diff --git a/.cspell.yml b/.cspell.yml deleted file mode 100644 index ae2482d..0000000 --- a/.cspell.yml +++ /dev/null @@ -1,8 +0,0 @@ -# cSpell:ignore textlintrc -# For settings, see -# https://www.streetsidesoftware.com/vscode-spell-checker/docs/configuration/ -version: '0.2' -caseSensitive: true -words: - - Docsy - - Goldydocs diff --git a/.github/dependabot.yml b/.github/dependabot.yml deleted file mode 100644 index 573e2f0..0000000 --- a/.github/dependabot.yml +++ /dev/null @@ -1,14 +0,0 @@ -version: 2 -updates: -- package-ecosystem: npm - directory: "/" - schedule: - interval: daily - time: '20:00' - open-pull-requests-limit: 10 -- package-ecosystem: bundler - directory: "/" - schedule: - interval: daily - time: '20:00' - open-pull-requests-limit: 10 diff --git a/.github/workflows/deploy-github-pages.yml b/.github/workflows/deploy-github-pages.yml deleted file mode 100644 index 0f41e7e..0000000 --- a/.github/workflows/deploy-github-pages.yml +++ /dev/null @@ -1,51 +0,0 @@ -name: Deployment to GitHub Pages - -on: - workflow_dispatch: - push: - branches: - - main # <-- specify the branch you want to deploy from - pull_request: - -permissions: read-all - -env: - REPO_NAME: ${{ github.event.repository.name }} - REPO_OWNER: ${{ github.repository_owner }} - -jobs: - deploy: - runs-on: ubuntu-22.04 - timeout-minutes: 60 - permissions: - contents: write - pull-requests: write - concurrency: - group: ${{ github.workflow }}-${{ github.ref }} - - steps: - - uses: actions/checkout@v4 - with: - fetch-depth: 0 # Fetch all history for .GitInfo and .Lastmod - - - name: Setup Hugo - uses: peaceiris/actions-hugo@v3 - with: - hugo-version: '0.148.0' - extended: true - - - name: Setup Node - uses: actions/setup-node@v4 - with: - node-version: '20' - cache: 'npm' - cache-dependency-path: '**/package-lock.json' - - - run: npm ci - - run: hugo --baseURL https://IntelPython.github.io/portable-data-parallel-extensions-europython-2025 --minify - - - name: Deploy - uses: peaceiris/actions-gh-pages@v4 - if: ${{ github.ref == 'refs/heads/main' }} # <-- specify same branch as above here - with: - github_token: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/openssf-scorecard.yml b/.github/workflows/openssf-scorecard.yml deleted file mode 100644 index 28141f7..0000000 --- a/.github/workflows/openssf-scorecard.yml +++ /dev/null @@ -1,75 +0,0 @@ -# This workflow uses actions that are not certified by GitHub. 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See - # https://github.com/ossf/scorecard/blob/main/docs/checks.md#maintained - schedule: - - cron: '28 2 * * 1' - - cron: '28 2 * * 4' - push: - branches: [ "master" ] - workflow_dispatch: - -# Declare default permissions as read only. -permissions: read-all - -jobs: - analysis: - name: Scorecard analysis - runs-on: ubuntu-latest - timeout-minutes: 30 - permissions: - # Needed to upload the results to code-scanning dashboard. - security-events: write - # Needed to publish results and get a badge (see publish_results below). - id-token: write - # Uncomment the permissions below if installing in a private repository. - # contents: read - # actions: read - - steps: - - name: "Checkout code" - uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7 - with: - persist-credentials: false - - - name: "Run analysis" - uses: ossf/scorecard-action@05b42c624433fc40578a4040d5cf5e36ddca8cde # v2.4.2 - with: - results_file: results.sarif - results_format: sarif - # (Optional) "write" PAT token. Uncomment the `repo_token` line below if: - # - you want to enable the Branch-Protection check on a *public* repository, or - # - you are installing Scorecard on a *private* repository - # To create the PAT, follow the steps in https://github.com/ossf/scorecard-action#authentication-with-pat. - # repo_token: ${{ secrets.SCORECARD_TOKEN }} - - # Public repositories: - # - Publish results to OpenSSF REST API for easy access by consumers - # - Allows the repository to include the Scorecard badge. - # - See https://github.com/ossf/scorecard-action#publishing-results. - # For private repositories: - # - `publish_results` will always be set to `false`, regardless - # of the value entered here. - publish_results: true - - # Upload the results as artifacts (optional). 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Categories

\ No newline at end of file diff --git a/categories/index.xml b/categories/index.xml new file mode 100644 index 0000000..242dcea --- /dev/null +++ b/categories/index.xml @@ -0,0 +1 @@ +Categories on Portable Data-Parallel Python Extensions with oneAPIhttps://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/categories/Recent content in Categories on Portable Data-Parallel Python Extensions with oneAPIHugoen \ No newline at end of file diff --git a/config.yaml b/config.yaml deleted file mode 100644 index 9070e38..0000000 --- a/config.yaml +++ /dev/null @@ -1,15 +0,0 @@ -# THIS IS A TEST CONFIG ONLY! -# FOR THE CONFIGURATION OF YOUR SITE USE hugo.yaml. -# -# As of Docsy 0.7.0, Hugo 0.110.0 or later must be used. -# -# The sole purpose of this config file is to detect Hugo-module builds that use -# an older version of Hugo. -# -# DO NOT add any config parameters to this file. You can safely delete this file -# if your project is using the required Hugo version. - -module: - hugoVersion: - extended: true - min: 0.110.0 diff --git a/content/en/_index.md b/content/en/_index.md deleted file mode 100644 index e8c161b..0000000 --- a/content/en/_index.md +++ /dev/null @@ -1,54 +0,0 @@ ---- -title: Portable Data-Parallel Python Extensions with oneAPI ---- - -{{}} -
-
-

Portable Data-Parallel Python Extensions with oneAPI

-

-

- Nikita Grigorian and - Oleksandr Pavlyk -

-

Sources and details for the Portable Data-Parallel Python Extensions with oneAPI poster at the EuroPython 2025 conference. This poster presents ongoing work to enable writing, building, and implementing portable extensions for data-parallel computation in Python. -

-

-
- -
-{{< /blocks/cover >}} - -{{% blocks/section color="secondary" %}} -

About the authors

-

-This poster was made by and is being presented on behalf of the Python team at Intel. -

-
-{{< cardpane >}} - {{< card header=`

dpctl

` - footer=` [Github](https://github.com/IntelPython/dpctl) [Docs](https://intelpython.github.io/dpctl)` >}} -

- A package with Python bindings for DPC++ runtime classes and an Array API Specification conformant tensor submodule built on pure SYCL for portability. -

- {{< /card >}} - {{< card header=`

dpnp

` - footer=` [Github](https://github.com/IntelPython/dpnp) [Docs](https://intelpython.github.io/dpnp/)` >}} -

- An oneAPI- and oneMKL-powered array library built as a drop-in replacement for Numpy that can be executed on any data-parallel device. -

- {{< /card >}} -{{< /cardpane >}} -
- -{.h1 .text-center} -{{% /blocks/section %}} diff --git a/content/en/docs/_index.md b/content/en/docs/_index.md deleted file mode 100755 index d322c7f..0000000 --- a/content/en/docs/_index.md +++ /dev/null @@ -1,15 +0,0 @@ ---- -title: EuroPython 2025 poster content -linkTitle: Details -menu: {main: {weight: 20}} ---- - -Portable Data-Parallel Extensions with oneAPI ---- -by [Nikita Grigorian](https://github.com/ndgrigorian) and [Oleksandr Pavlyk](https://github.com/oleksandr-pavlyk) - -This poster is intended to introduce writing portable data-parallel Python extensions using oneAPI. - -We present several examples, starting with the basics of initializing a USM (unified shared memory) array, then a KDE (kernel density estimation) with pure DPC++/Sycl, then a KDE Python extension, and finally how to write a portable Python extension which uses oneMath. - -The examples can be found [here](https://github.com/IntelPython/example-portable-data-parallel-extensions). diff --git a/content/en/docs/first-app.md b/content/en/docs/first-app.md deleted file mode 100755 index f114b48..0000000 --- a/content/en/docs/first-app.md +++ /dev/null @@ -1,102 +0,0 @@ ---- -title: First DPC++ app -description: A SYCL and DPC++ "Hello, World!" example. -date: 2025-07-10 -weight: 2 ---- - -For an in-depth introduction to SYCL and to accelerators programming please refer to the "[Data Parallel C++](https://link.springer.com/book/10.1007/978-1-4842-9691-2)" open access e-book. - -A SYCL application runs on SYCL platform (host, connected to one or more heterogeneous devices). The application is structured in three scopes: application scope, command group scope, and kernel scope. The kernel scope specifies a single kernel function that will be compiled by the device -compiler and executed on the device. The command group scope specifies a unit work which includes the kernel function, preparation of -its arguments and specifying execution ordering information. The application scope specifies all the other code outside of command group scope. -Execution of SYCL application begins in the application scope. - -```cpp -// Compile: icpx -fsycl first.cpp -o first -#include - -int main(void) { - // queue to enqueue work to - // default-selected device - sycl::queue q{sycl::default_selector_v}; - - // allocation device - size_t data_size = 256; - int *data = sycl::malloc_device(data_size, q); - - // submit a task to populate - // device allocation - sycl::event e_fill = - q.fill(data, 42, data_size); // built-in kernel - - // submit kernel to modify device allocation - sycl::event e_comp = - q.submit([&](sycl::handler &cgh) { // command-group scope - // order execution after - // fill task completes - cgh.depends_on(e_fill); - - sycl::range<1> global_iter_range{data_size}; - cgh.parallel_for( - global_iter_range, - [=](sycl::item<1> it) { // kernel scope - int i = it.get_id(0); - data[i] += i; - } - ); - }); - - // copy from device to host - // order execution after modification task completes - int *host_data = new int[data_size]; - - q.copy( // built-in kernel - data, host_data, data_size, {e_comp} - ).wait(); - sycl::free(data, q); - - // Output content of the array - output_array(host_data, data_size); - delete[] host_data; - - return 0; -} -``` - -The device where the kernel functions is executed is controlled by a device selector function, ``sycl::default_selector_v``. -The default selector assigns scores to every device recognized by the runtime, and selects the one with the highest score. -A list of devices recognized by the DPC++ runtime can be obtained by running ``sycl-ls`` command. - -A user of SYCL application compiled with DPC++ may restrict the set of devices discoverable by the runtime using -``ONEAPI_DEVICE_SELECTOR`` environment variable. For example: - -```bash -# execute on GPU -ONEAPI_DEVICE_SELECTOR=*:gpu ./first -# execute on CPU -ONEAPI_DEVICE_SELECTOR=*:cpu ./first -``` - -By default, DPC++ compiler generates offload code for [SPIR64](https://www.khronos.org/spir/) SYCL target, supported by -Intel GPUs as well as by CPU devices of x86_64 architecture. An attempt to execute SYCL program while -selecting only devices that do not support SPIR language would result in an error. - -### Targeting other GPUs - -DPC++ supports generation of offload sections for multiple targets. For example, to compile for both SPIR and NVPTX targets (oneAPI for NVidia(R) GPUs is assumed installed): - -```bash -icpx -fsycl -Xsycl-targets="nvptx64-nvidia-cuda,spir64-unknown-unknown" first.cpp -o first.out -``` - -To compile for both SPIR and AMD GCN targets (oneAPI for AMD GPUs is assumed installed): - -```bash -icpx -fsycl -Xsycl-targets="amdgcn-amd-amdhsa,spir64-unknown-unknown" first.cpp -o first.out -``` - -It is possible to pass additional arguments to the specific SYCL target backend. For example, to target specific architecture use: - -- ``-Xsycl-target-backend=amdgcn-amd-amdhsa --offload-arch=gfx1030`` for AMD GPUs -- ``-Xsycl-target-backend=nvptx64-nvidia-cuda --cuda-gpu-arch=sm_80`` for NVidia GPUs diff --git a/content/en/docs/kde-cpp.md b/content/en/docs/kde-cpp.md deleted file mode 100644 index 9495543..0000000 --- a/content/en/docs/kde-cpp.md +++ /dev/null @@ -1,128 +0,0 @@ ---- -title: KDE DPC++ example -description: KDE (kernel density estimation) example using SYCL and DPC++. -date: 2025-07-10 -weight: 2 ---- - -Given a sample of \\(n\\) observations \\(x_i\\) drawn from an unknown underlying continuous distribution \\(f(x)\\), -the kernel density estimate of that density function is computed as follows, for some kernel -smoothing parameter \\(h \in \mathbb{R}\\): - -$$ - \hat{f}(x) = \frac{1}{n} \sum_{i=1}^{n} \frac{1}{h} K\left(\frac{x - x_i}{h}\right) -$$ - -An example of NumPy code performing the estimation, for a common choice of kernel function as standard -\\(d\\)-dimensional Gaussian distribution: - - - -```python -def kde(poi : np.ndarray, sample : np.ndarray, h : float) -> np.ndarray: - """Given a sample from underlying continuous distribution and - a smoothing parameter `h`, evaluate density estimate at each point of - interest `poi`. - """ - assert sample.ndim == 2 - assert poi.ndim == 2 - m, d1 = poi.shape - n, d2 = sample.shape - assert d1 == d2 - assert h > 0 - dm = np.sum(np.square(poi[:, np.newaxis, ...] - sample[np.newaxis, ...]), axis=-1) - return np.mean(np.exp(dm/(-2*h*h)), axis=-1)/np.power(np.sqrt(2*np.pi) * h, d1) -``` - -The code above evaluates \\(f(x)\\) for \\(m\\) values of points of interest \\(y_t\\). - -$$ - f(y_t) = \frac{1}{n} \sum_{i=1}^{n} \frac{1}{h} K\left( \frac{1}{h^2} \left\lVert y_t - x_i \right\rVert^{2} \right), \;\;\; \forall 0 \leq t \le m -$$ - -Evaluating such an expression can be done in parallel. Evaluation can be done independently for each \\(t\\). -Furthermore, summation over \\(i\\) can be partitioned among work-items, each summing \\(n_{wi}\\) distinct terms. -Such work partitioning would generate \\(m \cdot \left\lceil {n}/{n_{wi}}\right\rceil\\) independent tasks. -Each work-item could write its partial sum into a dedicated temporary memory location to avoid race condition -for further summation by another kernel operating in a similar fashion. - -```cpp - parallel_for( - range<2>(m, ((n + n_wi - 1) / n_wi)), - [=](sycl::item<2> it) { - auto t = it.get_id(0); - auto i_block = it.get_id(1); - - T local_partial_sum = ...; - - partial_sums[t * ((n + n_wi - 1) / n_wi) + i_block] = local_partial_sum; - } - ); -``` - -Such an approach, known as tree reduction, is implemented in ``kernel_density_esimation_temps`` function found in -[``"steps/kernel_density_estimation_cpp/kde.hpp"``](https://github.com/IntelPython/example-portable-data-parallel-extensions/blob/main/steps/kernel_density_estimation_cpp/kde.hpp). - -Use of temporary allocation can be avoided if each work-item atomically adds the value of the local sum to the -appropriate zero-initialized location in the output array, as in implementation ``kernel_density_estimation_atomic_ref`` -in the same header file: - -```cpp - parallel_for( - range<2>(m, ((n + n_wi - 1) / n_wi)), - [=](sycl::item<2> it) { - auto t = it.get_id(0); - auto i_block = it.get_id(1); - - T local_partial_sum = ...; - - sycl::atomic_ref<...> f_aref(f[t]); - f_aref += local_partial_sum; - } - ); -``` - -Multiple work-items may concurrently updating the same location in global memory would produce the correct result due to -use of ``sycl::atomic_ref`` but at the expense of increased number of attempts, phenomenon known as atomic pressure. -Atomic pressure leads to thread divergence and degrades performance. - -To reduce the atomic pressure work-items can be organized into work-groups. Every work-item in a work-group has access -to local shared memory, dedicated on-chip memory, which can be used to cooperatively combine values held by work-items -in the work-group without accessing the global memory. This could be done efficiently by calling group function -``sycl::reduce_over_group``. To be able to call it, we must specify iteration range using ``sycl::nd_range`` rather than -``sycl::range`` as we did earlier. - -```cpp - auto wg = 256; // work-group-size - auto n_data_per_wg = n_wi * wg; - auto n_groups = ((n + n_data_per_wg - 1) / n_data_per_Wg); - - range<2> gRange(m, n_groups * wg); - range<2> lRange(1, wg); - - parallel_for( - nd_range<2>(gRange, lRange), - [=](sycl::nd_item<2> it) { - auto t = it.get_global_id(0); - - T local_partial_sum = ...; - - auto work_group = it.get_group(); - T sum_over_wg = sycl::reduce_over_group(work_group, local_sum, sycl::plus<>()); - - if (work_group.leader()) { - sycl::atomic_ref<...> f_aref(f[t]); - f_aref += sum_over_wg; - } - } - ); -``` - -Complete implementation can be found in ``kernel_density_estimation_work_group_reduce_and_atomic_ref`` function -in [``"steps/kernel_density_estimation_cpp/kde.hpp"``](https://github.com/IntelPython/example-portable-data-parallel-extensions/blob/main/steps/kernel_density_estimation_cpp/kde.hpp). - -These implementations are called from C++ application [``"steps/kernel_density_estimation_cpp/app.cpp"``](https://github.com/IntelPython/example-portable-data-parallel-extensions/blob/main/steps/kernel_density_estimation_cpp/app.cpp), which -samples data uniformly distributed over unit cuboid, and estimates the density using Kernel Density Estimation -and spherically symmetric multivariate Gaussian probability density function as the kernel. - -The application can be built using `CMake`, or `Meson`, please refer to [README](https://github.com/IntelPython/example-portable-data-parallel-extensions/blob/main/steps/kernel_density_estimation_cpp/README.md) document in that folder. diff --git a/content/en/docs/kde-python.md b/content/en/docs/kde-python.md deleted file mode 100644 index 4984654..0000000 --- a/content/en/docs/kde-python.md +++ /dev/null @@ -1,118 +0,0 @@ ---- -title: KDE Python extension -description: KDE (kernel density estimation) Python extension example. -date: 2025-07-10 -weight: 3 ---- - -Since SYCL builds on C++, we are going to use the `pybind11` project to generate a Python extension. -We also need Python objects to carry USM allocations of input and output data, such as `dpctl` ([Data Parallel Control](https://github.com/IntelPython/dpctl.git) Python package). The `dpctl` package also provides Python objects corresponding to DPC++ runtime objects: - -| Python object | SYCL C++ object | -| --------------------- | ----------------- | -| ``dpctl.SyclQueue`` | ``sycl::queue`` | -| ``dpctl.SyclDevice`` | ``sycl::device`` | -| ``dpctl.SyclContext`` | ``sycl::context`` | -| ``dpctl.SyclEvent`` | ``sycl::event`` | - -`dpctl` provides integration with `pybind11` supporting castings between `dpctl` Python objects and corresponding C++ SYCL classes listed in the table above. Furthermore, the integration provides the C++ class ``dpctl::tensor::usm_ndarray`` which derives from ``pybind11::object``. -It stores the `dpctl.tensor.usm_ndarray` object and provides methods to query its attributes, such as data pointer, dimensionality, shape, strides -and elemental type information. Underlying `dpctl.tensor.usm_ndarray` is a SYCL unified shared memory (USM) allocation. See the [SYCL standard](https://registry.khronos.org/SYCL/specs/sycl-2020/html/sycl-2020.html#sec:usm) or [dpctl.memory documentation](https://intelpython.github.io/dpctl/latest/api_reference/dpctl/memory.html#dpctl-memory-pyapi) for more details. - -For illustration purpose, here is a sample extension source code: - -```cpp -#include -#include -#include -#include "dpctl4pybind11.hpp" -#include - -sycl::event -py_foo(dpctl::tensor::usm_ndarray inp, - dpctl::tensor::usm_ndarray out, - const std::vector &deps) { - // validation steps skipped - - // Execution queue is the queue associated with input arrays - // these are expected to be the same and checked during validation - sycl::queue exec_q = inp.get_queue(); - - const std::int64_t *inp_ptr = inp.get_data(); - std::int64_t *out_ptr = out.get_data(); - - // submit tasks for execution and obtain the event signaling - // the status of execution of the set of tasks - sycl::event e_impl = impl_fn(exec_q, inp_ptr, out_ptr, depends); - - return e_impl; -} - -PYBIND11_MODULE(_ext, m) { - m.def("foo", &py_foo); -} -``` - -On the Python side, the function would be called as follows - -```python -import dpctl.tensor as dpt -import _ext - -# Allocate input and output arrays on the -# default-selected device -inp = dpt.arange(100, dtype=dpt.int64) -out = dpt.empty_like(inp) -ev = _ext.foo(inp, out, []) - -# ... -``` - -Since execution is offloaded to a device, it is our responsibility to ensure that USM data being worked on -is not deallocated until after offloaded tasks complete the execution. The simplest way to assure of this is -to wait on the event with `ev.wait()` on the Python side, or with `e_impl.wait()` on the C++ side. - -Alternatively, one can assure the lifetime of arrays asynchronously, by using `sycl::handler::host_task` to -schedule code execution on the host ordering it after kernel execution completion: - -```cpp -// increment reference counts of input and output arrays - -sycl::event ht_ev = - exec_q.submit([&](sycl::handler &cgh) { - // execute host_task once e_impl signals completion - cgh.depends_on(e_impl); - - cgh.host_task([=]() { - // we must acquire GIL to be able to safely - // manipulate reference counts of Python objects - pybind11::gil_scoped_acquire guard; - - // decrement ref-counts - }); - }); -``` - -Since the host task may execute in a thread different from that of the Python interpreter (the main thread), care must be taken -to avoid deadlocks: synchronization operation that call `ht_ev.wait()` from the main thread must release GIL to afford the body -of the host task a chance at execution. - -Of course, if USM memory is not managed by Python, it may be possible to avoid using GIL altogether. - -An example of Python extension `"kde_sycl_ext"` that exposes kernel density estimation code from previous -section can be found in [`"steps/sycl_python_extension"`](https://github.com/IntelPython/example-portable-data-parallel-extensions/tree/main/steps/sycl_python_extension) folder (see [README](https://github.com/IntelPython/example-portable-data-parallel-extensions/blob/main/steps/sycl_python_extension/README.md)). - -The folder contains comparison between `dpctl`-based implementation of the KDE implementation following the NumPy -implementation [above](#kde_numpy) and the dedicated C++ code: - -```bash -KDE for n_sample = 1000000, n_est = 17, n_dim = 7, h = 0.05 -Result agreed. -kde_dpctl took 0.3404452269896865 seconds -kde_ext[mode=0] 0.02209925901843235 seconds -kde_ext[mode=1] 0.02560457994695753 seconds -kde_ext[mode=2] 0.02815118699800223 seconds -kde_numpy 0.7227164240321144 seconds -``` - -This sample run was obtained on a laptop with 11th Gen Intel(R) Core(TM) i7-1185G7 CPU @ 3.00GHz, 32 GB of RAM, and the integrated Intel(R) Iris(R) Xe GPU, with stock NumPy 1.26.4, and development build of dpctl 0.17 built with oneAPI DPC++ 2024.1.0. diff --git a/content/en/docs/oneAPI.md b/content/en/docs/oneAPI.md deleted file mode 100644 index 589d341..0000000 --- a/content/en/docs/oneAPI.md +++ /dev/null @@ -1,34 +0,0 @@ ---- -title: About oneAPI -linkTitle: About oneAPI -description: A brief overview of oneAPI and the programming model -date: 2025-07-10 -weight: 1 ---- - -The Unified Acceleration Foundation ([UXL](https://uxlfoundation.org/)) under the umbrella of Linux Foundation is driving an open standard accelerator software ecosystem that includes compilers and performance libraries. This software ecosystem standardizes programming of different types of accelerators, such as multi-core CPUs, GPUs, some FPGAs, etc. from different vendors. - -Intel's oneAPI DPC++ compiler is an implementation of the [SYCL-2020 standard](https://registry.khronos.org/SYCL/specs/sycl-2020/html/sycl-2020.html) that is a part of UXL foundation's overall language design standardization for accelerator programming in C++. The compiler is being developed in https://github.com/intel/llvm, and supports offloading to Intel(R) GPUs, NVidia(R) GPUs, and AMD GPUs. - -The oneAPI DPC++ compiler can be installed on Linux and Windows as part of [oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) and requires a system compiler to be installed, such as GCC on Linux and MSVC on Windows. Since DPC++ builds -on the conventional toolchain, Python extensions built with DPC++ are compatible with the Python stack out of the box. DPC++ also requires that OpenCL and Level Zero drivers are installed. For NVidia(R) or AMD devices, appropriate drivers must be installed. - -To offload to devices, host code and device code are compiled separately, then combined by the linker into a single fat binary. - -Support for offloading to GPUs from vendors other than Intel can be enabled by additionally installing plugins from CodePlay: - -- [oneAPI for NVidia(R) GPUs](https://developer.codeplay.com/products/oneapi/nvidia/home/) -- [oneAPI for AMD GPUs](https://developer.codeplay.com/products/oneapi/amd/home/) - -Using these plugins and the appropriate compiler options, the fat binary for a DPC++ application will also include the necessary sections for offloading a kernel to NVidia(R) or AMD GPUs. - -A list of devices available to DPC++ can be obtained using ``sycl-ls``. - -```bash -sycl-ls -[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2024.17.3.0.08_160000] -[opencl:cpu:1] Intel(R) OpenCL, 12th Gen Intel(R) Core(TM) i9-12900 OpenCL 3.0 (Build 0) [2024.17.3.0.08_160000] -[ext_oneapi_cuda:gpu:0] NVIDIA CUDA BACKEND, NVIDIA GeForce GT 1030 6.1 [CUDA 12.2] -``` - -A list of SYCL projects can be found on https://sycl.tech/projects diff --git a/content/en/docs/oneMath.md b/content/en/docs/oneMath.md deleted file mode 100755 index 6f20ba4..0000000 --- a/content/en/docs/oneMath.md +++ /dev/null @@ -1,55 +0,0 @@ ---- -title: oneMath Python extension -description: A Python extension written using oneMath. -date: 2025-07-10 -weight: 4 ---- - -Given a matrix \\(A\\), the QR decomposition of \\(A\\) is defined as the decomposition of \\(A\\) into the product of matrices \\(Q\\) and \\(R\\) such that \\(Q\\) is orthonormal and \\(R\\) is upper-triangular. - -QR factorization is a common routine in more optimized LAPACK libraries, so rather than write and implement an algorithm ourselves, it would be preferable to find a suitable library routine. - -Since `dpctl.tensor.usm_ndarray` is a Python object with an underlying USM allocation, it is possible to write extensions which wrap `oneAPI Math Library` ([oneMath](https://github.com/uxlfoundation/oneMath)) USM routines and then call them on the `dpctl.tensor.usm_ndarray` from Python. These low-level routines can greatly improve the performance of an extension. - -`oneMath` can be built to dispatch to a variety of backends including `cuBLAS` and `rocBLAS` (see [oneMath README](https://github.com/uxlfoundation/oneMath?tab=readme-ov-file#oneapi-math-library-onemath)). The [`portBLAS`](https://github.com/codeplaysoftware/portBLAS) backend is also notable as it is open-source and written in pure SYCL. - -`oneMath` routines are essentially wrappers for the same routine in an underlying backend library, depending on the targeted device. This means that the same code can be used for NVidia, AMD, and Intel devices, making it highly portable. - -Looking to the `oneMath` documentation on [`geqrf`](https://oneapi-spec.uxlfoundation.org/specifications/oneapi/latest/elements/onemath/source/domains/lapack/geqrf#geqrf-usm-version): - -```cpp -namespace oneapi::math::lapack { - cl::sycl::event geqrf(cl::sycl::queue &queue, - std::int64_t m, - std::int64_t n, - T *a, - std::int64_t lda, - T *tau, - T *scratchpad, - std::int64_t scratchpad_size, - const std::vector &events = {}) -} -``` - -This general format (``sycl::queue``, arguments, and a vector of ``sycl::event``s) is more or less the same throughout the `oneMath` USM routines. - -The `pybind11` castings discussed in the previous section enable us to write a simple wrapper function for this routine with ``dpctl::tensor::usm_ndarray`` inputs and outputs, so long as we take the same precautions to avoid deadlocks. As a result, we can write the extension in much the same way as the `"kde_sycl_ext"` extension in the previous chapter. - -An example of a Python extension `"mkl_interface_ext"` that uses `oneMath` calls to implement a QR decomposition can be found in [`"steps/mkl_interface"`](https://github.com/IntelPython/example-portable-data-parallel-extensions/tree/main/steps/mkl_interface) folder (see [README](https://github.com/IntelPython/example-portable-data-parallel-extensions/blob/main/steps/mkl_interface/README.md)). - -The folder executes the tests found in [`"steps/mkl_interface/tests"`](https://github.com/IntelPython/example-portable-data-parallel-extensions/tree/main/steps/mkl_interface/tests) as well as running a larger benchmark which compares Numpy's `linalg.qr` (for reference) to the extension's implementation: - -```bash -$ python run.py -Using device NVIDIA GeForce GT 1030 -================================================= test session starts ================================================== -collected 8 items - -tests/test_qr.py ........ [100%] - -================================================== 8 passed in 0.45s =================================================== -QR decomposition for matrix of size = (3000, 3000) -Result agreed. -qr took 0.016026005148887634 seconds -np.linalg.qr took 0.5165981948375702 seconds -``` diff --git a/content/en/search.md b/content/en/search.md deleted file mode 100644 index 394feea..0000000 --- a/content/en/search.md +++ /dev/null @@ -1,4 +0,0 @@ ---- -title: Search Results -layout: search ---- diff --git a/css/katex.min.css b/css/katex.min.css new file mode 100644 index 0000000..865330a --- /dev/null +++ b/css/katex.min.css @@ -0,0 +1 @@ +@font-face{font-display:block;font-family:KaTeX_AMS;font-style:normal;font-weight:400;src:url(https://github.com/fonts/KaTeX_AMS-Regular.woff2) format("woff2"),url(https://github.com/fonts/KaTeX_AMS-Regular.woff) format("woff"),url(https://github.com/fonts/KaTeX_AMS-Regular.ttf) 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- build: - context: . - command: server - ports: - - "1313:1313" - volumes: - - .:/src diff --git a/docs/first-app/index.html b/docs/first-app/index.html new file mode 100644 index 0000000..f63e7ee --- /dev/null +++ b/docs/first-app/index.html @@ -0,0 +1,70 @@ +First DPC++ app | Portable Data-Parallel Python Extensions with oneAPI

First DPC++ app

A SYCL and DPC++ “Hello, World!” example.

For an in-depth introduction to SYCL and to accelerators programming please refer to the “Data Parallel C++” open access e-book.

A SYCL application runs on SYCL platform (host, connected to one or more heterogeneous devices). The application is structured in three scopes: application scope, command group scope, and kernel scope. The kernel scope specifies a single kernel function that will be compiled by the device +compiler and executed on the device. The command group scope specifies a unit work which includes the kernel function, preparation of +its arguments and specifying execution ordering information. The application scope specifies all the other code outside of command group scope. +Execution of SYCL application begins in the application scope.

// Compile: icpx -fsycl first.cpp -o first
+#include <sycl/sycl.hpp>
+
+int main(void) {
+    // queue to enqueue work to
+    // default-selected device
+    sycl::queue q{sycl::default_selector_v};
+
+    // allocation device
+    size_t data_size = 256;
+    int *data = sycl::malloc_device<int>(data_size, q);
+
+    // submit a task to populate
+    // device allocation
+    sycl::event e_fill =
+        q.fill<int>(data, 42, data_size);   // built-in kernel
+
+    // submit kernel to modify device allocation
+    sycl::event e_comp =
+        q.submit([&](sycl::handler &cgh) {   // command-group scope
+            // order execution after
+            // fill task completes
+            cgh.depends_on(e_fill);
+
+            sycl::range<1> global_iter_range{data_size};
+            cgh.parallel_for(
+                global_iter_range,
+                [=](sycl::item<1> it) {      // kernel scope
+                    int i = it.get_id(0);
+                    data[i] += i;
+                }
+            );
+        });
+
+    // copy from device to host
+    // order execution after modification task completes
+    int *host_data = new int[data_size];
+
+    q.copy<int>(                              // built-in kernel
+        data, host_data, data_size, {e_comp}
+    ).wait();
+    sycl::free(data, q);
+
+    // Output content of the array
+    output_array(host_data, data_size);
+    delete[] host_data;
+
+    return 0;
+}
+

The device where the kernel functions is executed is controlled by a device selector function, sycl::default_selector_v. +The default selector assigns scores to every device recognized by the runtime, and selects the one with the highest score. +A list of devices recognized by the DPC++ runtime can be obtained by running sycl-ls command.

A user of SYCL application compiled with DPC++ may restrict the set of devices discoverable by the runtime using +ONEAPI_DEVICE_SELECTOR environment variable. For example:

# execute on GPU
+ONEAPI_DEVICE_SELECTOR=*:gpu ./first
+# execute on CPU
+ONEAPI_DEVICE_SELECTOR=*:cpu ./first
+

By default, DPC++ compiler generates offload code for SPIR64 SYCL target, supported by +Intel GPUs as well as by CPU devices of x86_64 architecture. An attempt to execute SYCL program while +selecting only devices that do not support SPIR language would result in an error.

Targeting other GPUs

DPC++ supports generation of offload sections for multiple targets. For example, to compile for both SPIR and NVPTX targets (oneAPI for NVidia(R) GPUs is assumed installed):

icpx -fsycl -Xsycl-targets="nvptx64-nvidia-cuda,spir64-unknown-unknown" first.cpp -o first.out
+

To compile for both SPIR and AMD GCN targets (oneAPI for AMD GPUs is assumed installed):

icpx -fsycl -Xsycl-targets="amdgcn-amd-amdhsa,spir64-unknown-unknown" first.cpp -o first.out
+

It is possible to pass additional arguments to the specific SYCL target backend. For example, to target specific architecture use:

  • -Xsycl-target-backend=amdgcn-amd-amdhsa --offload-arch=gfx1030 for AMD GPUs
  • -Xsycl-target-backend=nvptx64-nvidia-cuda --cuda-gpu-arch=sm_80 for NVidia GPUs
\ No newline at end of file diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 0000000..baa6bbc --- /dev/null +++ b/docs/index.html @@ -0,0 +1,19 @@ +EuroPython 2025 poster content | Portable Data-Parallel Python Extensions with oneAPI

EuroPython 2025 poster content

Portable Data-Parallel Extensions with oneAPI

by Nikita Grigorian and Oleksandr Pavlyk

This poster is intended to introduce writing portable data-parallel Python extensions using oneAPI.

We present several examples, starting with the basics of initializing a USM (unified shared memory) array, then a KDE (kernel density estimation) with pure DPC++/Sycl, then a KDE Python extension, and finally how to write a portable Python extension which uses oneMath.

The examples can be found here.


About oneAPI

A brief overview of oneAPI and the programming model

First DPC++ app

A SYCL and DPC++ “Hello, World!” example.

KDE DPC++ example

KDE (kernel density estimation) example using SYCL and DPC++.

KDE Python extension

KDE (kernel density estimation) Python extension example.

oneMath Python extension

A Python extension written using oneMath.

\ No newline at end of file diff --git a/docs/index.xml b/docs/index.xml new file mode 100644 index 0000000..d7fd13c --- /dev/null +++ b/docs/index.xml @@ -0,0 +1,47 @@ +EuroPython 2025 poster content on Portable Data-Parallel Python Extensions with oneAPIhttps://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/Recent content in EuroPython 2025 poster content on Portable Data-Parallel Python Extensions with oneAPIHugoenThu, 10 Jul 2025 00:00:00 +0000About oneAPIhttps://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/oneapi/Thu, 10 Jul 2025 00:00:00 +0000https://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/oneapi/<p>The Unified Acceleration Foundation (<a href="https://uxlfoundation.org/">UXL</a>) under the umbrella of Linux Foundation is driving an open standard accelerator software ecosystem that includes compilers and performance libraries. This software ecosystem standardizes programming of different types of accelerators, such as multi-core CPUs, GPUs, some FPGAs, etc. from different vendors.</p> +<p>Intel&rsquo;s oneAPI DPC++ compiler is an implementation of the <a href="https://registry.khronos.org/SYCL/specs/sycl-2020/html/sycl-2020.html">SYCL-2020 standard</a> that is a part of UXL foundation&rsquo;s overall language design standardization for accelerator programming in C++. The compiler is being developed in <a href="https://github.com/intel/llvm">https://github.com/intel/llvm</a>, and supports offloading to Intel(R) GPUs, NVidia(R) GPUs, and AMD GPUs.</p>First DPC++ apphttps://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/first-app/Thu, 10 Jul 2025 00:00:00 +0000https://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/first-app/<p>For an in-depth introduction to SYCL and to accelerators programming please refer to the &ldquo;<a href="https://link.springer.com/book/10.1007/978-1-4842-9691-2">Data Parallel C++</a>&rdquo; open access e-book.</p> +<p>A SYCL application runs on SYCL platform (host, connected to one or more heterogeneous devices). The application is structured in three scopes: application scope, command group scope, and kernel scope. The kernel scope specifies a single kernel function that will be compiled by the device +compiler and executed on the device. The command group scope specifies a unit work which includes the kernel function, preparation of +its arguments and specifying execution ordering information. The application scope specifies all the other code outside of command group scope. +Execution of SYCL application begins in the application scope.</p>KDE DPC++ examplehttps://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/kde-cpp/Thu, 10 Jul 2025 00:00:00 +0000https://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/kde-cpp/<p>Given a sample of \(n\) observations \(x_i\) drawn from an unknown underlying continuous distribution \(f(x)\), +the kernel density estimate of that density function is computed as follows, for some kernel +smoothing parameter \(h \in \mathbb{R}\):</p> +<p>$$ +\hat{f}(x) = \frac{1}{n} \sum_{i=1}^{n} \frac{1}{h} K\left(\frac{x - x_i}{h}\right) +$$</p> +<p>An example of NumPy code performing the estimation, for a common choice of kernel function as standard +\(d\)-dimensional Gaussian distribution:</p> +<!-- See https://stackoverflow.com/questions/5319754/cross-reference-named-anchor-in-markdown //--> +<p><a id="kde_numpy" href=""></a></p>KDE Python extensionhttps://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/kde-python/Thu, 10 Jul 2025 00:00:00 +0000https://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/kde-python/<p>Since SYCL builds on C++, we are going to use the <code>pybind11</code> project to generate a Python extension. +We also need Python objects to carry USM allocations of input and output data, such as <code>dpctl</code> (<a href="https://github.com/IntelPython/dpctl.git">Data Parallel Control</a> Python package). The <code>dpctl</code> package also provides Python objects corresponding to DPC++ runtime objects:</p> +<table> + <thead> + <tr> + <th>Python object</th> + <th>SYCL C++ object</th> + </tr> + </thead> + <tbody> + <tr> + <td><code>dpctl.SyclQueue</code></td> + <td><code>sycl::queue</code></td> + </tr> + <tr> + <td><code>dpctl.SyclDevice</code></td> + <td><code>sycl::device</code></td> + </tr> + <tr> + <td><code>dpctl.SyclContext</code></td> + <td><code>sycl::context</code></td> + </tr> + <tr> + <td><code>dpctl.SyclEvent</code></td> + <td><code>sycl::event</code></td> + </tr> + </tbody> +</table> +<p><code>dpctl</code> provides integration with <code>pybind11</code> supporting castings between <code>dpctl</code> Python objects and corresponding C++ SYCL classes listed in the table above. Furthermore, the integration provides the C++ class <code>dpctl::tensor::usm_ndarray</code> which derives from <code>pybind11::object</code>. +It stores the <code>dpctl.tensor.usm_ndarray</code> object and provides methods to query its attributes, such as data pointer, dimensionality, shape, strides +and elemental type information. Underlying <code>dpctl.tensor.usm_ndarray</code> is a SYCL unified shared memory (USM) allocation. See the <a href="https://registry.khronos.org/SYCL/specs/sycl-2020/html/sycl-2020.html#sec:usm">SYCL standard</a> or <a href="https://intelpython.github.io/dpctl/latest/api_reference/dpctl/memory.html#dpctl-memory-pyapi">dpctl.memory documentation</a> for more details.</p>oneMath Python extensionhttps://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/onemath/Thu, 10 Jul 2025 00:00:00 +0000https://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/onemath/<p>Given a matrix \(A\), the QR decomposition of \(A\) is defined as the decomposition of \(A\) into the product of matrices \(Q\) and \(R\) such that \(Q\) is orthonormal and \(R\) is upper-triangular.</p> +<p>QR factorization is a common routine in more optimized LAPACK libraries, so rather than write and implement an algorithm ourselves, it would be preferable to find a suitable library routine.</p> +<p>Since <code>dpctl.tensor.usm_ndarray</code> is a Python object with an underlying USM allocation, it is possible to write extensions which wrap <code>oneAPI Math Library</code> (<a href="https://github.com/uxlfoundation/oneMath">oneMath</a>) USM routines and then call them on the <code>dpctl.tensor.usm_ndarray</code> from Python. These low-level routines can greatly improve the performance of an extension.</p> \ No newline at end of file diff --git a/docs/kde-cpp/index.html b/docs/kde-cpp/index.html new file mode 100644 index 0000000..b75b48a --- /dev/null +++ b/docs/kde-cpp/index.html @@ -0,0 +1,89 @@ +KDE DPC++ example | Portable Data-Parallel Python Extensions with oneAPI

KDE DPC++ example

KDE (kernel density estimation) example using SYCL and DPC++.

Given a sample of \(n\) observations \(x_i\) drawn from an unknown underlying continuous distribution \(f(x)\), +the kernel density estimate of that density function is computed as follows, for some kernel +smoothing parameter \(h \in \mathbb{R}\):

$$ +\hat{f}(x) = \frac{1}{n} \sum_{i=1}^{n} \frac{1}{h} K\left(\frac{x - x_i}{h}\right) +$$

An example of NumPy code performing the estimation, for a common choice of kernel function as standard +\(d\)-dimensional Gaussian distribution:

def kde(poi : np.ndarray, sample : np.ndarray, h : float) -> np.ndarray:
+    """Given a sample from underlying continuous distribution and
+    a smoothing parameter `h`, evaluate density estimate at each point of
+    interest `poi`.
+    """
+    assert sample.ndim == 2
+    assert poi.ndim == 2
+    m, d1 = poi.shape
+    n, d2 = sample.shape
+    assert d1 == d2
+    assert h > 0
+    dm = np.sum(np.square(poi[:, np.newaxis, ...] - sample[np.newaxis, ...]), axis=-1)
+    return np.mean(np.exp(dm/(-2*h*h)), axis=-1)/np.power(np.sqrt(2*np.pi) * h, d1)
+

The code above evaluates \(f(x)\) for \(m\) values of points of interest \(y_t\).

$$ +f(y_t) = \frac{1}{n} \sum_{i=1}^{n} \frac{1}{h} K\left( \frac{1}{h^2} \left\lVert y_t - x_i \right\rVert^{2} \right), ;;; \forall 0 \leq t \le m +$$

Evaluating such an expression can be done in parallel. Evaluation can be done independently for each \(t\). +Furthermore, summation over \(i\) can be partitioned among work-items, each summing \(n_{wi}\) distinct terms. +Such work partitioning would generate \(m \cdot \left\lceil {n}/{n_{wi}}\right\rceil\) independent tasks. +Each work-item could write its partial sum into a dedicated temporary memory location to avoid race condition +for further summation by another kernel operating in a similar fashion.

    parallel_for(
+        range<2>(m, ((n + n_wi - 1) / n_wi)),
+        [=](sycl::item<2> it) {
+            auto t = it.get_id(0);
+            auto i_block = it.get_id(1);
+
+            T local_partial_sum = ...;
+
+            partial_sums[t * ((n + n_wi - 1) / n_wi) + i_block] = local_partial_sum;
+        }
+    );
+

Such an approach, known as tree reduction, is implemented in kernel_density_esimation_temps function found in +"steps/kernel_density_estimation_cpp/kde.hpp".

Use of temporary allocation can be avoided if each work-item atomically adds the value of the local sum to the +appropriate zero-initialized location in the output array, as in implementation kernel_density_estimation_atomic_ref +in the same header file:

    parallel_for(
+        range<2>(m, ((n + n_wi - 1) / n_wi)),
+        [=](sycl::item<2> it) {
+            auto t = it.get_id(0);
+            auto i_block = it.get_id(1);
+
+            T local_partial_sum = ...;
+
+            sycl::atomic_ref<...> f_aref(f[t]);
+            f_aref += local_partial_sum;
+        }
+    );
+

Multiple work-items may concurrently updating the same location in global memory would produce the correct result due to +use of sycl::atomic_ref but at the expense of increased number of attempts, phenomenon known as atomic pressure. +Atomic pressure leads to thread divergence and degrades performance.

To reduce the atomic pressure work-items can be organized into work-groups. Every work-item in a work-group has access +to local shared memory, dedicated on-chip memory, which can be used to cooperatively combine values held by work-items +in the work-group without accessing the global memory. This could be done efficiently by calling group function +sycl::reduce_over_group. To be able to call it, we must specify iteration range using sycl::nd_range rather than +sycl::range as we did earlier.

    auto wg = 256; // work-group-size
+    auto n_data_per_wg = n_wi * wg;
+    auto n_groups = ((n + n_data_per_wg - 1) / n_data_per_Wg);
+
+    range<2> gRange(m, n_groups * wg);
+    range<2> lRange(1, wg);
+
+    parallel_for(
+        nd_range<2>(gRange, lRange),
+        [=](sycl::nd_item<2> it) {
+            auto t = it.get_global_id(0);
+
+            T local_partial_sum = ...;
+
+            auto work_group = it.get_group();
+            T sum_over_wg = sycl::reduce_over_group(work_group, local_sum, sycl::plus<>());
+
+            if (work_group.leader()) {
+                sycl::atomic_ref<...> f_aref(f[t]);
+                f_aref += sum_over_wg;
+            }
+        }
+    );
+

Complete implementation can be found in kernel_density_estimation_work_group_reduce_and_atomic_ref function +in "steps/kernel_density_estimation_cpp/kde.hpp".

These implementations are called from C++ application "steps/kernel_density_estimation_cpp/app.cpp", which +samples data uniformly distributed over unit cuboid, and estimates the density using Kernel Density Estimation +and spherically symmetric multivariate Gaussian probability density function as the kernel.

The application can be built using CMake, or Meson, please refer to README document in that folder.

\ No newline at end of file diff --git a/docs/kde-python/index.html b/docs/kde-python/index.html new file mode 100644 index 0000000..1cdcd34 --- /dev/null +++ b/docs/kde-python/index.html @@ -0,0 +1,78 @@ +KDE Python extension | Portable Data-Parallel Python Extensions with oneAPI

KDE Python extension

KDE (kernel density estimation) Python extension example.

Since SYCL builds on C++, we are going to use the pybind11 project to generate a Python extension. +We also need Python objects to carry USM allocations of input and output data, such as dpctl (Data Parallel Control Python package). The dpctl package also provides Python objects corresponding to DPC++ runtime objects:

Python objectSYCL C++ object
dpctl.SyclQueuesycl::queue
dpctl.SyclDevicesycl::device
dpctl.SyclContextsycl::context
dpctl.SyclEventsycl::event

dpctl provides integration with pybind11 supporting castings between dpctl Python objects and corresponding C++ SYCL classes listed in the table above. Furthermore, the integration provides the C++ class dpctl::tensor::usm_ndarray which derives from pybind11::object. +It stores the dpctl.tensor.usm_ndarray object and provides methods to query its attributes, such as data pointer, dimensionality, shape, strides +and elemental type information. Underlying dpctl.tensor.usm_ndarray is a SYCL unified shared memory (USM) allocation. See the SYCL standard or dpctl.memory documentation for more details.

For illustration purpose, here is a sample extension source code:

#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <sycl/sycl.hpp>
+#include "dpctl4pybind11.hpp"
+#include <vector>
+
+sycl::event
+py_foo(dpctl::tensor::usm_ndarray inp,
+       dpctl::tensor::usm_ndarray out,
+       const std::vector<sycl::event> &deps) {
+    // validation steps skipped
+
+    // Execution queue is the queue associated with input arrays
+    // these are expected to be the same and checked during validation
+    sycl::queue exec_q = inp.get_queue();
+
+    const std::int64_t *inp_ptr = inp.get_data<std::int64_t>();
+    std::int64_t *out_ptr = out.get_data<std::int64_t>();
+
+    // submit tasks for execution and obtain the event signaling
+    // the status of execution of the set of tasks
+    sycl::event e_impl =  impl_fn(exec_q, inp_ptr, out_ptr, depends);
+
+    return e_impl;
+}
+
+PYBIND11_MODULE(_ext, m) {
+    m.def("foo", &py_foo);
+}
+

On the Python side, the function would be called as follows

import dpctl.tensor as dpt
+import _ext
+
+# Allocate input and output arrays on the
+# default-selected device
+inp = dpt.arange(100, dtype=dpt.int64)
+out = dpt.empty_like(inp)
+ev = _ext.foo(inp, out, [])
+
+# ...
+

Since execution is offloaded to a device, it is our responsibility to ensure that USM data being worked on +is not deallocated until after offloaded tasks complete the execution. The simplest way to assure of this is +to wait on the event with ev.wait() on the Python side, or with e_impl.wait() on the C++ side.

Alternatively, one can assure the lifetime of arrays asynchronously, by using sycl::handler::host_task to +schedule code execution on the host ordering it after kernel execution completion:

// increment reference counts of input and output arrays
+
+sycl::event ht_ev =
+    exec_q.submit([&](sycl::handler &cgh) {
+        // execute host_task once e_impl signals completion
+        cgh.depends_on(e_impl);
+
+        cgh.host_task([=]() {
+            // we must acquire GIL to be able to safely
+            // manipulate reference counts of Python objects
+            pybind11::gil_scoped_acquire guard;
+
+            // decrement ref-counts
+        });
+    });
+

Since the host task may execute in a thread different from that of the Python interpreter (the main thread), care must be taken +to avoid deadlocks: synchronization operation that call ht_ev.wait() from the main thread must release GIL to afford the body +of the host task a chance at execution.

Of course, if USM memory is not managed by Python, it may be possible to avoid using GIL altogether.

An example of Python extension "kde_sycl_ext" that exposes kernel density estimation code from previous +section can be found in "steps/sycl_python_extension" folder (see README).

The folder contains comparison between dpctl-based implementation of the KDE implementation following the NumPy +implementation above and the dedicated C++ code:

KDE for n_sample = 1000000, n_est = 17, n_dim = 7, h = 0.05
+Result agreed.
+kde_dpctl took 0.3404452269896865 seconds
+kde_ext[mode=0] 0.02209925901843235 seconds
+kde_ext[mode=1] 0.02560457994695753 seconds
+kde_ext[mode=2] 0.02815118699800223 seconds
+kde_numpy 0.7227164240321144 seconds
+

This sample run was obtained on a laptop with 11th Gen Intel(R) Core(TM) i7-1185G7 CPU @ 3.00GHz, 32 GB of RAM, and the integrated Intel(R) Iris(R) Xe GPU, with stock NumPy 1.26.4, and development build of dpctl 0.17 built with oneAPI DPC++ 2024.1.0.

\ No newline at end of file diff --git a/docs/oneapi/index.html b/docs/oneapi/index.html new file mode 100644 index 0000000..4b013a8 --- /dev/null +++ b/docs/oneapi/index.html @@ -0,0 +1,12 @@ +About oneAPI | Portable Data-Parallel Python Extensions with oneAPI

About oneAPI

A brief overview of oneAPI and the programming model

The Unified Acceleration Foundation (UXL) under the umbrella of Linux Foundation is driving an open standard accelerator software ecosystem that includes compilers and performance libraries. This software ecosystem standardizes programming of different types of accelerators, such as multi-core CPUs, GPUs, some FPGAs, etc. from different vendors.

Intel’s oneAPI DPC++ compiler is an implementation of the SYCL-2020 standard that is a part of UXL foundation’s overall language design standardization for accelerator programming in C++. The compiler is being developed in https://github.com/intel/llvm, and supports offloading to Intel(R) GPUs, NVidia(R) GPUs, and AMD GPUs.

The oneAPI DPC++ compiler can be installed on Linux and Windows as part of oneAPI Base Toolkit and requires a system compiler to be installed, such as GCC on Linux and MSVC on Windows. Since DPC++ builds +on the conventional toolchain, Python extensions built with DPC++ are compatible with the Python stack out of the box. DPC++ also requires that OpenCL and Level Zero drivers are installed. For NVidia(R) or AMD devices, appropriate drivers must be installed.

To offload to devices, host code and device code are compiled separately, then combined by the linker into a single fat binary.

Support for offloading to GPUs from vendors other than Intel can be enabled by additionally installing plugins from CodePlay:

Using these plugins and the appropriate compiler options, the fat binary for a DPC++ application will also include the necessary sections for offloading a kernel to NVidia(R) or AMD GPUs.

A list of devices available to DPC++ can be obtained using sycl-ls.

sycl-ls
+[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2024.17.3.0.08_160000]
+[opencl:cpu:1] Intel(R) OpenCL, 12th Gen Intel(R) Core(TM) i9-12900 OpenCL 3.0 (Build 0) [2024.17.3.0.08_160000]
+[ext_oneapi_cuda:gpu:0] NVIDIA CUDA BACKEND, NVIDIA GeForce GT 1030 6.1 [CUDA 12.2]
+

A list of SYCL projects can be found on https://sycl.tech/projects

\ No newline at end of file diff --git a/docs/onemath/index.html b/docs/onemath/index.html new file mode 100644 index 0000000..2d95ffa --- /dev/null +++ b/docs/onemath/index.html @@ -0,0 +1,30 @@ +oneMath Python extension | Portable Data-Parallel Python Extensions with oneAPI

oneMath Python extension

A Python extension written using oneMath.

Given a matrix \(A\), the QR decomposition of \(A\) is defined as the decomposition of \(A\) into the product of matrices \(Q\) and \(R\) such that \(Q\) is orthonormal and \(R\) is upper-triangular.

QR factorization is a common routine in more optimized LAPACK libraries, so rather than write and implement an algorithm ourselves, it would be preferable to find a suitable library routine.

Since dpctl.tensor.usm_ndarray is a Python object with an underlying USM allocation, it is possible to write extensions which wrap oneAPI Math Library (oneMath) USM routines and then call them on the dpctl.tensor.usm_ndarray from Python. These low-level routines can greatly improve the performance of an extension.

oneMath can be built to dispatch to a variety of backends including cuBLAS and rocBLAS (see oneMath README). The portBLAS backend is also notable as it is open-source and written in pure SYCL.

oneMath routines are essentially wrappers for the same routine in an underlying backend library, depending on the targeted device. This means that the same code can be used for NVidia, AMD, and Intel devices, making it highly portable.

Looking to the oneMath documentation on geqrf:

namespace oneapi::math::lapack {
+  cl::sycl::event geqrf(cl::sycl::queue &queue,
+                        std::int64_t m,
+                        std::int64_t n,
+                        T *a,
+                        std::int64_t lda,
+                        T *tau,
+                        T *scratchpad,
+                        std::int64_t scratchpad_size,
+                        const std::vector<cl::sycl::event> &events = {})
+}
+

This general format (sycl::queue, arguments, and a vector of sycl::events) is more or less the same throughout the oneMath USM routines.

The pybind11 castings discussed in the previous section enable us to write a simple wrapper function for this routine with dpctl::tensor::usm_ndarray inputs and outputs, so long as we take the same precautions to avoid deadlocks. As a result, we can write the extension in much the same way as the "kde_sycl_ext" extension in the previous chapter.

An example of a Python extension "mkl_interface_ext" that uses oneMath calls to implement a QR decomposition can be found in "steps/mkl_interface" folder (see README).

The folder executes the tests found in "steps/mkl_interface/tests" as well as running a larger benchmark which compares Numpy’s linalg.qr (for reference) to the extension’s implementation:

$ python run.py
+Using device NVIDIA GeForce GT 1030
+================================================= test session starts ==================================================
+collected 8 items
+
+tests/test_qr.py ........                                                                                        [100%]
+
+================================================== 8 passed in 0.45s ===================================================
+QR decomposition for matrix of size = (3000, 3000)
+Result agreed.
+qr took 0.016026005148887634 seconds
+np.linalg.qr took 0.5165981948375702 seconds
+
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-go 1.12 - -require github.com/google/docsy v0.12.0 diff --git a/go.sum b/go.sum deleted file mode 100644 index 8a42dcd..0000000 --- a/go.sum +++ /dev/null @@ -1,6 +0,0 @@ -github.com/FortAwesome/Font-Awesome v0.0.0-20241216213156-af620534bfc3 h1:/iluJkJiyTAdnqrw3Yi9rH2HNHhrrtCmj8VJe7I6o3w= -github.com/FortAwesome/Font-Awesome v0.0.0-20241216213156-af620534bfc3/go.mod h1:IUgezN/MFpCDIlFezw3L8j83oeiIuYoj28Miwr/KUYo= -github.com/google/docsy v0.12.0 h1:CddZKL39YyJzawr8GTVaakvcUTCJRAAYdz7W0qfZ2P4= -github.com/google/docsy v0.12.0/go.mod h1:1bioDqA493neyFesaTvQ9reV0V2vYy+xUAnlnz7+miM= -github.com/twbs/bootstrap v5.3.6+incompatible h1:efmXVyq839m5QQ0+JBUdQQ1TrmoBqvQ5kRhUueKsH+4= -github.com/twbs/bootstrap v5.3.6+incompatible/go.mod h1:fZTSrkpSf0/HkL0IIJzvVspTt1r9zuf7XlZau8kpcY0= diff --git a/hugo-disabled.toml b/hugo-disabled.toml deleted file mode 100644 index 6538922..0000000 --- a/hugo-disabled.toml +++ /dev/null @@ -1,201 +0,0 @@ -baseURL = "https://intelpython.github.io/portable-data-parallel-extensions-europython-2025" -title = "Portable Data-Parallel Python Extensions with oneAPI" - -# Language settings -contentDir = "content/en" -defaultContentLanguage = "en" -defaultContentLanguageInSubdir = false -# Useful when translating. -enableMissingTranslationPlaceholders = true - -enableRobotsTXT = true - -# Will give values to .Lastmod etc. -enableGitInfo = true - -# Comment out to disable taxonomies in Docsy -# disableKinds = ["taxonomy"] - -# You can add your own taxonomies -[taxonomies] -tag = "" -category = "" - -[params.taxonomy] -# set taxonomyCloud = [] to hide taxonomy clouds -taxonomyCloud = [] - -# If used, must have same length as taxonomyCloud -taxonomyCloudTitle = [] - -# set taxonomyPageHeader = [] to hide taxonomies on the page headers -taxonomyPageHeader = [] - -# Highlighting config -pygmentsCodeFences = true -pygmentsUseClasses = false -# Use the new Chroma Go highlighter in Hugo. -pygmentsUseClassic = false -#pygmentsOptions = "linenos=table" -# See https://help.farbox.com/pygments.html -pygmentsStyle = "tango" - -# Configure how URLs look like per section. -[permalinks] -blog = "/:section/:year/:month/:day/:slug/" - -# Image processing configuration. -[imaging] -resampleFilter = "CatmullRom" -quality = 75 -anchor = "smart" - -# Language configuration - -[languages] -[languages.en] -languageName ="English" -title = "Goldydocs" -[languages.en.params] -description = "A Docsy example site" - -[markup] - [markup.goldmark] - [markup.goldmark.parser.attribute] - block = true - [markup.goldmark.renderer] - unsafe = true - [markup.highlight] - # See a complete list of available styles at https://xyproto.github.io/splash/docs/all.html - style = "tango" - # Uncomment if you want your chosen highlight style used for code blocks without a specified language - # guessSyntax = "true" - -# Comment out if you don't want the "print entire section" link enabled. -[outputs] -# section = ["HTML", "print", "RSS"] - -# Everything below this are Site Params - -[params] -privacy_policy = "https://policies.google.com/privacy" - -# First one is picked as the Twitter card image if not set on page. -# images = ["images/project-illustration.png"] - -# Menu title if your navbar has a versions selector to access old versions of your site. -# This menu appears only if you have at least one [params.versions] set. -version_menu = "Releases" - -# Flag used in the "version-banner" partial to decide whether to display a -# banner on every page indicating that this is an archived version of the docs. -# Set this flag to "true" if you want to display the banner. -archived_version = false - -# The version number for the version of the docs represented in this doc set. -# Used in the "version-banner" partial to display a version number for the -# current doc set. -version = "0.7.1" - -# A link to latest version of the docs. Used in the "version-banner" partial to -# point people to the main doc site. -url_latest_version = "https://example.com/" - -# Repository configuration (URLs for in-page links to opening issues and suggesting changes) -github_repo = "https://github.com/google/docsy-example" -# An optional link to a related project repo. For example, the sibling repository where your product code lives. -github_project_repo = "https://github.com/google/docsy" - -# Specify a value here if your content directory is not in your repo's root directory -# github_subdir = "" - -# Uncomment this if your GitHub repo does not have "main" as the default branch, -# or specify a new value if you want to reference another branch in your GitHub links -github_branch= "main" - -# Google Custom Search Engine ID. Remove or comment out to disable search. -gcs_engine_id = "d72aa9b2712488cc3" - -# Enable Lunr.js offline search -offlineSearch = false - -# Enable syntax highlighting and copy buttons on code blocks with Prism -prism_syntax_highlighting = false - -[params.copyright] - authors = "Docsy Authors | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) | " - from_year = 2018 - -# User interface configuration -[params.ui] -# Set to true to disable breadcrumb navigation. -breadcrumb_disable = false -# Set to false if you don't want to display a logo (/assets/icons/logo.svg) in the top navbar -navbar_logo = true -# Set to true if you don't want the top navbar to be translucent when over a `block/cover`, like on the homepage. -navbar_translucent_over_cover_disable = false -# Enable to show the side bar menu in its compact state. -sidebar_menu_compact = false -# Set to true to hide the sidebar search box (the top nav search box will still be displayed if search is enabled) -sidebar_search_disable = false - -# Adds a H2 section titled "Feedback" to the bottom of each doc. The responses are sent to Google Analytics as events. -# This feature depends on [services.googleAnalytics] and will be disabled if "services.googleAnalytics.id" is not set. -# If you want this feature, but occasionally need to remove the "Feedback" section from a single page, -# add "hide_feedback: true" to the page's front matter. -[params.ui.feedback] -enable = false -# The responses that the user sees after clicking "yes" (the page was helpful) or "no" (the page was not helpful). -yes = 'Glad to hear it! Please tell us how we can improve.' -no = 'Sorry to hear that. Please tell us how we can improve.' - -# Adds a reading time to the top of each doc. -# If you want this feature, but occasionally need to remove the Reading time from a single page, -# add "hide_readingtime: true" to the page's front matter -[params.ui.readingtime] -enable = false - -[params.links] -# End user relevant links. These will show up on left side of footer and in the community page if you have one. -[[params.links.user]] - name = "User mailing list" - url = "https://example.org/mail" - icon = "fa fa-envelope" - desc = "Discussion and help from your fellow users" -[[params.links.user]] - name ="Twitter" - url = "https://example.org/twitter" - icon = "fab fa-twitter" - desc = "Follow us on Twitter to get the latest news!" -[[params.links.user]] - name = "Stack Overflow" - url = "https://example.org/stack" - icon = "fab fa-stack-overflow" - desc = "Practical questions and curated answers" -# Developer relevant links. These will show up on right side of footer and in the community page if you have one. -[[params.links.developer]] - name = "GitHub" - url = "https://github.com/google/docsy" - icon = "fab fa-github" - desc = "Development takes place here!" -[[params.links.developer]] - name = "Slack" - url = "https://example.org/slack" - icon = "fab fa-slack" - desc = "Chat with other project developers" -[[params.links.developer]] - name = "Developer mailing list" - url = "https://example.org/mail" - icon = "fa fa-envelope" - desc = "Discuss development issues around the project" - -# hugo module configuration -[module] - # Uncomment the next line to build and serve using local docsy clone declared in the named Hugo workspace: - # workspace = "docsy.work" - [module.hugoVersion] - extended = true - min = "0.146.0" - [[module.imports]] - path = "github.com/google/docsy" - disable = false diff --git a/hugo.yaml b/hugo.yaml deleted file mode 100644 index aba9553..0000000 --- a/hugo.yaml +++ /dev/null @@ -1,192 +0,0 @@ -baseURL: https://IntelPython.github.io/portable-data-parallel-extensions-europython-2025 -title: Portable Data-Parallel Python Extensions with oneAPI - -# cSpell:ignore goldmark github hugo readingtime docsy subdir lastmod pygments linenos catmullrom gu - -# Language settings -contentDir: content/en -defaultContentLanguage: en -defaultContentLanguageInSubdir: false -# Useful when translating. -enableMissingTranslationPlaceholders: true - -enableRobotsTXT: true - -# Will give values to .Lastmod etc. -enableGitInfo: false - -# Comment out to disable taxonomies in Docsy -# disableKinds: [taxonomy] - -# You can add your own taxonomies -taxonomies: - tag: tags - category: categories - -# Highlighting config -pygmentsCodeFences: true -pygmentsUseClasses: false -# Use the new Chroma Go highlighter in Hugo. -pygmentsUseClassic: false -# pygmentsOptions: "linenos=table" -# See https://help.farbox.com/pygments.html -pygmentsStyle: tango - -# Configure how URLs look like per section. -permalinks: - blog: /:section/:year/:month/:day/:slug/ - -# Image processing configuration. -imaging: - resampleFilter: CatmullRom - quality: 75 - anchor: smart - -# Language configuration -languages: - en: - languageName: English - title: Portable Data-Parallel Python Extensions with oneAPI - params: - description: Material for EuroPython 2025 poster - -markup: - goldmark: - parser: - attribute: - block: true - renderer: - unsafe: true - highlight: - # See a complete list of available styles at https://xyproto.github.io/splash/docs/all.html - style: tango - # Uncomment if you want your chosen highlight style used for code blocks without a specified language - # guessSyntax: true - -# Everything below this are Site Params - -# Comment out if you don't want the "print entire section" link enabled. -outputs: - -params: - taxonomy: - # set taxonomyCloud = [] to hide taxonomy clouds - taxonomyCloud: [] - - # If used, must have same length as taxonomyCloud - taxonomyCloudTitle: [] - - # set taxonomyPageHeader = [] to hide taxonomies on the page headers - taxonomyPageHeader: [] - -params: - katex: - enable: true - - privacy_policy: https://policies.google.com/privacy - - # First one is picked as the Twitter card image if not set on page. - # images: [images/project-illustration.png] - - # Menu title if your navbar has a versions selector to access old versions of your site. - # This menu appears only if you have at least one [params.versions] set. - version_menu: Releases - - # Flag used in the "version-banner" partial to decide whether to display a - # banner on every page indicating that this is an archived version of the docs. - # Set this flag to "true" if you want to display the banner. - archived_version: false - - # The version number for the version of the docs represented in this doc set. - # Used in the "version-banner" partial to display a version number for the - # current doc set. - version: 0.0 - - # A link to latest version of the docs. Used in the "version-banner" partial to - # point people to the main doc site. - url_latest_version: https://example.com - - # Repository configuration (URLs for in-page links to opening issues and suggesting changes) - github_repo: https://github.com/google/docsy-example - - # An optional link to a related project repo. For example, the sibling repository where your product code lives. - github_project_repo: https://github.com/google/docsy - - # Specify a value here if your content directory is not in your repo's root directory - # github_subdir: "" - - # Uncomment this if your GitHub repo does not have "main" as the default branch, - # or specify a new value if you want to reference another branch in your GitHub links - github_branch: main - - # Google Custom Search Engine ID. Remove or comment out to disable search. - gcs_engine_id: d72aa9b2712488cc3 - - # Enable Lunr.js offline search - offlineSearch: false - - # Enable syntax highlighting and copy buttons on code blocks with Prism - prism_syntax_highlighting: false - - copyright: - authors: Intel Corp. All Rights Reserved - year: 2025 - - # User interface configuration - ui: - # Set to true to disable breadcrumb navigation. - breadcrumb_disable: false - # Set to false if you don't want to display a logo (/assets/icons/logo.svg) in the top navbar - navbar_logo: true - # Set to true if you don't want the top navbar to be translucent when over a `block/cover`, like on the homepage. - navbar_translucent_over_cover_disable: false - # Enable to show the side bar menu in its compact state. - sidebar_menu_compact: true - # Set to true to hide the sidebar search box (the top nav search box will still be displayed if search is enabled) - sidebar_search_disable: false - - # Adds a H2 section titled "Feedback" to the bottom of each doc. The responses are sent to Google Analytics as events. - # This feature depends on [services.googleAnalytics] and will be disabled if "services.googleAnalytics.id" is not set. - # If you want this feature, but occasionally need to remove the "Feedback" section from a single page, - # add "hide_feedback: true" to the page's front matter. - feedback: - enable: false - # The responses that the user sees after clicking "yes" (the page was helpful) or "no" (the page was not helpful). - 'yes': >- - Glad to hear it! Please tell us how we can improve. - 'no': >- - Sorry to hear that. Please tell us how we can improve. - - # Adds a reading time to the top of each doc. - # If you want this feature, but occasionally need to remove the Reading time from a single page, - # add "hide_readingtime: true" to the page's front matter - readingtime: - enable: false - - links: - # End user relevant links. These will show up on left side of footer and in the community page if you have one. - user: - # Developer relevant links. These will show up on right side of footer and in the community page if you have one. - developer: - - name: GitHub - url: https://github.com/google/docsy - icon: fab fa-github - desc: Development takes place here! - - name: Slack - url: https://example.org/slack - icon: fab fa-slack - desc: Chat with other project developers - - name: Developer mailing list - url: https://example.org/mail - icon: fa fa-envelope - desc: Discuss development issues around the project - -module: - # Uncomment the next line to build and serve using local docsy clone declared in the named Hugo workspace: - # workspace: docsy.work - hugoVersion: - extended: true - min: 0.146.0 - imports: - - path: github.com/google/docsy - disable: false diff --git a/index.html b/index.html new file mode 100644 index 0000000..ccc75e4 --- /dev/null +++ b/index.html @@ -0,0 +1,9 @@ +Portable Data-Parallel Python Extensions with oneAPI

Portable Data-Parallel Python Extensions with oneAPI

Nikita Grigorian and +Oleksandr Pavlyk

Sources and details for the Portable Data-Parallel Python Extensions with oneAPI poster at the EuroPython 2025 conference. This poster presents ongoing work to enable writing, building, and implementing portable extensions for data-parallel computation in Python.

About the authors

This poster was made by and is being presented on behalf of the Python team at Intel.

dpctl

A package with Python bindings for DPC++ runtime classes and an Array API Specification conformant tensor submodule built on pure SYCL for portability.

dpnp

An oneAPI- and oneMKL-powered array library built as a drop-in replacement for Numpy that can be executed on any data-parallel device.

\ No newline at end of file diff --git a/index.xml b/index.xml new file mode 100644 index 0000000..e318c98 --- /dev/null +++ b/index.xml @@ -0,0 +1,47 @@ +Portable Data-Parallel Python Extensions with oneAPIhttps://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/Recent content on Portable Data-Parallel Python Extensions with oneAPIHugoenThu, 10 Jul 2025 00:00:00 +0000About oneAPIhttps://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/oneapi/Thu, 10 Jul 2025 00:00:00 +0000https://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/oneapi/<p>The Unified Acceleration Foundation (<a href="https://uxlfoundation.org/">UXL</a>) under the umbrella of Linux Foundation is driving an open standard accelerator software ecosystem that includes compilers and performance libraries. This software ecosystem standardizes programming of different types of accelerators, such as multi-core CPUs, GPUs, some FPGAs, etc. from different vendors.</p> +<p>Intel&rsquo;s oneAPI DPC++ compiler is an implementation of the <a href="https://registry.khronos.org/SYCL/specs/sycl-2020/html/sycl-2020.html">SYCL-2020 standard</a> that is a part of UXL foundation&rsquo;s overall language design standardization for accelerator programming in C++. The compiler is being developed in <a href="https://github.com/intel/llvm">https://github.com/intel/llvm</a>, and supports offloading to Intel(R) GPUs, NVidia(R) GPUs, and AMD GPUs.</p>First DPC++ apphttps://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/first-app/Thu, 10 Jul 2025 00:00:00 +0000https://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/first-app/<p>For an in-depth introduction to SYCL and to accelerators programming please refer to the &ldquo;<a href="https://link.springer.com/book/10.1007/978-1-4842-9691-2">Data Parallel C++</a>&rdquo; open access e-book.</p> +<p>A SYCL application runs on SYCL platform (host, connected to one or more heterogeneous devices). The application is structured in three scopes: application scope, command group scope, and kernel scope. The kernel scope specifies a single kernel function that will be compiled by the device +compiler and executed on the device. The command group scope specifies a unit work which includes the kernel function, preparation of +its arguments and specifying execution ordering information. The application scope specifies all the other code outside of command group scope. +Execution of SYCL application begins in the application scope.</p>KDE DPC++ examplehttps://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/kde-cpp/Thu, 10 Jul 2025 00:00:00 +0000https://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/kde-cpp/<p>Given a sample of \(n\) observations \(x_i\) drawn from an unknown underlying continuous distribution \(f(x)\), +the kernel density estimate of that density function is computed as follows, for some kernel +smoothing parameter \(h \in \mathbb{R}\):</p> +<p>$$ +\hat{f}(x) = \frac{1}{n} \sum_{i=1}^{n} \frac{1}{h} K\left(\frac{x - x_i}{h}\right) +$$</p> +<p>An example of NumPy code performing the estimation, for a common choice of kernel function as standard +\(d\)-dimensional Gaussian distribution:</p> +<!-- See https://stackoverflow.com/questions/5319754/cross-reference-named-anchor-in-markdown //--> +<p><a id="kde_numpy" href=""></a></p>KDE Python extensionhttps://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/kde-python/Thu, 10 Jul 2025 00:00:00 +0000https://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/kde-python/<p>Since SYCL builds on C++, we are going to use the <code>pybind11</code> project to generate a Python extension. +We also need Python objects to carry USM allocations of input and output data, such as <code>dpctl</code> (<a href="https://github.com/IntelPython/dpctl.git">Data Parallel Control</a> Python package). The <code>dpctl</code> package also provides Python objects corresponding to DPC++ runtime objects:</p> +<table> + <thead> + <tr> + <th>Python object</th> + <th>SYCL C++ object</th> + </tr> + </thead> + <tbody> + <tr> + <td><code>dpctl.SyclQueue</code></td> + <td><code>sycl::queue</code></td> + </tr> + <tr> + <td><code>dpctl.SyclDevice</code></td> + <td><code>sycl::device</code></td> + </tr> + <tr> + <td><code>dpctl.SyclContext</code></td> + <td><code>sycl::context</code></td> + </tr> + <tr> + <td><code>dpctl.SyclEvent</code></td> + <td><code>sycl::event</code></td> + </tr> + </tbody> +</table> +<p><code>dpctl</code> provides integration with <code>pybind11</code> supporting castings between <code>dpctl</code> Python objects and corresponding C++ SYCL classes listed in the table above. Furthermore, the integration provides the C++ class <code>dpctl::tensor::usm_ndarray</code> which derives from <code>pybind11::object</code>. +It stores the <code>dpctl.tensor.usm_ndarray</code> object and provides methods to query its attributes, such as data pointer, dimensionality, shape, strides +and elemental type information. Underlying <code>dpctl.tensor.usm_ndarray</code> is a SYCL unified shared memory (USM) allocation. See the <a href="https://registry.khronos.org/SYCL/specs/sycl-2020/html/sycl-2020.html#sec:usm">SYCL standard</a> or <a href="https://intelpython.github.io/dpctl/latest/api_reference/dpctl/memory.html#dpctl-memory-pyapi">dpctl.memory documentation</a> for more details.</p>oneMath Python extensionhttps://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/onemath/Thu, 10 Jul 2025 00:00:00 +0000https://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/docs/onemath/<p>Given a matrix \(A\), the QR decomposition of \(A\) is defined as the decomposition of \(A\) into the product of matrices \(Q\) and \(R\) such that \(Q\) is orthonormal and \(R\) is upper-triangular.</p> +<p>QR factorization is a common routine in more optimized LAPACK libraries, so rather than write and implement an algorithm ourselves, it would be preferable to find a suitable library routine.</p> +<p>Since <code>dpctl.tensor.usm_ndarray</code> is a Python object with an underlying USM allocation, it is possible to write extensions which wrap <code>oneAPI Math Library</code> (<a href="https://github.com/uxlfoundation/oneMath">oneMath</a>) USM routines and then call them on the <code>dpctl.tensor.usm_ndarray</code> from Python. These low-level routines can greatly improve the performance of an extension.</p>Search Resultshttps://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/search/Mon, 01 Jan 0001 00:00:00 +0000https://IntelPython.github.io/portable-data-parallel-extensions-europython-2025/search/ \ No newline at end of file diff --git a/js/auto-render.min.js b/js/auto-render.min.js new file mode 100644 index 0000000..32a7dd8 --- /dev/null +++ b/js/auto-render.min.js @@ -0,0 +1 @@ +!function(e,t){"object"==typeof exports&&"object"==typeof module?module.exports=t(require("katex")):"function"==typeof define&&define.amd?define(["katex"],t):"object"==typeof exports?exports.renderMathInElement=t(require("katex")):e.renderMathInElement=t(e.katex)}("undefined"!=typeof self?self:this,(function(e){return function(){"use strict";var t={757:function(t){t.exports=e}},n={};function r(e){var o=n[e];if(void 0!==o)return o.exports;var i=n[e]={exports:{}};return t[e](i,i.exports,r),i.exports}r.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return r.d(t,{a:t}),t},r.d=function(e,t){for(var n in t)r.o(t,n)&&!r.o(e,n)&&Object.defineProperty(e,n,{enumerable:!0,get:t[n]})},r.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)};var o={};r.d(o,{default:function(){return p}});var i=r(757),a=r.n(i);const l=function(e,t,n){let r=n,o=0;const i=e.length;for(;re.left.replace(/[-/\\^$*+?.()|[\]{}]/g,"\\$&"))).join("|")+")");for(;n=e.search(o),-1!==n;){n>0&&(r.push({type:"text",data:e.slice(0,n)}),e=e.slice(n));const o=t.findIndex((t=>e.startsWith(t.left)));if(n=l(t[o].right,e,t[o].left.length),-1===n)break;const i=e.slice(0,n+t[o].right.length),a=s.test(i)?i:e.slice(t[o].left.length,n);r.push({type:"math",data:a,rawData:i,display:t[o].display}),e=e.slice(n+t[o].right.length)}return""!==e&&r.push({type:"text",data:e}),r};const c=function(e,t){const n=d(e,t.delimiters);if(1===n.length&&"text"===n[0].type)return null;const r=document.createDocumentFragment();for(let e=0;e-1===e.indexOf(" "+t+" ")))&&f(r,t)}}};var p=function(e,t){if(!e)throw new Error("No element provided to render");const n={};for(const e in t)t.hasOwnProperty(e)&&(n[e]=t[e]);n.delimiters=n.delimiters||[{left:"$$",right:"$$",display:!0},{left:"\\(",right:"\\)",display:!1},{left:"\\begin{equation}",right:"\\end{equation}",display:!0},{left:"\\begin{align}",right:"\\end{align}",display:!0},{left:"\\begin{alignat}",right:"\\end{alignat}",display:!0},{left:"\\begin{gather}",right:"\\end{gather}",display:!0},{left:"\\begin{CD}",right:"\\end{CD}",display:!0},{left:"\\[",right:"\\]",display:!0}],n.ignoredTags=n.ignoredTags||["script","noscript","style","textarea","pre","code","option"],n.ignoredClasses=n.ignoredClasses||[],n.errorCallback=n.errorCallback||console.error,n.macros=n.macros||{},f(e,n)};return o=o.default}()})); 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+*/ +const deflate = (function () { + /* constant parameters */ + var zip_WSIZE = 32768; // Sliding Window size + var zip_STORED_BLOCK = 0; + var zip_STATIC_TREES = 1; + var zip_DYN_TREES = 2; + + /* for deflate */ + var zip_DEFAULT_LEVEL = 6; + var zip_FULL_SEARCH = true; + var zip_INBUFSIZ = 32768; // Input buffer size + var zip_INBUF_EXTRA = 64; // Extra buffer + var zip_OUTBUFSIZ = 1024 * 8; + var zip_window_size = 2 * zip_WSIZE; + var zip_MIN_MATCH = 3; + var zip_MAX_MATCH = 258; + var zip_BITS = 16; + // for SMALL_MEM + var zip_LIT_BUFSIZE = 0x2000; + var zip_HASH_BITS = 13; + // for MEDIUM_MEM + // var zip_LIT_BUFSIZE = 0x4000; + // var zip_HASH_BITS = 14; + // for BIG_MEM + // var zip_LIT_BUFSIZE = 0x8000; + // var zip_HASH_BITS = 15; + //if(zip_LIT_BUFSIZE > zip_INBUFSIZ) + // alert("error: zip_INBUFSIZ is too small"); + //if((zip_WSIZE<<1) > (1< zip_BITS-1) + // alert("error: zip_HASH_BITS is too large"); + //if(zip_HASH_BITS < 8 || zip_MAX_MATCH != 258) + // alert("error: Code too clever"); + var zip_DIST_BUFSIZE = zip_LIT_BUFSIZE; + var zip_HASH_SIZE = 1 << zip_HASH_BITS; + var zip_HASH_MASK = zip_HASH_SIZE - 1; + var zip_WMASK = zip_WSIZE - 1; + var zip_NIL = 0; // Tail of hash chains + var zip_TOO_FAR = 4096; + var zip_MIN_LOOKAHEAD = zip_MAX_MATCH + zip_MIN_MATCH + 1; + var zip_MAX_DIST = zip_WSIZE - zip_MIN_LOOKAHEAD; + var zip_SMALLEST = 1; + var zip_MAX_BITS = 15; + var zip_MAX_BL_BITS = 7; + var zip_LENGTH_CODES = 29; + var zip_LITERALS = 256; + var zip_END_BLOCK = 256; + var zip_L_CODES = zip_LITERALS + 1 + zip_LENGTH_CODES; + var zip_D_CODES = 30; + var zip_BL_CODES = 19; + var zip_REP_3_6 = 16; + var zip_REPZ_3_10 = 17; + var zip_REPZ_11_138 = 18; + var zip_HEAP_SIZE = 2 * zip_L_CODES + 1; + var zip_H_SHIFT = parseInt((zip_HASH_BITS + zip_MIN_MATCH - 1) / + zip_MIN_MATCH); + + /* variables */ + var zip_free_queue; + var zip_qhead, zip_qtail; + var zip_initflag; + var zip_outbuf = null; + var zip_outcnt, zip_outoff; + var zip_complete; + var zip_window; + var zip_d_buf; + var zip_l_buf; + var zip_prev; + var zip_bi_buf; + var zip_bi_valid; + var zip_block_start; + var zip_ins_h; + var zip_hash_head; + var zip_prev_match; + var zip_match_available; + var zip_match_length; + var zip_prev_length; + var zip_strstart; + var zip_match_start; + var zip_eofile; + var zip_lookahead; + var zip_max_chain_length; + var zip_max_lazy_match; + var zip_compr_level; + var zip_good_match; + var zip_nice_match; + var zip_dyn_ltree; + var zip_dyn_dtree; + var zip_static_ltree; + var zip_static_dtree; + var zip_bl_tree; + var zip_l_desc; + var zip_d_desc; + var zip_bl_desc; + var zip_bl_count; + var zip_heap; + var zip_heap_len; + var zip_heap_max; + var zip_depth; + var zip_length_code; + var zip_dist_code; + var zip_base_length; + var zip_base_dist; + var zip_flag_buf; + var zip_last_lit; + var zip_last_dist; + var zip_last_flags; + var zip_flags; + var zip_flag_bit; + var zip_opt_len; + var zip_static_len; + var zip_deflate_data; + var zip_deflate_pos; + + /* objects (deflate) */ + + function zip_DeflateCT() { + this.fc = 0; // frequency count or bit string + this.dl = 0; // father node in Huffman tree or length of bit string + } + + function zip_DeflateTreeDesc() { + this.dyn_tree = null; // the dynamic tree + this.static_tree = null; // corresponding static tree or NULL + this.extra_bits = null; // extra bits for each code or NULL + this.extra_base = 0; // base index for extra_bits + this.elems = 0; // max number of elements in the tree + this.max_length = 0; // max bit length for the codes + this.max_code = 0; // largest code with non zero frequency + } + + /* Values for max_lazy_match, good_match and max_chain_length, depending on + * the desired pack level (0..9). The values given below have been tuned to + * exclude worst case performance for pathological files. Better values may be + * found for specific files. + */ + function zip_DeflateConfiguration(a, b, c, d) { + this.good_length = a; // reduce lazy search above this match length + this.max_lazy = b; // do not perform lazy search above this match length + this.nice_length = c; // quit search above this match length + this.max_chain = d; + } + + function zip_DeflateBuffer() { + this.next = null; + this.len = 0; + this.ptr = new Array(zip_OUTBUFSIZ); + this.off = 0; + } + + /* constant tables */ + var zip_extra_lbits = [ + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 0]; + var zip_extra_dbits = [ + 0, 0, 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13]; + var zip_extra_blbits = [ + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 7]; + var zip_bl_order = [16, 17, 18, 0, 8, 7, 9, 6, 10, 5, 11, 4, 12, 3, 13, 2, 14, 1, 15]; + var zip_configuration_table = [ + new zip_DeflateConfiguration(0, 0, 0, 0), + new zip_DeflateConfiguration(4, 4, 8, 4), + new zip_DeflateConfiguration(4, 5, 16, 8), + new zip_DeflateConfiguration(4, 6, 32, 32), + new zip_DeflateConfiguration(4, 4, 16, 16), + new zip_DeflateConfiguration(8, 16, 32, 32), + new zip_DeflateConfiguration(8, 16, 128, 128), + new zip_DeflateConfiguration(8, 32, 128, 256), + new zip_DeflateConfiguration(32, 128, 258, 1024), + new zip_DeflateConfiguration(32, 258, 258, 4096)]; + + + /* routines (deflate) */ + + function zip_deflate_start(level) { + var i; + + if (!level) + level = zip_DEFAULT_LEVEL; + else if (level < 1) + level = 1; + else if (level > 9) + level = 9; + + zip_compr_level = level; + zip_initflag = false; + zip_eofile = false; + if (zip_outbuf != null) + return; + + zip_free_queue = zip_qhead = zip_qtail = null; + zip_outbuf = new Array(zip_OUTBUFSIZ); + zip_window = new Array(zip_window_size); + zip_d_buf = new Array(zip_DIST_BUFSIZE); + zip_l_buf = new Array(zip_INBUFSIZ + zip_INBUF_EXTRA); + zip_prev = new Array(1 << zip_BITS); + zip_dyn_ltree = new Array(zip_HEAP_SIZE); + for (i = 0; i < zip_HEAP_SIZE; i++) + zip_dyn_ltree[i] = new zip_DeflateCT(); + zip_dyn_dtree = new Array(2 * zip_D_CODES + 1); + for (i = 0; i < 2 * zip_D_CODES + 1; i++) + zip_dyn_dtree[i] = new zip_DeflateCT(); + zip_static_ltree = new Array(zip_L_CODES + 2); + for (i = 0; i < zip_L_CODES + 2; i++) + zip_static_ltree[i] = new zip_DeflateCT(); + zip_static_dtree = new Array(zip_D_CODES); + for (i = 0; i < zip_D_CODES; i++) + zip_static_dtree[i] = new zip_DeflateCT(); + zip_bl_tree = new Array(2 * zip_BL_CODES + 1); + for (i = 0; i < 2 * zip_BL_CODES + 1; i++) + zip_bl_tree[i] = new zip_DeflateCT(); + zip_l_desc = new zip_DeflateTreeDesc(); + zip_d_desc = new zip_DeflateTreeDesc(); + zip_bl_desc = new zip_DeflateTreeDesc(); + zip_bl_count = new Array(zip_MAX_BITS + 1); + zip_heap = new Array(2 * zip_L_CODES + 1); + zip_depth = new Array(2 * zip_L_CODES + 1); + zip_length_code = new Array(zip_MAX_MATCH - zip_MIN_MATCH + 1); + zip_dist_code = new Array(512); + zip_base_length = new Array(zip_LENGTH_CODES); + zip_base_dist = new Array(zip_D_CODES); + zip_flag_buf = new Array(parseInt(zip_LIT_BUFSIZE / 8)); + } + + function zip_deflate_end() { + zip_free_queue = zip_qhead = zip_qtail = null; + zip_outbuf = null; + zip_window = null; + zip_d_buf = null; + zip_l_buf = null; + zip_prev = null; + zip_dyn_ltree = null; + zip_dyn_dtree = null; + zip_static_ltree = null; + zip_static_dtree = null; + zip_bl_tree = null; + zip_l_desc = null; + zip_d_desc = null; + zip_bl_desc = null; + zip_bl_count = null; + zip_heap = null; + zip_depth = null; + zip_length_code = null; + zip_dist_code = null; + zip_base_length = null; + zip_base_dist = null; + zip_flag_buf = null; + } + + function zip_reuse_queue(p) { + p.next = zip_free_queue; + zip_free_queue = p; + } + + function zip_new_queue() { + var p; + + if (zip_free_queue != null) { + p = zip_free_queue; + zip_free_queue = zip_free_queue.next; + } + else + p = new zip_DeflateBuffer(); + p.next = null; + p.len = p.off = 0; + + return p; + } + + function zip_head1(i) { + return zip_prev[zip_WSIZE + i]; + } + + function zip_head2(i, val) { + return zip_prev[zip_WSIZE + i] = val; + } + + /* put_byte is used for the compressed output, put_ubyte for the + * uncompressed output. However unlzw() uses window for its + * suffix table instead of its output buffer, so it does not use put_ubyte + * (to be cleaned up). + */ + function zip_put_byte(c) { + zip_outbuf[zip_outoff + zip_outcnt++] = c; + if (zip_outoff + zip_outcnt == zip_OUTBUFSIZ) + zip_qoutbuf(); + } + + /* Output a 16 bit value, lsb first */ + function zip_put_short(w) { + w &= 0xffff; + if (zip_outoff + zip_outcnt < zip_OUTBUFSIZ - 2) { + zip_outbuf[zip_outoff + zip_outcnt++] = (w & 0xff); + zip_outbuf[zip_outoff + zip_outcnt++] = (w >>> 8); + } else { + zip_put_byte(w & 0xff); + zip_put_byte(w >>> 8); + } + } + + /* ========================================================================== + * Insert string s in the dictionary and set match_head to the previous head + * of the hash chain (the most recent string with same hash key). Return + * the previous length of the hash chain. + * IN assertion: all calls to to INSERT_STRING are made with consecutive + * input characters and the first MIN_MATCH bytes of s are valid + * (except for the last MIN_MATCH-1 bytes of the input file). + */ + function zip_INSERT_STRING() { + zip_ins_h = ((zip_ins_h << zip_H_SHIFT) + ^ (zip_window[zip_strstart + zip_MIN_MATCH - 1] & 0xff)) + & zip_HASH_MASK; + zip_hash_head = zip_head1(zip_ins_h); + zip_prev[zip_strstart & zip_WMASK] = zip_hash_head; + zip_head2(zip_ins_h, zip_strstart); + } + + /* Send a code of the given tree. c and tree must not have side effects */ + function zip_SEND_CODE(c, tree) { + zip_send_bits(tree[c].fc, tree[c].dl); + } + + /* Mapping from a distance to a distance code. dist is the distance - 1 and + * must not have side effects. dist_code[256] and dist_code[257] are never + * used. + */ + function zip_D_CODE(dist) { + return (dist < 256 ? zip_dist_code[dist] + : zip_dist_code[256 + (dist >> 7)]) & 0xff; + } + + /* ========================================================================== + * Compares to subtrees, using the tree depth as tie breaker when + * the subtrees have equal frequency. This minimizes the worst case length. + */ + function zip_SMALLER(tree, n, m) { + return tree[n].fc < tree[m].fc || + (tree[n].fc == tree[m].fc && zip_depth[n] <= zip_depth[m]); + } + + /* ========================================================================== + * read string data + */ + function zip_read_buff(buff, offset, n) { + var i; + for (i = 0; i < n && zip_deflate_pos < zip_deflate_data.length; i++) + buff[offset + i] = + zip_deflate_data.charCodeAt(zip_deflate_pos++) & 0xff; + return i; + } + + /* ========================================================================== + * Initialize the "longest match" routines for a new file + */ + function zip_lm_init() { + var j; + + /* Initialize the hash table. */ + for (j = 0; j < zip_HASH_SIZE; j++) + // zip_head2(j, zip_NIL); + zip_prev[zip_WSIZE + j] = 0; + /* prev will be initialized on the fly */ + + /* Set the default configuration parameters: + */ + zip_max_lazy_match = zip_configuration_table[zip_compr_level].max_lazy; + zip_good_match = zip_configuration_table[zip_compr_level].good_length; + if (!zip_FULL_SEARCH) + zip_nice_match = zip_configuration_table[zip_compr_level].nice_length; + zip_max_chain_length = zip_configuration_table[zip_compr_level].max_chain; + + zip_strstart = 0; + zip_block_start = 0; + + zip_lookahead = zip_read_buff(zip_window, 0, 2 * zip_WSIZE); + if (zip_lookahead <= 0) { + zip_eofile = true; + zip_lookahead = 0; + return; + } + zip_eofile = false; + /* Make sure that we always have enough lookahead. This is important + * if input comes from a device such as a tty. + */ + while (zip_lookahead < zip_MIN_LOOKAHEAD && !zip_eofile) + zip_fill_window(); + + /* If lookahead < MIN_MATCH, ins_h is garbage, but this is + * not important since only literal bytes will be emitted. + */ + zip_ins_h = 0; + for (j = 0; j < zip_MIN_MATCH - 1; j++) { + // UPDATE_HASH(ins_h, window[j]); + zip_ins_h = ((zip_ins_h << zip_H_SHIFT) ^ (zip_window[j] & 0xff)) & zip_HASH_MASK; + } + } + + /* ========================================================================== + * Set match_start to the longest match starting at the given string and + * return its length. Matches shorter or equal to prev_length are discarded, + * in which case the result is equal to prev_length and match_start is + * garbage. + * IN assertions: cur_match is the head of the hash chain for the current + * string (strstart) and its distance is <= MAX_DIST, and prev_length >= 1 + */ + function zip_longest_match(cur_match) { + var chain_length = zip_max_chain_length; // max hash chain length + var scanp = zip_strstart; // current string + var matchp; // matched string + var len; // length of current match + var best_len = zip_prev_length; // best match length so far + + /* Stop when cur_match becomes <= limit. To simplify the code, + * we prevent matches with the string of window index 0. + */ + var limit = (zip_strstart > zip_MAX_DIST ? zip_strstart - zip_MAX_DIST : zip_NIL); + + var strendp = zip_strstart + zip_MAX_MATCH; + var scan_end1 = zip_window[scanp + best_len - 1]; + var scan_end = zip_window[scanp + best_len]; + + /* Do not waste too much time if we already have a good match: */ + if (zip_prev_length >= zip_good_match) + chain_length >>= 2; + + // Assert(encoder->strstart <= window_size-MIN_LOOKAHEAD, "insufficient lookahead"); + + do { + // Assert(cur_match < encoder->strstart, "no future"); + matchp = cur_match; + + /* Skip to next match if the match length cannot increase + * or if the match length is less than 2: + */ + if (zip_window[matchp + best_len] != scan_end || + zip_window[matchp + best_len - 1] != scan_end1 || + zip_window[matchp] != zip_window[scanp] || + zip_window[++matchp] != zip_window[scanp + 1]) { + continue; + } + + /* The check at best_len-1 can be removed because it will be made + * again later. (This heuristic is not always a win.) + * It is not necessary to compare scan[2] and match[2] since they + * are always equal when the other bytes match, given that + * the hash keys are equal and that HASH_BITS >= 8. + */ + scanp += 2; + matchp++; + + /* We check for insufficient lookahead only every 8th comparison; + * the 256th check will be made at strstart+258. + */ + do { + } while (zip_window[++scanp] == zip_window[++matchp] && + zip_window[++scanp] == zip_window[++matchp] && + zip_window[++scanp] == zip_window[++matchp] && + zip_window[++scanp] == zip_window[++matchp] && + zip_window[++scanp] == zip_window[++matchp] && + zip_window[++scanp] == zip_window[++matchp] && + zip_window[++scanp] == zip_window[++matchp] && + zip_window[++scanp] == zip_window[++matchp] && + scanp < strendp); + + len = zip_MAX_MATCH - (strendp - scanp); + scanp = strendp - zip_MAX_MATCH; + + if (len > best_len) { + zip_match_start = cur_match; + best_len = len; + if (zip_FULL_SEARCH) { + if (len >= zip_MAX_MATCH) break; + } else { + if (len >= zip_nice_match) break; + } + + scan_end1 = zip_window[scanp + best_len - 1]; + scan_end = zip_window[scanp + best_len]; + } + } while ((cur_match = zip_prev[cur_match & zip_WMASK]) > limit + && --chain_length != 0); + + return best_len; + } + + /* ========================================================================== + * Fill the window when the lookahead becomes insufficient. + * Updates strstart and lookahead, and sets eofile if end of input file. + * IN assertion: lookahead < MIN_LOOKAHEAD && strstart + lookahead > 0 + * OUT assertions: at least one byte has been read, or eofile is set; + * file reads are performed for at least two bytes (required for the + * translate_eol option). + */ + function zip_fill_window() { + var n, m; + + // Amount of free space at the end of the window. + var more = zip_window_size - zip_lookahead - zip_strstart; + + /* If the window is almost full and there is insufficient lookahead, + * move the upper half to the lower one to make room in the upper half. + */ + if (more == -1) { + /* Very unlikely, but possible on 16 bit machine if strstart == 0 + * and lookahead == 1 (input done one byte at time) + */ + more--; + } else if (zip_strstart >= zip_WSIZE + zip_MAX_DIST) { + /* By the IN assertion, the window is not empty so we can't confuse + * more == 0 with more == 64K on a 16 bit machine. + */ + // Assert(window_size == (ulg)2*WSIZE, "no sliding with BIG_MEM"); + + // System.arraycopy(window, WSIZE, window, 0, WSIZE); + for (n = 0; n < zip_WSIZE; n++) + zip_window[n] = zip_window[n + zip_WSIZE]; + + zip_match_start -= zip_WSIZE; + zip_strstart -= zip_WSIZE; /* we now have strstart >= MAX_DIST: */ + zip_block_start -= zip_WSIZE; + + for (n = 0; n < zip_HASH_SIZE; n++) { + m = zip_head1(n); + zip_head2(n, m >= zip_WSIZE ? m - zip_WSIZE : zip_NIL); + } + for (n = 0; n < zip_WSIZE; n++) { + /* If n is not on any hash chain, prev[n] is garbage but + * its value will never be used. + */ + m = zip_prev[n]; + zip_prev[n] = (m >= zip_WSIZE ? m - zip_WSIZE : zip_NIL); + } + more += zip_WSIZE; + } + // At this point, more >= 2 + if (!zip_eofile) { + n = zip_read_buff(zip_window, zip_strstart + zip_lookahead, more); + if (n <= 0) + zip_eofile = true; + else + zip_lookahead += n; + } + } + + /* ========================================================================== + * Processes a new input file and return its compressed length. This + * function does not perform lazy evaluationof matches and inserts + * new strings in the dictionary only for unmatched strings or for short + * matches. It is used only for the fast compression options. + */ + function zip_deflate_fast() { + while (zip_lookahead != 0 && zip_qhead == null) { + var flush; // set if current block must be flushed + + /* Insert the string window[strstart .. strstart+2] in the + * dictionary, and set hash_head to the head of the hash chain: + */ + zip_INSERT_STRING(); + + /* Find the longest match, discarding those <= prev_length. + * At this point we have always match_length < MIN_MATCH + */ + if (zip_hash_head != zip_NIL && + zip_strstart - zip_hash_head <= zip_MAX_DIST) { + /* To simplify the code, we prevent matches with the string + * of window index 0 (in particular we have to avoid a match + * of the string with itself at the start of the input file). + */ + zip_match_length = zip_longest_match(zip_hash_head); + /* longest_match() sets match_start */ + if (zip_match_length > zip_lookahead) + zip_match_length = zip_lookahead; + } + if (zip_match_length >= zip_MIN_MATCH) { + // check_match(strstart, match_start, match_length); + + flush = zip_ct_tally(zip_strstart - zip_match_start, + zip_match_length - zip_MIN_MATCH); + zip_lookahead -= zip_match_length; + + /* Insert new strings in the hash table only if the match length + * is not too large. This saves time but degrades compression. + */ + if (zip_match_length <= zip_max_lazy_match) { + zip_match_length--; // string at strstart already in hash table + do { + zip_strstart++; + zip_INSERT_STRING(); + /* strstart never exceeds WSIZE-MAX_MATCH, so there are + * always MIN_MATCH bytes ahead. If lookahead < MIN_MATCH + * these bytes are garbage, but it does not matter since + * the next lookahead bytes will be emitted as literals. + */ + } while (--zip_match_length != 0); + zip_strstart++; + } else { + zip_strstart += zip_match_length; + zip_match_length = 0; + zip_ins_h = zip_window[zip_strstart] & 0xff; + // UPDATE_HASH(ins_h, window[strstart + 1]); + zip_ins_h = ((zip_ins_h << zip_H_SHIFT) ^ (zip_window[zip_strstart + 1] & 0xff)) & zip_HASH_MASK; + + //#if MIN_MATCH != 3 + // Call UPDATE_HASH() MIN_MATCH-3 more times + //#endif + + } + } else { + /* No match, output a literal byte */ + flush = zip_ct_tally(0, zip_window[zip_strstart] & 0xff); + zip_lookahead--; + zip_strstart++; + } + if (flush) { + zip_flush_block(0); + zip_block_start = zip_strstart; + } + + /* Make sure that we always have enough lookahead, except + * at the end of the input file. We need MAX_MATCH bytes + * for the next match, plus MIN_MATCH bytes to insert the + * string following the next match. + */ + while (zip_lookahead < zip_MIN_LOOKAHEAD && !zip_eofile) + zip_fill_window(); + } + } + + function zip_deflate_better() { + /* Process the input block. */ + while (zip_lookahead != 0 && zip_qhead == null) { + /* Insert the string window[strstart .. strstart+2] in the + * dictionary, and set hash_head to the head of the hash chain: + */ + zip_INSERT_STRING(); + + /* Find the longest match, discarding those <= prev_length. + */ + zip_prev_length = zip_match_length; + zip_prev_match = zip_match_start; + zip_match_length = zip_MIN_MATCH - 1; + + if (zip_hash_head != zip_NIL && + zip_prev_length < zip_max_lazy_match && + zip_strstart - zip_hash_head <= zip_MAX_DIST) { + /* To simplify the code, we prevent matches with the string + * of window index 0 (in particular we have to avoid a match + * of the string with itself at the start of the input file). + */ + zip_match_length = zip_longest_match(zip_hash_head); + /* longest_match() sets match_start */ + if (zip_match_length > zip_lookahead) + zip_match_length = zip_lookahead; + + /* Ignore a length 3 match if it is too distant: */ + if (zip_match_length == zip_MIN_MATCH && + zip_strstart - zip_match_start > zip_TOO_FAR) { + /* If prev_match is also MIN_MATCH, match_start is garbage + * but we will ignore the current match anyway. + */ + zip_match_length--; + } + } + /* If there was a match at the previous step and the current + * match is not better, output the previous match: + */ + if (zip_prev_length >= zip_MIN_MATCH && + zip_match_length <= zip_prev_length) { + var flush; // set if current block must be flushed + + // check_match(strstart - 1, prev_match, prev_length); + flush = zip_ct_tally(zip_strstart - 1 - zip_prev_match, + zip_prev_length - zip_MIN_MATCH); + + /* Insert in hash table all strings up to the end of the match. + * strstart-1 and strstart are already inserted. + */ + zip_lookahead -= zip_prev_length - 1; + zip_prev_length -= 2; + do { + zip_strstart++; + zip_INSERT_STRING(); + /* strstart never exceeds WSIZE-MAX_MATCH, so there are + * always MIN_MATCH bytes ahead. If lookahead < MIN_MATCH + * these bytes are garbage, but it does not matter since the + * next lookahead bytes will always be emitted as literals. + */ + } while (--zip_prev_length != 0); + zip_match_available = 0; + zip_match_length = zip_MIN_MATCH - 1; + zip_strstart++; + if (flush) { + zip_flush_block(0); + zip_block_start = zip_strstart; + } + } else if (zip_match_available != 0) { + /* If there was no match at the previous position, output a + * single literal. If there was a match but the current match + * is longer, truncate the previous match to a single literal. + */ + if (zip_ct_tally(0, zip_window[zip_strstart - 1] & 0xff)) { + zip_flush_block(0); + zip_block_start = zip_strstart; + } + zip_strstart++; + zip_lookahead--; + } else { + /* There is no previous match to compare with, wait for + * the next step to decide. + */ + zip_match_available = 1; + zip_strstart++; + zip_lookahead--; + } + + /* Make sure that we always have enough lookahead, except + * at the end of the input file. We need MAX_MATCH bytes + * for the next match, plus MIN_MATCH bytes to insert the + * string following the next match. + */ + while (zip_lookahead < zip_MIN_LOOKAHEAD && !zip_eofile) + zip_fill_window(); + } + } + + function zip_init_deflate() { + if (zip_eofile) + return; + zip_bi_buf = 0; + zip_bi_valid = 0; + zip_ct_init(); + zip_lm_init(); + + zip_qhead = null; + zip_outcnt = 0; + zip_outoff = 0; + + if (zip_compr_level <= 3) { + zip_prev_length = zip_MIN_MATCH - 1; + zip_match_length = 0; + } + else { + zip_match_length = zip_MIN_MATCH - 1; + zip_match_available = 0; + } + + zip_complete = false; + } + + /* ========================================================================== + * Same as above, but achieves better compression. We use a lazy + * evaluation for matches: a match is finally adopted only if there is + * no better match at the next window position. + */ + function zip_deflate_internal(buff, off, buff_size) { + var n; + + if (!zip_initflag) { + zip_init_deflate(); + zip_initflag = true; + if (zip_lookahead == 0) { // empty + zip_complete = true; + return 0; + } + } + + if ((n = zip_qcopy(buff, off, buff_size)) == buff_size) + return buff_size; + + if (zip_complete) + return n; + + if (zip_compr_level <= 3) // optimized for speed + zip_deflate_fast(); + else + zip_deflate_better(); + if (zip_lookahead == 0) { + if (zip_match_available != 0) + zip_ct_tally(0, zip_window[zip_strstart - 1] & 0xff); + zip_flush_block(1); + zip_complete = true; + } + return n + zip_qcopy(buff, n + off, buff_size - n); + } + + function zip_qcopy(buff, off, buff_size) { + var n, i, j; + + n = 0; + while (zip_qhead != null && n < buff_size) { + i = buff_size - n; + if (i > zip_qhead.len) + i = zip_qhead.len; + // System.arraycopy(qhead.ptr, qhead.off, buff, off + n, i); + for (j = 0; j < i; j++) + buff[off + n + j] = zip_qhead.ptr[zip_qhead.off + j]; + + zip_qhead.off += i; + zip_qhead.len -= i; + n += i; + if (zip_qhead.len == 0) { + var p; + p = zip_qhead; + zip_qhead = zip_qhead.next; + zip_reuse_queue(p); + } + } + + if (n == buff_size) + return n; + + if (zip_outoff < zip_outcnt) { + i = buff_size - n; + if (i > zip_outcnt - zip_outoff) + i = zip_outcnt - zip_outoff; + // System.arraycopy(outbuf, outoff, buff, off + n, i); + for (j = 0; j < i; j++) + buff[off + n + j] = zip_outbuf[zip_outoff + j]; + zip_outoff += i; + n += i; + if (zip_outcnt == zip_outoff) + zip_outcnt = zip_outoff = 0; + } + return n; + } + + /* ========================================================================== + * Allocate the match buffer, initialize the various tables and save the + * location of the internal file attribute (ascii/binary) and method + * (DEFLATE/STORE). + */ + function zip_ct_init() { + var n; // iterates over tree elements + var bits; // bit counter + var length; // length value + var code; // code value + var dist; // distance index + + if (zip_static_dtree[0].dl != 0) return; // ct_init already called + + zip_l_desc.dyn_tree = zip_dyn_ltree; + zip_l_desc.static_tree = zip_static_ltree; + zip_l_desc.extra_bits = zip_extra_lbits; + zip_l_desc.extra_base = zip_LITERALS + 1; + zip_l_desc.elems = zip_L_CODES; + zip_l_desc.max_length = zip_MAX_BITS; + zip_l_desc.max_code = 0; + + zip_d_desc.dyn_tree = zip_dyn_dtree; + zip_d_desc.static_tree = zip_static_dtree; + zip_d_desc.extra_bits = zip_extra_dbits; + zip_d_desc.extra_base = 0; + zip_d_desc.elems = zip_D_CODES; + zip_d_desc.max_length = zip_MAX_BITS; + zip_d_desc.max_code = 0; + + zip_bl_desc.dyn_tree = zip_bl_tree; + zip_bl_desc.static_tree = null; + zip_bl_desc.extra_bits = zip_extra_blbits; + zip_bl_desc.extra_base = 0; + zip_bl_desc.elems = zip_BL_CODES; + zip_bl_desc.max_length = zip_MAX_BL_BITS; + zip_bl_desc.max_code = 0; + + // Initialize the mapping length (0..255) -> length code (0..28) + length = 0; + for (code = 0; code < zip_LENGTH_CODES - 1; code++) { + zip_base_length[code] = length; + for (n = 0; n < (1 << zip_extra_lbits[code]); n++) + zip_length_code[length++] = code; + } + // Assert (length == 256, "ct_init: length != 256"); + + /* Note that the length 255 (match length 258) can be represented + * in two different ways: code 284 + 5 bits or code 285, so we + * overwrite length_code[255] to use the best encoding: + */ + zip_length_code[length - 1] = code; + + /* Initialize the mapping dist (0..32K) -> dist code (0..29) */ + dist = 0; + for (code = 0; code < 16; code++) { + zip_base_dist[code] = dist; + for (n = 0; n < (1 << zip_extra_dbits[code]); n++) { + zip_dist_code[dist++] = code; + } + } + // Assert (dist == 256, "ct_init: dist != 256"); + dist >>= 7; // from now on, all distances are divided by 128 + for (; code < zip_D_CODES; code++) { + zip_base_dist[code] = dist << 7; + for (n = 0; n < (1 << (zip_extra_dbits[code] - 7)); n++) + zip_dist_code[256 + dist++] = code; + } + // Assert (dist == 256, "ct_init: 256+dist != 512"); + + // Construct the codes of the static literal tree + for (bits = 0; bits <= zip_MAX_BITS; bits++) + zip_bl_count[bits] = 0; + n = 0; + while (n <= 143) { zip_static_ltree[n++].dl = 8; zip_bl_count[8]++; } + while (n <= 255) { zip_static_ltree[n++].dl = 9; zip_bl_count[9]++; } + while (n <= 279) { zip_static_ltree[n++].dl = 7; zip_bl_count[7]++; } + while (n <= 287) { zip_static_ltree[n++].dl = 8; zip_bl_count[8]++; } + /* Codes 286 and 287 do not exist, but we must include them in the + * tree construction to get a canonical Huffman tree (longest code + * all ones) + */ + zip_gen_codes(zip_static_ltree, zip_L_CODES + 1); + + /* The static distance tree is trivial: */ + for (n = 0; n < zip_D_CODES; n++) { + zip_static_dtree[n].dl = 5; + zip_static_dtree[n].fc = zip_bi_reverse(n, 5); + } + + // Initialize the first block of the first file: + zip_init_block(); + } + + /* ========================================================================== + * Initialize a new block. + */ + function zip_init_block() { + var n; // iterates over tree elements + + // Initialize the trees. + for (n = 0; n < zip_L_CODES; n++) zip_dyn_ltree[n].fc = 0; + for (n = 0; n < zip_D_CODES; n++) zip_dyn_dtree[n].fc = 0; + for (n = 0; n < zip_BL_CODES; n++) zip_bl_tree[n].fc = 0; + + zip_dyn_ltree[zip_END_BLOCK].fc = 1; + zip_opt_len = zip_static_len = 0; + zip_last_lit = zip_last_dist = zip_last_flags = 0; + zip_flags = 0; + zip_flag_bit = 1; + } + + /* ========================================================================== + * Restore the heap property by moving down the tree starting at node k, + * exchanging a node with the smallest of its two sons if necessary, stopping + * when the heap property is re-established (each father smaller than its + * two sons). + */ + function zip_pqdownheap( + tree, // the tree to restore + k) { // node to move down + var v = zip_heap[k]; + var j = k << 1; // left son of k + + while (j <= zip_heap_len) { + // Set j to the smallest of the two sons: + if (j < zip_heap_len && + zip_SMALLER(tree, zip_heap[j + 1], zip_heap[j])) + j++; + + // Exit if v is smaller than both sons + if (zip_SMALLER(tree, v, zip_heap[j])) + break; + + // Exchange v with the smallest son + zip_heap[k] = zip_heap[j]; + k = j; + + // And continue down the tree, setting j to the left son of k + j <<= 1; + } + zip_heap[k] = v; + } + + /* ========================================================================== + * Compute the optimal bit lengths for a tree and update the total bit length + * for the current block. + * IN assertion: the fields freq and dad are set, heap[heap_max] and + * above are the tree nodes sorted by increasing frequency. + * OUT assertions: the field len is set to the optimal bit length, the + * array bl_count contains the frequencies for each bit length. + * The length opt_len is updated; static_len is also updated if stree is + * not null. + */ + function zip_gen_bitlen(desc) { // the tree descriptor + var tree = desc.dyn_tree; + var extra = desc.extra_bits; + var base = desc.extra_base; + var max_code = desc.max_code; + var max_length = desc.max_length; + var stree = desc.static_tree; + var h; // heap index + var n, m; // iterate over the tree elements + var bits; // bit length + var xbits; // extra bits + var f; // frequency + var overflow = 0; // number of elements with bit length too large + + for (bits = 0; bits <= zip_MAX_BITS; bits++) + zip_bl_count[bits] = 0; + + /* In a first pass, compute the optimal bit lengths (which may + * overflow in the case of the bit length tree). + */ + tree[zip_heap[zip_heap_max]].dl = 0; // root of the heap + + for (h = zip_heap_max + 1; h < zip_HEAP_SIZE; h++) { + n = zip_heap[h]; + bits = tree[tree[n].dl].dl + 1; + if (bits > max_length) { + bits = max_length; + overflow++; + } + tree[n].dl = bits; + // We overwrite tree[n].dl which is no longer needed + + if (n > max_code) + continue; // not a leaf node + + zip_bl_count[bits]++; + xbits = 0; + if (n >= base) + xbits = extra[n - base]; + f = tree[n].fc; + zip_opt_len += f * (bits + xbits); + if (stree != null) + zip_static_len += f * (stree[n].dl + xbits); + } + if (overflow == 0) + return; + + // This happens for example on obj2 and pic of the Calgary corpus + + // Find the first bit length which could increase: + do { + bits = max_length - 1; + while (zip_bl_count[bits] == 0) + bits--; + zip_bl_count[bits]--; // move one leaf down the tree + zip_bl_count[bits + 1] += 2; // move one overflow item as its brother + zip_bl_count[max_length]--; + /* The brother of the overflow item also moves one step up, + * but this does not affect bl_count[max_length] + */ + overflow -= 2; + } while (overflow > 0); + + /* Now recompute all bit lengths, scanning in increasing frequency. + * h is still equal to HEAP_SIZE. (It is simpler to reconstruct all + * lengths instead of fixing only the wrong ones. This idea is taken + * from 'ar' written by Haruhiko Okumura.) + */ + for (bits = max_length; bits != 0; bits--) { + n = zip_bl_count[bits]; + while (n != 0) { + m = zip_heap[--h]; + if (m > max_code) + continue; + if (tree[m].dl != bits) { + zip_opt_len += (bits - tree[m].dl) * tree[m].fc; + tree[m].fc = bits; + } + n--; + } + } + } + + /* ========================================================================== + * Generate the codes for a given tree and bit counts (which need not be + * optimal). + * IN assertion: the array bl_count contains the bit length statistics for + * the given tree and the field len is set for all tree elements. + * OUT assertion: the field code is set for all tree elements of non + * zero code length. + */ + function zip_gen_codes(tree, // the tree to decorate + max_code) { // largest code with non zero frequency + var next_code = new Array(zip_MAX_BITS + 1); // next code value for each bit length + var code = 0; // running code value + var bits; // bit index + var n; // code index + + /* The distribution counts are first used to generate the code values + * without bit reversal. + */ + for (bits = 1; bits <= zip_MAX_BITS; bits++) { + code = ((code + zip_bl_count[bits - 1]) << 1); + next_code[bits] = code; + } + + /* Check that the bit counts in bl_count are consistent. The last code + * must be all ones. + */ + // Assert (code + encoder->bl_count[MAX_BITS]-1 == (1<> 1; n >= 1; n--) + zip_pqdownheap(tree, n); + + /* Construct the Huffman tree by repeatedly combining the least two + * frequent nodes. + */ + do { + n = zip_heap[zip_SMALLEST]; + zip_heap[zip_SMALLEST] = zip_heap[zip_heap_len--]; + zip_pqdownheap(tree, zip_SMALLEST); + + m = zip_heap[zip_SMALLEST]; // m = node of next least frequency + + // keep the nodes sorted by frequency + zip_heap[--zip_heap_max] = n; + zip_heap[--zip_heap_max] = m; + + // Create a new node father of n and m + tree[node].fc = tree[n].fc + tree[m].fc; + // depth[node] = (char)(MAX(depth[n], depth[m]) + 1); + if (zip_depth[n] > zip_depth[m] + 1) + zip_depth[node] = zip_depth[n]; + else + zip_depth[node] = zip_depth[m] + 1; + tree[n].dl = tree[m].dl = node; + + // and insert the new node in the heap + zip_heap[zip_SMALLEST] = node++; + zip_pqdownheap(tree, zip_SMALLEST); + + } while (zip_heap_len >= 2); + + zip_heap[--zip_heap_max] = zip_heap[zip_SMALLEST]; + + /* At this point, the fields freq and dad are set. We can now + * generate the bit lengths. + */ + zip_gen_bitlen(desc); + + // The field len is now set, we can generate the bit codes + zip_gen_codes(tree, max_code); + } + + /* ========================================================================== + * Scan a literal or distance tree to determine the frequencies of the codes + * in the bit length tree. Updates opt_len to take into account the repeat + * counts. (The contribution of the bit length codes will be added later + * during the construction of bl_tree.) + */ + function zip_scan_tree(tree,// the tree to be scanned + max_code) { // and its largest code of non zero frequency + var n; // iterates over all tree elements + var prevlen = -1; // last emitted length + var curlen; // length of current code + var nextlen = tree[0].dl; // length of next code + var count = 0; // repeat count of the current code + var max_count = 7; // max repeat count + var min_count = 4; // min repeat count + + if (nextlen == 0) { + max_count = 138; + min_count = 3; + } + tree[max_code + 1].dl = 0xffff; // guard + + for (n = 0; n <= max_code; n++) { + curlen = nextlen; + nextlen = tree[n + 1].dl; + if (++count < max_count && curlen == nextlen) + continue; + else if (count < min_count) + zip_bl_tree[curlen].fc += count; + else if (curlen != 0) { + if (curlen != prevlen) + zip_bl_tree[curlen].fc++; + zip_bl_tree[zip_REP_3_6].fc++; + } else if (count <= 10) + zip_bl_tree[zip_REPZ_3_10].fc++; + else + zip_bl_tree[zip_REPZ_11_138].fc++; + count = 0; prevlen = curlen; + if (nextlen == 0) { + max_count = 138; + min_count = 3; + } else if (curlen == nextlen) { + max_count = 6; + min_count = 3; + } else { + max_count = 7; + min_count = 4; + } + } + } + + /* ========================================================================== + * Send a literal or distance tree in compressed form, using the codes in + * bl_tree. + */ + function zip_send_tree(tree, // the tree to be scanned + max_code) { // and its largest code of non zero frequency + var n; // iterates over all tree elements + var prevlen = -1; // last emitted length + var curlen; // length of current code + var nextlen = tree[0].dl; // length of next code + var count = 0; // repeat count of the current code + var max_count = 7; // max repeat count + var min_count = 4; // min repeat count + + /* tree[max_code+1].dl = -1; */ /* guard already set */ + if (nextlen == 0) { + max_count = 138; + min_count = 3; + } + + for (n = 0; n <= max_code; n++) { + curlen = nextlen; + nextlen = tree[n + 1].dl; + if (++count < max_count && curlen == nextlen) { + continue; + } else if (count < min_count) { + do { zip_SEND_CODE(curlen, zip_bl_tree); } while (--count != 0); + } else if (curlen != 0) { + if (curlen != prevlen) { + zip_SEND_CODE(curlen, zip_bl_tree); + count--; + } + // Assert(count >= 3 && count <= 6, " 3_6?"); + zip_SEND_CODE(zip_REP_3_6, zip_bl_tree); + zip_send_bits(count - 3, 2); + } else if (count <= 10) { + zip_SEND_CODE(zip_REPZ_3_10, zip_bl_tree); + zip_send_bits(count - 3, 3); + } else { + zip_SEND_CODE(zip_REPZ_11_138, zip_bl_tree); + zip_send_bits(count - 11, 7); + } + count = 0; + prevlen = curlen; + if (nextlen == 0) { + max_count = 138; + min_count = 3; + } else if (curlen == nextlen) { + max_count = 6; + min_count = 3; + } else { + max_count = 7; + min_count = 4; + } + } + } + + /* ========================================================================== + * Construct the Huffman tree for the bit lengths and return the index in + * bl_order of the last bit length code to send. + */ + function zip_build_bl_tree() { + var max_blindex; // index of last bit length code of non zero freq + + // Determine the bit length frequencies for literal and distance trees + zip_scan_tree(zip_dyn_ltree, zip_l_desc.max_code); + zip_scan_tree(zip_dyn_dtree, zip_d_desc.max_code); + + // Build the bit length tree: + zip_build_tree(zip_bl_desc); + /* opt_len now includes the length of the tree representations, except + * the lengths of the bit lengths codes and the 5+5+4 bits for the counts. + */ + + /* Determine the number of bit length codes to send. The pkzip format + * requires that at least 4 bit length codes be sent. (appnote.txt says + * 3 but the actual value used is 4.) + */ + for (max_blindex = zip_BL_CODES - 1; max_blindex >= 3; max_blindex--) { + if (zip_bl_tree[zip_bl_order[max_blindex]].dl != 0) break; + } + /* Update opt_len to include the bit length tree and counts */ + zip_opt_len += 3 * (max_blindex + 1) + 5 + 5 + 4; + // Tracev((stderr, "\ndyn trees: dyn %ld, stat %ld", + // encoder->opt_len, encoder->static_len)); + + return max_blindex; + } + + /* ========================================================================== + * Send the header for a block using dynamic Huffman trees: the counts, the + * lengths of the bit length codes, the literal tree and the distance tree. + * IN assertion: lcodes >= 257, dcodes >= 1, blcodes >= 4. + */ + function zip_send_all_trees(lcodes, dcodes, blcodes) { // number of codes for each tree + var rank; // index in bl_order + + // Assert (lcodes >= 257 && dcodes >= 1 && blcodes >= 4, "not enough codes"); + // Assert (lcodes <= L_CODES && dcodes <= D_CODES && blcodes <= BL_CODES, + // "too many codes"); + // Tracev((stderr, "\nbl counts: ")); + zip_send_bits(lcodes - 257, 5); // not +255 as stated in appnote.txt + zip_send_bits(dcodes - 1, 5); + zip_send_bits(blcodes - 4, 4); // not -3 as stated in appnote.txt + for (rank = 0; rank < blcodes; rank++) { + // Tracev((stderr, "\nbl code %2d ", bl_order[rank])); + zip_send_bits(zip_bl_tree[zip_bl_order[rank]].dl, 3); + } + + // send the literal tree + zip_send_tree(zip_dyn_ltree, lcodes - 1); + + // send the distance tree + zip_send_tree(zip_dyn_dtree, dcodes - 1); + } + + /* ========================================================================== + * Determine the best encoding for the current block: dynamic trees, static + * trees or store, and output the encoded block to the zip file. + */ + function zip_flush_block(eof) { // true if this is the last block for a file + var opt_lenb, static_lenb; // opt_len and static_len in bytes + var max_blindex; // index of last bit length code of non zero freq + var stored_len; // length of input block + + stored_len = zip_strstart - zip_block_start; + zip_flag_buf[zip_last_flags] = zip_flags; // Save the flags for the last 8 items + + // Construct the literal and distance trees + zip_build_tree(zip_l_desc); + // Tracev((stderr, "\nlit data: dyn %ld, stat %ld", + // encoder->opt_len, encoder->static_len)); + + zip_build_tree(zip_d_desc); + // Tracev((stderr, "\ndist data: dyn %ld, stat %ld", + // encoder->opt_len, encoder->static_len)); + /* At this point, opt_len and static_len are the total bit lengths of + * the compressed block data, excluding the tree representations. + */ + + /* Build the bit length tree for the above two trees, and get the index + * in bl_order of the last bit length code to send. + */ + max_blindex = zip_build_bl_tree(); + + // Determine the best encoding. Compute first the block length in bytes + opt_lenb = (zip_opt_len + 3 + 7) >> 3; + static_lenb = (zip_static_len + 3 + 7) >> 3; + + // Trace((stderr, "\nopt %lu(%lu) stat %lu(%lu) stored %lu lit %u dist %u ", + // opt_lenb, encoder->opt_len, + // static_lenb, encoder->static_len, stored_len, + // encoder->last_lit, encoder->last_dist)); + + if (static_lenb <= opt_lenb) + opt_lenb = static_lenb; + if (stored_len + 4 <= opt_lenb // 4: two words for the lengths + && zip_block_start >= 0) { + var i; + + /* The test buf != NULL is only necessary if LIT_BUFSIZE > WSIZE. + * Otherwise we can't have processed more than WSIZE input bytes since + * the last block flush, because compression would have been + * successful. If LIT_BUFSIZE <= WSIZE, it is never too late to + * transform a block into a stored block. + */ + zip_send_bits((zip_STORED_BLOCK << 1) + eof, 3); /* send block type */ + zip_bi_windup(); /* align on byte boundary */ + zip_put_short(stored_len); + zip_put_short(~stored_len); + + // copy block + /* + p = &window[block_start]; + for(i = 0; i < stored_len; i++) + put_byte(p[i]); + */ + for (i = 0; i < stored_len; i++) + zip_put_byte(zip_window[zip_block_start + i]); + + } else if (static_lenb == opt_lenb) { + zip_send_bits((zip_STATIC_TREES << 1) + eof, 3); + zip_compress_block(zip_static_ltree, zip_static_dtree); + } else { + zip_send_bits((zip_DYN_TREES << 1) + eof, 3); + zip_send_all_trees(zip_l_desc.max_code + 1, + zip_d_desc.max_code + 1, + max_blindex + 1); + zip_compress_block(zip_dyn_ltree, zip_dyn_dtree); + } + + zip_init_block(); + + if (eof != 0) + zip_bi_windup(); + } + + /* ========================================================================== + * Save the match info and tally the frequency counts. Return true if + * the current block must be flushed. + */ + function zip_ct_tally( + dist, // distance of matched string + lc) { // match length-MIN_MATCH or unmatched char (if dist==0) + zip_l_buf[zip_last_lit++] = lc; + if (dist == 0) { + // lc is the unmatched char + zip_dyn_ltree[lc].fc++; + } else { + // Here, lc is the match length - MIN_MATCH + dist--; // dist = match distance - 1 + // Assert((ush)dist < (ush)MAX_DIST && + // (ush)lc <= (ush)(MAX_MATCH-MIN_MATCH) && + // (ush)D_CODE(dist) < (ush)D_CODES, "ct_tally: bad match"); + + zip_dyn_ltree[zip_length_code[lc] + zip_LITERALS + 1].fc++; + zip_dyn_dtree[zip_D_CODE(dist)].fc++; + + zip_d_buf[zip_last_dist++] = dist; + zip_flags |= zip_flag_bit; + } + zip_flag_bit <<= 1; + + // Output the flags if they fill a byte + if ((zip_last_lit & 7) == 0) { + zip_flag_buf[zip_last_flags++] = zip_flags; + zip_flags = 0; + zip_flag_bit = 1; + } + // Try to guess if it is profitable to stop the current block here + if (zip_compr_level > 2 && (zip_last_lit & 0xfff) == 0) { + // Compute an upper bound for the compressed length + var out_length = zip_last_lit * 8; + var in_length = zip_strstart - zip_block_start; + var dcode; + + for (dcode = 0; dcode < zip_D_CODES; dcode++) { + out_length += zip_dyn_dtree[dcode].fc * (5 + zip_extra_dbits[dcode]); + } + out_length >>= 3; + // Trace((stderr,"\nlast_lit %u, last_dist %u, in %ld, out ~%ld(%ld%%) ", + // encoder->last_lit, encoder->last_dist, in_length, out_length, + // 100L - out_length*100L/in_length)); + if (zip_last_dist < parseInt(zip_last_lit / 2) && + out_length < parseInt(in_length / 2)) + return true; + } + return (zip_last_lit == zip_LIT_BUFSIZE - 1 || + zip_last_dist == zip_DIST_BUFSIZE); + /* We avoid equality with LIT_BUFSIZE because of wraparound at 64K + * on 16 bit machines and because stored blocks are restricted to + * 64K-1 bytes. + */ + } + + /* ========================================================================== + * Send the block data compressed using the given Huffman trees + */ + function zip_compress_block( + ltree, // literal tree + dtree) { // distance tree + var dist; // distance of matched string + var lc; // match length or unmatched char (if dist == 0) + var lx = 0; // running index in l_buf + var dx = 0; // running index in d_buf + var fx = 0; // running index in flag_buf + var flag = 0; // current flags + var code; // the code to send + var extra; // number of extra bits to send + + if (zip_last_lit != 0) do { + if ((lx & 7) == 0) + flag = zip_flag_buf[fx++]; + lc = zip_l_buf[lx++] & 0xff; + if ((flag & 1) == 0) { + zip_SEND_CODE(lc, ltree); /* send a literal byte */ + // Tracecv(isgraph(lc), (stderr," '%c' ", lc)); + } else { + // Here, lc is the match length - MIN_MATCH + code = zip_length_code[lc]; + zip_SEND_CODE(code + zip_LITERALS + 1, ltree); // send the length code + extra = zip_extra_lbits[code]; + if (extra != 0) { + lc -= zip_base_length[code]; + zip_send_bits(lc, extra); // send the extra length bits + } + dist = zip_d_buf[dx++]; + // Here, dist is the match distance - 1 + code = zip_D_CODE(dist); + // Assert (code < D_CODES, "bad d_code"); + + zip_SEND_CODE(code, dtree); // send the distance code + extra = zip_extra_dbits[code]; + if (extra != 0) { + dist -= zip_base_dist[code]; + zip_send_bits(dist, extra); // send the extra distance bits + } + } // literal or match pair ? + flag >>= 1; + } while (lx < zip_last_lit); + + zip_SEND_CODE(zip_END_BLOCK, ltree); + } + + /* ========================================================================== + * Send a value on a given number of bits. + * IN assertion: length <= 16 and value fits in length bits. + */ + var zip_Buf_size = 16; // bit size of bi_buf + function zip_send_bits( + value, // value to send + length) { // number of bits + /* If not enough room in bi_buf, use (valid) bits from bi_buf and + * (16 - bi_valid) bits from value, leaving (width - (16-bi_valid)) + * unused bits in value. + */ + if (zip_bi_valid > zip_Buf_size - length) { + zip_bi_buf |= (value << zip_bi_valid); + zip_put_short(zip_bi_buf); + zip_bi_buf = (value >> (zip_Buf_size - zip_bi_valid)); + zip_bi_valid += length - zip_Buf_size; + } else { + zip_bi_buf |= value << zip_bi_valid; + zip_bi_valid += length; + } + } + + /* ========================================================================== + * Reverse the first len bits of a code, using straightforward code (a faster + * method would use a table) + * IN assertion: 1 <= len <= 15 + */ + function zip_bi_reverse( + code, // the value to invert + len) { // its bit length + var res = 0; + do { + res |= code & 1; + code >>= 1; + res <<= 1; + } while (--len > 0); + return res >> 1; + } + + /* ========================================================================== + * Write out any remaining bits in an incomplete byte. + */ + function zip_bi_windup() { + if (zip_bi_valid > 8) { + zip_put_short(zip_bi_buf); + } else if (zip_bi_valid > 0) { + zip_put_byte(zip_bi_buf); + } + zip_bi_buf = 0; + zip_bi_valid = 0; + } + + function zip_qoutbuf() { + if (zip_outcnt != 0) { + var q, i; + q = zip_new_queue(); + if (zip_qhead == null) + zip_qhead = zip_qtail = q; + else + zip_qtail = zip_qtail.next = q; + q.len = zip_outcnt - zip_outoff; + // System.arraycopy(zip_outbuf, zip_outoff, q.ptr, 0, q.len); + for (i = 0; i < q.len; i++) + q.ptr[i] = zip_outbuf[zip_outoff + i]; + zip_outcnt = zip_outoff = 0; + } + } + + return function deflate(str, level) { + var i, j; + + zip_deflate_data = str; + zip_deflate_pos = 0; + if (typeof level == "undefined") + level = zip_DEFAULT_LEVEL; + zip_deflate_start(level); + + var buff = new Array(1024); + var aout = []; + while ((i = zip_deflate_internal(buff, 0, buff.length)) > 0) { + var cbuf = new Array(i); + for (j = 0; j < i; j++) { + cbuf[j] = String.fromCharCode(buff[j]); + } + aout[aout.length] = cbuf.join(""); + } + zip_deflate_data = null; // G.C. + return aout.join(""); + }; +})(); \ No newline at end of file diff --git a/js/katex.min.js b/js/katex.min.js new file mode 100644 index 0000000..babf959 --- /dev/null +++ b/js/katex.min.js @@ -0,0 +1 @@ +!function(e,t){"object"==typeof exports&&"object"==typeof module?module.exports=t():"function"==typeof define&&define.amd?define([],t):"object"==typeof exports?exports.katex=t():e.katex=t()}("undefined"!=typeof self?self:this,(function(){return function(){"use strict";var e={d:function(t,r){for(var n in r)e.o(r,n)&&!e.o(t,n)&&Object.defineProperty(t,n,{enumerable:!0,get:r[n]})},o:function(e,t){return Object.prototype.hasOwnProperty.call(e,t)}},t={};e.d(t,{default:function(){return Wn}});class r{constructor(e,t){this.name=void 0,this.position=void 0,this.length=void 0,this.rawMessage=void 0;let n,o,s="KaTeX parse error: "+e;const i=t&&t.loc;if(i&&i.start<=i.end){const e=i.lexer.input;n=i.start,o=i.end,n===e.length?s+=" at end of input: ":s+=" at position "+(n+1)+": ";const t=e.slice(n,o).replace(/[^]/g,"$&\u0332");let r,a;r=n>15?"\u2026"+e.slice(n-15,n):e.slice(0,n),a=o+15":">","<":"<",'"':""","'":"'"},i=/[&><"']/g;const a=function(e){return"ordgroup"===e.type||"color"===e.type?1===e.body.length?a(e.body[0]):e:"font"===e.type?a(e.body):e};var l={deflt:function(e,t){return void 0===e?t:e},escape:function(e){return String(e).replace(i,(e=>s[e]))},hyphenate:function(e){return e.replace(o,"-$1").toLowerCase()},getBaseElem:a,isCharacterBox:function(e){const t=a(e);return"mathord"===t.type||"textord"===t.type||"atom"===t.type},protocolFromUrl:function(e){const t=/^[\x00-\x20]*([^\\/#?]*?)(:|�*58|�*3a|&colon)/i.exec(e);return t?":"!==t[2]?null:/^[a-zA-Z][a-zA-Z0-9+\-.]*$/.test(t[1])?t[1].toLowerCase():null:"_relative"}};const h={displayMode:{type:"boolean",description:"Render math in display mode, which puts the math in display style (so \\int and \\sum are large, for example), and centers the math on the page on its own line.",cli:"-d, --display-mode"},output:{type:{enum:["htmlAndMathml","html","mathml"]},description:"Determines the markup language of the output.",cli:"-F, --format "},leqno:{type:"boolean",description:"Render display math in leqno style (left-justified tags)."},fleqn:{type:"boolean",description:"Render display math flush left."},throwOnError:{type:"boolean",default:!0,cli:"-t, --no-throw-on-error",cliDescription:"Render errors (in the color given by --error-color) instead of throwing a ParseError exception when encountering an error."},errorColor:{type:"string",default:"#cc0000",cli:"-c, --error-color ",cliDescription:"A color string given in the format 'rgb' or 'rrggbb' (no #). 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h{constructor(e,t){super(e,t),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return qi}static get DefaultType(){return Yi}static get NAME(){return ki}toggle(e){return this._isShown?this.hide():this.show(e)}show(t){if(this._isShown)return;const n=e.trigger(this._element,Pi,{relatedTarget:t});if(n.defaultPrevented)return;this._isShown=!0,this._backdrop.show(),this._config.scroll||(new qe).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(on);const s=()=>{(!this._config.scroll||this._config.backdrop)&&this._focustrap.activate(),this._element.classList.add(qt),this._element.classList.remove(on),e.trigger(this._element,Hi,{relatedTarget:t})};this._queueCallback(s,this._element,!0)}hide(){if(!this._isShown)return;const t=e.trigger(this._element,Ii);if(t.defaultPrevented)return;this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(an),this._backdrop.hide();const n=()=>{this._element.classList.remove(qt,an),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new qe).reset(),e.trigger(this._element,ln)};this._queueCallback(n,this._element,!0)}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const n=()=>{if(this._config.backdrop==="static"){e.trigger(this._element,cn);return}this.hide()},t=Boolean(this._config.backdrop);return new xt({className:Li,isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?n:null})}_initializeFocusTrap(){return new Dt({trapElement:this._element})}_addEventListeners(){e.on(this._element,Ui,t=>{if(t.key!==Ti)return;if(this._config.keyboard){this.hide();return}e.trigger(this._element,cn)})}static 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Xi=/^aria-[\w-]*$/i,dn={"*":["class","dir","id","lang","role",Xi],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],dd:[],div:[],dl:[],dt:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Zi=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Ji=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,ea=(e,t)=>{const n=e.nodeName.toLowerCase();return t.includes(n)?!Zi.has(n)||Boolean(Ji.test(e.nodeValue)):t.filter(e=>e instanceof RegExp).some(e=>e.test(n))};function ta(e,t,n){if(!e.length)return e;if(n&&typeof n=="function")return n(e);const o=new window.DOMParser,s=o.parseFromString(e,"text/html"),i=[].concat(...s.body.querySelectorAll("*"));for(const e of i){const n=e.nodeName.toLowerCase();if(!Object.keys(t).includes(n)){e.remove();continue}const s=[].concat(...e.attributes),o=[].concat(t["*"]||[],t[n]||[]);for(const t of s)ea(t,o)||e.removeAttribute(t.nodeName)}return s.body.innerHTML}const na="TemplateFactory",sa={allowList:dn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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Set(["sanitize","allowList","sanitizeFn"]),Ze="fade",da="modal",ye="show",ha=".tooltip-inner",hn=`.${da}`,mn="hide.bs.modal",Z="hover",at="focus",va="click",ba="manual",ja="hide",ya="hidden",_a="show",wa="shown",Oa="inserted",xa="click",Ca="focusin",Ea="focusout",ka="mouseenter",Aa="mouseleave",Sa={AUTO:"auto",TOP:"top",RIGHT:c()?"left":"right",BOTTOM:"bottom",LEFT:c()?"right":"left"},Ma={allowList:dn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover 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t=this._getTipElement();this._element.setAttribute("aria-describedby",t.getAttribute("id"));const{container:i}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(i.append(t),e.trigger(this._element,this.constructor.eventName(Oa))),this._popper=this._createPopper(t),t.classList.add(ye),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))e.on(t,"mouseover",le);const a=()=>{e.trigger(this._element,this.constructor.eventName(wa)),this._isHovered===!1&&this._leave(),this._isHovered=!1};this._queueCallback(a,this.tip,this._isAnimated())}hide(){if(!this._isShown())return;const t=e.trigger(this._element,this.constructor.eventName(ja));if(t.defaultPrevented)return;const n=this._getTipElement();if(n.classList.remove(ye),"ontouchstart"in document.documentElement)for(const t 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t.setAttribute("id",n),this._isAnimated()&&t.classList.add(Ze),t}setContent(e){this._newContent=e,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(e){return this._templateFactory?this._templateFactory.changeContent(e):this._templateFactory=new aa({...this._config,content:e,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{[ha]:this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(e){return this.constructor.getOrCreateInstance(e.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(Ze)}_isShown(){return this.tip&&this.tip.classList.contains(ye)}_createPopper(e){const t=o(this._config.placement,[this,e,this._element]),n=Sa[t.toUpperCase()];return 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t=this._initializeOnDelegatedTarget(e);t.toggle()});else if(n!==ba){const t=n===Z?this.constructor.eventName(ka):this.constructor.eventName(Ca),s=n===Z?this.constructor.eventName(Aa):this.constructor.eventName(Ea);e.on(this._element,t,this._config.selector,e=>{const t=this._initializeOnDelegatedTarget(e);t._activeTrigger[e.type==="focusin"?at:Z]=!0,t._enter()}),e.on(this._element,s,this._config.selector,e=>{const t=this._initializeOnDelegatedTarget(e);t._activeTrigger[e.type==="focusout"?at:Z]=t._element.contains(e.relatedTarget),t._leave()})}this._hideModalHandler=()=>{this._element&&this.hide()},e.on(this._element.closest(hn),mn,this._hideModalHandler)}_fixTitle(){const e=this._element.getAttribute("title");if(!e)return;!this._element.getAttribute("aria-label")&&!this._element.textContent.trim()&&this._element.setAttribute("aria-label",e),this._element.setAttribute("data-bs-original-title",e),this._element.removeAttribute("title")}_enter(){if(this._isShown()||this._isHovered){this._isHovered=!0;return}this._isHovered=!0,this._setTimeout(()=>{this._isHovered&&this.show()},this._config.delay.show)}_leave(){if(this._isWithActiveTrigger())return;this._isHovered=!1,this._setTimeout(()=>{this._isHovered||this.hide()},this._config.delay.hide)}_setTimeout(e,t){clearTimeout(this._timeout),this._timeout=setTimeout(e,t)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(e){const t=v.getDataAttributes(this._element);for(const e of Object.keys(t))ca.has(e)&&delete t[e];return e={...t,...typeof e=="object"&&e?e:{}},e=this._mergeConfigObj(e),e=this._configAfterMerge(e),this._typeCheckConfig(e),e}_configAfterMerge(e){return e.container=e.container===!1?document.body:w(e.container),typeof e.delay=="number"&&(e.delay={show:e.delay,hide:e.delay}),typeof e.title=="number"&&(e.title=e.title.toString()),typeof e.content=="number"&&(e.content=e.content.toString()),e}_getDelegateConfig(){const e={};for(const[t,n]of Object.entries(this._config))this.constructor.Default[t]!==n&&(e[t]=n);return e.selector=!1,e.trigger="manual",e}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(e){return this.each(function(){const t=H.getOrCreateInstance(this,e);if(typeof e!="string")return;if(typeof t[e]=="undefined")throw new TypeError(`No method named "${e}"`);t[e]()})}}u(H);const za="popover",Da=".popover-header",Na=".popover-body",La={...H.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},Ra={...H.DefaultType,content:"(null|string|element|function)"};class mt extends H{static get Default(){return La}static get 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e.threshold=="string"&&(e.threshold=e.threshold.split(",").map(e=>Number.parseFloat(e))),e}_maybeEnableSmoothScroll(){if(!this._config.smoothScroll)return;e.off(this._config.target,pn),e.on(this._config.target,pn,Ge,e=>{const t=this._observableSections.get(e.target.hash);if(t){e.preventDefault();const n=this._rootElement||window,s=t.offsetTop-this._element.offsetTop;if(n.scrollTo){n.scrollTo({top:s,behavior:"smooth"});return}n.scrollTop=s}})}_getNewObserver(){const e={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver(e=>this._observerCallback(e),e)}_observerCallback(e){const n=e=>this._targetLinks.get(`#${e.target.id}`),s=e=>{this._previousScrollData.visibleEntryTop=e.target.offsetTop,this._process(n(e))},t=(this._rootElement||document.documentElement).scrollTop,o=t>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=t;for(const i of e){if(!i.isIntersecting){this._activeTarget=null,this._clearActiveClass(n(i));continue}const a=i.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(o&&a){if(s(i),!t)return;continue}!o&&!a&&s(i)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const e=t.find(Ge,this._config.target);for(const n of e){if(!n.hash||y(n))continue;const s=t.findOne(decodeURI(n.hash),this._element);R(s)&&(this._targetLinks.set(decodeURI(n.hash),n),this._observableSections.set(n.hash,s))}}_process(t){if(this._activeTarget===t)return;this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(W),this._activateParents(t),e.trigger(this._element,$a,{relatedTarget:t})}_activateParents(e){if(e.classList.contains(Ka)){t.findOne(nr,e.closest(tr)).classList.add(W);return}for(const n of t.parents(e,Xa))for(const e of t.prev(n,er))e.classList.add(W)}_clearActiveClass(e){e.classList.remove(W);const n=t.find(`${Ge}.${W}`,e);for(const e of n)e.classList.remove(W)}static jQueryInterface(e){return this.each(function(){const t=xe.getOrCreateInstance(this,e);if(typeof e!="string")return;if(t[e]===0[0]||e.startsWith("_")||e==="constructor")throw new TypeError(`No method named "${e}"`);t[e]()})}}e.on(window,Ua,()=>{for(const e of t.find(Ya))xe.getOrCreateInstance(e)}),u(xe);const ar="tab",rr="bs.tab",M=`.${rr}`,lr=`hide${M}`,dr=`hidden${M}`,ur=`show${M}`,hr=`shown${M}`,mr=`click${M}`,fr=`keydown${M}`,pr=`load${M}`,gr="ArrowLeft",vn="ArrowRight",br="ArrowUp",yn="ArrowDown",ft="Home",wn="End",S="active",Mn="fade",Ye="show",Cr="dropdown",Wn=".dropdown-toggle",kr=".dropdown-menu",Ne=`:not(${Wn})`,Sr='.list-group, .nav, [role="tablist"]',Mr=".nav-item, .list-group-item",Fr=`.nav-link${Ne}, .list-group-item${Ne}, [role="tab"]${Ne}`,Gn='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Pe=`${Fr}, ${Gn}`,Dr=`.${S}[data-bs-toggle="tab"], .${S}[data-bs-toggle="pill"], .${S}[data-bs-toggle="list"]`;class B extends h{constructor(t){if(super(t),this._parent=this._element.closest(Sr),!this._parent)return;this._setInitialAttributes(this._parent,this._getChildren()),e.on(this._element,fr,e=>this._keydown(e))}static get NAME(){return ar}show(){const t=this._element;if(this._elemIsActive(t))return;const n=this._getActiveElem(),s=n?e.trigger(n,lr,{relatedTarget:t}):null,o=e.trigger(t,ur,{relatedTarget:n});if(o.defaultPrevented||s&&s.defaultPrevented)return;this._deactivate(n,t),this._activate(t,n)}_activate(n,s){if(!n)return;n.classList.add(S),this._activate(t.getElementFromSelector(n));const o=()=>{if(n.getAttribute("role")!=="tab"){n.classList.add(Ye);return}n.removeAttribute("tabindex"),n.setAttribute("aria-selected",!0),this._toggleDropDown(n,!0),e.trigger(n,hr,{relatedTarget:s})};this._queueCallback(o,n,n.classList.contains(Mn))}_deactivate(n,s){if(!n)return;n.classList.remove(S),n.blur(),this._deactivate(t.getElementFromSelector(n));const o=()=>{if(n.getAttribute("role")!=="tab"){n.classList.remove(Ye);return}n.setAttribute("aria-selected",!1),n.setAttribute("tabindex","-1"),this._toggleDropDown(n,!1),e.trigger(n,dr,{relatedTarget:s})};this._queueCallback(o,n,n.classList.contains(Mn))}_keydown(e){if(![gr,vn,br,yn,ft,wn].includes(e.key))return;e.stopPropagation(),e.preventDefault();const n=this._getChildren().filter(e=>!y(e));let t;if([ft,wn].includes(e.key))t=n[e.key===ft?0:n.length-1];else{const s=[vn,yn].includes(e.key);t=$e(n,e.target,s,!0)}t&&(t.focus({preventScroll:!0}),B.getOrCreateInstance(t).show())}_getChildren(){return t.find(Pe,this._parent)}_getActiveElem(){return this._getChildren().find(e=>this._elemIsActive(e))||null}_setInitialAttributes(e,t){this._setAttributeIfNotExists(e,"role","tablist");for(const e of t)this._setInitialAttributesOnChild(e)}_setInitialAttributesOnChild(e){e=this._getInnerElement(e);const t=this._elemIsActive(e),n=this._getOuterElement(e);e.setAttribute("aria-selected",t),n!==e&&this._setAttributeIfNotExists(n,"role","presentation"),t||e.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(e,"role","tab"),this._setInitialAttributesOnTargetPanel(e)}_setInitialAttributesOnTargetPanel(e){const n=t.getElementFromSelector(e);if(!n)return;this._setAttributeIfNotExists(n,"role","tabpanel"),e.id&&this._setAttributeIfNotExists(n,"aria-labelledby",`${e.id}`)}_toggleDropDown(e,n){const s=this._getOuterElement(e);if(!s.classList.contains(Cr))return;const o=(e,o)=>{const i=t.findOne(e,s);i&&i.classList.toggle(o,n)};o(Wn,S),o(kr,Ye),s.setAttribute("aria-expanded",n)}_setAttributeIfNotExists(e,t,n){e.hasAttribute(t)||e.setAttribute(t,n)}_elemIsActive(e){return e.classList.contains(S)}_getInnerElement(e){return e.matches(Pe)?e:t.findOne(Pe,e)}_getOuterElement(e){return e.closest(Mr)||e}static jQueryInterface(e){return this.each(function(){const t=B.getOrCreateInstance(this);if(typeof e!="string")return;if(t[e]===0[0]||e.startsWith("_")||e==="constructor")throw new TypeError(`No method named "${e}"`);t[e]()})}}e.on(document,mr,Gn,function(e){if(["A","AREA"].includes(this.tagName)&&e.preventDefault(),y(this))return;B.getOrCreateInstance(this).show()}),e.on(window,pr,()=>{for(const e of t.find(Dr))B.getOrCreateInstance(e)}),u(B);const Lr="toast",Rr="bs.toast",x=`.${Rr}`,Hr=`mouseover${x}`,Ir=`mouseout${x}`,Br=`focusin${x}`,Vr=`focusout${x}`,$r=`hide${x}`,Wr=`hidden${x}`,Ur=`show${x}`,Kr=`shown${x}`,qr="fade",os="hide",ve="show",he="showing",Qr={animation:"boolean",autohide:"boolean",delay:"number"},Zr={animation:!0,autohide:!0,delay:5e3};class ue extends h{constructor(e,t){super(e,t),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return Zr}static get DefaultType(){return Qr}static get NAME(){return Lr}show(){const t=e.trigger(this._element,Ur);if(t.defaultPrevented)return;this._clearTimeout(),this._config.animation&&this._element.classList.add(qr);const n=()=>{this._element.classList.remove(he),e.trigger(this._element,Kr),this._maybeScheduleHide()};this._element.classList.remove(os),oe(this._element),this._element.classList.add(ve,he),this._queueCallback(n,this._element,this._config.animation)}hide(){if(!this.isShown())return;const t=e.trigger(this._element,$r);if(t.defaultPrevented)return;const n=()=>{this._element.classList.add(os),this._element.classList.remove(he,ve),e.trigger(this._element,Wr)};this._element.classList.add(he),this._queueCallback(n,this._element,this._config.animation)}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(ve),super.dispose()}isShown(){return this._element.classList.contains(ve)}_maybeScheduleHide(){if(!this._config.autohide)return;if(this._hasMouseInteraction||this._hasKeyboardInteraction)return;this._timeout=setTimeout(()=>{this.hide()},this._config.delay)}_onInteraction(e,t){switch(e.type){case"mouseover":case"mouseout":{this._hasMouseInteraction=t;break}case"focusin":case"focusout":{this._hasKeyboardInteraction=t;break}}if(t){this._clearTimeout();return}const n=e.relatedTarget;if(this._element===n||this._element.contains(n))return;this._maybeScheduleHide()}_setListeners(){e.on(this._element,Hr,e=>this._onInteraction(e,!0)),e.on(this._element,Ir,e=>this._onInteraction(e,!1)),e.on(this._element,Br,e=>this._onInteraction(e,!0)),e.on(this._element,Vr,e=>this._onInteraction(e,!1))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(e){return this.each(function(){const t=ue.getOrCreateInstance(this,e);if(typeof e=="string"){if(typeof t[e]=="undefined")throw new TypeError(`No method named 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a/js/tabpane-persist.js b/js/tabpane-persist.js new file mode 100644 index 0000000..2362130 --- /dev/null +++ b/js/tabpane-persist.js @@ -0,0 +1,116 @@ +// Storage key names and data attribute name: +const td_persistStorageKeyNameBase = 'td-tp-persist'; +const td_persistCounterStorageKeyName = `${td_persistStorageKeyNameBase}-count`; +const td_persistDataAttrName = `data-${td_persistStorageKeyNameBase}`; + +// Utilities + +const _tdPersistCssSelector = (attrValue) => + attrValue + ? `[${td_persistDataAttrName}="${attrValue}"]` + : `[${td_persistDataAttrName}]`; + +const _tdStoragePersistKey = (tabKey) => + td_persistStorageKeyNameBase + ':' + (tabKey || ''); + +const _tdSupportsLocalStorage = () => typeof Storage !== 'undefined'; + +// Helpers + +function tdPersistKey(key, value) { + // @requires: tdSupportsLocalStorage(); + + try { + if (value) { + localStorage.setItem(key, value); + } else { + localStorage.removeItem(key); + } + } catch (error) { + const action = value ? 'add' : 'remove'; + console.error( + `Docsy tabpane: unable to ${action} localStorage key '${key}': `, + error + ); + } +} + +// Retrieve, increment, and store tab-select event count, then returns it. +function tdGetTabSelectEventCountAndInc() { + // @requires: tdSupportsLocalStorage(); + + const storedCount = localStorage.getItem(td_persistCounterStorageKeyName); + let numTabSelectEvents = parseInt(storedCount) || 0; + numTabSelectEvents++; + tdPersistKey(td_persistCounterStorageKeyName, numTabSelectEvents.toString()); + return numTabSelectEvents; +} + +// Main functions + +function tdActivateTabsWithKey(key) { + if (!key) return; + + document.querySelectorAll(_tdPersistCssSelector(key)).forEach((element) => { + new bootstrap.Tab(element).show(); + }); +} + +function tdPersistActiveTab(activeTabKey) { + if (!_tdSupportsLocalStorage()) return; + + tdPersistKey( + _tdStoragePersistKey(activeTabKey), + tdGetTabSelectEventCountAndInc() + ); + tdActivateTabsWithKey(activeTabKey); +} + +// Handlers + +function tdGetAndActivatePersistedTabs(tabs) { + // Get unique persistence keys of tabs in this page + var keyOfTabsInThisPage = [ + ...new Set( + Array.from(tabs).map((el) => el.getAttribute(td_persistDataAttrName)) + ), + ]; + + // Create a list of active tabs with their age: + let key_ageList = keyOfTabsInThisPage + // Map to [tab-key, last-activated-age] + .map((k) => [ + k, + parseInt(localStorage.getItem(_tdStoragePersistKey(k))) || 0, + ]) + // Exclude tabs that have never been activated + .filter(([k, v]) => v) + // Sort from oldest selected to most recently selected + .sort((a, b) => a[1] - b[1]); + + // Activate tabs from the oldest to the newest + key_ageList.forEach(([key]) => { + tdActivateTabsWithKey(key); + }); + + return key_ageList; +} + +function tdRegisterTabClickHandler(tabs) { + tabs.forEach((tab) => { + tab.addEventListener('click', () => { + const activeTabKey = tab.getAttribute(td_persistDataAttrName); + tdPersistActiveTab(activeTabKey); + }); + }); +} + +// Register listeners and activate tabs + +window.addEventListener('DOMContentLoaded', () => { + if (!_tdSupportsLocalStorage()) return; + + var allTabsInThisPage = document.querySelectorAll(_tdPersistCssSelector()); + tdRegisterTabClickHandler(allTabsInThisPage); + tdGetAndActivatePersistedTabs(allTabsInThisPage); +}); diff --git a/layouts/404.html b/layouts/404.html deleted file mode 100644 index d32828c..0000000 --- a/layouts/404.html +++ /dev/null @@ -1,6 +0,0 @@ -{{ define "main" -}} -
-

Not found

-

Oops! This page doesn't exist. Try going back to the home page.

-
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.badge,div.drawio button .badge,.td-blog .td-rss-button .badge{position:relative;top:-1px}.alert{--bs-alert-bg:transparent;--bs-alert-padding-x:1rem;--bs-alert-padding-y:1rem;--bs-alert-margin-bottom:1rem;--bs-alert-color:inherit;--bs-alert-border-color:transparent;--bs-alert-border:var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius:var(--bs-border-radius);--bs-alert-link-color:inherit;position:relative;padding:var(--bs-alert-padding-y)var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-primary,.pageinfo-primary{--bs-alert-color:var(--bs-primary-text-emphasis);--bs-alert-bg:var(--bs-primary-bg-subtle);--bs-alert-border-color:var(--bs-primary-border-subtle);--bs-alert-link-color:var(--bs-primary-text-emphasis)}.alert-secondary,.pageinfo-secondary{--bs-alert-color:var(--bs-secondary-text-emphasis);--bs-alert-bg:var(--bs-secondary-bg-subtle);--bs-alert-border-color:var(--bs-secondary-border-subtle);--bs-alert-link-color:var(--bs-secondary-text-emphasis)}.alert-success,.pageinfo-success{--bs-alert-color:var(--bs-success-text-emphasis);--bs-alert-bg:var(--bs-success-bg-subtle);--bs-alert-border-color:var(--bs-success-border-subtle);--bs-alert-link-color:var(--bs-success-text-emphasis)}.alert-info,.pageinfo-info{--bs-alert-color:var(--bs-info-text-emphasis);--bs-alert-bg:var(--bs-info-bg-subtle);--bs-alert-border-color:var(--bs-info-border-subtle);--bs-alert-link-color:var(--bs-info-text-emphasis)}.alert-warning,.pageinfo-warning{--bs-alert-color:var(--bs-warning-text-emphasis);--bs-alert-bg:var(--bs-warning-bg-subtle);--bs-alert-border-color:var(--bs-warning-border-subtle);--bs-alert-link-color:var(--bs-warning-text-emphasis)}.alert-danger,.pageinfo-danger{--bs-alert-color:var(--bs-danger-text-emphasis);--bs-alert-bg:var(--bs-danger-bg-subtle);--bs-alert-border-color:var(--bs-danger-border-subtle);--bs-alert-link-color:var(--bs-danger-text-emphasis)}.alert-light,.pageinfo-light{--bs-alert-color:var(--bs-light-text-emphasis);--bs-alert-bg:var(--bs-light-bg-subtle);--bs-alert-border-color:var(--bs-light-border-subtle);--bs-alert-link-color:var(--bs-light-text-emphasis)}.alert-dark,.pageinfo-dark{--bs-alert-color:var(--bs-dark-text-emphasis);--bs-alert-bg:var(--bs-dark-bg-subtle);--bs-alert-border-color:var(--bs-dark-border-subtle);--bs-alert-link-color:var(--bs-dark-text-emphasis)}@keyframes 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ease;display:flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius);box-shadow:var(--bs-progress-box-shadow)}.progress-bar{display:flex;flex-direction:column;justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion:reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-size:var(--bs-progress-height)var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite 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";counter-increment:section}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y)var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width)solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * 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* var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width:576px){.list-group-horizontal-sm{flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * 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btn-close{--bs-btn-close-color:#000;--bs-btn-close-bg:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414'/%3e%3c/svg%3e");--bs-btn-close-opacity:0.5;--bs-btn-close-hover-opacity:0.75;--bs-btn-close-focus-shadow:0 0 0 0.25rem rgba(48, 99, 142, 0.25);--bs-btn-close-focus-opacity:1;--bs-btn-close-disabled-opacity:0.25;box-sizing:content-box;width:1em;height:1em;padding:.25em;color:var(--bs-btn-close-color);background:var(--bs-btn-close-bg)50%/1em 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