `np.errstate` has us covered here too: @@ -165,9 +165,9 @@ FloatingPointError: invalid value encountered in sqrt ## scipy.special -NumPy has over 60 ufuncs available [] for a range of mathematical functions and operations, but more specialized +NumPy has over 60 ufuncs available [^4] for a range of mathematical functions and operations, but more specialized functions useful in the sciences and engineering are out of scope. Ufuncs for over 230 such functions can be found -instead in [`scipy.special`](https://docs.scipy.org/doc/scipy/reference/special.html) []. +instead in `scipy.special`.[^5] Just for fun, let's use [`scipy.special.gamma`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gamma.html), which implements a @@ -221,7 +221,7 @@ In [1]: import numpy as np In [2]: import scipy.special as special -In [3]: x = np.linspace(-4, 4, 5) +In [3]: x = np.array([-4., -2., 0., 2., 4.]) In [4]: special.gamma(x) @@ -235,13 +235,13 @@ In [6]: ``` -`np.errstate` had no impact on error handling, there were no warnings, but we do see `NaN`s in the output []. What's +`np.errstate` had no impact on error handling, there were no warnings, but we do see `NaN`s in the output.[^6] What's going on? Well, NumPy's ufuncs are provided in a C [extension module](https://docs.python.org/3/extending/extending.html) that's part of NumPy. There's some C code in this extension module for managing the state for error handling policies. The ufuncs in `scipy.special` come from a different extension module in SciPy. Extension modules from separate projects are like separate worlds, and cannot communicate with one -another except through their Python interfaces []. SciPy instead has its own context manager: -[`scipy.special.errstate`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.errstate.html) that +another except through their Python interfaces. SciPy instead has its own context manager: +[scipy.special.errstate](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.errstate.html) that mirrors `np.errstate`. ``` @@ -288,7 +288,7 @@ In the Spring of 2024, things moved fairly quickly because Irwin was able to put things off the ground. He found that the existing ufunc infrastructure was not flexible enough for work he had planned involving [Generalized universal functions](https://numpy.org/doc/stable/reference/c-api/generalized-ufuncs.html) (gufuncs for short) so he wrote new machinery from scratch. Ufuncs and gufuncs created with the new machinery live in a -separate extension module from those created with the old machinery []. It will be a great win in terms of simplifying +separate extension module from those created with the old machinery.[^7] It will be a great win in terms of simplifying `scipy.special`'s build process when all ufuncs can be moved over to the new infrastructure but in the short term there was a problem. @@ -296,7 +296,7 @@ was a problem. After splitting them between multiple extension modules, the error policy state could no longer be easily shared between ufuncs. We noticed something was wrong due to a failure in a doctest for -[`special.seterr`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.seterr.html#scipy.special.seterr). +[special.seterr](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.seterr.html#scipy.special.seterr). The expected error was not being raised. ``` @@ -313,7 +313,7 @@ The expected error was not being raised. >>> _ = sc.seterr(**olderr) ``` -[`gammaln`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gammaln.html) was one of a handful of +[gammaln](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gammaln.html) was one of a handful of ufuncs moved to the new extension module. Both extension modules contained a separate copy of the state for managing error handling policies, but the user facing @@ -334,10 +334,10 @@ We saw three options: 3. Keep the error handling state in one of the extension modules and have it retrieved from there by the others. -We chose to create a shared library because it seemed like the more principled option []. As the saying goes: we do -things not because they are easy, but because we think they will be easy []. Quansight Labs Co-director Ralf Gommers -(rgommers) knew from long experience that this may be difficult to get right, but trusted that we'd be able to figure -it out. +We chose to create a shared library because it seemed like the more principled option. As the saying goes: we do these +things not because they are easy, but because we thought they were going to be easy.[^8] Quansight Labs Co-director Ralf +Gommers (rgommers) knew from long experience that this may be difficult to get right, but trusted that we'd be able to +figure it out. ## Static vs dynamic linking @@ -351,14 +351,14 @@ particular platform, giving explicit commands directly to the CPU. A program cal combining the generated object files into a single program. A library is a collection of code containing functions and datatypes which can be used in programs, but which itself doesn't have a `main` function. -There are two ways library code can be linked into a program. The simplest way is _static linking_, where the library +There are two ways library code can be linked into a program. The simplest way is **static linking**, where the library code is bundled directly into the program. Two programs statically linked with the same library will each have their own separate copy of the library. Special function error handling broke because the code responsible for it was statically -linked into each separate extension module. By contrast, when library code is _dynamically linked_, it is not included +linked into each separate extension module. By contrast, when library code is **dynamically linked**, it is not included in the executable binary file for the program at compile time. It instead lives in a separate binary file, called a -_shared library_, which can be loaded into the program at runtime. +**shared library**, which can be loaded into the program at runtime. -In addition to executable machine instructions, each object file contains a _symbol table_ mapping each identifier used +In addition to executable machine instructions, each object file contains a **symbol table** mapping each identifier used in the original source file (e.g. a function name) to either the position in the executable code where the identifier is defined (e.g. the function's definition) or a placeholder if there's no definition in the source file. When combining object files into a single program through static linking, the linker fills these placeholders by searching the symbol @@ -368,7 +368,7 @@ Object files from a statically linked library are treated no differently from ob source code. By contrast: for dynamic linking, at compile time, the linker only checks the shared library's symbol table for entries that could fill placeholders, but does not link the shared library into the program. Shared libraries are instead loaded by programs at runtime. Function names, variable names, and other identifiers a shared library makes -available to programs are referred to as _symbols exported by the library_. +available to programs are referred to as **symbols exported by the library**. Some benefits of shared libraries are that: @@ -398,7 +398,7 @@ modules with a shared library. It's perfectly valid to link one shared library w ## The relevant code Having decided on the shared library approach, we looked into code `scipy.special` used for managing error handling state. -In essence, things were very simple. Let's walk through the core lines. +In essence, things were very simple. Let's walk through the core lines.[^9] There is an enumeration type `sf_action_t` giving human readable names to the different error handling policies: @@ -480,7 +480,7 @@ then somewhere down in the stack, the following call will occur to update the er scipy_sf_error_set_action(SF_ERROR_LOSS, SF_ERROR_WARN) ``` -If `special.gamma` is evaluated at an array containing a negative integer, then when the scalar kernel is evaluating the +If `special.gamma` is evaluated at an array containing a negative integer, then when the scalar kernel is called on the offending entry, it will signal `SF_ERROR_SINGULAR` and `sf_error_actions` will be consulted to figure out what action to take. @@ -494,7 +494,7 @@ as we thought everything was finally working, another quirk would pop up that ne ### Path troubles -The initial challenge was finding the right invocations to give to the meson build system used by SciPy []. Extension +The initial challenge was finding the right invocations to give to the meson build system used by SciPy.[^10] Extension modules are configured in the `meson.build` files spread across SciPy's source tree and we needed to figure out how to set up a shared library and link it into each of the special function ufunc extension modules. Irwin and I begin working on this independently, comparing notes as we went. @@ -558,10 +558,9 @@ sf_error_state_lib = shared_library('sf_error_state', # Name of the library ) ``` -But even after this, I was still receiving the same error. After consulting `meson`'s excellent documentation and -looking at some related issues, it turns out that the `pip` takes care to set the -`RPATH` [] for each extension module, which tells the dynamic linker where to look -for shared libraries. +But even after this, I was still receiving the same error. After consulting `meson`'s +[excellent documentation](https://mesonbuild.com/) and looking at some related issues, it turns out that the `pip` takes +care to set the `RPATH` for each extension module,[^11] which tells the dynamic linker where to look for shared libraries. To get things to work with `dev.py`, I needed to explicitly set the `RPATH` in `meson` by adding `install_rpath: '$ORIGIN'` to each extension module. `'$ORIGIN'` means to search in the same folder as the extension @@ -572,16 +571,16 @@ module. After setting `install_dir` and `install_rpath` correctly, all but one of SciPy's CI jobs were passing. The sole failing job involved building a wheel on Windows. A wheel can be thought of as a -precompiled binary for a Python package. The underlying issue was that Windows does not have support for something like -`RPATH`, following a less configurable set of rules for determining the search path for shared libraries []. +precompiled binary for a Python package.[^12] The underlying issue was that Windows does not have support for something like +`RPATH`, following a less configurable set of rules for determining the search path for shared libraries.[^13] It took us a day of work to get to this point. Since things were not as straightforward as expected; I took it from here. At the time I didn't really have any experience developing for Windows and didn't even have a Windows machine available -at home to use []. I looked up how to run Windows in a VM and got to work. +at home to use. I looked up how to run Windows in a VM and got to work. -I found a solution using delvewheel, a tool for bundling shared libraries into wheels on Windows []. It worked on my -machine but wasn't viable for production []. Fortunately, Ralf had seen this problem before and had a ready made solution: +I found a solution using delvewheel, a tool for bundling shared libraries into wheels on Windows.[^14] It worked on my +machine but wasn't viable for production.[^15] Fortunately, Ralf had seen this problem before and had a ready made solution: manually loading the shared library from within `scipy/special/__init__.py` so it would be available when needed: ```python @@ -630,13 +629,13 @@ lld-link: error: undefined symbol: scipy_sf_error_get_action How could this be? SciPy's own CI jobs were passing on Windows, but here, symbols from `lib_sf_error_state` were clearly not being found. The thing is, at that point in time there was still a key gap in SciPy's CI coverage. Although there -were Windows builds in CI, they all used the MinGW compiler toolchain [], with no jobs using MSVC (Microsoft Visual C++) -[]. We had run into another platform specific difference. +were Windows builds in CI, they all used the MinGW compiler toolchain, with no jobs using MSVC (Microsoft Visual C++). +We had run into another platform specific difference. Fortunately, Axel knew what the problem was. On Linux, Mac OS (and Windows while using MinGW), symbols from shared libraries are exported by default, but with MSVC they must be explicitly exported from shared libraries and explicitly imported into consumers by annotating source code with special compiler directives: `__declspec(dllexport)` for exports -and and `__declspec(dllimport)` for imports. +and and `__declspec(dllimport)` for imports.[^16] He had a recipe ready to use: defining and using macros which conditionally compiled to the right thing depending on their context. @@ -680,7 +679,7 @@ it, extensions need to be rewritten with thread safety in mind. `libsf_error_state` is obviously not thread safe. There is a global array carrying the current state of special function error handling policies with nothing to stop competing threads from trying to access the same memory location. Simultaneous modifications could even leave an entry in some corrupt and indeterminate state. This situation -is known as a data race and leads to undefined behavior in C and C++ []. Weird things can happen when there is a data race. +is known as a data race and leads to undefined behavior in C and C++.[^17] Weird things can happen when there is a data race. The latest tale in the saga of `libsf_error_state` is [a PR](https://github.com/scipy/scipy/pull/21956) from Edgar Margffoy (andfoy)—a member of Quansight Labs' free-threaded Python team—to ensure thread safety by declaring the array @@ -689,7 +688,7 @@ the array. Edgar and the others on the free-threaded Python team have been doing free-threaded Python across the ecosystem for much of this past year. In a curious reversal, it is now (the `win32` flavor of) MinGW that is causing trouble due to lack of proper threading -support []. +support.[^18] ## Reflections @@ -732,13 +731,58 @@ calling scalar versions of special function from Cython. To quote: between the two extension modules. On gcc we could do this with -rpath, but coming up with a portable solution seems messy. Is there a nice way out of this?" -A _nice_ way? Perhaps not. He wisely chose instead to disable error handling in `cython_special` entirely. Now that -we know _a_ portable way to share global state between extension modules, maybe more possibilities will open up -though. +A _nice_ way? Perhaps not. He wisely chose instead to disable error handling in `cython_special` entirely. But maybe now +that we at least have _a_ portable way to share global state between extension modules, this is worth revisiting. Before we finish, one more gem: in [another GitHub comment from 2016](https://github.com/scipy/scipy/issues/6681#issuecomment-254192679), currently inactive SciPy BDFL Pauli Virtanen suggested to "make `sf_error` error handling thread-local (which probably should be done in any case)". Over eight years later this is finally happening. I should probably go back and read old comment threads more often. +--- + [^1]: The first regular shared library, not including Python extension modules. +[^2]: Travis Oliphant, Pearu Peterson, and Eric Jones. https://en.wikipedia.org/wiki/SciPy#History) +[^3]: + Having `math.sqrt(-1.0)` evaluate to the complex number `1j` instead of raising would violate a principle that the + output type should depend only on the input type, not the input value. Allowing the latter makes code more difficult to + reason about, both for programmers and for tools like static type checkers and jit compilers. + +[^4]: https://numpy.org/doc/stable/reference/ufuncs.html#available-ufuncs +[^5]: https://docs.scipy.org/doc/scipy/reference/special.html +[^6]: + Those using `scipy<1.15` will see `inf` instead of `nan` at negative integers due to a bug in `special.gamma` + which was fixed in [the PR scipy/#21827](https://github.com/scipy/scipy/pull/21827).If you're curious why + `special.gamma(0.)` evaluated to `inf`, note that the IEE-754 floating point standard requires signed zeros `+0.` + and `-0.` as described in [Signed zero Wikipedia article](https://en.wikipedia.org/wiki/Signed_zero). + `special.gamma(+0.)` evaluates the limit of the Gamma function as `x` approaches `0` from the right. Signed zeros + can be used to ensure the correct sign is preserved when underflow occurs. + +[^7]: + We could have stuck to one extension module by making it more complicated, but the idea was to build the simpler + extension module that would be possible after all scalar kernels are written in C++ headers, move ufuncs over + to it incrementally, and then remove the old extension module after this process is completed. + +[^8]: The Programmers Credo. From a 2016 twitter post by Maciej Cegłowski. +[^9]: + Some small simplifications have been made to keep the explanations concise. To see the library files as they + exist at the moment I'm writing this see the current GitHub permalinks for + [scipy/special/sf_error_state.c](https://github.com/scipy/scipy/blob/03cdb807958066d1af6a2c624803d066c7ab0bce/scipy/special/sf_error_state.c) + and the corresponding header + [scipy/special/sf_error_state.h](https://github.com/scipy/scipy/blob/03cdb807958066d1af6a2c624803d066c7ab0bce/scipy/special/sf_error_state.h). + +[^10]: + For more info on SciPy's move to meson, see this [2021 Quansight blog post](https://labs.quansight.org/blog/2021/07/moving-scipy-to-meson) + from labs Co-Director Ralf Gommers. + +[^11]: https://en.wikipedia.org/wiki/Rpath +[^12]: https://packaging.python.org/en/latest/specifications/binary-distribution-format/ +[^13]: https://learn.microsoft.com/en-us/windows/win32/dlls/dynamic-link-library-search-order +[^14]: https://github.com/adang1345/delvewheel +[^15]: https://github.com/scipy/scipy/pull/20321#pullrequestreview-1969657606 +[^16]: + https://learn.microsoft.com/en-us/cpp/build/exporting-from-a-dll-using-declspec-dllexport?view=msvc-170 + https://learn.microsoft.com/en-us/cpp/build/importing-into-an-application-using-declspec-dllimport?view=msvc-170 + +[^17]: https://en.wikipedia.org/wiki/Race_condition#Data_race +[^18]: https://github.com/scipy/scipy/pull/21956#pullrequestreview-2476680430 From 4c539c74bfeb887d1ed28e1b98940ca365c2c92d Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Thu, 19 Dec 2024 12:36:46 -0500 Subject: [PATCH 07/22] Apply easier to implement suggestions from review comments --- apps/labs/posts/libsf-error-state.md | 75 ++++++++++++++-------------- 1 file changed, 38 insertions(+), 37 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index 92d5e2a71..d46f6c0e6 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -18,11 +18,11 @@ This is the story of the first[^1] shared library to be shipped within SciPy. It complexity SciPy tries to hide from users, while previewing some exciting developments in [scipy.special](https://docs.scipy.org/doc/scipy/reference/special.html). -Python has become wildly popular as a scripting language for scientific and other data intensive applications. It owes +Python has become wildly popular as a language for scientific and other data-intensive applications. It owes much of its success to an exemplary execution of the "compiled core, interpreted shell" principle. One can orchestrate simulation pipelines, preprocess and munge data, set up and use explanatory or predictive statistical models, make plots -and tables, and more, in an expressive high level language—while delegating computationally intensive tasks to compiled -libraries. +and tables, and more, in an expressive high level language — while delegating computationally intensive tasks to +compiled libraries. The default CPython interpreter was designed specifically to make it easy to extend Python programs with efficient native code. The Python C API is well thought out and straightforward to work with. The controversial global interpreter @@ -30,8 +30,8 @@ lock or GIL, a barrier to free threading within Python, has the benefit of makin code, since one need not worry about the thread-safety of wrapped compiled libraries. By wrapping, filling in gaps, and providing user friendly Python APIs to a wide range of battle tested, permissively -licensed and public domain scientific software tools—SciPy's founding developers[^2] were able to kickstart the growth of -the Scientific Python ecosystem. There are now worlds of scientific and data intensive software available in +licensed and public domain scientific software tools — SciPy's founding developers[^2] were able to kickstart the growth +of the Scientific Python ecosystem. There are now worlds of scientific and data intensive software available in Python. Users who spend their time in the interpreted shell may not be aware of the complexity that lies underneath. Journeying deeper into the stack, it can be surprising to see the level of work that can go into making even a relatively simple feature work smoothly. @@ -267,16 +267,17 @@ SpecialFunctionError: scipy.special/Gamma: singularity To create a ufunc for a mathematical function, one needs a scalar implementation of this function, known as a scalar kernel, that's written in a compiled language. Until recently, `scipy.special` had scalar kernels written in all of C, -C++, Fortran 77, and Cython. In August of 2023, Irwin Zaid (izaid) at Oxford proposed rewriting all of the scalar -kernels in C++ header files in such a way that they could be included in both C++ and CUDA programs. This would allow -these scalar kernels to also be used in GPU-aware array libraries like CuPy and PyTorch, improving special function -support across array library backends. I've been supported by the 2020 NASA ROSES grant, _Reinforcing the Foundations of -Scientific Python_ (with travel and compute costs partially covered by the 2023 NumFocus SDG _Streamlined Special -Function Development in SciPy_), to work together with Irwin to put this plan into action. With additional help from -SciPy maintainer Ilhan Polat (ilayn), who made a heroic effort to translate over twenty thousand lines of Fortran scalar -kernel code to C, and contributions from other volunteers, substantial progress has been made. We're now in the process -of splitting these headers into a separate library called xsf. This is a story deserving of its own post. Until then, -see [the first issue in the xsf repo](https://github.com/scipy/xsf/issues/1) for more info. +C++, Fortran 77, and Cython. In August of 2023, Irwin Zaid ([@izaid](https://github.com/izaid)) at Oxford proposed +rewriting all of the scalar kernels in C++ header files in such a way that they could be included in both C++ and CUDA +programs. This would allow these scalar kernels to also be used in GPU-aware array libraries like CuPy and PyTorch, +improving special function support across array library backends. I've been supported by the 2020 NASA ROSES grant, +_Reinforcing the Foundations of Scientific Python_ (with travel and compute costs partially covered by the 2023 NumFocus +SDG _Streamlined Special Function Development in SciPy_), to work together with Irwin to put this plan into action. With +additional help from SciPy maintainer Ilhan Polat ([@ilayn](https://github.com/ilayn)), who made a heroic effort to +translate over twenty thousand lines of Fortran scalar kernel code to C, and contributions from other volunteers, +substantial progress has been made. We're now in the process of splitting these headers into a separate library called +xsf. This is a story deserving of its own post. Until then, see [the first issue in the xsf +repo](https://github.com/scipy/xsf/issues/1) for more info. At the outset of this project, `scipy.special` had been under-maintained for several years. I'm not sure if there were any active maintainers left who fully understood the details of the infrastucture `scipy.special` has been using for @@ -336,8 +337,8 @@ We saw three options: We chose to create a shared library because it seemed like the more principled option. As the saying goes: we do these things not because they are easy, but because we thought they were going to be easy.[^8] Quansight Labs Co-director Ralf -Gommers (rgommers) knew from long experience that this may be difficult to get right, but trusted that we'd be able to -figure it out. +Gommers ([@rgommers](https://github.com/rgommers)) knew from long experience that this may be difficult to get right, +but trusted that we'd be able to figure it out. ## Static vs dynamic linking @@ -411,7 +412,7 @@ enum sf_action_t { ``` And then an array `sf_error_actions` storing the error handling policy associated to each special function error -handling type. We see that the default is to ignore errors all errors, matching what we observed in the example with +handling type. We see that the default is to ignore all errors, matching what we observed in the example with `special.gamma`. ```c @@ -494,7 +495,7 @@ as we thought everything was finally working, another quirk would pop up that ne ### Path troubles -The initial challenge was finding the right invocations to give to the meson build system used by SciPy.[^10] Extension +The initial challenge was finding the right invocations to give to the Meson build system used by SciPy.[^10] Extension modules are configured in the `meson.build` files spread across SciPy's source tree and we needed to figure out how to set up a shared library and link it into each of the special function ufunc extension modules. Irwin and I begin working on this independently, comparing notes as we went. @@ -503,7 +504,7 @@ The first hiccup is that the following invocations were working on Irwin's Mac. Setting up the shared library like this. -```meson +```python sf_error_state_lib = shared_library('sf_error_state', # Name of the library # Implementation files contained it. ['sf_error_state.c'], @@ -515,7 +516,7 @@ sf_error_state_lib = shared_library('sf_error_state', # Name of the library and adding a `link_with` entry to the creation of each extension module: -``` +```python py3.extension_module('_special_ufuncs', ['_special_ufuncs.cpp', '_special_ufuncs_docs.cpp', 'sf_error.cc'], include_directories: ['../_lib', '../_build_utils/src'], @@ -559,7 +560,7 @@ sf_error_state_lib = shared_library('sf_error_state', # Name of the library ``` But even after this, I was still receiving the same error. After consulting `meson`'s -[excellent documentation](https://mesonbuild.com/) and looking at some related issues, it turns out that the `pip` takes +[excellent documentation](https://mesonbuild.com/) and looking at some related issues, it turns out that `pip` takes care to set the `RPATH` for each extension module,[^11] which tells the dynamic linker where to look for shared libraries. To get things to work with `dev.py`, I needed to explicitly set the `RPATH` in `meson` by adding @@ -614,14 +615,14 @@ _load_libsf_error_state() One week in, all CI jobs were passing and the [PR](https://github.com/scipy/scipy/pull/20321) creating `libsf_error_state` was merged. We did it! We fixed the bug we'd introduced in `special.errstate` with months to go -before the next SciPy release—or so we thought. +before the next SciPy release — or so we thought. ### Breaking SciPy for MSVC builds -On May 30th, SciPy maintainer and Conda-Forge core member Axel Obermeir (h-vetinari) posted a comment on his open PR, -[scipy-feedstock/gh-277](https://github.com/conda-forge/scipy-feedstock/pull/277). The first release candidate for -SciPy 1.14.0 had just come out, he was starting the process to add version 1.14.0 to Conda-Forge, and noticed that -Windows builds for SciPy 1.14.0 were failing with the following error: +On May 30th, SciPy maintainer and conda-forge core member [@h-vetinari](https://github.com/h-vetinari) posted a comment +on his open PR, [scipy-feedstock/gh-277](https://github.com/conda-forge/scipy-feedstock/pull/277). The first release +candidate for SciPy 1.14.0 had just come out, he was starting the process to add version 1.14.0 to Conda-Forge, and +noticed that Windows builds for SciPy 1.14.0 were failing with the following error: ``` lld-link: error: undefined symbol: scipy_sf_error_get_action @@ -635,7 +636,7 @@ We had run into another platform specific difference. Fortunately, Axel knew what the problem was. On Linux, Mac OS (and Windows while using MinGW), symbols from shared libraries are exported by default, but with MSVC they must be explicitly exported from shared libraries and explicitly imported into consumers by annotating source code with special compiler directives: `__declspec(dllexport)` for exports -and and `__declspec(dllimport)` for imports.[^16] +and `__declspec(dllimport)` for imports.[^16] He had a recipe ready to use: defining and using macros which conditionally compiled to the right thing depending on their context. @@ -661,8 +662,8 @@ He had a recipe ready to use: defining and using macros which conditionally comp As soon as I had a chance, I fired up a Windows VM again and put together [a PR](https://github.com/scipy/scipy/pull/20937) implementing Axel's solution. After a couple misteps, MSVC builds were working again. There would be no need to push back the release date. A couple weeks later, fellow Quansight Labs member and LFortran/LPython core developer Gagandeep -Singh (czgdp1807) submitted [a PR](https://github.com/scipy/scipy/pull/20985) to add an MSVC CI job, plugging the gap in -SciPy's coverage. +Singh ([@czgdp1807](https://github.com/czgdp1807)) submitted [a PR](https://github.com/scipy/scipy/pull/20985) to add an +MSVC CI job, plugging the gap in SciPy's coverage. ### Thread safety @@ -682,10 +683,10 @@ location. Simultaneous modifications could even leave an entry in some corrupt a is known as a data race and leads to undefined behavior in C and C++.[^17] Weird things can happen when there is a data race. The latest tale in the saga of `libsf_error_state` is [a PR](https://github.com/scipy/scipy/pull/21956) from Edgar -Margffoy (andfoy)—a member of Quansight Labs' free-threaded Python team—to ensure thread safety by declaring the array -`sf_error_actions` thread local. This eliminates the data-race by making it so each thread gets its own separate copy of -the array. Edgar and the others on the free-threaded Python team have been doing great work improving support for -free-threaded Python across the ecosystem for much of this past year. +Margffoy ([@andfoy](https://github.com/andfoy)) — a member of Quansight Labs' free-threaded Python team — to ensure +thread safety by declaring the array `sf_error_actions` thread local. This eliminates the data-race by making it so each +thread gets its own separate copy of the array. Edgar and the others on the free-threaded Python team have been doing +great work improving support for free-threaded Python across the ecosystem for much of this past year. In a curious reversal, it is now (the `win32` flavor of) MinGW that is causing trouble due to lack of proper threading support.[^18] @@ -694,9 +695,9 @@ support.[^18] Now that the dust has mostly settled, it's valuable to look back and try to judge whether we made the right choice. Over the entire timeframe, the primary goal for Irwin and I was always to make as much of SciPy special available on GPUs as -possible—with secondary goals of simplifying SciPy special's build process and improving the scalar kernel codebase. The -story of `libsf_error_state` is that of a side quest—a story of needing to fix something we broke in pursuit of bigger -things—and the challenges we faced and brought to others because we underestimated the difficulty of our chosen +possible, with secondary goals of simplifying SciPy special's build process and improving the scalar kernel codebase. The +story of `libsf_error_state` is that of a side quest — a story of needing to fix something we broke in pursuit of bigger +things — and the challenges we faced and brought to others because we underestimated the difficulty of our chosen solution. Having a single array for managing shared state between the extension modules appeals to a desire to have a single source of truth [], but looking back, it would have taken much less time to have each extension module carry its own copy of the array, and to always update them all together. From ef8c63001136d2d52d09926501f6f5d5715b034e Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Thu, 19 Dec 2024 12:49:45 -0500 Subject: [PATCH 08/22] Make mentions of Meson consistent --- apps/labs/posts/libsf-error-state.md | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index d46f6c0e6..92674eb60 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -545,7 +545,7 @@ using the `"python dev.py build"` workflow and he was using an editable install: For the editable install, SciPy is installed directly in its own project folder, and the shared library and the relevant extension modules were all being installed next to each other in `~/scipy/scipy/special`. For the `dev.py` workflow, SciPy is installed elsewhere. Since I didn't specify where to install the shared library, it got put in the wrong -place. It turned out what I had to do was configure the `install_dir` in `meson` like this: +place. It turned out what I had to do was configure the `install_dir` in Meson like this: ``` sf_error_state_lib = shared_library('sf_error_state', # Name of the library @@ -559,11 +559,11 @@ sf_error_state_lib = shared_library('sf_error_state', # Name of the library ) ``` -But even after this, I was still receiving the same error. After consulting `meson`'s +But even after this, I was still receiving the same error. After consulting Meson's [excellent documentation](https://mesonbuild.com/) and looking at some related issues, it turns out that `pip` takes care to set the `RPATH` for each extension module,[^11] which tells the dynamic linker where to look for shared libraries. -To get things to work with `dev.py`, I needed to explicitly set the `RPATH` in `meson` by adding +To get things to work with `dev.py`, I needed to explicitly set the `RPATH` in Meson by adding `install_rpath: '$ORIGIN'` to each extension module. `'$ORIGIN'` means to search in the same folder as the extension module. @@ -772,10 +772,7 @@ later this is finally happening. I should probably go back and read old comment and the corresponding header [scipy/special/sf_error_state.h](https://github.com/scipy/scipy/blob/03cdb807958066d1af6a2c624803d066c7ab0bce/scipy/special/sf_error_state.h). -[^10]: - For more info on SciPy's move to meson, see this [2021 Quansight blog post](https://labs.quansight.org/blog/2021/07/moving-scipy-to-meson) - from labs Co-Director Ralf Gommers. - +[^10]: For more info on SciPy's move to Meson, see Ralf's [2021 Quansight blog post](https://labs.quansight.org/blog/2021/07/moving-scipy-to-meson) [^11]: https://en.wikipedia.org/wiki/Rpath [^12]: https://packaging.python.org/en/latest/specifications/binary-distribution-format/ [^13]: https://learn.microsoft.com/en-us/windows/win32/dlls/dynamic-link-library-search-order From 5974e5cf420950bfac1dd1f54798bfa098acb4ba Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Thu, 19 Dec 2024 12:50:29 -0500 Subject: [PATCH 09/22] Move funding acknowledgement to end --- apps/labs/posts/libsf-error-state.md | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index 92674eb60..a46b9dea6 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -270,14 +270,12 @@ kernel, that's written in a compiled language. Until recently, `scipy.special` h C++, Fortran 77, and Cython. In August of 2023, Irwin Zaid ([@izaid](https://github.com/izaid)) at Oxford proposed rewriting all of the scalar kernels in C++ header files in such a way that they could be included in both C++ and CUDA programs. This would allow these scalar kernels to also be used in GPU-aware array libraries like CuPy and PyTorch, -improving special function support across array library backends. I've been supported by the 2020 NASA ROSES grant, -_Reinforcing the Foundations of Scientific Python_ (with travel and compute costs partially covered by the 2023 NumFocus -SDG _Streamlined Special Function Development in SciPy_), to work together with Irwin to put this plan into action. With -additional help from SciPy maintainer Ilhan Polat ([@ilayn](https://github.com/ilayn)), who made a heroic effort to -translate over twenty thousand lines of Fortran scalar kernel code to C, and contributions from other volunteers, -substantial progress has been made. We're now in the process of splitting these headers into a separate library called -xsf. This is a story deserving of its own post. Until then, see [the first issue in the xsf -repo](https://github.com/scipy/xsf/issues/1) for more info. +improving special function support across array library backends. Over the past year, I've been working together with +Irwin to put this plan into action. With additional help from SciPy maintainer Ilhan Polat +([@ilayn](https://github.com/ilayn)), who made a heroic effort to translate over twenty thousand lines of Fortran scalar +kernel code to C, and contributions from other volunteers, substantial progress has been made. We're now in the process +of splitting these headers into a separate library called xsf. This is a story deserving of its own post. Until then, +see [the first issue in the xsf repo](https://github.com/scipy/xsf/issues/1) for more info. At the outset of this project, `scipy.special` had been under-maintained for several years. I'm not sure if there were any active maintainers left who fully understood the details of the infrastucture `scipy.special` has been using for @@ -740,6 +738,10 @@ Before we finish, one more gem: in [another GitHub comment from suggested to "make `sf_error` error handling thread-local (which probably should be done in any case)". Over eight years later this is finally happening. I should probably go back and read old comment threads more often. +### Funding acknowledgements + +This work was supported by the 2020 NASA ROSES grant, _Reinforcing the Foundations of Scientific Python_. + --- [^1]: The first regular shared library, not including Python extension modules. From 949f4a46f6abba09a666c74257835854d226f5c2 Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Thu, 19 Dec 2024 13:02:20 -0500 Subject: [PATCH 10/22] Some small fixes --- apps/labs/posts/libsf-error-state.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index a46b9dea6..5c5e2dfa8 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -631,7 +631,7 @@ not being found. The thing is, at that point in time there was still a key gap i were Windows builds in CI, they all used the MinGW compiler toolchain, with no jobs using MSVC (Microsoft Visual C++). We had run into another platform specific difference. -Fortunately, Axel knew what the problem was. On Linux, Mac OS (and Windows while using MinGW), symbols from shared +Fortunately, h-vetinari knew what the problem was. On Linux, Mac OS (and Windows while using MinGW), symbols from shared libraries are exported by default, but with MSVC they must be explicitly exported from shared libraries and explicitly imported into consumers by annotating source code with special compiler directives: `__declspec(dllexport)` for exports and `__declspec(dllimport)` for imports.[^16] @@ -658,7 +658,7 @@ He had a recipe ready to use: defining and using macros which conditionally comp ``` As soon as I had a chance, I fired up a Windows VM again and put together [a PR](https://github.com/scipy/scipy/pull/20937) -implementing Axel's solution. After a couple misteps, MSVC builds were working again. There would be no need to push +implementing h-vetinari's solution. After a couple missteps, MSVC builds were working again. There would be no need to push back the release date. A couple weeks later, fellow Quansight Labs member and LFortran/LPython core developer Gagandeep Singh ([@czgdp1807](https://github.com/czgdp1807)) submitted [a PR](https://github.com/scipy/scipy/pull/20985) to add an MSVC CI job, plugging the gap in SciPy's coverage. From e2974eb9a1acc49bb5eb98f6ea050c4373a2e2ea Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Thu, 19 Dec 2024 13:02:55 -0500 Subject: [PATCH 11/22] Make it clear that we're not aware of anyone doing this first --- apps/labs/posts/libsf-error-state.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index 5c5e2dfa8..e4473bcb6 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -486,8 +486,9 @@ to take. ## The many battles to actually ship it The shared library `libsf_error_state`'s contents are fairly simple, but how does one actually ship a shared library -within a Python package like SciPy? The process for creating and using shared libraries depends on the operating system -and compiler toolchain. SciPy supports a wide range of platforms in aim of its goal to democratize access to scientific +within a Python package like SciPy? Even now, we've yet to find any prior examples of a Python package that contains +an internal shared library. The process for creating and using shared libraries depends on the operating system and +compiler toolchain. SciPy supports a wide range of platforms in aim of its goal to democratize access to scientific computing tools and the greatest challenge turned out to be getting things to work on each of them. Several times, just as we thought everything was finally working, another quirk would pop up that needed to be addressed. From 07c5fcbe70acc249bb9a24b7153477abd5016d59 Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Thu, 19 Dec 2024 13:03:43 -0500 Subject: [PATCH 12/22] Change a word choice back --- apps/labs/posts/libsf-error-state.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index e4473bcb6..ed56b6fac 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -670,7 +670,7 @@ In the introduction I'd mentioned that CPython's global interpreter lock (GIL) m or other compiled languages since one doesn't need to worry about the thread safety of wrapped code. Still, being able to have only one thread in a running Python process execute Python code at a time is greatly limiting for multicore applications. In October 2023, a Python Enhancement Proposal (PEP) was accepted to make the GIL optional. This proposal, -[PEP 703](https://peps.python.org/pep-0703/), is well worth reading for its in-depth summary of the surrounding +[PEP 703](https://peps.python.org/pep-0703/), is well worth reading for its thoughtful summary of the surrounding issues. CPython 3.13 launched with an optional [free-threaded mode](https://docs.python.org/3.13/whatsnew/3.13.html#free-threaded-cpython) which supports running with the GIL disabled. Free-threading in Python offers great promise, but to take advantage of From 0f0e4f224d73ebb13ecd219954b48de1aaf0f8e7 Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Thu, 19 Dec 2024 13:18:25 -0500 Subject: [PATCH 13/22] Make transition to ufuncs less abrupt --- apps/labs/posts/libsf-error-state.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index ed56b6fac..804081ba4 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -38,6 +38,9 @@ a relatively simple feature work smoothly. ## NumPy Universal functions +The simple feature in this case is error handling for mathematical functions in `scipy.special` which operate +elementwise over NumPy arrays. Let's look into this more closely. + A NumPy [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) represents an arbitrary dimensional array of elements of the same data type, stored in a continguous buffer at the machine level. A [universal function](https://numpy.org/doc/stable/reference/ufuncs.html) or ufunc for short is a Python level function which From 1b026bf0efbc07d5bdba754be3e195543291f2c0 Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Thu, 19 Dec 2024 14:00:26 -0500 Subject: [PATCH 14/22] Fix incorrect explanation for why pip was working --- apps/labs/posts/libsf-error-state.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index 804081ba4..514530034 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -561,13 +561,13 @@ sf_error_state_lib = shared_library('sf_error_state', # Name of the library ) ``` -But even after this, I was still receiving the same error. After consulting Meson's -[excellent documentation](https://mesonbuild.com/) and looking at some related issues, it turns out that `pip` takes -care to set the `RPATH` for each extension module,[^11] which tells the dynamic linker where to look for shared libraries. +But even after this, I was still receiving the same error. I asked Ralf about it, and found what happens is that `pip` +goes through [meson-python](https://github.com/mesonbuild/meson-python) while `dev.py` uses Meson directly. +`meson-python` takes care to set the `RPATH` for each extension module, which tells the dynamic linker where to look +for shared libraries.[^11] -To get things to work with `dev.py`, I needed to explicitly set the `RPATH` in Meson by adding -`install_rpath: '$ORIGIN'` to each extension module. `'$ORIGIN'` means to search in the same folder as the extension -module. +To get things to work with `dev.py`, I needed to explicitly set the `RPATH` in Meson by adding `install_rpath:'$ORIGIN'` +to each extension module. `'$ORIGIN'` in this case means to search in the same folder as the extension module. ### Building Wheels on Windows From d5dbf8bb32a447dc8ff1f7f29942b1bbbc2fbe3d Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Thu, 19 Dec 2024 14:15:26 -0500 Subject: [PATCH 15/22] Fix more inaccuracies --- apps/labs/posts/libsf-error-state.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index 514530034..70f2f5aaf 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -632,13 +632,13 @@ lld-link: error: undefined symbol: scipy_sf_error_get_action How could this be? SciPy's own CI jobs were passing on Windows, but here, symbols from `lib_sf_error_state` were clearly not being found. The thing is, at that point in time there was still a key gap in SciPy's CI coverage. Although there -were Windows builds in CI, they all used the MinGW compiler toolchain, with no jobs using MSVC (Microsoft Visual C++). -We had run into another platform specific difference. +were Windows builds in CI, they all used the MinGW compiler toolchain. There were no jobs using Clang-cl, the toolchain +used for Windows builds in conda-forge. We had run into another platform specific difference. Fortunately, h-vetinari knew what the problem was. On Linux, Mac OS (and Windows while using MinGW), symbols from shared -libraries are exported by default, but with MSVC they must be explicitly exported from shared libraries and explicitly -imported into consumers by annotating source code with special compiler directives: `__declspec(dllexport)` for exports -and `__declspec(dllimport)` for imports.[^16] +libraries are exported by default, but on Windows with Clang-cl or MSVC they must be explicitly exported from shared +libraries and explicitly imported into consumers by annotating source code with special compiler directives: +`__declspec(dllexport)` for exports and `__declspec(dllimport)` for imports.[^16] He had a recipe ready to use: defining and using macros which conditionally compiled to the right thing depending on their context. @@ -662,10 +662,10 @@ He had a recipe ready to use: defining and using macros which conditionally comp ``` As soon as I had a chance, I fired up a Windows VM again and put together [a PR](https://github.com/scipy/scipy/pull/20937) -implementing h-vetinari's solution. After a couple missteps, MSVC builds were working again. There would be no need to push +implementing h-vetinari's solution. After a couple missteps, Clang-cl builds were working again. There would be no need to push back the release date. A couple weeks later, fellow Quansight Labs member and LFortran/LPython core developer Gagandeep Singh ([@czgdp1807](https://github.com/czgdp1807)) submitted [a PR](https://github.com/scipy/scipy/pull/20985) to add an -MSVC CI job, plugging the gap in SciPy's coverage. +MSVC CI job, helping plug the gap in SciPy's coverage. ### Thread safety @@ -690,8 +690,8 @@ thread safety by declaring the array `sf_error_actions` thread local. This elimi thread gets its own separate copy of the array. Edgar and the others on the free-threaded Python team have been doing great work improving support for free-threaded Python across the ecosystem for much of this past year. -In a curious reversal, it is now (the `win32` flavor of) MinGW that is causing trouble due to lack of proper threading -support.[^18] +In a curious reversal, it is now (the `win32` flavor of) MinGW that is causing trouble due to lack of support for +thread local variables. [^18] ## Reflections From 5ef681bf0149b5f440b770d730910525544285d7 Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Thu, 19 Dec 2024 15:47:18 -0500 Subject: [PATCH 16/22] Further updates --- apps/labs/posts/libsf-error-state.md | 114 +++++++++++++-------------- 1 file changed, 56 insertions(+), 58 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index 70f2f5aaf..3e698a52d 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -16,7 +16,7 @@ hero: This is the story of the first[^1] shared library to be shipped within SciPy. It offers a glimpse at some of the complexity SciPy tries to hide from users, while previewing some exciting developments in -[scipy.special](https://docs.scipy.org/doc/scipy/reference/special.html). +[`scipy.special`](https://docs.scipy.org/doc/scipy/reference/special.html). Python has become wildly popular as a language for scientific and other data-intensive applications. It owes much of its success to an exemplary execution of the "compiled core, interpreted shell" principle. One can orchestrate @@ -41,13 +41,13 @@ a relatively simple feature work smoothly. The simple feature in this case is error handling for mathematical functions in `scipy.special` which operate elementwise over NumPy arrays. Let's look into this more closely. -A NumPy [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) represents an arbitrary +A NumPy [`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) represents an arbitrary dimensional array of elements of the same data type, stored in a continguous buffer at the machine level. A [universal function](https://numpy.org/doc/stable/reference/ufuncs.html) or ufunc for short is a Python level function which applies a transform to each element of an `ndarray` by looping and operating elementwise over the underlying contiguous buffer in efficient native code. -Here's the ufunc [np.sqrt](https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html) in action +Here's the ufunc [`np.sqrt`](https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html) in action: ``` In [1]: import numpy as np @@ -65,7 +65,7 @@ array([1. , 1.41421356, 1.73205081, 2. , 2.23606798, ``` For large arrays, the speedup from applying `np.sqrt` over an `ndarray` rather than -[math.sqrt](https://docs.python.org/3/library/math.html#math.sqrt) over each element of a list is significant. On my +[`math.sqrt`](https://docs.python.org/3/library/math.html#math.sqrt) over each element of a list is significant. On my laptop I witnessed: ``` @@ -85,8 +85,8 @@ In [5]: %timeit [math.sqrt(t) for t in B] ## Error handling What should happen if one gives invalid input to a ufunc? If we pass a negative -[float](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex) to `math.sqrt` a -[ValueError](https://docs.python.org/3/library/exceptions.html#ValueError) is raised.[^3] +[`float`](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex) to `math.sqrt` a +[`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) is raised.[^3] ``` In [1]: import math @@ -168,9 +168,9 @@ FloatingPointError: invalid value encountered in sqrt ## scipy.special -NumPy has over 60 ufuncs available [^4] for a range of mathematical functions and operations, but more specialized -functions useful in the sciences and engineering are out of scope. Ufuncs for over 230 such functions can be found -instead in `scipy.special`.[^5] +NumPy has over 60 [ufuncs available](https://numpy.org/doc/stable/reference/ufuncs.html#available-ufuncs) for a range +of mathematical functions and operations, but more specialized functions useful in the sciences and engineering are out +of scope. Ufuncs for over 230 such functions can be found instead in `scipy.special`. Just for fun, let's use [`scipy.special.gamma`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gamma.html), which implements a @@ -238,7 +238,7 @@ In [6]: ``` -`np.errstate` had no impact on error handling, there were no warnings, but we do see `NaN`s in the output.[^6] What's +`np.errstate` had no impact on error handling, there were no warnings, but we do see `NaN`s in the output.[^4] What's going on? Well, NumPy's ufuncs are provided in a C [extension module](https://docs.python.org/3/extending/extending.html) that's part of NumPy. There's some C code in this extension module for managing the state for error handling policies. The ufuncs in `scipy.special` come from a different extension @@ -290,7 +290,7 @@ In the Spring of 2024, things moved fairly quickly because Irwin was able to put things off the ground. He found that the existing ufunc infrastructure was not flexible enough for work he had planned involving [Generalized universal functions](https://numpy.org/doc/stable/reference/c-api/generalized-ufuncs.html) (gufuncs for short) so he wrote new machinery from scratch. Ufuncs and gufuncs created with the new machinery live in a -separate extension module from those created with the old machinery.[^7] It will be a great win in terms of simplifying +separate extension module from those created with the old machinery.[^5] It will be a great win in terms of simplifying `scipy.special`'s build process when all ufuncs can be moved over to the new infrastructure but in the short term there was a problem. @@ -298,7 +298,7 @@ was a problem. After splitting them between multiple extension modules, the error policy state could no longer be easily shared between ufuncs. We noticed something was wrong due to a failure in a doctest for -[special.seterr](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.seterr.html#scipy.special.seterr). +[`special.seterr`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.seterr.html#scipy.special.seterr). The expected error was not being raised. ``` @@ -315,7 +315,7 @@ The expected error was not being raised. >>> _ = sc.seterr(**olderr) ``` -[gammaln](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gammaln.html) was one of a handful of +[`gammaln`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gammaln.html) was one of a handful of ufuncs moved to the new extension module. Both extension modules contained a separate copy of the state for managing error handling policies, but the user facing @@ -337,7 +337,7 @@ We saw three options: 3. Keep the error handling state in one of the extension modules and have it retrieved from there by the others. We chose to create a shared library because it seemed like the more principled option. As the saying goes: we do these -things not because they are easy, but because we thought they were going to be easy.[^8] Quansight Labs Co-director Ralf +things not because they are easy, but because we thought they were going to be easy.[^6] Quansight Labs Co-director Ralf Gommers ([@rgommers](https://github.com/rgommers)) knew from long experience that this may be difficult to get right, but trusted that we'd be able to figure it out. @@ -400,7 +400,7 @@ modules with a shared library. It's perfectly valid to link one shared library w ## The relevant code Having decided on the shared library approach, we looked into code `scipy.special` used for managing error handling state. -In essence, things were very simple. Let's walk through the core lines.[^9] +In essence, things were very simple. Let's walk through the core lines.[^7] There is an enumeration type `sf_action_t` giving human readable names to the different error handling policies: @@ -497,7 +497,7 @@ as we thought everything was finally working, another quirk would pop up that ne ### Path troubles -The initial challenge was finding the right invocations to give to the Meson build system used by SciPy.[^10] Extension +The initial challenge was finding the right invocations to give to the [Meson build system](https://mesonbuild.com/) used by SciPy.[^8] Extension modules are configured in the `meson.build` files spread across SciPy's source tree and we needed to figure out how to set up a shared library and link it into each of the special function ufunc extension modules. Irwin and I begin working on this independently, comparing notes as we went. @@ -547,7 +547,7 @@ using the `"python dev.py build"` workflow and he was using an editable install: For the editable install, SciPy is installed directly in its own project folder, and the shared library and the relevant extension modules were all being installed next to each other in `~/scipy/scipy/special`. For the `dev.py` workflow, SciPy is installed elsewhere. Since I didn't specify where to install the shared library, it got put in the wrong -place. It turned out what I had to do was configure the `install_dir` in Meson like this: +place. I fixed things by directly configuring the `install_dir` in Meson like this: ``` sf_error_state_lib = shared_library('sf_error_state', # Name of the library @@ -561,10 +561,10 @@ sf_error_state_lib = shared_library('sf_error_state', # Name of the library ) ``` -But even after this, I was still receiving the same error. I asked Ralf about it, and found what happens is that `pip` +Even after this, I was still seeing the same error. I asked Ralf about it, and found what happens is that `pip` goes through [meson-python](https://github.com/mesonbuild/meson-python) while `dev.py` uses Meson directly. -`meson-python` takes care to set the `RPATH` for each extension module, which tells the dynamic linker where to look -for shared libraries.[^11] +`meson-python` takes care to set the [`RPATH`](https://en.wikipedia.org/wiki/Rpath) for each extension module, +which tells the dynamic linker where to look for shared libraries. To get things to work with `dev.py`, I needed to explicitly set the `RPATH` in Meson by adding `install_rpath:'$ORIGIN'` to each extension module. `'$ORIGIN'` in this case means to search in the same folder as the extension module. @@ -573,18 +573,21 @@ to each extension module. `'$ORIGIN'` in this case means to search in the same f After setting `install_dir` and `install_rpath` correctly, all but one of SciPy's CI jobs were passing. The sole failing job involved building a wheel on Windows. A -wheel can be thought of as a -precompiled binary for a Python package.[^12] The underlying issue was that Windows does not have support for something like -`RPATH`, following a less configurable set of rules for determining the search path for shared libraries.[^13] +[wheel](https://packaging.python.org/en/latest/specifications/binary-distribution-format/) can be thought of as a +precompiled binary for a Python package. The underlying issue was that Windows does not have support for something like +`RPATH`, following a less configurable [set of rules](https://learn.microsoft.com/en-us/windows/win32/dlls/dynamic-link-library-search-order) +for determining the search path for shared libraries. It took us a day of work to get to this point. Since things were not as straightforward as expected; I took it from here. At the time I didn't really have any experience developing for Windows and didn't even have a Windows machine available at home to use. I looked up how to run Windows in a VM and got to work. -I found a solution using delvewheel, a tool for bundling shared libraries into wheels on Windows.[^14] It worked on my -machine but wasn't viable for production.[^15] Fortunately, Ralf had seen this problem before and had a ready made solution: -manually loading the shared library from within `scipy/special/__init__.py` so it would be available when needed: +I found a solution using [delvewheel](https://github.com/adang1345/delvewheel), a tool for bundling shared libraries +into wheels on Windows. It worked on my machine but +[wasn't viable for production](https://github.com/scipy/scipy/pull/20321#pullrequestreview-1969657606). Fortunately, +Ralf had seen this problem before and had a ready made solution: manually loading the shared library from within +`scipy/special/__init__.py` so it would be available when needed: ```python def _load_libsf_error_state(): @@ -632,13 +635,18 @@ lld-link: error: undefined symbol: scipy_sf_error_get_action How could this be? SciPy's own CI jobs were passing on Windows, but here, symbols from `lib_sf_error_state` were clearly not being found. The thing is, at that point in time there was still a key gap in SciPy's CI coverage. Although there -were Windows builds in CI, they all used the MinGW compiler toolchain. There were no jobs using Clang-cl, the toolchain -used for Windows builds in conda-forge. We had run into another platform specific difference. +were Windows builds in CI, they all used the [MinGW](https://en.wikipedia.org/wiki/MinGW) compiler toolchain. There were no jobs using +[MSVC](https://visualstudio.microsoft.com/vs/features/cplusplus/) or [Clang-cl](https://clang.llvm.org/docs/MSVCCompatibility.html), +a Clang compiler driver that aims for compatability with MSVC. Clang-cl is used for Windows builds in conda-forge and we had run into +another platform specific difference. Fortunately, h-vetinari knew what the problem was. On Linux, Mac OS (and Windows while using MinGW), symbols from shared -libraries are exported by default, but on Windows with Clang-cl or MSVC they must be explicitly exported from shared +libraries are exported by default, but on Windows with MSVC or Clang-cl they must be explicitly exported from shared libraries and explicitly imported into consumers by annotating source code with special compiler directives: -`__declspec(dllexport)` for exports and `__declspec(dllimport)` for imports.[^16] +[`__declspec(dllexport)`](https://learn.microsoft.com/en-us/cpp/build/exporting-from-a-dll-using-declspec-dllexport?view=msvc-170) +for exports and +[`__declspec(dllimport)`](https://learn.microsoft.com/en-us/cpp/build/importing-into-an-application-using-declspec-dllimport?view=msvc-170) +for imports. He had a recipe ready to use: defining and using macros which conditionally compiled to the right thing depending on their context. @@ -664,8 +672,8 @@ He had a recipe ready to use: defining and using macros which conditionally comp As soon as I had a chance, I fired up a Windows VM again and put together [a PR](https://github.com/scipy/scipy/pull/20937) implementing h-vetinari's solution. After a couple missteps, Clang-cl builds were working again. There would be no need to push back the release date. A couple weeks later, fellow Quansight Labs member and LFortran/LPython core developer Gagandeep -Singh ([@czgdp1807](https://github.com/czgdp1807)) submitted [a PR](https://github.com/scipy/scipy/pull/20985) to add an -MSVC CI job, helping plug the gap in SciPy's coverage. +Singh ([@czgdp1807](https://github.com/czgdp1807)) submitted [a PR](https://github.com/scipy/scipy/pull/20985) to plug the +gap in SciPy's CI coverage. ### Thread safety @@ -682,16 +690,18 @@ it, extensions need to be rewritten with thread safety in mind. `libsf_error_state` is obviously not thread safe. There is a global array carrying the current state of special function error handling policies with nothing to stop competing threads from trying to access the same memory location. Simultaneous modifications could even leave an entry in some corrupt and indeterminate state. This situation -is known as a data race and leads to undefined behavior in C and C++.[^17] Weird things can happen when there is a data race. +is known as a [data race](https://en.wikipedia.org/wiki/Race_condition#Data_race) and leads to +[undefined behavior](https://en.wikipedia.org/wiki/Undefined_behavior) in C and C++. Weird things can happen when there +is a data race. The latest tale in the saga of `libsf_error_state` is [a PR](https://github.com/scipy/scipy/pull/21956) from Edgar Margffoy ([@andfoy](https://github.com/andfoy)) — a member of Quansight Labs' free-threaded Python team — to ensure thread safety by declaring the array `sf_error_actions` thread local. This eliminates the data-race by making it so each thread gets its own separate copy of the array. Edgar and the others on the free-threaded Python team have been doing -great work improving support for free-threaded Python across the ecosystem for much of this past year. +great work improving support for free-threaded Python across the ecosystem for much of this past year.[^9] In a curious reversal, it is now (the `win32` flavor of) MinGW that is causing trouble due to lack of support for -thread local variables. [^18] +thread local variables. ## Reflections @@ -701,7 +711,7 @@ possible, with secondary goals of simplifying SciPy special's build process and story of `libsf_error_state` is that of a side quest — a story of needing to fix something we broke in pursuit of bigger things — and the challenges we faced and brought to others because we underestimated the difficulty of our chosen solution. Having a single array for managing shared state between the extension modules appeals to a desire to have a -single source of truth [], but looking back, it would have taken much less time to have each extension module carry its +single source of truth, but looking back, it would have taken much less time to have each extension module carry its own copy of the array, and to always update them all together. While conducting research for this blog post, I discovered another wrinkle. Just beneath our noses, there was another @@ -724,9 +734,9 @@ From [github.com/scipy/.../\_ufuncs_extra_code.pxi](https://github.com/scipy/sci ``` I also found [an October 2016 comment on GitHub](https://github.com/scipy/scipy/pull/6722#issuecomment-256539142) from -currently inactive SciPy maintainer Josh Wilson (person142), the original author of SciPy special's error handling -features, which shows he was already aware of the problem we ended up facing. He was trying to make special function -error handling work correctly in +currently inactive SciPy maintainer Josh Wilson ([@person142](https://github.com/person142)), the original author of +SciPy special's error handling features, which shows he was already aware of the problem we ended up facing. He was +trying to make special function error handling work correctly in [`scipy.special.cython_special`](https://docs.scipy.org/doc/scipy/reference/special.cython_special.html), a module for calling scalar versions of special function from Cython. To quote: @@ -755,9 +765,7 @@ This work was supported by the 2020 NASA ROSES grant, _Reinforcing the Foundatio output type should depend only on the input type, not the input value. Allowing the latter makes code more difficult to reason about, both for programmers and for tools like static type checkers and jit compilers. -[^4]: https://numpy.org/doc/stable/reference/ufuncs.html#available-ufuncs -[^5]: https://docs.scipy.org/doc/scipy/reference/special.html -[^6]: +[^4]: Those using `scipy<1.15` will see `inf` instead of `nan` at negative integers due to a bug in `special.gamma` which was fixed in [the PR scipy/#21827](https://github.com/scipy/scipy/pull/21827).If you're curious why `special.gamma(0.)` evaluated to `inf`, note that the IEE-754 floating point standard requires signed zeros `+0.` @@ -765,28 +773,18 @@ This work was supported by the 2020 NASA ROSES grant, _Reinforcing the Foundatio `special.gamma(+0.)` evaluates the limit of the Gamma function as `x` approaches `0` from the right. Signed zeros can be used to ensure the correct sign is preserved when underflow occurs. -[^7]: +[^5]: We could have stuck to one extension module by making it more complicated, but the idea was to build the simpler extension module that would be possible after all scalar kernels are written in C++ headers, move ufuncs over to it incrementally, and then remove the old extension module after this process is completed. -[^8]: The Programmers Credo. From a 2016 twitter post by Maciej Cegłowski. -[^9]: +[^6]: The Programmers Credo. From a 2016 twitter post by Maciej Cegłowski. +[^7]: Some small simplifications have been made to keep the explanations concise. To see the library files as they - exist at the moment I'm writing this see the current GitHub permalinks for + exist at the moment I'm writing this, see these GitHub permalinks for [scipy/special/sf_error_state.c](https://github.com/scipy/scipy/blob/03cdb807958066d1af6a2c624803d066c7ab0bce/scipy/special/sf_error_state.c) and the corresponding header [scipy/special/sf_error_state.h](https://github.com/scipy/scipy/blob/03cdb807958066d1af6a2c624803d066c7ab0bce/scipy/special/sf_error_state.h). -[^10]: For more info on SciPy's move to Meson, see Ralf's [2021 Quansight blog post](https://labs.quansight.org/blog/2021/07/moving-scipy-to-meson) -[^11]: https://en.wikipedia.org/wiki/Rpath -[^12]: https://packaging.python.org/en/latest/specifications/binary-distribution-format/ -[^13]: https://learn.microsoft.com/en-us/windows/win32/dlls/dynamic-link-library-search-order -[^14]: https://github.com/adang1345/delvewheel -[^15]: https://github.com/scipy/scipy/pull/20321#pullrequestreview-1969657606 -[^16]: - https://learn.microsoft.com/en-us/cpp/build/exporting-from-a-dll-using-declspec-dllexport?view=msvc-170 - https://learn.microsoft.com/en-us/cpp/build/importing-into-an-application-using-declspec-dllimport?view=msvc-170 - -[^17]: https://en.wikipedia.org/wiki/Race_condition#Data_race -[^18]: https://github.com/scipy/scipy/pull/21956#pullrequestreview-2476680430 +[^8]: For more info on SciPy's move to Meson, see Ralf's [2021 Quansight blog post](https://labs.quansight.org/blog/2021/07/moving-scipy-to-meson) +[^9]: https://labs.quansight.org/blog/free-threaded-python-rollout From 164ce1ec791083a019f08228e61ed9302bf1d9ec Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Thu, 19 Dec 2024 15:51:27 -0500 Subject: [PATCH 17/22] Minor edit --- apps/labs/posts/libsf-error-state.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index 3e698a52d..a1b2a9a49 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -781,7 +781,7 @@ This work was supported by the 2020 NASA ROSES grant, _Reinforcing the Foundatio [^6]: The Programmers Credo. From a 2016 twitter post by Maciej Cegłowski. [^7]: Some small simplifications have been made to keep the explanations concise. To see the library files as they - exist at the moment I'm writing this, see these GitHub permalinks for + exist at the moment I'm writing this, see the the following GitHub permalinks for [scipy/special/sf_error_state.c](https://github.com/scipy/scipy/blob/03cdb807958066d1af6a2c624803d066c7ab0bce/scipy/special/sf_error_state.c) and the corresponding header [scipy/special/sf_error_state.h](https://github.com/scipy/scipy/blob/03cdb807958066d1af6a2c624803d066c7ab0bce/scipy/special/sf_error_state.h). From 96858baedc46c3a56952360a84bac535d86f67c5 Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Thu, 19 Dec 2024 18:43:47 -0500 Subject: [PATCH 18/22] Move another footnote to a link in the text --- apps/labs/posts/libsf-error-state.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index a1b2a9a49..1d4a2a0c1 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -695,7 +695,8 @@ is known as a [data race](https://en.wikipedia.org/wiki/Race_condition#Data_race is a data race. The latest tale in the saga of `libsf_error_state` is [a PR](https://github.com/scipy/scipy/pull/21956) from Edgar -Margffoy ([@andfoy](https://github.com/andfoy)) — a member of Quansight Labs' free-threaded Python team — to ensure +Margffoy ([@andfoy](https://github.com/andfoy)) — a member of Quansight Labs' +[free-threaded Python team](https://labs.quansight.org/blog/free-threaded-python-rollout) — to ensure thread safety by declaring the array `sf_error_actions` thread local. This eliminates the data-race by making it so each thread gets its own separate copy of the array. Edgar and the others on the free-threaded Python team have been doing great work improving support for free-threaded Python across the ecosystem for much of this past year.[^9] @@ -787,4 +788,3 @@ This work was supported by the 2020 NASA ROSES grant, _Reinforcing the Foundatio [scipy/special/sf_error_state.h](https://github.com/scipy/scipy/blob/03cdb807958066d1af6a2c624803d066c7ab0bce/scipy/special/sf_error_state.h). [^8]: For more info on SciPy's move to Meson, see Ralf's [2021 Quansight blog post](https://labs.quansight.org/blog/2021/07/moving-scipy-to-meson) -[^9]: https://labs.quansight.org/blog/free-threaded-python-rollout From 046a070c0d67a778c38e143d3a9be68ac5d1cd30 Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Fri, 20 Dec 2024 00:03:10 -0500 Subject: [PATCH 19/22] Another round of small updates --- apps/labs/posts/libsf-error-state.md | 35 +++++++++++++++------------- 1 file changed, 19 insertions(+), 16 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index 1d4a2a0c1..0171c0277 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -38,7 +38,7 @@ a relatively simple feature work smoothly. ## NumPy Universal functions -The simple feature in this case is error handling for mathematical functions in `scipy.special` which operate +The simple feature in question is error handling for mathematical functions in `scipy.special` that operate elementwise over NumPy arrays. Let's look into this more closely. A NumPy [`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) represents an arbitrary @@ -174,7 +174,8 @@ of scope. Ufuncs for over 230 such functions can be found instead in `scipy.spec Just for fun, let's use [`scipy.special.gamma`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gamma.html), which implements a -continuous extension of the factorial function called the Gamma function, to reproduce a plot from the Wikipedia article +continuous extension of the factorial function called the [Gamma function](https://en.wikipedia.org/wiki/Gamma_function), +to reproduce a plot from the Wikipedia article ["_Volume of an n-ball_"](https://en.wikipedia.org/wiki/Volume_of_an_n-ball). The plot shows how the volume of a solid multi-dimensional sphere depends on the dimension `n` when the radius `R` is one of 0.9, 1.0, or 1.1. @@ -244,7 +245,7 @@ module](https://docs.python.org/3/extending/extending.html) that's part of NumPy module for managing the state for error handling policies. The ufuncs in `scipy.special` come from a different extension module in SciPy. Extension modules from separate projects are like separate worlds, and cannot communicate with one another except through their Python interfaces. SciPy instead has its own context manager: -[scipy.special.errstate](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.errstate.html) that +[`scipy.special.errstate`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.errstate.html) that mirrors `np.errstate`. ``` @@ -468,6 +469,8 @@ sf_action_t scipy_sf_error_get_action(sf_error_t code) } ``` +Using the terminology from before, these are the symbols that we want to export from the shared library. + If a user runs the following in an IPython session ```python @@ -489,18 +492,18 @@ to take. ## The many battles to actually ship it The shared library `libsf_error_state`'s contents are fairly simple, but how does one actually ship a shared library -within a Python package like SciPy? Even now, we've yet to find any prior examples of a Python package that contains -an internal shared library. The process for creating and using shared libraries depends on the operating system and +within a Python package like SciPy? When we started out, we weren't even aware of any Python packages that contain an +internal shared library. The process for creating and using shared libraries depends on the operating system and compiler toolchain. SciPy supports a wide range of platforms in aim of its goal to democratize access to scientific computing tools and the greatest challenge turned out to be getting things to work on each of them. Several times, just as we thought everything was finally working, another quirk would pop up that needed to be addressed. ### Path troubles -The initial challenge was finding the right invocations to give to the [Meson build system](https://mesonbuild.com/) used by SciPy.[^8] Extension -modules are configured in the `meson.build` files spread across SciPy's source tree and we needed to figure out how to -set up a shared library and link it into each of the special function ufunc extension modules. Irwin and I begin working -on this independently, comparing notes as we went. +The initial challenge was finding the right invocations to give to the [Meson build system](https://mesonbuild.com/) +used by SciPy.[^8] Extension modules are configured in the `meson.build` files spread across SciPy's source tree and we +needed to figure out how to set up a shared library and link it into each of the special function ufunc extension +modules. Irwin and I begin working on this independently, comparing notes as we went. The first hiccup is that the following invocations were working on Irwin's Mac. @@ -563,8 +566,8 @@ sf_error_state_lib = shared_library('sf_error_state', # Name of the library Even after this, I was still seeing the same error. I asked Ralf about it, and found what happens is that `pip` goes through [meson-python](https://github.com/mesonbuild/meson-python) while `dev.py` uses Meson directly. -`meson-python` takes care to set the [`RPATH`](https://en.wikipedia.org/wiki/Rpath) for each extension module, -which tells the dynamic linker where to look for shared libraries. +`meson-python` takes care to set the [`RPATH`](https://en.wikipedia.org/wiki/Rpath) for each extension module. +The `RPATH` tells the dynamic linker where to look for shared libraries. To get things to work with `dev.py`, I needed to explicitly set the `RPATH` in Meson by adding `install_rpath:'$ORIGIN'` to each extension module. `'$ORIGIN'` in this case means to search in the same folder as the extension module. @@ -768,11 +771,11 @@ This work was supported by the 2020 NASA ROSES grant, _Reinforcing the Foundatio [^4]: Those using `scipy<1.15` will see `inf` instead of `nan` at negative integers due to a bug in `special.gamma` - which was fixed in [the PR scipy/#21827](https://github.com/scipy/scipy/pull/21827).If you're curious why - `special.gamma(0.)` evaluated to `inf`, note that the IEE-754 floating point standard requires signed zeros `+0.` - and `-0.` as described in [Signed zero Wikipedia article](https://en.wikipedia.org/wiki/Signed_zero). - `special.gamma(+0.)` evaluates the limit of the Gamma function as `x` approaches `0` from the right. Signed zeros - can be used to ensure the correct sign is preserved when underflow occurs. + which was fixed in the PR [scipy/#21827](https://github.com/scipy/scipy/pull/21827).If you're curious why + `special.gamma(0.)` evaluated to `+inf`, note that the IEE-754 floating point standard requires + [signed zeros](https://en.wikipedia.org/wiki/Signed_zero) `+0.` and `-0.`. + `special.gamma(-0.)` and `special.gamma(+0.)` evaluate the limit of the Gamma function as `x` approaches `0` from the + left and right respectively. [^5]: We could have stuck to one extension module by making it more complicated, but the idea was to build the simpler From 7d93087b4d84d8a0a76d576720bff544c654dd38 Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Fri, 20 Dec 2024 00:05:53 -0500 Subject: [PATCH 20/22] Add github handle for pv --- apps/labs/posts/libsf-error-state.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index 0171c0277..7d5accfad 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -753,8 +753,9 @@ that we at least have _a_ portable way to share global state between extension m Before we finish, one more gem: in [another GitHub comment from 2016](https://github.com/scipy/scipy/issues/6681#issuecomment-254192679), currently inactive SciPy BDFL Pauli Virtanen -suggested to "make `sf_error` error handling thread-local (which probably should be done in any case)". Over eight years -later this is finally happening. I should probably go back and read old comment threads more often. +([@pv](https://github.com/pv)) suggested to "make `sf_error` error handling thread-local (which probably should be done +in any case)". Over eight years later this is finally happening. I should probably go back and read old comment threads +more often. ### Funding acknowledgements From 3ea1696b495e699a28b040ce5b781dc48422f911 Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Fri, 20 Dec 2024 11:36:10 -0500 Subject: [PATCH 21/22] Respond to review feedback --- apps/labs/posts/libsf-error-state.md | 58 +++++++++++++--------------- 1 file changed, 26 insertions(+), 32 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index 7d5accfad..39d4fae9a 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -36,20 +36,20 @@ Python. Users who spend their time in the interpreted shell may not be aware of underneath. Journeying deeper into the stack, it can be surprising to see the level of work that can go into making even a relatively simple feature work smoothly. -## NumPy Universal functions - -The simple feature in question is error handling for mathematical functions in `scipy.special` that operate +Here, the simple feature in question is error handling for mathematical functions in `scipy.special` that operate elementwise over NumPy arrays. Let's look into this more closely. +## NumPy Universal functions + A NumPy [`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) represents an arbitrary dimensional array of elements of the same data type, stored in a continguous buffer at the machine level. A [universal function](https://numpy.org/doc/stable/reference/ufuncs.html) or ufunc for short is a Python level function which applies a transform to each element of an `ndarray` by looping and operating elementwise over the underlying contiguous buffer in efficient native code. -Here's the ufunc [`np.sqrt`](https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html) in action: +Here's the ufunc `np.sqrt` in action: -``` +```python In [1]: import numpy as np In [2]: A = np.arange(1, 11, dtype=float) @@ -64,11 +64,10 @@ array([1. , 1.41421356, 1.73205081, 2. , 2.23606798, ``` -For large arrays, the speedup from applying `np.sqrt` over an `ndarray` rather than -[`math.sqrt`](https://docs.python.org/3/library/math.html#math.sqrt) over each element of a list is significant. On my -laptop I witnessed: +For large arrays, the speedup from applying `np.sqrt` over an `ndarray` rather than `math.sqrt` over each element of +a list is significant. On my laptop I witnessed: -``` +```python In [1]: import numpy as np In [2]: A = np.arange(1, 1000000, dtype=float) @@ -84,11 +83,10 @@ In [5]: %timeit [math.sqrt(t) for t in B] ## Error handling -What should happen if one gives invalid input to a ufunc? If we pass a negative -[`float`](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex) to `math.sqrt` a -[`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) is raised.[^3] +What should happen if one gives invalid input to a ufunc? If we pass a negative `float` to `math.sqrt` +a `ValueError` is raised.[^3] -``` +```python In [1]: import math In [2]: math.sqrt(-1.0) @@ -103,7 +101,7 @@ What if one applies a ufunc over a large array and only a small number of inputs raise a `ValueError` even though almost every calculation succeeded and produced a useful result? Fortunately, that's not what happens: -``` +```python In [1]: import numpy as np In [2]: A = np.arange(-1, 1000, dtype=float) @@ -172,10 +170,8 @@ NumPy has over 60 [ufuncs available](https://numpy.org/doc/stable/reference/ufun of mathematical functions and operations, but more specialized functions useful in the sciences and engineering are out of scope. Ufuncs for over 230 such functions can be found instead in `scipy.special`. -Just for fun, let's use -[`scipy.special.gamma`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gamma.html), which implements a -continuous extension of the factorial function called the [Gamma function](https://en.wikipedia.org/wiki/Gamma_function), -to reproduce a plot from the Wikipedia article +Just for fun, let's use `scipy.special.gamma`, which implements a continuous extension of the factorial function called +the [Gamma function](https://en.wikipedia.org/wiki/Gamma_function), to reproduce a plot from the Wikipedia article ["_Volume of an n-ball_"](https://en.wikipedia.org/wiki/Volume_of_an_n-ball). The plot shows how the volume of a solid multi-dimensional sphere depends on the dimension `n` when the radius `R` is one of 0.9, 1.0, or 1.1. @@ -220,7 +216,7 @@ which outputs: What if one of the ufuncs in `scipy.special` recieves an array with some invalid elements? The Gamma function has singularities at non-positive integers, similar to how `1 / x` has a singularity at 0. -``` +```python In [1]: import numpy as np In [2]: import scipy.special as special @@ -316,21 +312,19 @@ The expected error was not being raised. >>> _ = sc.seterr(**olderr) ``` -[`gammaln`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gammaln.html) was one of a handful of -ufuncs moved to the new extension module. +`special.gammaln` was one of a handful of ufuncs moved to the new extension module. Both extension modules contained a separate copy of the state for managing error handling policies, but the user facing `special.errstate` could only see and change the state from the first extension module. While investigating, we also found that, for some reason, there was a separate extension module just for the function -[`ellip_harm_2`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.ellip_harm_2.html#scipy.special.ellip_harm_2). +[`special.ellip_harm_2`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.ellip_harm_2.html#scipy.special.ellip_harm_2). As expected, `special.errstate` did not and had never worked for `ellip_harm_2` either, but since there were no relevant tests, no one knew. We saw three options: -1. Have the Python interface for modifying the error handling state (`special.errstate` and - [`special.seterr`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.seterr.html)) update the state - in each extension module, taking care that the state remains synchronized. +1. Have the Python interface for modifying the error handling state (`special.errstate` and `special.seterr` update the + state in each extension module, taking care that the state remains synchronized. 2. Extract the error handling state and primitives for managing it into a shared library that would be linked with each extension module. @@ -498,7 +492,7 @@ compiler toolchain. SciPy supports a wide range of platforms in aim of its goal computing tools and the greatest challenge turned out to be getting things to work on each of them. Several times, just as we thought everything was finally working, another quirk would pop up that needed to be addressed. -### Path troubles +## Path troubles The initial challenge was finding the right invocations to give to the [Meson build system](https://mesonbuild.com/) used by SciPy.[^8] Extension modules are configured in the `meson.build` files spread across SciPy's source tree and we @@ -572,7 +566,7 @@ The `RPATH` tells the dynamic linker where to look for shared libraries. To get things to work with `dev.py`, I needed to explicitly set the `RPATH` in Meson by adding `install_rpath:'$ORIGIN'` to each extension module. `'$ORIGIN'` in this case means to search in the same folder as the extension module. -### Building Wheels on Windows +## Building Wheels on Windows After setting `install_dir` and `install_rpath` correctly, all but one of SciPy's CI jobs were passing. The sole failing job involved building a wheel on Windows. A @@ -625,7 +619,7 @@ One week in, all CI jobs were passing and the [PR](https://github.com/scipy/scip `libsf_error_state` was merged. We did it! We fixed the bug we'd introduced in `special.errstate` with months to go before the next SciPy release — or so we thought. -### Breaking SciPy for MSVC builds +## Breaking SciPy for MSVC builds On May 30th, SciPy maintainer and conda-forge core member [@h-vetinari](https://github.com/h-vetinari) posted a comment on his open PR, [scipy-feedstock/gh-277](https://github.com/conda-forge/scipy-feedstock/pull/277). The first release @@ -643,7 +637,7 @@ were Windows builds in CI, they all used the [MinGW](https://en.wikipedia.org/wi a Clang compiler driver that aims for compatability with MSVC. Clang-cl is used for Windows builds in conda-forge and we had run into another platform specific difference. -Fortunately, h-vetinari knew what the problem was. On Linux, Mac OS (and Windows while using MinGW), symbols from shared +Fortunately, h-vetinari knew what the problem was. On Linux, macOS (and Windows while using MinGW), symbols from shared libraries are exported by default, but on Windows with MSVC or Clang-cl they must be explicitly exported from shared libraries and explicitly imported into consumers by annotating source code with special compiler directives: [`__declspec(dllexport)`](https://learn.microsoft.com/en-us/cpp/build/exporting-from-a-dll-using-declspec-dllexport?view=msvc-170) @@ -653,7 +647,7 @@ for imports. He had a recipe ready to use: defining and using macros which conditionally compiled to the right thing depending on their context. -```C++ +```c // SCIPY_DLL // inspired by https://github.com/abseil/abseil-cpp/blob/20240116.2/absl/base/config.h#L736-L753 // @@ -678,7 +672,7 @@ back the release date. A couple weeks later, fellow Quansight Labs member and LF Singh ([@czgdp1807](https://github.com/czgdp1807)) submitted [a PR](https://github.com/scipy/scipy/pull/20985) to plug the gap in SciPy's CI coverage. -### Thread safety +## Thread safety In the introduction I'd mentioned that CPython's global interpreter lock (GIL) makes it easier to extend Python with C or other compiled languages since one doesn't need to worry about the thread safety of wrapped code. Still, being able @@ -702,7 +696,7 @@ Margffoy ([@andfoy](https://github.com/andfoy)) — a member of Quansight Labs' [free-threaded Python team](https://labs.quansight.org/blog/free-threaded-python-rollout) — to ensure thread safety by declaring the array `sf_error_actions` thread local. This eliminates the data-race by making it so each thread gets its own separate copy of the array. Edgar and the others on the free-threaded Python team have been doing -great work improving support for free-threaded Python across the ecosystem for much of this past year.[^9] +great work improving support for free-threaded Python across the ecosystem for much of this past year. In a curious reversal, it is now (the `win32` flavor of) MinGW that is causing trouble due to lack of support for thread local variables. From 4447651717f41c350f38760951d38452b0e48912 Mon Sep 17 00:00:00 2001 From: steppi <1953382+steppi@users.noreply.github.com> Date: Fri, 20 Dec 2024 11:37:59 -0500 Subject: [PATCH 22/22] Remove another unnecessary link --- apps/labs/posts/libsf-error-state.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/apps/labs/posts/libsf-error-state.md b/apps/labs/posts/libsf-error-state.md index 39d4fae9a..3cad08710 100644 --- a/apps/labs/posts/libsf-error-state.md +++ b/apps/labs/posts/libsf-error-state.md @@ -316,9 +316,8 @@ The expected error was not being raised. Both extension modules contained a separate copy of the state for managing error handling policies, but the user facing `special.errstate` could only see and change the state from the first extension module. While investigating, we also -found that, for some reason, there was a separate extension module just for the function -[`special.ellip_harm_2`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.ellip_harm_2.html#scipy.special.ellip_harm_2). -As expected, `special.errstate` did not and had never worked for `ellip_harm_2` either, but since there were no relevant +found that, for some reason, there was a separate extension module just for the function `special.ellip_harm_2`. As +expected, `special.errstate` did not and had never worked for `ellip_harm_2` either, but since there were no relevant tests, no one knew. We saw three options: