diff --git a/.all-contributorsrc b/.all-contributorsrc index eee250a4..b33d332f 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -27,7 +27,8 @@ "tool", "test", "bug", - "platform" + "platform", + "review" ] }, { @@ -37,7 +38,8 @@ "profile": "https://github.com/mdhaber", "contributions": [ "code", - "ideas" + "ideas", + "test" ] }, { @@ -47,7 +49,8 @@ "profile": "https://github.com/j-bowhay", "contributions": [ "code", - "review" + "review", + "tool" ] }, { @@ -90,14 +93,20 @@ ] }, { - "login": "rgommers", - "name": "Ralf Gommers", - "avatar_url": "https://avatars.githubusercontent.com/u/98330?v=4", - "profile": "https://github.com/rgommers/", + "login": "crusaderky", + "name": "Guido Imperiale", + "avatar_url": "https://avatars.githubusercontent.com/u/6213168?v=4", + "profile": "https://github.com/crusaderky", "contributions": [ + "code", + "doc", + "tool", + "test", + "example", + "bug", "ideas", - "infra", - "review" + "review", + "maintenance" ] }, { @@ -155,6 +164,17 @@ "code" ] }, + { + "login": "rgommers", + "name": "Ralf Gommers", + "avatar_url": "https://avatars.githubusercontent.com/u/98330?v=4", + "profile": "https://github.com/rgommers/", + "contributions": [ + "ideas", + "infra", + "review" + ] + }, { "login": "ogrisel", "name": "Olivier Grisel", @@ -163,6 +183,72 @@ "contributions": [ "code" ] + }, + { + "login": "ev-br", + "name": "Evgeni Burovski", + "avatar_url": "https://avatars.githubusercontent.com/u/2133832?v=4", + "profile": "https://github.com/ev-br", + "contributions": [ + "code", + "doc" + ] + }, + { + "login": "jakevdp", + "name": "Jake Vanderplas", + "avatar_url": "https://avatars.githubusercontent.com/u/781659?v=4", + "profile": "http://www.vanderplas.com/", + "contributions": [ + "review" + ] + }, + { + "login": "kotlinisland", + "name": "kotlinisland", + "avatar_url": "https://avatars.githubusercontent.com/u/65446343?v=4", + "profile": "https://github.com/KotlinIsland", + "contributions": [ + "review" + ] + }, + { + "login": "detachhead", + "name": "detachhead", + "avatar_url": "https://avatars.githubusercontent.com/u/57028336?v=4", + "profile": "https://github.com/DetachHead", + "contributions": [ + "review" + ] + }, + { + "login": "NeilGirdhar", + "name": "Neil Girdhar", + "avatar_url": "https://avatars.githubusercontent.com/u/730137?v=4", + "profile": "https://github.com/NeilGirdhar", + "contributions": [ + "code", + "test" + ] + }, + { + "login": "lithomas1", + "name": "Thomas Li", + "avatar_url": "https://avatars.githubusercontent.com/u/47963215?v=4", + "profile": "https://github.com/lithomas1", + "contributions": [ + "bug", + "tool" + ] + }, + { + "login": "pearu", + "name": "Pearu Peterson", + "avatar_url": "https://avatars.githubusercontent.com/u/402156?v=4", + "profile": "https://github.com/pearu", + "contributions": [ + "review" + ] } ] } diff --git a/.dprint.jsonc b/.dprint.jsonc new file mode 100644 index 00000000..c3642905 --- /dev/null +++ b/.dprint.jsonc @@ -0,0 +1,25 @@ +{ + "json": { + "lineWidth": 100, + "array.preferSingleLine": true, + }, + "markdown": { + "lineWidth": 200, + "emphasisKind": "asterisks", + }, + "toml": { + "lineWidth": 100, + }, + "yaml": { + "printWidth": 100, + "formatComments": true, + "braceSpacing": false, + }, + "excludes": ["**/*-lock.json", "**/*.lock", "**/node_modules/", "dist/"], + "plugins": [ + "https://plugins.dprint.dev/g-plane/pretty_yaml-v0.5.1.wasm", + "https://plugins.dprint.dev/json-0.20.0.wasm", + "https://plugins.dprint.dev/markdown-0.19.0.wasm", + "https://plugins.dprint.dev/toml-0.7.0.wasm", + ], +} diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 00000000..46f23570 --- /dev/null +++ b/.editorconfig @@ -0,0 +1,14 @@ +root = true + +[*.{py,md}] +indent_style = space +end_of_line = lf +trim_trailing_whitespace = true +insert_final_newline = true + +[*.py] +max_line_length = 88 +indent_size = 4 + +[*.md] +max_line_length = 80 diff --git a/.github/dependabot.yml b/.github/dependabot.yml deleted file mode 100644 index 6c4b3695..00000000 --- a/.github/dependabot.yml +++ /dev/null @@ -1,11 +0,0 @@ -version: 2 -updates: - # Maintain dependencies for GitHub Actions - - package-ecosystem: "github-actions" - directory: "/" - schedule: - interval: "weekly" - groups: - actions: - patterns: - - "*" diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 00000000..014064f0 --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,5 @@ + diff --git a/.github/workflows/cd.yml b/.github/workflows/cd.yml index e00708a5..68bd3738 100644 --- a/.github/workflows/cd.yml +++ b/.github/workflows/cd.yml @@ -1,5 +1,8 @@ name: CD +permissions: + contents: read + on: workflow_dispatch: pull_request: @@ -14,11 +17,11 @@ jobs: dist: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - - uses: hynek/build-and-inspect-python-package@v2 + - uses: hynek/build-and-inspect-python-package@c52c3a4710070b50470d903818a7b25115dcd076 # v2.13.0 publish: needs: [dist] @@ -31,14 +34,14 @@ jobs: if: github.event_name == 'release' && github.event.action == 'published' steps: - - uses: actions/download-artifact@v4 + - uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0 with: name: Packages path: dist - name: Generate artifact attestation for sdist and wheel - uses: actions/attest-build-provenance@v1.4.4 + uses: actions/attest-build-provenance@e8998f949152b193b063cb0ec769d69d929409be # v2.4.0 with: subject-path: "dist/*" - - uses: pypa/gh-action-pypi-publish@release/v1 + - uses: pypa/gh-action-pypi-publish@76f52bc884231f62b9a034ebfe128415bbaabdfc # v1.12.4 diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 50952923..7ed9676d 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -1,5 +1,8 @@ name: CI +permissions: + contents: read + on: workflow_dispatch: pull_request: @@ -17,54 +20,59 @@ env: FORCE_COLOR: 3 jobs: - pre-commit-and-lint: - name: Format + lint: + name: Lint runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - - uses: actions/setup-python@v5 - with: - python-version: "3.x" - - uses: pre-commit/action@v3.0.1 - with: - extra_args: --hook-stage manual --all-files - - uses: prefix-dev/setup-pixi@v0.8.1 + + - uses: prefix-dev/setup-pixi@14c8aabd75893f83f4ab30c03e7cf853c8208961 # v0.8.10 with: - pixi-version: v0.37.0 + pixi-version: v0.49.0 cache: true - - name: Run Pylint, Mypy & Pyright - run: | - pixi run -e lint pylint - pixi run -e lint mypy - pixi run -e lint pyright + environments: lint + + - name: Lint + run: pixi run -e lint lint checks: - name: Check ${{ matrix.environment }} + name: Test ${{ matrix.environment }} runs-on: ${{ matrix.runs-on }} - needs: [pre-commit-and-lint] strategy: fail-fast: false matrix: - environment: [ci-py310, ci-py313] + environment: + - tests-py310 + - tests-py313 + - tests-numpy1 + - tests-backends + - tests-nogil runs-on: [ubuntu-latest] steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - - uses: prefix-dev/setup-pixi@v0.8.1 + - uses: prefix-dev/setup-pixi@14c8aabd75893f83f4ab30c03e7cf853c8208961 # v0.8.10 with: - pixi-version: v0.37.0 + pixi-version: v0.49.0 cache: true environments: ${{ matrix.environment }} - name: Test package + # Save some time; also at the moment of writing coverage crashes on python 3.13t + if: ${{ matrix.environment != 'tests-nogil' }} run: pixi run -e ${{ matrix.environment }} tests-ci + - name: Test free-threading + if: ${{ matrix.environment == 'tests-nogil' }} + run: pixi run -e tests-nogil tests --parallel-threads=4 + - name: Upload coverage report - uses: codecov/codecov-action@v5.0.7 + if: ${{ matrix.environment != 'tests-nogil' }} + uses: codecov/codecov-action@18283e04ce6e62d37312384ff67231eb8fd56d24 # v5.4.3 with: token: ${{ secrets.CODECOV_TOKEN }} diff --git a/.github/workflows/dependabot-auto-merge.yml b/.github/workflows/dependabot-auto-merge.yml deleted file mode 100644 index bd29e25b..00000000 --- a/.github/workflows/dependabot-auto-merge.yml +++ /dev/null @@ -1,23 +0,0 @@ -# https://docs.github.com/en/code-security/dependabot/working-with-dependabot/automating-dependabot-with-github-actions#approve-a-pull-request -name: Dependabot auto-merge -on: pull_request - -permissions: - contents: write - pull-requests: write - -jobs: - dependabot: - runs-on: ubuntu-latest - if: github.actor == 'dependabot[bot]' - steps: - - name: Dependabot metadata - id: metadata - uses: dependabot/fetch-metadata@v2 - with: - github-token: "${{ secrets.GITHUB_TOKEN }}" - - name: Enable auto-merge for Dependabot PRs - run: gh pr merge --auto --merge "$PR_URL" - env: - PR_URL: ${{github.event.pull_request.html_url}} - GH_TOKEN: ${{secrets.GITHUB_TOKEN}} diff --git a/.github/workflows/docs-build.yml b/.github/workflows/docs-build.yml index 1cdf221e..81073742 100644 --- a/.github/workflows/docs-build.yml +++ b/.github/workflows/docs-build.yml @@ -1,20 +1,27 @@ name: Docs Build +permissions: + contents: read + on: [push, pull_request] jobs: docs-build: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - uses: prefix-dev/setup-pixi@v0.8.1 + - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + + - uses: prefix-dev/setup-pixi@14c8aabd75893f83f4ab30c03e7cf853c8208961 # v0.8.10 with: - pixi-version: v0.37.0 + pixi-version: v0.49.0 cache: true + environments: docs + - name: Build Docs run: pixi run -e docs docs + - name: Upload Artifact - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2 with: name: docs-build path: docs/build/ diff --git a/.github/workflows/docs-deploy.yml b/.github/workflows/docs-deploy.yml index 5ba6577d..844031bd 100644 --- a/.github/workflows/docs-deploy.yml +++ b/.github/workflows/docs-deploy.yml @@ -1,5 +1,8 @@ name: Docs Deploy +permissions: + contents: write # needed for the deploy step + on: workflow_run: workflows: ["Docs Build"] @@ -14,9 +17,10 @@ jobs: environment: name: docs-deploy steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + - name: Download Artifact - uses: dawidd6/action-download-artifact@v6 + uses: dawidd6/action-download-artifact@ac66b43f0e6a346234dd65d4d0c8fbb31cb316e5 # v11 with: workflow: docs-build.yml name: docs-build @@ -26,7 +30,7 @@ jobs: # See # https://github.com/JamesIves/github-pages-deploy-action/tree/dev#using-an-ssh-deploy-key- - name: Deploy - uses: JamesIves/github-pages-deploy-action@v4 + uses: JamesIves/github-pages-deploy-action@6c2d9db40f9296374acc17b90404b6e8864128c8 # v4.7.3 with: folder: docs/build/ ssh-key: ${{ secrets.DEPLOY_KEY }} diff --git a/.gitignore b/.gitignore index eccba9dc..0b377833 100644 --- a/.gitignore +++ b/.gitignore @@ -114,9 +114,11 @@ ENV/ env.bak/ venv.bak/ -# Spyder project settings +# IDE project settings +.idea/ .spyderproject .spyproject +.vscode/ # Rope project settings .ropeproject @@ -160,3 +162,12 @@ Thumbs.db # pixi environments .pixi *.egg-info + +# Vendor tests +vendor_tests/array_api_compat/ +vendor_tests/array_api_extra/ + +# all-contributors +node_modules +package.json +yarn.lock diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml deleted file mode 100644 index 41beb381..00000000 --- a/.pre-commit-config.yaml +++ /dev/null @@ -1,80 +0,0 @@ -ci: - autoupdate_commit_msg: "chore: update pre-commit hooks" - autofix_commit_msg: "style: pre-commit fixes" - -exclude: ^.cruft.json|.copier-answers.yml$ - -repos: - - repo: https://github.com/adamchainz/blacken-docs - rev: "1.18.0" - hooks: - - id: blacken-docs - additional_dependencies: [black==24.*] - - - repo: https://github.com/pre-commit/pre-commit-hooks - rev: "v4.6.0" - hooks: - - id: check-added-large-files - - id: check-case-conflict - - id: check-merge-conflict - - id: check-symlinks - - id: check-yaml - - id: debug-statements - - id: end-of-file-fixer - - id: mixed-line-ending - - id: name-tests-test - args: ["--pytest-test-first"] - - id: requirements-txt-fixer - - id: trailing-whitespace - - - repo: https://github.com/pre-commit/pygrep-hooks - rev: "v1.10.0" - hooks: - - id: rst-backticks - - id: rst-directive-colons - - id: rst-inline-touching-normal - - - repo: https://github.com/rbubley/mirrors-prettier - rev: "v3.3.3" - hooks: - - id: prettier - types_or: [yaml, markdown, html, css, scss, javascript, json] - args: [--prose-wrap=always] - - - repo: https://github.com/astral-sh/ruff-pre-commit - rev: "v0.6.1" - hooks: - - id: ruff - args: ["--fix", "--show-fixes"] - - id: ruff-format - - - repo: https://github.com/codespell-project/codespell - rev: "v2.3.0" - hooks: - - id: codespell - exclude: pixi.lock - - - repo: https://github.com/shellcheck-py/shellcheck-py - rev: "v0.10.0.1" - hooks: - - id: shellcheck - - - repo: local - hooks: - - id: disallow-caps - name: Disallow improper capitalization - language: pygrep - entry: PyBind|Numpy|Cmake|CCache|Github|PyTest - exclude: .pre-commit-config.yaml - - - repo: https://github.com/abravalheri/validate-pyproject - rev: "v0.19" - hooks: - - id: validate-pyproject - additional_dependencies: ["validate-pyproject-schema-store[all]"] - - - repo: https://github.com/python-jsonschema/check-jsonschema - rev: "0.29.1" - hooks: - - id: check-dependabot - - id: check-github-workflows diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index c3f99188..cc93de44 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -9,16 +9,16 @@ This project exists thanks to the following contributors - - - + + + - + @@ -27,7 +27,17 @@ This project exists thanks to the following contributors + + + + + + + + + +
Lucas Colley
Lucas Colley
πŸ’» πŸ“– πŸ’‘ πŸ€” πŸš‡ 🚧 πŸ”§ ⚠️ πŸ› πŸ“¦		Matt Haberland
Matt Haberland
πŸ’» πŸ€”		Jake Bowhay
Jake Bowhay
πŸ’» πŸ‘€		Lucas Colley
Lucas Colley
πŸ’» πŸ“– πŸ’‘ πŸ€” πŸš‡ 🚧 πŸ”§ ⚠️ πŸ› πŸ“¦ πŸ‘€		Matt Haberland
Matt Haberland
πŸ’» πŸ€” ⚠️		Jake Bowhay
Jake Bowhay
πŸ’» πŸ‘€ πŸ”§		 Aaron Meurer
Aaron Meurer
πŸ‘€ πŸ€” πŸš‡		 Tim Head
Tim Head
πŸ€”		 Athan
Athan
πŸ‘€ πŸ€”		 Carlos Ramos CarreΓ±o
Carlos Ramos CarreΓ±o
πŸ€”
Ralf Gommers
Ralf Gommers
πŸ€” πŸš‡ πŸ‘€		Guido Imperiale
Guido Imperiale
πŸ’» πŸ“– πŸ”§ ⚠️ πŸ’‘ πŸ› πŸ€” πŸ‘€ 🚧		 Joren Hammudoglu
Joren Hammudoglu
πŸ‘€		 Thomas J. Fan
Thomas J. Fan
πŸ€”		 Irwin Zaid
Irwin Zaid
πŸ€”		Omar Salman
Omar Salman
πŸ’»
Ralf Gommers
Ralf Gommers
πŸ€” πŸš‡ πŸ‘€		 Olivier Grisel
Olivier Grisel
πŸ’»		Evgeni Burovski
Evgeni Burovski
πŸ’» πŸ“–		Jake Vanderplas
Jake Vanderplas
πŸ‘€		kotlinisland
kotlinisland
πŸ‘€		detachhead
detachhead
πŸ‘€		Neil Girdhar
Neil Girdhar
πŸ’» ⚠️
Thomas Li
Thomas Li
πŸ› πŸ”§		Pearu Peterson
Pearu Peterson
πŸ‘€
diff --git a/README.md b/README.md index 35521b3f..cef223d3 100644 --- a/README.md +++ b/README.md @@ -4,27 +4,30 @@ [![docs - here!][docs-badge]][docs-link] [![Pixi Badge](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/prefix-dev/pixi/main/assets/badge/v0.json)](https://pixi.sh) [![All Contributors](https://img.shields.io/github/all-contributors/data-apis/array-api-extra?color=ee8449&style=flat-square)](#contributors) -[![Scientific Python Ecosystem Coordination](https://img.shields.io/badge/SPEC-0-green?labelColor=%23004811&color=%235CA038)](https://scientific-python.org/specs/) +[![Typing](https://img.shields.io/pypi/types/array-api-extra)](https://typing.python.org/) [![codecov](https://codecov.io/github/data-apis/array-api-extra/graph/badge.svg)](https://codecov.io/github/data-apis/array-api-extra) [![PyPI version][pypi-version]][pypi-link] [![Conda-Forge][conda-badge]][conda-link] [![PyPI platforms][pypi-platforms]][pypi-link] +[![EffVer Versioning](https://img.shields.io/badge/version_scheme-EffVer-0097a7)](https://jacobtomlinson.dev/effver) +[![SPEC 8 β€” Securing the Release Process](https://img.shields.io/badge/SPEC-8-green?labelColor=%23004811&color=%235CA038)](https://scientific-python.org/specs/spec-0008/) -[actions-badge]: https://github.com/data-apis/array-api-extra/workflows/CI/badge.svg -[actions-link]: https://github.com/data-apis/array-api-extra/actions -[conda-badge]: https://img.shields.io/conda/vn/conda-forge/array-api-extra -[conda-link]: https://github.com/conda-forge/array-api-extra-feedstock + +[actions-badge]: https://github.com/data-apis/array-api-extra/workflows/CI/badge.svg +[actions-link]: https://github.com/data-apis/array-api-extra/actions +[conda-badge]: https://img.shields.io/conda/vn/conda-forge/array-api-extra +[conda-link]: https://github.com/conda-forge/array-api-extra-feedstock [github-discussions-badge]: https://img.shields.io/static/v1?label=Discussions&message=Ask&color=blue&logo=github -[github-discussions-link]: https://github.com/data-apis/array-api-extra/discussions -[pypi-link]: https://pypi.org/project/array-api-extra/ -[pypi-platforms]: https://img.shields.io/pypi/pyversions/array-api-extra -[pypi-version]: https://img.shields.io/pypi/v/array-api-extra -[docs-badge]: https://img.shields.io/badge/docs-here!-2ea44f -[docs-link]: https://data-apis.org/array-api-extra/index.html +[github-discussions-link]: https://github.com/data-apis/array-api-extra/discussions +[pypi-link]: https://pypi.org/project/array-api-extra/ +[pypi-platforms]: https://img.shields.io/pypi/pyversions/array-api-extra +[pypi-version]: https://img.shields.io/pypi/v/array-api-extra +[docs-badge]: https://img.shields.io/badge/docs-here!-2ea44f +[docs-link]: https://data-apis.org/array-api-extra/index.html @@ -32,9 +35,24 @@ Extra array functions built on top of the array API standard. Used by: -- [SciPy](https://github.com/scipy/scipy) - Fundamental algorithms for +- [SciPy](https://github.com/scipy/scipy) β€” Fundamental algorithms for scientific computing. -- ... +- [scikit-learn](https://github.com/scikit-learn/scikit-learn)Β β€” Machine + Learning in Python. +- *your library? Let us know!* + +## Installation + +See https://data-apis.org/array-api-extra/#installation. + +## Versioning + +This project uses [EffVer](https://jacobtomlinson.dev/effver/). TL;DR: + +- To bump from version `x.y.z` to `x.(y+1).0` of array-api-extra, expect that + you may need to put in some work. +- To bump from version `x.y.z` to `x.y.(z+1)`, any work needed on your side + should be minimal. Please open an issue if this is not the case! ## Contributors @@ -47,16 +65,16 @@ This project exists thanks to the following contributors - - - + + + - + @@ -65,7 +83,17 @@ This project exists thanks to the following contributors + + + + + + + + + +
Lucas Colley
Lucas Colley
πŸ’» πŸ“– πŸ’‘ πŸ€” πŸš‡ 🚧 πŸ”§ ⚠️ πŸ› πŸ“¦		Matt Haberland
Matt Haberland
πŸ’» πŸ€”		Jake Bowhay
Jake Bowhay
πŸ’» πŸ‘€		Lucas Colley
Lucas Colley
πŸ’» πŸ“– πŸ’‘ πŸ€” πŸš‡ 🚧 πŸ”§ ⚠️ πŸ› πŸ“¦ πŸ‘€		Matt Haberland
Matt Haberland
πŸ’» πŸ€” ⚠️		Jake Bowhay
Jake Bowhay
πŸ’» πŸ‘€ πŸ”§		 Aaron Meurer
Aaron Meurer
πŸ‘€ πŸ€” πŸš‡		 Tim Head
Tim Head
πŸ€”		 Athan
Athan
πŸ‘€ πŸ€”		 Carlos Ramos CarreΓ±o
Carlos Ramos CarreΓ±o
πŸ€”
Ralf Gommers
Ralf Gommers
πŸ€” πŸš‡ πŸ‘€		Guido Imperiale
Guido Imperiale
πŸ’» πŸ“– πŸ”§ ⚠️ πŸ’‘ πŸ› πŸ€” πŸ‘€ 🚧		 Joren Hammudoglu
Joren Hammudoglu
πŸ‘€		 Thomas J. Fan
Thomas J. Fan
πŸ€”		 Irwin Zaid
Irwin Zaid
πŸ€”		Omar Salman
Omar Salman
πŸ’»
Ralf Gommers
Ralf Gommers
πŸ€” πŸš‡ πŸ‘€		 Olivier Grisel
Olivier Grisel
πŸ’»		Evgeni Burovski
Evgeni Burovski
πŸ’» πŸ“–		Jake Vanderplas
Jake Vanderplas
πŸ‘€		kotlinisland
kotlinisland
πŸ‘€		detachhead
detachhead
πŸ‘€		Neil Girdhar
Neil Girdhar
πŸ’» ⚠️
Thomas Li
Thomas Li
πŸ› πŸ”§		Pearu Peterson
Pearu Peterson
πŸ‘€
diff --git a/RELEASING.md b/RELEASING.md new file mode 100644 index 00000000..564fc682 --- /dev/null +++ b/RELEASING.md @@ -0,0 +1,8 @@ +1. Update the version in `src/array_api_extra/__init__.py` +2. Update the lockfile with `pixi update && pixi install` +3. Push your changes +4. Cut a release via the GitHub GUI +5. Update the version to `{next micro version}.dev0` +6. `pixi clean cache --pypi && pixi update && pixi install` +7. Push your changes +8. Merge the automated PR to conda-forge/array-api-extra-feedstock diff --git a/codecov.yml b/codecov.yml index 1a517b3a..dc9b47cd 100644 --- a/codecov.yml +++ b/codecov.yml @@ -1,4 +1,9 @@ comment: false +github_checks: + annotations: false ignore: - "src/array_api_extra/_lib/_compat" - "src/array_api_extra/_lib/_typing" +coverage: + status: + project: off diff --git a/docs/api-lazy.md b/docs/api-lazy.md new file mode 100644 index 00000000..abea5cbb --- /dev/null +++ b/docs/api-lazy.md @@ -0,0 +1,15 @@ +# Tools for lazy backends + +These additional functions are meant to be used to support compatibility with +lazy backends, e.g. Dask or JAX: + +```{eval-rst} +.. currentmodule:: array_api_extra +.. autosummary:: + :nosignatures: + :toctree: generated + + lazy_apply + testing.lazy_xp_function + testing.patch_lazy_xp_functions +``` diff --git a/docs/api-reference.md b/docs/api-reference.md index ffe68f24..38d0d26e 100644 --- a/docs/api-reference.md +++ b/docs/api-reference.md @@ -6,11 +6,19 @@ :nosignatures: :toctree: generated + apply_where + at atleast_nd + broadcast_shapes cov create_diagonal + default_dtype expand_dims + isclose kron + nunique + one_hot + pad setdiff1d sinc ``` diff --git a/docs/conf.py b/docs/conf.py index 5b2fdf05..e2cab35b 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -1,9 +1,11 @@ -from __future__ import annotations +"""Sphinx config.""" import importlib.metadata from typing import Any project = "array-api-extra" +copyright = "Consortium for Python Data API Standards" +author = "Consortium for Python Data API Standards" version = release = importlib.metadata.version("array_api_extra") extensions = [ @@ -38,7 +40,7 @@ - """, + """, # noqa: E501 "class": "", }, ], @@ -53,6 +55,10 @@ intersphinx_mapping = { "python": ("https://docs.python.org/3", None), + "array-api": ("https://data-apis.org/array-api/draft", None), + "dask": ("https://docs.dask.org/en/stable", None), + "numpy": ("https://numpy.org/doc/stable", None), + "jax": ("https://jax.readthedocs.io/en/latest", None), } nitpick_ignore = [ diff --git a/docs/contributing.md b/docs/contributing.md index b1f8b90d..bfcfd0d0 100644 --- a/docs/contributing.md +++ b/docs/contributing.md @@ -6,11 +6,14 @@ converting code to consume the standard. Thanks to [all contributors](contributors.md) so far! -## How to contribute a function +## How to contribute a new function - [Open an issue](https://github.com/data-apis/array-api-extra/issues/new) to - propose the new function. -- Add the implementation of your function to `src/array_api_extra/_funcs.py`. + propose the new function. You may want to wait for initial feedback on the + issue before diving into an implementation. Feel free to skip this step if + there is already an open issue for the function. +- Add the implementation of your function to + `src/array_api_extra/_lib/_funcs.py`. - Ensure that your function includes type annotations and a [numpydoc-style docstring](https://numpydoc.readthedocs.io/en/latest/format.html). - Add your function to `__all__` at the top of the file. @@ -20,6 +23,33 @@ Thanks to [all contributors](contributors.md) so far! - Add your function to `docs/api-reference.md`. - [Make a PR!](https://github.com/data-apis/array-api-extra/pulls) +## How to add delegation to a function + +See [the tracker for adding delegation][delegation-tracker]. + +[delegation-tracker]: https://github.com/data-apis/array-api-extra/issues/100 + +- If you would like to discuss the task before diving into the implementation, + click on the three dots next to the function on the tracker issue, and choose + "Convert to sub-issue". +- Create a function in `src/array_api_extra/_delegation.py` with a signature + matching the function in `src/array_api_extra/_lib/_funcs.py`, and move the + docstring to the new function. Leave a one-line docstring in `_funcs.py`, + pointing to `_delegation.py` to see the full docstring. +- Also move the initial `array_namespace` call and any input validation over to + the new function. +- Add delegation to backends using the `if _delegate` pattern. See + `src/array_api_extra/_lib/_backends.py` for the full list of backends we have + worked with so far. +- After all delegation layers, return the result from the implementation in + `_funcs`. +- Simplify the signature in `_funcs.py` to remove impossible arguments now that + it is only called internally via `_delegation`. For example, the `xp` + parameter can be changed from type `ModuleType | None` to `ModuleType`. +- Don't worry if you are not sure how to do some of the above steps or think you + might have done something wrong - + [make a PR!](https://github.com/data-apis/array-api-extra/pulls) + ## Development workflow If you are an experienced contributor to Python packages, feel free to develop @@ -31,7 +61,7 @@ development of array-api-extra is made easy with at . - `cd array-api-extra`. - [Install Pixi](https://pixi.sh/latest/#installation). -- To enter a development environment: +- To enter a development environment (if you prefer this to the `pixi run` interface): ``` pixi shell -e dev @@ -49,22 +79,28 @@ pixi run tests pixi run docs ``` -- To open and preview the locally-built docs: +- To build and preview the docs locally: ``` pixi run open-docs ``` -- To install a [pre-commit](https://pre-commit.com) hook: +- To install pre-commit hooks: + +``` +pixi run hooks +``` + +- To run pre-commit checks on staged files: ``` -pixi run pre-commit-install +pixi run pre-commit ``` -- To run the lint suite: +- To run the full lint suite: ``` -pixi run lint +pixi run --environment=lint lint ``` - To enter an interactive Python prompt: @@ -73,20 +109,39 @@ pixi run lint pixi run ipython ``` -- To run individual parts of the lint suite separately: +- To run individual parts of the lint suite separately (for example): ``` -pixi run pre-commit -pixi run pylint -pixi run mypy -pixi run pyright +pixi run --environment=lint pyright +pixi run --environment=lint dprint +``` + +- To generate the coverage report: + +``` +pixi run coverage +``` + +- To generate and display the coverage report: + +``` +pixi run open-coverage ``` Alternative environments are available with a subset of the dependencies and tasks available in the `dev` environment: ``` -pixi shell -e docs -pixi shell -e tests -pixi shell -e lint +pixi shell --environment=docs +pixi shell --environment=tests +pixi shell --environment=tests-backends +pixi shell --environment=lint +``` + +If you run on a host with CUDA hardware, you can enable extra tests: + +``` +pixi shell --environment=dev-cuda +pixi shell --environment=tests-cuda +pixi run --environment=tests-cuda tests ``` diff --git a/docs/contributors.md b/docs/contributors.md index 724cdb77..c8e001d3 100644 --- a/docs/contributors.md +++ b/docs/contributors.md @@ -1,3 +1,2 @@ ```{include} ../CONTRIBUTORS.md - ``` diff --git a/docs/index.md b/docs/index.md index 11c29162..5168e973 100644 --- a/docs/index.md +++ b/docs/index.md @@ -5,25 +5,29 @@ :hidden: self api-reference.md +api-lazy.md contributing.md contributors.md ``` This is a library housing "array-agnostic" implementations of functions built on -top of [the Python array API standard](https://data-apis.org/array-api/). +top of [the Python array API standard](https://data-apis.org/array-api/), as +well as delegation to existing implementations for known array library backends. The intended users of this library are "array-consuming" libraries which are using [array-api-compat](https://data-apis.org/array-api-compat/) to make their own library's functions array-agnostic. In this library, they will find a set of -tools which provide _extra_ functionality on top of the array API standard, +tools which provide *extra* functionality on top of the array API standard, which other array-consuming libraries in a similar position have found useful themselves. It is currently used by: -- [SciPy](https://github.com/scipy/scipy) - Fundamental algorithms for +- [SciPy](https://github.com/scipy/scipy) β€” Fundamental algorithms for scientific computing. -- ... +- [scikit-learn](https://github.com/scikit-learn/scikit-learn) β€” Machine + Learning in Python. +- *your library? Let us know!* (installation)= @@ -33,6 +37,8 @@ It is currently used by: [on PyPI](https://pypi.org/project/array-api-extra/): ```shell +uv add array-api-extra +# or python -m pip install array-api-extra ``` @@ -40,9 +46,9 @@ And [on conda-forge](https://prefix.dev/channels/conda-forge/packages/array-api-extra): ```shell -micromamba install -c https://repo.prefix.dev/conda-forge array-api-extra -# or pixi add array-api-extra +# or +mamba install array-api-extra ``` ```{warning} @@ -51,18 +57,74 @@ If you require stability, it is recommended to pin `array-api-extra` to a specific version, or vendor the library inside your own. ``` +```{note} +This library depends on `array-api-compat`. We aim for compatibility with +the latest released version of array-api-compat, and your mileage may vary +with older or dev versions. +``` + (vendoring)= ## Vendoring To vendor the library, clone -[the repository](https://github.com/data-apis/array-api-extra) and copy it into -the appropriate place in your library, like: +[the array-api-extra repository](https://github.com/data-apis/array-api-extra) +and copy it into the appropriate place in your library, like: + +``` +cp -a array-api-extra/src/array_api_extra mylib/vendored/ +``` + +You may either add a dependency to array-api-compat in your own project, or +vendor it too: + +1. Clone + [the array-api-compat repository](https://github.com/data-apis/array-api-compat) + and copy it next to your vendored array-api-extra: + + ``` + cp -a array-api-compat/array_api_compat mylib/vendored/ + ``` + +2. Create a new hook file which array-api-extra will use instead of the + top-level array-api-compat if present: + + ``` + echo 'from mylib.vendored.array_api_compat import *' > mylib/vendored/_array_api_compat_vendor.py + ``` +This also allows overriding array-api-compat functions if you so wish. E.g. your +`mylib/vendored/_array_api_compat_vendor.py` could look like this: + +```python +from mylib.vendored.array_api_compat import * +from mylib.vendored.array_api_compat import array_namespace as _array_namespace_orig + + +def array_namespace(*xs, **kwargs): + import mylib + + if any(isinstance(x, mylib.MyArray) for x in xs): + return mylib + else: + return _array_namespace_orig(*xs, **kwargs) ``` -cp -R array-api-extra/ mylib/vendored/array_api_extra + +```{tip} +See [an example of this in SciPy][scipy-vendor-example]. ``` +[scipy-vendor-example]: https://github.com/scipy/scipy/blob/main/scipy/_lib/_array_api_compat_vendor.py + +## Versioning + +This project uses [EffVer](https://jacobtomlinson.dev/effver/). TL;DR: + +- To bump from version `x.y.z` to `x.(y+1).0` of array-api-extra, expect that + you may need to put in some work. +- To bump from version `x.y.z` to `x.y.(z+1)`, any work needed on your side + should be minimal. Please open an issue if this is not the case! + (usage)= ## Usage @@ -74,23 +136,29 @@ import array_api_extra as xpx ... xp = array_namespace(x) -y = xp.sum(x) +y = xp.sum(x) # use functions from `xp` as normal ... -return xpx.atleast_nd(y, ndim=2, xp=xp) +return xpx.atleast_nd(y, ndim=2, xp=xp) # use functions from `xpx`, passing `xp=xp` ``` ```{note} Functions in this library assume input arrays *are arrays* (not "array-likes") and that -the namespace passed as `xp` is compatible with the standard. This means that -the namespace you pass as `xp` should come from array-api-compat's ``array_namespace``, +the namespace passed as `xp` (if given) is compatible with the standard - +this means that it should come from array-api-compat's `array_namespace`, or otherwise be compatible with the standard. + +Calling functions without providing an `xp` argument means that `array_namespace` +is called internally to determine the namespace. ``` In the examples shown in the docstrings of functions from this library, -[`array-api-strict`](https://data-apis.org/array-api-strict/) is used as the -array namespace `xp`. In reality, code using this library will be written to -work with any compatible array namespace as `xp`, not any particular -implementation. +[array-api-strict](https://data-apis.org/array-api-strict/) is used as the array +namespace `xp`. In reality, code using this library will be written to work with +any compatible array namespace as `xp`, not any particular implementation. + +Some functions may only work with specific array libraries supported by +array-api-compat. This should be clearly indicated in the docs - please open an +issue if this is not the case! (scope)= @@ -102,35 +170,35 @@ Functions that are in-scope for this library will: standard. - Implement functionality which may be generally useful across various libraries. -- Be implemented purely in terms of the array API standard. - Be implemented with type annotations and [numpydoc-style docstrings](https://numpydoc.readthedocs.io/en/latest/format.html). -- Be tested against `array-api-strict`. +- Be tested against array-api-strict and various existing backends. + +Functions are implemented purely in terms of the array API standard where +possible. Where functions must use library-specific helpers for libraries +supported by array-api-compat, this will be clearly marked in their API +reference page. + +Delegation is added for some functions to use native implementations for the +given array type, instead of the array-agnostic implementations, as this may +increase performance. In particular, the following kinds of function are also in-scope: - Functions which implement - [array API standard extension](https://data-apis.org/array-api/2023.12/extensions/index.html) + [array API standard extension](https://data-apis.org/array-api/latest/extensions/index.html) functions in terms of functions from the base standard. - Functions which add functionality (e.g. extra parameters) to functions from the standard. The following features are currently out-of-scope for this library: -- Delegation to known, existing array libraries. - - It is quite simple to wrap functions in this library to also use existing - implementations where possible. Such delegation will not live in this - library for now, but the array-agnostic functions in this library could form - an array-agnostic backend for such delegating functions in the future, here - or elsewhere. - Functions which accept "array-like" input, or standard-incompatible namespaces. - It is possible to prepare input arrays and a standard-compatible namespace - via `array-api-compat` downstream in consumer libraries. Avoiding use of - `array-api-compat` in this library makes it easier to vendor and reduces - potential redundant calls to `xp.asarray` and `array_namespace`. - - For proposed alternatives to the `xp=xp` interface, see - [this issue](https://github.com/data-apis/array-api-extra/issues/6). + via array-api-compat downstream in consumer libraries. The `xp` argument can + also be omitted to infer the standard-compatible namespace using + `array_namespace` internally. - Functions which are specific to a particular domain. - These functions may belong better in an array-consuming library which is specific to that domain. diff --git a/docs/requirements.txt b/docs/requirements.txt deleted file mode 100644 index 77237968..00000000 --- a/docs/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -furo>=2023.08.17 -myst_parser>=0.13 -sphinx>=7.0 -sphinx_autodoc_typehints -sphinx_copybutton diff --git a/lefthook.yml b/lefthook.yml new file mode 100644 index 00000000..4797a99e --- /dev/null +++ b/lefthook.yml @@ -0,0 +1,43 @@ +lefthook: pixi run --environment=lint lefthook + +templates: + run: run --environment=lint + +pre-commit: + parallel: true + jobs: + - name: dprint + glob: "*.{json,jsonc,md,toml,yaml,yml}" + stage_fixed: true + run: pixi {run} dprint + - name: ruff + glob: "*.{py,pyi}" + stage_fixed: true + group: + piped: true + jobs: + - name: ruff check + run: pixi {run} ruff-check {staged_files} + - name: ruff format + run: pixi {run} ruff-format {staged_files} + - name: pyright + glob: "*.{py,pyi}" + run: pixi {run} pyright + - name: mypy + glob: "*.{py,pyi}" + run: pixi {run} mypy + - name: typos + stage_fixed: true + run: pixi {run} typos + - name: actionlint + run: pixi {run} actionlint + - name: blacken-docs + glob: "*.md" + stage_fixed: true + run: pixi {run} blacken-docs {staged_files} + - name: validate-pyproject + glob: "pyproject.toml" + run: pixi {run} validate-pyproject + - name: numpydoc + glob: "*.py" + run: pixi {run} numpydoc {staged_files} diff --git a/pixi.lock b/pixi.lock index e37d0d0d..78fde7bb 100644 --- a/pixi.lock +++ b/pixi.lock @@ -1,6 +1,6 @@ -version: 5 +version: 6 environments: - ci-py310: + default: channels: - url: https://prefix.dev/conda-forge/ indexes: @@ -9,254 +9,1488 @@ environments: linux-64: - conda: https://prefix.dev/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 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6432cb5d4ac0046c3ac0a8a0f95842f9 depends: - - __osx >=11.0 - - libzlib >=1.2.13,<2.0.0a0 - arch: arm64 - platform: osx + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + - libstdcxx >=13 + - libzlib >=1.3.1,<2.0a0 license: BSD-3-Clause license_family: BSD purls: [] - size: 405089 - timestamp: 1714723101397 + size: 567578 + timestamp: 1742433379869 diff --git a/pyproject.toml b/pyproject.toml index abe1a87d..69c080e4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,116 +5,136 @@ build-backend = "hatchling.build" [project] name = "array-api-extra" authors = [ - { name = "Lucas Colley", email = "lucas.colley8@gmail.com" }, - { name = "Open Source Contributors" }, + { name = "Lucas Colley", email = "lucas.colley8@gmail.com" }, + # { name = "Open Source Contributors" }, # https://github.com/pypi/warehouse/issues/14813 ] description = "Extra array functions built on top of the array API standard." readme = "README.md" license.file = "LICENSE" requires-python = ">=3.10" classifiers = [ - "Intended Audience :: Developers", - "License :: OSI Approved :: MIT License", - "Operating System :: OS Independent", - "Programming Language :: Python", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3 :: Only", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3.13", - "Typing :: Typed", + "Intended Audience :: Developers", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: Free Threading :: 3 - Stable", + "Typing :: Typed", ] dynamic = ["version"] -dependencies = ["typing-extensions"] - -[project.optional-dependencies] -tests = [ - "pytest >=6", - "pytest-cov >=3", - "array-api-strict", - "numpy", -] -docs = [ - "sphinx>=7.0", - "myst_parser>=0.13", - "sphinx_copybutton", - "sphinx_autodoc_typehints", - "furo>=2023.08.17", -] +dependencies = ["array-api-compat>=1.12.0,<2"] [project.urls] Homepage = "https://github.com/data-apis/array-api-extra" "Bug Tracker" = "https://github.com/data-apis/array-api-extra/issues" -Discussions = "https://github.com/data-apis/array-api-extra/discussions" Changelog = "https://github.com/data-apis/array-api-extra/releases" - # Hatch [tool.hatch] version.path = "src/array_api_extra/__init__.py" +[tool.hatch.build.targets.sdist] +exclude = ["codecov.yml", "pixi.lock", "RELEASING.md", "renovate.json"] -#Β Pixi +# Β Pixi [tool.pixi.project] channels = ["https://prefix.dev/conda-forge"] -platforms = ["linux-64", "osx-arm64", "win-64"] +platforms = ["linux-64", "osx-64", "osx-arm64", "win-64"] [tool.pixi.dependencies] -python = ">=3.10.15,<3.14" -typing_extensions = ">=4.12.2,<4.13" +array-api-compat = ">=1.12.0,<2" [tool.pixi.pypi-dependencies] array-api-extra = { path = ".", editable = true } [tool.pixi.feature.lint.dependencies] -pre-commit = "*" -pylint = "*" -basedmypy = "*" -basedpyright = "*" +typing-extensions = ">=4.14.0" +pylint = ">=3.3.7" +mypy = ">=1.16.1" +basedpyright = ">=1.29.5" +numpydoc = ">=1.9.0,<2" # import dependencies for mypy: -array-api-strict = "*" -numpy = "*" -pytest = "*" +array-api-strict = ">=2.3.1,<2.4" +numpy = ">=2.1.3" +hypothesis = ">=6.135.16" +dask-core = ">=2025.5.1" # No distributed, tornado, etc. +dprint = ">=0.50.0,<0.51" +lefthook = ">=1.11.14,<2" +ruff = ">=0.12.1,<0.13" +typos = ">=1.34.0,<2" +actionlint = ">=1.7.7,<2" +blacken-docs = ">=1.19.1,<2" +pytest = ">=8.4.1,<9" +validate-pyproject = ">=0.24.1,<0.25" +# NOTE: don't add cupy, jax, pytorch, or sparse here, +# as they slow down mypy and are not portable across target OSs [tool.pixi.feature.lint.tasks] -pre-commit-install = { cmd = "pre-commit install" } -pre-commit = { cmd = "pre-commit run -v --all-files --show-diff-on-failure" } -mypy = { cmd = "mypy", cwd = "." } -pylint = { cmd = ["pylint", "array_api_extra"], cwd = "src" } -pyright = { cmd = "basedpyright", cwd = "." } -lint = { depends-on = ["pre-commit", "pylint", "mypy", "pyright"] } +lefthook = { cmd = "lefthook", description = "Run lefthook" } +hooks = { cmd = "lefthook install", description = "Install pre-commit hooks" } +pre-commit = { cmd = "lefthook run pre-commit", description = "Run pre-commit checks" } +pylint = { cmd = "pylint array_api_extra", cwd = "src", description = "Lint with pylint" } +mypy = { cmd = "mypy", description = "Type check with mypy" } +pyright = { cmd = "basedpyright", description = "Type check with basedpyright" } +ruff-check = { cmd = "ruff check --fix", description = "Lint with ruff" } +ruff-format = { cmd = "ruff format", description = "Format with ruff" } +dprint = { cmd = "dprint fmt", description = "Format with dprint" } +typos = { cmd = "typos --write-changes --force-exclude", description = "Fix typos" } +actionlint = { cmd = "actionlint", description = "Lint actions with actionlint" } +blacken-docs = { cmd = "blacken-docs", description = "Format Python markdown blocks with Black" } +validate-pyproject = { cmd = "validate-pyproject pyproject.toml", description = "Validate pyproject.toml" } +numpydoc = { cmd = "numpydoc lint", description = "Validate docstrings with numpydoc" } +lint = { cmd = "lefthook run pre-commit --all-files --force", description = "Run all linters" } [tool.pixi.feature.tests.dependencies] -pytest = ">=6" -pytest-cov = ">=3" -array-api-strict = "*" -numpy = "*" +pytest = ">=8.4.1" +pytest-cov = ">=6.2.1" +hypothesis = ">=6.135.16" +array-api-strict = ">=2.3.1,<2.4" +numpy = ">=1.22.0" [tool.pixi.feature.tests.tasks] -tests = { cmd = "pytest" } -tests-ci = { cmd = "pytest -ra --cov --cov-report=xml --cov-report=term --durations=20" } +tests = { cmd = "pytest -v", description = "Run tests" } +tests-cov = { cmd = "pytest -v -ra --cov --cov-report=xml --cov-report=term --durations=20", description = "Run tests with coverage" } -[tool.pixi.feature.docs.dependencies] -sphinx = ">=7.0" -furo = ">=2023.08.17" +clean-vendor-compat = { cmd = "rm -rf vendor_tests/array_api_compat", description = "Delete the existing vendored version of array-api-compat" } +clean-vendor-extra = { cmd = "rm -rf vendor_tests/array_api_extra", description = "Delete the existing vendored version of array-api-extra" } +copy-vendor-compat = { cmd = "cp -r $(python -c 'import site; print(site.getsitepackages()[0])')/array_api_compat vendor_tests/", depends-on = ["clean-vendor-compat"], description = "Vendor a clean copy of array-api-compat" } +copy-vendor-extra = { cmd = "cp -r src/array_api_extra vendor_tests/", depends-on = ["clean-vendor-extra"], description = "Vendor a clean copy of array-api-extra" } +tests-vendor = { cmd = "pytest -v vendor_tests", depends-on = ["copy-vendor-compat", "copy-vendor-extra"], description = "Check that array-api-extra and array-api-compat can be vendored together" } + +tests-ci = { depends-on = ["tests-cov", "tests-vendor"], description = "Run tests with coverage and vendor tests" } +coverage = { cmd = "coverage html", depends-on = ["tests-cov"], description = "Generate test coverage html report" } +open-coverage = { cmd = "open htmlcov/index.html", depends-on = ["coverage"], description = "Open test coverage report" } -[tool.pixi.feature.docs.pypi-dependencies] -myst_parser = ">=0.13" -sphinx_copybutton = "*" -sphinx_autodoc_typehints = "*" +[tool.pixi.feature.docs.dependencies] +sphinx = ">=7.4.7" +furo = ">=2024.8.6" +myst-parser = ">=4.0.1" +sphinx-copybutton = ">=0.5.2" +sphinx-autodoc-typehints = ">=1.25.3" +# Needed to import parsed modules with autodoc +dask-core = ">=2025.5.1" # No distributed, tornado, etc. +pytest = ">=8.4.1" +typing-extensions = ">=4.14.0" +numpy = ">=2.1.3" [tool.pixi.feature.docs.tasks] -docs = { cmd = ["sphinx-build", ".", "build/"], cwd = "docs" } -open-docs = { cmd = ["open", "build/index.html"], cwd = "docs" } +docs = { cmd = "sphinx-build -E -W . build/", cwd = "docs", description = "Build docs" } +open-docs = { cmd = "open build/index.html", cwd = "docs", depends-on = ["docs"], description = "Open the generated docs" } [tool.pixi.feature.dev.dependencies] -ipython = "*" +ipython = ">=7.33.0" [tool.pixi.feature.dev.tasks] -ipython = { cmd = "ipython" } +ipython = { cmd = "ipython", description = "Launch ipython" } [tool.pixi.feature.py310.dependencies] python = "~=3.10.0" @@ -122,15 +142,87 @@ python = "~=3.10.0" [tool.pixi.feature.py313.dependencies] python = "~=3.13.0" +[tool.pixi.feature.numpy1.dependencies] +# Oldest NumPy version supported by scikit-learn. +# Note that this is older than what SPEC0 recommends. +numpy = "=1.22.0" + +# Backends that can run on CPU-only hosts +# Note: JAX and PyTorch will install CPU variants. +[tool.pixi.feature.backends.dependencies] +pytorch = ">=2.7.0" +dask-core = ">=2025.5.1" # No distributed, tornado, etc. +sparse = ">=0.17.0" + +[tool.pixi.feature.backends.target.linux-64.dependencies] +jax = ">=0.6.0" + +[tool.pixi.feature.backends.target.osx-64.dependencies] +jax = ">=0.6.0" + +[tool.pixi.feature.backends.target.osx-arm64.dependencies] +jax = ">=0.6.0" + +[tool.pixi.feature.backends.target.win-64.dependencies] +# jax = "*" # unavailable + +# Backends that require a GPU host and a CUDA driver. +# Note that JAX and PyTorch automatically prefer CUDA variants +# thanks to the `system-requirements` below, *if available*. +# We request them explicitly below to ensure that we don't +# quietly revert to CPU-only in the future, e.g. when CUDA 13 +# is released and CUDA 12 builds are dropped upstream. +[tool.pixi.feature.cuda-backends] +system-requirements = { cuda = "12" } + +[tool.pixi.feature.cuda-backends.target.linux-64.dependencies] +cupy = ">=13.4.1" +jaxlib = { version = ">=0.6.0", build = "cuda12*" } +pytorch = { version = ">=2.7.0", build = "cuda12*" } + +[tool.pixi.feature.cuda-backends.target.osx-64.dependencies] +# cupy = "*" # unavailable +# jaxlib = { version = "*", build = "cuda12*" } # unavailable +# pytorch = { version = "*", build = "cuda12*" } # unavailable + +[tool.pixi.feature.cuda-backends.target.osx-arm64.dependencies] +# cupy = "*" # unavailable +# jaxlib = { version = "*", build = "cuda12*" } # unavailable +# pytorch = { version = "*", build = "cuda12*" } # unavailable + +[tool.pixi.feature.cuda-backends.target.win-64.dependencies] +cupy = ">=13.4.1" +# jaxlib = { version = "*", build = "cuda12*" } # unavailable +pytorch = { version = ">=2.7.0", build = "cuda12*" } + +[tool.pixi.feature.nogil.dependencies] +python-freethreading = "~=3.13.0" +pytest-run-parallel = ">=0.4.4" +numpy = ">=2.3.1" +# pytorch = "*" # Not available on Python 3.13t yet +dask-core = ">=2025.5.1" # No distributed, tornado, etc. +# sparse = "*" # numba not available on Python 3.13t yet +# jax = "*" # ml_dtypes not available on Python 3.13t yet + [tool.pixi.environments] -default = { solve-group = "default" } -lint = { features = ["lint"], solve-group = "default" } -tests = { features = ["tests"], solve-group = "default" } -docs = { features = ["docs"], solve-group = "default" } -dev = { features = ["lint", "tests", "docs", "dev"], solve-group = "default" } -ci-py310 = ["py310", "tests"] -ci-py313 = ["py313", "tests"] +default = { features = ["py313"], solve-group = "py313" } +lint = { features = ["py313", "lint"], solve-group = "py313" } +docs = { features = ["py313", "docs"], solve-group = "py313" } +tests = { features = ["py313", "tests"], solve-group = "py313" } +tests-py313 = { features = ["py313", "tests"], solve-group = "py313" } # alias of tests + +# Some backends may pin numpy; use separate solve-group +dev = { features = ["py310", "lint", "tests", "docs", "dev", "backends"], solve-group = "backends" } +tests-backends = { features = ["py310", "tests", "backends"], solve-group = "backends" } + +# CUDA not available on free github actions and on some developers' PCs +dev-cuda = { features = ["py310", "lint", "tests", "docs", "dev", "backends", "cuda-backends"], solve-group = "cuda" } +tests-cuda = { features = ["py310", "tests", "backends", "cuda-backends"], solve-group = "cuda" } +# Ungrouped environments +tests-numpy1 = ["py310", "tests", "numpy1"] +tests-py310 = ["py310", "tests"] +tests-nogil = ["nogil", "tests"] # pytest @@ -138,24 +230,18 @@ ci-py313 = ["py313", "tests"] minversion = "6.0" addopts = ["-ra", "--showlocals", "--strict-markers", "--strict-config"] xfail_strict = true -filterwarnings = [ - "error", -] +filterwarnings = ["error"] log_cli_level = "INFO" -testpaths = [ - "tests", +testpaths = ["tests"] +markers = [ + "skip_xp_backend(library, /, *, reason=None): Skip test for a specific backend", + "xfail_xp_backend(library, /, *, reason=None, strict=None): Xfail test for a specific backend", ] - # Coverage [tool.coverage] run.source = ["array_api_extra"] -report.exclude_also = [ - '\.\.\.', - 'if typing.TYPE_CHECKING:', -] - # mypy @@ -164,18 +250,17 @@ files = ["src", "tests"] python_version = "3.10" warn_unused_configs = true strict = true -enable_error_code = ["ignore-without-code", "redundant-expr", "truthy-bool"] -warn_unreachable = true -disallow_untyped_defs = false -disallow_incomplete_defs = false -# data-apis/array-api#589 -disallow_any_expr = false +enable_error_code = ["ignore-without-code", "truthy-bool"] +disable_error_code = ["no-any-return"] [[tool.mypy.overrides]] -module = "array_api_extra.*" -disallow_untyped_defs = true -disallow_incomplete_defs = true +# slow or unavailable on Windows; do not add to the lint env +module = ["cupy.*", "jax.*", "sparse.*", "torch.*"] +ignore_missing_imports = true +[[tool.mypy.overrides]] +module = ["tests/*"] +disable_error_code = ["no-untyped-def"] # test(...) without -> None # pyright @@ -185,65 +270,99 @@ pythonVersion = "3.10" pythonPlatform = "All" typeCheckingMode = "all" -# data-apis/array-api#589 +# https://github.com/data-apis/array-api-typing reportAny = false reportExplicitAny = false -# data-apis/array-api-strict#6 +# no array-api-strict type stubs; pytest fixtures reportUnknownMemberType = false - +# no array-api-compat type stubs; pytest fixtures +reportUnknownVariableType = false +# Redundant with mypy checks +reportMissingImports = false +reportMissingTypeStubs = false +# false positives for input validation +reportUnreachable = false +# ruff handles this +reportUnusedParameter = false +# cyclic imports inside function bodies +reportImportCycles = false +# PyRight can't trace types in lambdas +reportUnknownLambdaType = false + +executionEnvironments = [ + { root = "tests", reportPrivateUsage = false, reportUnknownArgumentType = false }, + { root = "src" }, +] # Ruff [tool.ruff] -target-version = "py39" +target-version = "py310" [tool.ruff.lint] extend-select = [ - "B", # flake8-bugbear - "I", # isort - "ARG", # flake8-unused-arguments - "C4", # flake8-comprehensions - "EM", # flake8-errmsg - "ICN", # flake8-import-conventions - "G", # flake8-logging-format - "PGH", # pygrep-hooks - "PIE", # flake8-pie - "PL", # pylint - "PT", # flake8-pytest-style - "PTH", # flake8-use-pathlib - "RET", # flake8-return - "RUF", # Ruff-specific - "SIM", # flake8-simplify - "T20", # flake8-print - "UP", # pyupgrade - "YTT", # flake8-2020 - "EXE", # flake8-executable - "NPY", # NumPy specific rules - "PD", # pandas-vet + "B", # flake8-bugbear + "F", # Pyflakes + "I", # isort + "E", # Pycodestyle + "W", # Pycodestyle + "N", # pep8-naming + "ARG", # flake8-unused-arguments + "C4", # flake8-comprehensions + "EM", # flake8-errmsg + "ICN", # flake8-import-conventions + "ISC", # flake8-implicit-str-concat + "G", # flake8-logging-format + "PGH", # pygrep-hooks + "PIE", # flake8-pie + "PL", # pylint + "PT", # flake8-pytest-style + "PTH", # flake8-use-pathlib + "RET", # flake8-return + "RUF", # Ruff-specific + "SIM", # flake8-simplify + "T20", # flake8-print + "UP", # pyupgrade + "YTT", # flake8-2020 + "EXE", # flake8-executable + "NPY", # NumPy specific rules + "PD", # pandas-vet + "UP", # Pyupgrade ] ignore = [ - "PLR09", # Too many <...> - "PLR2004", # Magic value used in comparison - "ISC001", # Conflicts with formatter + "PLR09", # Too many <...> + "PLR2004", # Magic value used in comparison + "ISC001", # Conflicts with formatter + "N801", # Class name should use CapWords convention + "N802", # Function name should be lowercase + "N806", # Variable in function should be lowercase + "PLC0415", # `import` should be at the top-level of a file ] -isort.required-imports = ["from __future__ import annotations"] - -[tool.ruff.lint.per-file-ignores] -"tests/**" = ["T20"] - # Pylint [tool.pylint] py-version = "3.10" -ignore-paths = [".*/_version.py"] reports.output-format = "colorized" -similarities.ignore-imports = "yes" messages_control.disable = [ - "design", - "fixme", - "line-too-long", - "missing-module-docstring", - "missing-function-docstring", - "wrong-import-position", + "design", # ignore heavily opinionated design checks + "fixme", # Β allow FIXME comments + "line-too-long", # ruff handles this + "unused-argument", # ruff handles this + "missing-function-docstring", # numpydoc handles this + "import-error", # mypy handles this + "import-outside-toplevel", # optional dependencies +] + +# numpydoc + +[tool.numpydoc_validation] +checks = [ + "all", # report on all checks, except the below + "EX01", # most docstrings do not need an example + "SA01", # data-apis/array-api-extra#87 + "ES01", # most docstrings do not need an extended summary +] +exclude = [ # don't report on objects that match any of these regex + '.*test_*', ] diff --git a/renovate.json b/renovate.json new file mode 100644 index 00000000..d81ec2b4 --- /dev/null +++ b/renovate.json @@ -0,0 +1,48 @@ +{ + "$schema": "https://docs.renovatebot.com/renovate-schema.json", + "extends": ["config:recommended", "helpers:pinGitHubActionDigests", ":automergeMinor"], + "dependencyDashboardTitle": "META: Dependency Dashboard", + "commitMessagePrefix": "deps:", + "labels": ["dependencies"], + "rangeStrategy": "bump", + "constraints": { + "pixi": ">=v0.45.0" + }, + "minimumReleaseAge": "14 days", + "packageRules": [{ + "description": "Do not bump deps pinned with '~=' or '='.", + "matchManagers": ["pixi"], + "matchCurrentValue": "/^~?=/", + "enabled": false + }, { + "description": "Do not bump requires-python.", + "matchManagers": ["pep621"], + "matchPackageNames": ["python"], + "enabled": false + }, { + "description": "Schedule automerged GHA updates for the 15th of each month.", + "matchManagers": ["github-actions"], + "groupName": "gha", + "schedule": ["* * 15 * *"], + "automerge": true + }, { + "description": "Block PRs for updates blocked on dropping Python 3.10.", + "matchManagers": ["pixi"], + "matchUpdateTypes": ["major", "minor"], + "matchPackageNames": ["numpy", "sphinx", "ipython", "sphinx-autodoc-typehints"], + "enabled": false + }, { + "description": "Group Dask packages.", + "matchPackageNames": ["dask", "dask-core"], + "groupName": "dask" + }, { + "description": "Group JAX packages.", + "matchPackageNames": ["jax", "jaxlib"], + "groupName": "jax" + }, { + "description": "Schedule hypothesis monthly as releases are frequent.", + "matchManagers": ["pixi"], + "matchPackageNames": ["hypothesis"], + "schedule": ["* * 10 * *"] + }] +} diff --git a/src/array_api_extra/__init__.py b/src/array_api_extra/__init__.py index 46b1388f..ddfc715e 100644 --- a/src/array_api_extra/__init__.py +++ b/src/array_api_extra/__init__.py @@ -1,17 +1,41 @@ -from __future__ import annotations +"""Extra array functions built on top of the array API standard.""" -from ._funcs import atleast_nd, cov, create_diagonal, expand_dims, kron, setdiff1d, sinc +from ._delegation import isclose, one_hot, pad +from ._lib._at import at +from ._lib._funcs import ( + apply_where, + atleast_nd, + broadcast_shapes, + cov, + create_diagonal, + default_dtype, + expand_dims, + kron, + nunique, + setdiff1d, + sinc, +) +from ._lib._lazy import lazy_apply -__version__ = "0.2.1.dev0" +__version__ = "0.8.1.dev0" # pylint: disable=duplicate-code __all__ = [ "__version__", + "apply_where", + "at", "atleast_nd", + "broadcast_shapes", "cov", "create_diagonal", + "default_dtype", "expand_dims", + "isclose", "kron", + "lazy_apply", + "nunique", + "one_hot", + "pad", "setdiff1d", "sinc", ] diff --git a/src/array_api_extra/_delegation.py b/src/array_api_extra/_delegation.py new file mode 100644 index 00000000..756841c8 --- /dev/null +++ b/src/array_api_extra/_delegation.py @@ -0,0 +1,249 @@ +"""Delegation to existing implementations for Public API Functions.""" + +from collections.abc import Sequence +from types import ModuleType +from typing import Literal + +from ._lib import _funcs +from ._lib._utils._compat import ( + array_namespace, + is_cupy_namespace, + is_dask_namespace, + is_jax_namespace, + is_numpy_namespace, + is_pydata_sparse_namespace, + is_torch_namespace, +) +from ._lib._utils._compat import device as get_device +from ._lib._utils._helpers import asarrays +from ._lib._utils._typing import Array, DType + +__all__ = ["isclose", "one_hot", "pad"] + + +def isclose( + a: Array | complex, + b: Array | complex, + *, + rtol: float = 1e-05, + atol: float = 1e-08, + equal_nan: bool = False, + xp: ModuleType | None = None, +) -> Array: + """ + Return a boolean array where two arrays are element-wise equal within a tolerance. + + The tolerance values are positive, typically very small numbers. The relative + difference ``(rtol * abs(b))`` and the absolute difference `atol` are added together + to compare against the absolute difference between `a` and `b`. + + NaNs are treated as equal if they are in the same place and if ``equal_nan=True``. + Infs are treated as equal if they are in the same place and of the same sign in both + arrays. + + Parameters + ---------- + a, b : Array | int | float | complex | bool + Input objects to compare. At least one must be an array. + rtol : array_like, optional + The relative tolerance parameter (see Notes). + atol : array_like, optional + The absolute tolerance parameter (see Notes). + equal_nan : bool, optional + Whether to compare NaN's as equal. If True, NaN's in `a` will be considered + equal to NaN's in `b` in the output array. + xp : array_namespace, optional + The standard-compatible namespace for `a` and `b`. Default: infer. + + Returns + ------- + Array + A boolean array of shape broadcasted from `a` and `b`, containing ``True`` where + `a` is close to `b`, and ``False`` otherwise. + + Warnings + -------- + The default `atol` is not appropriate for comparing numbers with magnitudes much + smaller than one (see notes). + + See Also + -------- + math.isclose : Similar function in stdlib for Python scalars. + + Notes + ----- + For finite values, `isclose` uses the following equation to test whether two + floating point values are equivalent:: + + absolute(a - b) <= (atol + rtol * absolute(b)) + + Unlike the built-in `math.isclose`, + the above equation is not symmetric in `a` and `b`, + so that ``isclose(a, b)`` might be different from ``isclose(b, a)`` in some rare + cases. + + The default value of `atol` is not appropriate when the reference value `b` has + magnitude smaller than one. For example, it is unlikely that ``a = 1e-9`` and + ``b = 2e-9`` should be considered "close", yet ``isclose(1e-9, 2e-9)`` is ``True`` + with default settings. Be sure to select `atol` for the use case at hand, especially + for defining the threshold below which a non-zero value in `a` will be considered + "close" to a very small or zero value in `b`. + + The comparison of `a` and `b` uses standard broadcasting, which means that `a` and + `b` need not have the same shape in order for ``isclose(a, b)`` to evaluate to + ``True``. + + `isclose` is not defined for non-numeric data types. + ``bool`` is considered a numeric data-type for this purpose. + """ + xp = array_namespace(a, b) if xp is None else xp + + if ( + is_numpy_namespace(xp) + or is_cupy_namespace(xp) + or is_dask_namespace(xp) + or is_jax_namespace(xp) + ): + return xp.isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan) + + if is_torch_namespace(xp): + a, b = asarrays(a, b, xp=xp) # Array API 2024.12 support + return xp.isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan) + + return _funcs.isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan, xp=xp) + + +def one_hot( + x: Array, + /, + num_classes: int, + *, + dtype: DType | None = None, + axis: int = -1, + xp: ModuleType | None = None, +) -> Array: + """ + One-hot encode the given indices. + + Each index in the input `x` is encoded as a vector of zeros of length `num_classes` + with the element at the given index set to one. + + Parameters + ---------- + x : array + An array with integral dtype whose values are between `0` and `num_classes - 1`. + num_classes : int + Number of classes in the one-hot dimension. + dtype : DType, optional + The dtype of the return value. Defaults to the default float dtype (usually + float64). + axis : int, optional + Position in the expanded axes where the new axis is placed. Default: -1. + xp : array_namespace, optional + The standard-compatible namespace for `x`. Default: infer. + + Returns + ------- + array + An array having the same shape as `x` except for a new axis at the position + given by `axis` having size `num_classes`. If `axis` is unspecified, it + defaults to -1, which appends a new axis. + + If ``x < 0`` or ``x >= num_classes``, then the result is undefined, may raise + an exception, or may even cause a bad state. `x` is not checked. + + Examples + -------- + >>> import array_api_extra as xpx + >>> import array_api_strict as xp + >>> xpx.one_hot(xp.asarray([1, 2, 0]), 3) + Array([[0., 1., 0.], + [0., 0., 1.], + [1., 0., 0.]], dtype=array_api_strict.float64) + """ + # Validate inputs. + if xp is None: + xp = array_namespace(x) + if not xp.isdtype(x.dtype, "integral"): + msg = "x must have an integral dtype." + raise TypeError(msg) + if dtype is None: + dtype = _funcs.default_dtype(xp, device=get_device(x)) + # Delegate where possible. + if is_jax_namespace(xp): + from jax.nn import one_hot as jax_one_hot + + return jax_one_hot(x, num_classes, dtype=dtype, axis=axis) + if is_torch_namespace(xp): + from torch.nn.functional import one_hot as torch_one_hot + + x = xp.astype(x, xp.int64) # PyTorch only supports int64 here. + try: + out = torch_one_hot(x, num_classes) + except RuntimeError as e: + raise IndexError from e + else: + out = _funcs.one_hot(x, num_classes, xp=xp) + out = xp.astype(out, dtype, copy=False) + if axis != -1: + out = xp.moveaxis(out, -1, axis) + return out + + +def pad( + x: Array, + pad_width: int | tuple[int, int] | Sequence[tuple[int, int]], + mode: Literal["constant"] = "constant", + *, + constant_values: complex = 0, + xp: ModuleType | None = None, +) -> Array: + """ + Pad the input array. + + Parameters + ---------- + x : array + Input array. + pad_width : int or tuple of ints or sequence of pairs of ints + Pad the input array with this many elements from each side. + If a sequence of tuples, ``[(before_0, after_0), ... (before_N, after_N)]``, + each pair applies to the corresponding axis of ``x``. + A single tuple, ``(before, after)``, is equivalent to a list of ``x.ndim`` + copies of this tuple. + mode : str, optional + Only "constant" mode is currently supported, which pads with + the value passed to `constant_values`. + constant_values : python scalar, optional + Use this value to pad the input. Default is zero. + xp : array_namespace, optional + The standard-compatible namespace for `x`. Default: infer. + + Returns + ------- + array + The input array, + padded with ``pad_width`` elements equal to ``constant_values``. + """ + xp = array_namespace(x) if xp is None else xp + + if mode != "constant": + msg = "Only `'constant'` mode is currently supported" + raise NotImplementedError(msg) + + if ( + is_numpy_namespace(xp) + or is_cupy_namespace(xp) + or is_jax_namespace(xp) + or is_pydata_sparse_namespace(xp) + ): + return xp.pad(x, pad_width, mode, constant_values=constant_values) + + # https://github.com/pytorch/pytorch/blob/cf76c05b4dc629ac989d1fb8e789d4fac04a095a/torch/_numpy/_funcs_impl.py#L2045-L2056 + if is_torch_namespace(xp): + pad_width = xp.asarray(pad_width) + pad_width = xp.broadcast_to(pad_width, (x.ndim, 2)) + pad_width = xp.flip(pad_width, axis=(0,)).flatten() + return xp.nn.functional.pad(x, tuple(pad_width), value=constant_values) # type: ignore[arg-type] # pyright: ignore[reportArgumentType] + + return _funcs.pad(x, pad_width, constant_values=constant_values, xp=xp) diff --git a/src/array_api_extra/_funcs.py b/src/array_api_extra/_funcs.py deleted file mode 100644 index 4062c56f..00000000 --- a/src/array_api_extra/_funcs.py +++ /dev/null @@ -1,541 +0,0 @@ -from __future__ import annotations # https://github.com/pylint-dev/pylint/pull/9990 - -import typing -import warnings - -if typing.TYPE_CHECKING: - from ._lib._typing import Array, ModuleType - -from ._lib import _utils - -__all__ = [ - "atleast_nd", - "cov", - "create_diagonal", - "expand_dims", - "kron", - "setdiff1d", - "sinc", -] - - -def atleast_nd(x: Array, /, *, ndim: int, xp: ModuleType) -> Array: - """ - Recursively expand the dimension of an array to at least `ndim`. - - Parameters - ---------- - x : array - ndim : int - The minimum number of dimensions for the result. - xp : array_namespace - The standard-compatible namespace for `x`. - - Returns - ------- - res : array - An array with ``res.ndim`` >= `ndim`. - If ``x.ndim`` >= `ndim`, `x` is returned. - If ``x.ndim`` < `ndim`, `x` is expanded by prepending new axes - until ``res.ndim`` equals `ndim`. - - Examples - -------- - >>> import array_api_strict as xp - >>> import array_api_extra as xpx - >>> x = xp.asarray([1]) - >>> xpx.atleast_nd(x, ndim=3, xp=xp) - Array([[[1]]], dtype=array_api_strict.int64) - - >>> x = xp.asarray([[[1, 2], - ... [3, 4]]]) - >>> xpx.atleast_nd(x, ndim=1, xp=xp) is x - True - - """ - if x.ndim < ndim: - x = xp.expand_dims(x, axis=0) - x = atleast_nd(x, ndim=ndim, xp=xp) - return x - - -def cov(m: Array, /, *, xp: ModuleType) -> Array: - """ - Estimate a covariance matrix. - - Covariance indicates the level to which two variables vary together. - If we examine N-dimensional samples, :math:`X = [x_1, x_2, ... x_N]^T`, - then the covariance matrix element :math:`C_{ij}` is the covariance of - :math:`x_i` and :math:`x_j`. The element :math:`C_{ii}` is the variance - of :math:`x_i`. - - This provides a subset of the functionality of ``numpy.cov``. - - Parameters - ---------- - m : array - A 1-D or 2-D array containing multiple variables and observations. - Each row of `m` represents a variable, and each column a single - observation of all those variables. - xp : array_namespace - The standard-compatible namespace for `m`. - - Returns - ------- - res : array - The covariance matrix of the variables. - - Examples - -------- - >>> import array_api_strict as xp - >>> import array_api_extra as xpx - - Consider two variables, :math:`x_0` and :math:`x_1`, which - correlate perfectly, but in opposite directions: - - >>> x = xp.asarray([[0, 2], [1, 1], [2, 0]]).T - >>> x - Array([[0, 1, 2], - [2, 1, 0]], dtype=array_api_strict.int64) - - Note how :math:`x_0` increases while :math:`x_1` decreases. The covariance - matrix shows this clearly: - - >>> xpx.cov(x, xp=xp) - Array([[ 1., -1.], - [-1., 1.]], dtype=array_api_strict.float64) - - - Note that element :math:`C_{0,1}`, which shows the correlation between - :math:`x_0` and :math:`x_1`, is negative. - - Further, note how `x` and `y` are combined: - - >>> x = xp.asarray([-2.1, -1, 4.3]) - >>> y = xp.asarray([3, 1.1, 0.12]) - >>> X = xp.stack((x, y), axis=0) - >>> xpx.cov(X, xp=xp) - Array([[11.71 , -4.286 ], - [-4.286 , 2.14413333]], dtype=array_api_strict.float64) - - >>> xpx.cov(x, xp=xp) - Array(11.71, dtype=array_api_strict.float64) - - >>> xpx.cov(y, xp=xp) - Array(2.14413333, dtype=array_api_strict.float64) - - """ - m = xp.asarray(m, copy=True) - dtype = ( - xp.float64 if xp.isdtype(m.dtype, "integral") else xp.result_type(m, xp.float64) - ) - - m = atleast_nd(m, ndim=2, xp=xp) - m = xp.astype(m, dtype) - - avg = _mean(m, axis=1, xp=xp) - fact = m.shape[1] - 1 - - if fact <= 0: - warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning, stacklevel=2) - fact = 0.0 - - m -= avg[:, None] - m_transpose = m.T - if xp.isdtype(m_transpose.dtype, "complex floating"): - m_transpose = xp.conj(m_transpose) - c = m @ m_transpose - c /= fact - axes = tuple(axis for axis, length in enumerate(c.shape) if length == 1) - return xp.squeeze(c, axis=axes) - - -def create_diagonal(x: Array, /, *, offset: int = 0, xp: ModuleType) -> Array: - """ - Construct a diagonal array. - - Parameters - ---------- - x : array - A 1-D array - offset : int, optional - Offset from the leading diagonal (default is ``0``). - Use positive ints for diagonals above the leading diagonal, - and negative ints for diagonals below the leading diagonal. - xp : array_namespace - The standard-compatible namespace for `x`. - - Returns - ------- - res : array - A 2-D array with `x` on the diagonal (offset by `offset`). - - Examples - -------- - >>> import array_api_strict as xp - >>> import array_api_extra as xpx - >>> x = xp.asarray([2, 4, 8]) - - >>> xpx.create_diagonal(x, xp=xp) - Array([[2, 0, 0], - [0, 4, 0], - [0, 0, 8]], dtype=array_api_strict.int64) - - >>> xpx.create_diagonal(x, offset=-2, xp=xp) - Array([[0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [2, 0, 0, 0, 0], - [0, 4, 0, 0, 0], - [0, 0, 8, 0, 0]], dtype=array_api_strict.int64) - - """ - if x.ndim != 1: - err_msg = "`x` must be 1-dimensional." - raise ValueError(err_msg) - n = x.shape[0] + abs(offset) - diag = xp.zeros(n**2, dtype=x.dtype) - i = offset if offset >= 0 else abs(offset) * n - diag[i : min(n * (n - offset), diag.shape[0]) : n + 1] = x - return xp.reshape(diag, (n, n)) - - -def _mean( - x: Array, - /, - *, - axis: int | tuple[int, ...] | None = None, - keepdims: bool = False, - xp: ModuleType, -) -> Array: - """ - Complex mean, https://github.com/data-apis/array-api/issues/846. - """ - if xp.isdtype(x.dtype, "complex floating"): - x_real = xp.real(x) - x_imag = xp.imag(x) - mean_real = xp.mean(x_real, axis=axis, keepdims=keepdims) - mean_imag = xp.mean(x_imag, axis=axis, keepdims=keepdims) - return mean_real + (mean_imag * xp.asarray(1j)) - return xp.mean(x, axis=axis, keepdims=keepdims) - - -def expand_dims( - a: Array, /, *, axis: int | tuple[int, ...] = (0,), xp: ModuleType -) -> Array: - """ - Expand the shape of an array. - - Insert (a) new axis/axes that will appear at the position(s) specified by - `axis` in the expanded array shape. - - This is ``xp.expand_dims`` for `axis` an int *or a tuple of ints*. - Roughly equivalent to ``numpy.expand_dims`` for NumPy arrays. - - Parameters - ---------- - a : array - axis : int or tuple of ints, optional - Position(s) in the expanded axes where the new axis (or axes) is/are placed. - If multiple positions are provided, they should be unique (note that a position - given by a positive index could also be referred to by a negative index - - that will also result in an error). - Default: ``(0,)``. - xp : array_namespace - The standard-compatible namespace for `a`. - - Returns - ------- - res : array - `a` with an expanded shape. - - Examples - -------- - >>> import array_api_strict as xp - >>> import array_api_extra as xpx - >>> x = xp.asarray([1, 2]) - >>> x.shape - (2,) - - The following is equivalent to ``x[xp.newaxis, :]`` or ``x[xp.newaxis]``: - - >>> y = xpx.expand_dims(x, axis=0, xp=xp) - >>> y - Array([[1, 2]], dtype=array_api_strict.int64) - >>> y.shape - (1, 2) - - The following is equivalent to ``x[:, xp.newaxis]``: - - >>> y = xpx.expand_dims(x, axis=1, xp=xp) - >>> y - Array([[1], - [2]], dtype=array_api_strict.int64) - >>> y.shape - (2, 1) - - ``axis`` may also be a tuple: - - >>> y = xpx.expand_dims(x, axis=(0, 1), xp=xp) - >>> y - Array([[[1, 2]]], dtype=array_api_strict.int64) - - >>> y = xpx.expand_dims(x, axis=(2, 0), xp=xp) - >>> y - Array([[[1], - [2]]], dtype=array_api_strict.int64) - - """ - if not isinstance(axis, tuple): - axis = (axis,) - ndim = a.ndim + len(axis) - if axis != () and (min(axis) < -ndim or max(axis) >= ndim): - err_msg = ( - f"a provided axis position is out of bounds for array of dimension {a.ndim}" - ) - raise IndexError(err_msg) - axis = tuple(dim % ndim for dim in axis) - if len(set(axis)) != len(axis): - err_msg = "Duplicate dimensions specified in `axis`." - raise ValueError(err_msg) - for i in sorted(axis): - a = xp.expand_dims(a, axis=i) - return a - - -def kron(a: Array, b: Array, /, *, xp: ModuleType) -> Array: - """ - Kronecker product of two arrays. - - Computes the Kronecker product, a composite array made of blocks of the - second array scaled by the first. - - Equivalent to ``numpy.kron`` for NumPy arrays. - - Parameters - ---------- - a, b : array - xp : array_namespace - The standard-compatible namespace for `a` and `b`. - - Returns - ------- - res : array - The Kronecker product of `a` and `b`. - - Notes - ----- - The function assumes that the number of dimensions of `a` and `b` - are the same, if necessary prepending the smallest with ones. - If ``a.shape = (r0,r1,..,rN)`` and ``b.shape = (s0,s1,...,sN)``, - the Kronecker product has shape ``(r0*s0, r1*s1, ..., rN*SN)``. - The elements are products of elements from `a` and `b`, organized - explicitly by:: - - kron(a,b)[k0,k1,...,kN] = a[i0,i1,...,iN] * b[j0,j1,...,jN] - - where:: - - kt = it * st + jt, t = 0,...,N - - In the common 2-D case (N=1), the block structure can be visualized:: - - [[ a[0,0]*b, a[0,1]*b, ... , a[0,-1]*b ], - [ ... ... ], - [ a[-1,0]*b, a[-1,1]*b, ... , a[-1,-1]*b ]] - - - Examples - -------- - >>> import array_api_strict as xp - >>> import array_api_extra as xpx - >>> xpx.kron(xp.asarray([1, 10, 100]), xp.asarray([5, 6, 7]), xp=xp) - Array([ 5, 6, 7, 50, 60, 70, 500, - 600, 700], dtype=array_api_strict.int64) - - >>> xpx.kron(xp.asarray([5, 6, 7]), xp.asarray([1, 10, 100]), xp=xp) - Array([ 5, 50, 500, 6, 60, 600, 7, - 70, 700], dtype=array_api_strict.int64) - - >>> xpx.kron(xp.eye(2), xp.ones((2, 2)), xp=xp) - Array([[1., 1., 0., 0.], - [1., 1., 0., 0.], - [0., 0., 1., 1.], - [0., 0., 1., 1.]], dtype=array_api_strict.float64) - - - >>> a = xp.reshape(xp.arange(100), (2, 5, 2, 5)) - >>> b = xp.reshape(xp.arange(24), (2, 3, 4)) - >>> c = xpx.kron(a, b, xp=xp) - >>> c.shape - (2, 10, 6, 20) - >>> I = (1, 3, 0, 2) - >>> J = (0, 2, 1) - >>> J1 = (0,) + J # extend to ndim=4 - >>> S1 = (1,) + b.shape - >>> K = tuple(xp.asarray(I) * xp.asarray(S1) + xp.asarray(J1)) - >>> c[K] == a[I]*b[J] - Array(True, dtype=array_api_strict.bool) - - """ - - b = xp.asarray(b) - singletons = (1,) * (b.ndim - a.ndim) - a = xp.broadcast_to(xp.asarray(a), singletons + a.shape) - - nd_b, nd_a = b.ndim, a.ndim - nd_max = max(nd_b, nd_a) - if nd_a == 0 or nd_b == 0: - return xp.multiply(a, b) - - a_shape = a.shape - b_shape = b.shape - - # Equalise the shapes by prepending smaller one with 1s - a_shape = (1,) * max(0, nd_b - nd_a) + a_shape - b_shape = (1,) * max(0, nd_a - nd_b) + b_shape - - # Insert empty dimensions - a_arr = expand_dims(a, axis=tuple(range(nd_b - nd_a)), xp=xp) - b_arr = expand_dims(b, axis=tuple(range(nd_a - nd_b)), xp=xp) - - # Compute the product - a_arr = expand_dims(a_arr, axis=tuple(range(1, nd_max * 2, 2)), xp=xp) - b_arr = expand_dims(b_arr, axis=tuple(range(0, nd_max * 2, 2)), xp=xp) - result = xp.multiply(a_arr, b_arr) - - # Reshape back and return - a_shape = xp.asarray(a_shape) - b_shape = xp.asarray(b_shape) - return xp.reshape(result, tuple(xp.multiply(a_shape, b_shape))) - - -def setdiff1d( - x1: Array, x2: Array, /, *, assume_unique: bool = False, xp: ModuleType -) -> Array: - """ - Find the set difference of two arrays. - - Return the unique values in `x1` that are not in `x2`. - - Parameters - ---------- - x1 : array - Input array. - x2 : array - Input comparison array. - assume_unique : bool - If ``True``, the input arrays are both assumed to be unique, which - can speed up the calculation. Default is ``False``. - xp : array_namespace - The standard-compatible namespace for `x1` and `x2`. - - Returns - ------- - res : array - 1D array of values in `x1` that are not in `x2`. The result - is sorted when `assume_unique` is ``False``, but otherwise only sorted - if the input is sorted. - - Examples - -------- - >>> import array_api_strict as xp - >>> import array_api_extra as xpx - - >>> x1 = xp.asarray([1, 2, 3, 2, 4, 1]) - >>> x2 = xp.asarray([3, 4, 5, 6]) - >>> xpx.setdiff1d(x1, x2, xp=xp) - Array([1, 2], dtype=array_api_strict.int64) - - """ - - if assume_unique: - x1 = xp.reshape(x1, (-1,)) - else: - x1 = xp.unique_values(x1) - x2 = xp.unique_values(x2) - return x1[_utils.in1d(x1, x2, assume_unique=True, invert=True, xp=xp)] - - -def sinc(x: Array, /, *, xp: ModuleType) -> Array: - r""" - Return the normalized sinc function. - - The sinc function is equal to :math:`\sin(\pi x)/(\pi x)` for any argument - :math:`x\ne 0`. ``sinc(0)`` takes the limit value 1, making ``sinc`` not - only everywhere continuous but also infinitely differentiable. - - .. note:: - - Note the normalization factor of ``pi`` used in the definition. - This is the most commonly used definition in signal processing. - Use ``sinc(x / xp.pi)`` to obtain the unnormalized sinc function - :math:`\sin(x)/x` that is more common in mathematics. - - Parameters - ---------- - x : array - Array (possibly multi-dimensional) of values for which to calculate - ``sinc(x)``. Must have a real floating point dtype. - xp : array_namespace - The standard-compatible namespace for `x`. - - Returns - ------- - res : array - ``sinc(x)`` calculated elementwise, which has the same shape as the input. - - Notes - ----- - The name sinc is short for "sine cardinal" or "sinus cardinalis". - - The sinc function is used in various signal processing applications, - including in anti-aliasing, in the construction of a Lanczos resampling - filter, and in interpolation. - - For bandlimited interpolation of discrete-time signals, the ideal - interpolation kernel is proportional to the sinc function. - - References - ---------- - #. Weisstein, Eric W. "Sinc Function." From MathWorld--A Wolfram Web - Resource. https://mathworld.wolfram.com/SincFunction.html - #. Wikipedia, "Sinc function", - https://en.wikipedia.org/wiki/Sinc_function - - Examples - -------- - >>> import array_api_strict as xp - >>> import array_api_extra as xpx - >>> x = xp.linspace(-4, 4, 41) - >>> xpx.sinc(x, xp=xp) - Array([-3.89817183e-17, -4.92362781e-02, - -8.40918587e-02, -8.90384387e-02, - -5.84680802e-02, 3.89817183e-17, - 6.68206631e-02, 1.16434881e-01, - 1.26137788e-01, 8.50444803e-02, - -3.89817183e-17, -1.03943254e-01, - -1.89206682e-01, -2.16236208e-01, - -1.55914881e-01, 3.89817183e-17, - 2.33872321e-01, 5.04551152e-01, - 7.56826729e-01, 9.35489284e-01, - 1.00000000e+00, 9.35489284e-01, - 7.56826729e-01, 5.04551152e-01, - 2.33872321e-01, 3.89817183e-17, - -1.55914881e-01, -2.16236208e-01, - -1.89206682e-01, -1.03943254e-01, - -3.89817183e-17, 8.50444803e-02, - 1.26137788e-01, 1.16434881e-01, - 6.68206631e-02, 3.89817183e-17, - -5.84680802e-02, -8.90384387e-02, - -8.40918587e-02, -4.92362781e-02, - -3.89817183e-17], dtype=array_api_strict.float64) - - """ - if not xp.isdtype(x.dtype, "real floating"): - err_msg = "`x` must have a real floating data type." - raise ValueError(err_msg) - # no scalars in `where` - array-api#807 - y = xp.pi * xp.where( - x, x, xp.asarray(xp.finfo(x.dtype).smallest_normal, dtype=x.dtype) - ) - return xp.sin(y) / y diff --git a/src/array_api_extra/_lib/__init__.py b/src/array_api_extra/_lib/__init__.py new file mode 100644 index 00000000..d7b32033 --- /dev/null +++ b/src/array_api_extra/_lib/__init__.py @@ -0,0 +1 @@ +"""Internals of array-api-extra.""" diff --git a/src/array_api_extra/_lib/_at.py b/src/array_api_extra/_lib/_at.py new file mode 100644 index 00000000..fb2d6ab7 --- /dev/null +++ b/src/array_api_extra/_lib/_at.py @@ -0,0 +1,463 @@ +"""Update operations for read-only arrays.""" + +from __future__ import annotations + +import operator +from collections.abc import Callable +from enum import Enum +from types import ModuleType +from typing import TYPE_CHECKING, ClassVar, cast + +from ._utils import _compat +from ._utils._compat import ( + array_namespace, + is_dask_array, + is_jax_array, + is_torch_array, + is_writeable_array, +) +from ._utils._helpers import meta_namespace +from ._utils._typing import Array, SetIndex + +if TYPE_CHECKING: # pragma: no cover + # TODO import from typing (requires Python >=3.11) + from typing_extensions import Self + + +class _AtOp(Enum): + """Operations for use in `xpx.at`.""" + + SET = "set" + ADD = "add" + SUBTRACT = "subtract" + MULTIPLY = "multiply" + DIVIDE = "divide" + POWER = "power" + MIN = "min" + MAX = "max" + + # @override from Python 3.12 + def __str__(self) -> str: # pyright: ignore[reportImplicitOverride] + """ + Return string representation (useful for pytest logs). + + Returns + ------- + str + The operation's name. + """ + return self.value + + +class Undef(Enum): + """Sentinel for undefined values.""" + + UNDEF = 0 + + +_undef = Undef.UNDEF + + +class at: # pylint: disable=invalid-name # numpydoc ignore=PR02 + """ + Update operations for read-only arrays. + + This implements ``jax.numpy.ndarray.at`` for all writeable + backends (those that support ``__setitem__``) and routes + to the ``.at[]`` method for JAX arrays. + + Parameters + ---------- + x : array + Input array. + idx : index, optional + Only `array API standard compliant indices + `_ + are supported. + + You may use two alternate syntaxes:: + + >>> import array_api_extra as xpx + >>> xpx.at(x, idx).set(value) # or add(value), etc. + >>> xpx.at(x)[idx].set(value) + + copy : bool, optional + None (default) + The array parameter *may* be modified in place if it is + possible and beneficial for performance. + You should not reuse it after calling this function. + True + Ensure that the inputs are not modified. + False + Ensure that the update operation writes back to the input. + Raise ``ValueError`` if a copy cannot be avoided. + + xp : array_namespace, optional + The standard-compatible namespace for `x`. Default: infer. + + Returns + ------- + Updated input array. + + Warnings + -------- + (a) When you omit the ``copy`` parameter, you should never reuse the parameter + array later on; ideally, you should reassign it immediately:: + + >>> import array_api_extra as xpx + >>> x = xpx.at(x, 0).set(2) + + The above best practice pattern ensures that the behaviour won't change depending + on whether ``x`` is writeable or not, as the original ``x`` object is dereferenced + as soon as ``xpx.at`` returns; this way there is no risk to accidentally update it + twice. + + On the reverse, the anti-pattern below must be avoided, as it will result in + different behaviour on read-only versus writeable arrays:: + + >>> x = xp.asarray([0, 0, 0]) + >>> y = xpx.at(x, 0).set(2) + >>> z = xpx.at(x, 1).set(3) + + In the above example, both calls to ``xpx.at`` update ``x`` in place *if possible*. + This causes the behaviour to diverge depending on whether ``x`` is writeable or not: + + - If ``x`` is writeable, then after the snippet above you'll have + ``x == y == z == [2, 3, 0]`` + - If ``x`` is read-only, then you'll end up with + ``x == [0, 0, 0]``, ``y == [2, 0, 0]`` and ``z == [0, 3, 0]``. + + The correct pattern to use if you want diverging outputs from the same input is + to enforce copies:: + + >>> x = xp.asarray([0, 0, 0]) + >>> y = xpx.at(x, 0).set(2, copy=True) # Never updates x + >>> z = xpx.at(x, 1).set(3) # May or may not update x in place + >>> del x # avoid accidental reuse of x as we don't know its state anymore + + (b) The array API standard does not support integer array indices. + The behaviour of update methods when the index is an array of integers is + undefined and will vary between backends; this is particularly true when the + index contains multiple occurrences of the same index, e.g.:: + + >>> import numpy as np + >>> import jax.numpy as jnp + >>> import array_api_extra as xpx + >>> xpx.at(np.asarray([123]), np.asarray([0, 0])).add(1) + array([124]) + >>> xpx.at(jnp.asarray([123]), jnp.asarray([0, 0])).add(1) + Array([125], dtype=int32) + + See Also + -------- + jax.numpy.ndarray.at : Equivalent array method in JAX. + + Notes + ----- + `sparse `_, as well as read-only arrays from libraries + not explicitly covered by ``array-api-compat``, are not supported by update + methods. + + Boolean masks are supported on Dask and jitted JAX arrays exclusively + when `idx` has the same shape as `x` and `y` is 0-dimensional. + Note that this support is not available in JAX's native + ``x.at[mask].set(y)``. + + This pattern:: + + >>> mask = m(x) + >>> x[mask] = f(x[mask]) + + Can't be replaced by `at`, as it won't work on Dask and JAX inside jax.jit:: + + >>> mask = m(x) + >>> x = xpx.at(x, mask).set(f(x[mask]) # Crash on Dask and jax.jit + + You should instead use:: + + >>> x = xp.where(m(x), f(x), x) + + Examples + -------- + Given either of these equivalent expressions:: + + >>> import array_api_extra as xpx + >>> x = xpx.at(x)[1].add(2) + >>> x = xpx.at(x, 1).add(2) + + If x is a JAX array, they are the same as:: + + >>> x = x.at[1].add(2) + + If x is a read-only NumPy array, they are the same as:: + + >>> x = x.copy() + >>> x[1] += 2 + + For other known backends, they are the same as:: + + >>> x[1] += 2 + """ + + _x: Array + _idx: SetIndex | Undef + __slots__: ClassVar[tuple[str, ...]] = ("_idx", "_x") + + def __init__( + self, x: Array, idx: SetIndex | Undef = _undef, / + ) -> None: # numpydoc ignore=GL08 + self._x = x + self._idx = idx + + def __getitem__(self, idx: SetIndex, /) -> Self: # numpydoc ignore=PR01,RT01 + """ + Allow for the alternate syntax ``at(x)[start:stop:step]``. + + It looks prettier than ``at(x, slice(start, stop, step))`` + and feels more intuitive coming from the JAX documentation. + """ + if self._idx is not _undef: + msg = "Index has already been set" + raise ValueError(msg) + return type(self)(self._x, idx) + + def _op( + self, + at_op: _AtOp, + in_place_op: Callable[[Array, Array | complex], Array] | None, + out_of_place_op: Callable[[Array, Array], Array] | None, + y: Array | complex, + /, + copy: bool | None, + xp: ModuleType | None, + ) -> Array: + """ + Implement all update operations. + + Parameters + ---------- + at_op : _AtOp + Method of JAX's Array.at[]. + in_place_op : Callable[[Array, Array | complex], Array] | None + In-place operation to apply on mutable backends:: + + x[idx] = in_place_op(x[idx], y) + + If None:: + + x[idx] = y + + out_of_place_op : Callable[[Array, Array], Array] | None + Out-of-place operation to apply when idx is a boolean mask and the backend + doesn't support in-place updates:: + + x = xp.where(idx, out_of_place_op(x, y), x) + + If None:: + + x = xp.where(idx, y, x) + + y : array or complex + Right-hand side of the operation. + copy : bool or None + Whether to copy the input array. See the class docstring for details. + xp : array_namespace, optional + The array namespace for the input array. Default: infer. + + Returns + ------- + Array + Updated `x`. + """ + from ._funcs import apply_where # pylint: disable=cyclic-import + + x, idx = self._x, self._idx + xp = array_namespace(x, y) if xp is None else xp + + if isinstance(idx, Undef): + msg = ( + "Index has not been set.\n" + "Usage: either\n" + " at(x, idx).set(value)\n" + "or\n" + " at(x)[idx].set(value)\n" + "(same for all other methods)." + ) + raise ValueError(msg) + + if copy not in (True, False, None): + msg = f"copy must be True, False, or None; got {copy!r}" + raise ValueError(msg) + + writeable = None if copy else is_writeable_array(x) + + # JAX inside jax.jit doesn't support in-place updates with boolean + # masks; Dask exclusively supports __setitem__ but not iops. + # We can handle the common special case of 0-dimensional y + # with where(idx, y, x) instead. + if ( + (is_dask_array(idx) or is_jax_array(idx)) + and idx.dtype == xp.bool + and idx.shape == x.shape + ): + y_xp = xp.asarray(y, dtype=x.dtype, device=_compat.device(x)) + if y_xp.ndim == 0: + if out_of_place_op: # add(), subtract(), ... + # suppress inf warnings on Dask + out = apply_where( + idx, (x, y_xp), out_of_place_op, fill_value=x, xp=xp + ) + # Undo int->float promotion on JAX after _AtOp.DIVIDE + out = xp.astype(out, x.dtype, copy=False) + else: # set() + out = xp.where(idx, y_xp, x) + + if copy is False: + x[()] = out + return x + return out + + # else: this will work on eager JAX and crash on jax.jit and Dask + + if copy or (copy is None and not writeable): + if is_jax_array(x): + # Use JAX's at[] + func = cast( + Callable[[Array | complex], Array], + getattr(x.at[idx], at_op.value), # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue,reportUnknownArgumentType] + ) + out = func(y) + # Undo int->float promotion on JAX after _AtOp.DIVIDE + return xp.astype(out, x.dtype, copy=False) + + # Emulate at[] behaviour for non-JAX arrays + # with a copy followed by an update + + x = xp.asarray(x, copy=True) + # A copy of a read-only numpy array is writeable + # Note: this assumes that a copy of a writeable array is writeable + assert not writeable + writeable = None + + if writeable is None: + writeable = is_writeable_array(x) + if not writeable: + # sparse crashes here + msg = f"Can't update read-only array {x}" + raise ValueError(msg) + + # Work around bug in PyTorch where __setitem__ doesn't + # always support mismatched dtypes + # https://github.com/pytorch/pytorch/issues/150017 + if is_torch_array(y): + y = xp.astype(y, x.dtype, copy=False) + + # Backends without boolean indexing (other than JAX) crash here + if in_place_op: # add(), subtract(), ... + x[idx] = in_place_op(x[idx], y) + else: # set() + x[idx] = y + return x + + def set( + self, + y: Array | complex, + /, + copy: bool | None = None, + xp: ModuleType | None = None, + ) -> Array: # numpydoc ignore=PR01,RT01 + """Apply ``x[idx] = y`` and return the update array.""" + return self._op(_AtOp.SET, None, None, y, copy=copy, xp=xp) + + def add( + self, + y: Array | complex, + /, + copy: bool | None = None, + xp: ModuleType | None = None, + ) -> Array: # numpydoc ignore=PR01,RT01 + """Apply ``x[idx] += y`` and return the updated array.""" + + # Note for this and all other methods based on _iop: + # operator.iadd and operator.add subtly differ in behaviour, as + # only iadd will trigger exceptions when y has an incompatible dtype. + return self._op(_AtOp.ADD, operator.iadd, operator.add, y, copy=copy, xp=xp) + + def subtract( + self, + y: Array | complex, + /, + copy: bool | None = None, + xp: ModuleType | None = None, + ) -> Array: # numpydoc ignore=PR01,RT01 + """Apply ``x[idx] -= y`` and return the updated array.""" + return self._op( + _AtOp.SUBTRACT, operator.isub, operator.sub, y, copy=copy, xp=xp + ) + + def multiply( + self, + y: Array | complex, + /, + copy: bool | None = None, + xp: ModuleType | None = None, + ) -> Array: # numpydoc ignore=PR01,RT01 + """Apply ``x[idx] *= y`` and return the updated array.""" + return self._op( + _AtOp.MULTIPLY, operator.imul, operator.mul, y, copy=copy, xp=xp + ) + + def divide( + self, + y: Array | complex, + /, + copy: bool | None = None, + xp: ModuleType | None = None, + ) -> Array: # numpydoc ignore=PR01,RT01 + """Apply ``x[idx] /= y`` and return the updated array.""" + return self._op( + _AtOp.DIVIDE, operator.itruediv, operator.truediv, y, copy=copy, xp=xp + ) + + def power( + self, + y: Array | complex, + /, + copy: bool | None = None, + xp: ModuleType | None = None, + ) -> Array: # numpydoc ignore=PR01,RT01 + """Apply ``x[idx] **= y`` and return the updated array.""" + return self._op(_AtOp.POWER, operator.ipow, operator.pow, y, copy=copy, xp=xp) + + def min( + self, + y: Array | complex, + /, + copy: bool | None = None, + xp: ModuleType | None = None, + ) -> Array: # numpydoc ignore=PR01,RT01 + """Apply ``x[idx] = minimum(x[idx], y)`` and return the updated array.""" + # On Dask, this function runs on the chunks, so we need to determine the + # namespace that Dask is wrapping. + # Note that da.minimum _incidentally_ works on NumPy, CuPy, and sparse + # thanks to all these meta-namespaces implementing the __array_ufunc__ + # interface, but there's no guarantee that it will work for other + # wrapped libraries in the future. + xp = array_namespace(self._x) if xp is None else xp + mxp = meta_namespace(self._x, xp=xp) + y = xp.asarray(y) + return self._op(_AtOp.MIN, mxp.minimum, mxp.minimum, y, copy=copy, xp=xp) + + def max( + self, + y: Array | complex, + /, + copy: bool | None = None, + xp: ModuleType | None = None, + ) -> Array: # numpydoc ignore=PR01,RT01 + """Apply ``x[idx] = maximum(x[idx], y)`` and return the updated array.""" + # See note on min() + xp = array_namespace(self._x) if xp is None else xp + mxp = meta_namespace(self._x, xp=xp) + y = xp.asarray(y) + return self._op(_AtOp.MAX, mxp.maximum, mxp.maximum, y, copy=copy, xp=xp) diff --git a/src/array_api_extra/_lib/_backends.py b/src/array_api_extra/_lib/_backends.py new file mode 100644 index 00000000..936f5dd0 --- /dev/null +++ b/src/array_api_extra/_lib/_backends.py @@ -0,0 +1,72 @@ +"""Backends against which array-api-extra runs its tests.""" + +from __future__ import annotations + +from enum import Enum +from typing import Any + +import numpy as np +import pytest + +__all__ = ["NUMPY_VERSION", "Backend"] + +NUMPY_VERSION = tuple(int(v) for v in np.__version__.split(".")[:3]) # pyright: ignore[reportUnknownArgumentType] + + +class Backend(Enum): # numpydoc ignore=PR02 + """ + All array library backends explicitly tested by array-api-extra. + + Parameters + ---------- + value : str + Tag of the backend's module, in the format ``[:]``. + """ + + # Use : to prevent Enum from deduplicating items with the same value + ARRAY_API_STRICT = "array_api_strict" + ARRAY_API_STRICTEST = "array_api_strict:strictest" + NUMPY = "numpy" + NUMPY_READONLY = "numpy:readonly" + CUPY = "cupy" + TORCH = "torch" + TORCH_GPU = "torch:gpu" + DASK = "dask.array" + SPARSE = "sparse" + JAX = "jax.numpy" + JAX_GPU = "jax.numpy:gpu" + + @property + def modname(self) -> str: # numpydoc ignore=RT01 + """Module name to be imported.""" + return self.value.split(":")[0] + + def like(self, *others: Backend) -> bool: # numpydoc ignore=PR01,RT01 + """Check if this backend uses the same module as others.""" + return any(self.modname == other.modname for other in others) + + def pytest_param(self) -> Any: + """ + Backend as a pytest parameter + + Returns + ------- + pytest.mark.ParameterSet + """ + id_ = ( + self.name.lower().replace("_gpu", ":gpu").replace("_readonly", ":readonly") + ) + + marks = [] + if self.like(Backend.ARRAY_API_STRICT): + marks.append( + pytest.mark.skipif( + NUMPY_VERSION < (1, 26), + reason="array_api_strict is untested on NumPy <1.26", + ) + ) + if self.like(Backend.DASK, Backend.JAX): + # Monkey-patched by lazy_xp_function + marks.append(pytest.mark.thread_unsafe) + + return pytest.param(self, id=id_, marks=marks) # pyright: ignore[reportUnknownArgumentType] diff --git a/src/array_api_extra/_lib/_compat.py b/src/array_api_extra/_lib/_compat.py deleted file mode 100644 index b9577ff3..00000000 --- a/src/array_api_extra/_lib/_compat.py +++ /dev/null @@ -1,168 +0,0 @@ -### Helpers borrowed from array-api-compat - -from __future__ import annotations # https://github.com/pylint-dev/pylint/pull/9990 - -import inspect -import sys -import typing - -from typing_extensions import override - -if typing.TYPE_CHECKING: - from ._typing import Array, Device - -__all__ = ["device"] - - -# Placeholder object to represent the dask device -# when the array backend is not the CPU. -# (since it is not easy to tell which device a dask array is on) -class _dask_device: # pylint: disable=invalid-name - @override - def __repr__(self) -> str: - return "DASK_DEVICE" - - -_DASK_DEVICE = _dask_device() - - -# device() is not on numpy.ndarray or dask.array and to_device() is not on numpy.ndarray -# or cupy.ndarray. They are not included in array objects of this library -# because this library just reuses the respective ndarray classes without -# wrapping or subclassing them. These helper functions can be used instead of -# the wrapper functions for libraries that need to support both NumPy/CuPy and -# other libraries that use devices. -def device(x: Array, /) -> Device: - """ - Hardware device the array data resides on. - - This is equivalent to `x.device` according to the `standard - `__. - This helper is included because some array libraries either do not have - the `device` attribute or include it with an incompatible API. - - Parameters - ---------- - x: array - array instance from an array API compatible library. - - Returns - ------- - out: device - a ``device`` object (see the `Device Support `__ - section of the array API specification). - - Notes - ----- - - For NumPy the device is always `"cpu"`. For Dask, the device is always a - special `DASK_DEVICE` object. - - See Also - -------- - - to_device : Move array data to a different device. - - """ - if _is_numpy_array(x): - return "cpu" - if _is_dask_array(x): - # Peek at the metadata of the jax array to determine type - try: - import numpy as np # pylint: disable=import-outside-toplevel - - if isinstance(x._meta, np.ndarray): # pylint: disable=protected-access - # Must be on CPU since backed by numpy - return "cpu" - except ImportError: - pass - return _DASK_DEVICE - if _is_jax_array(x): - # JAX has .device() as a method, but it is being deprecated so that it - # can become a property, in accordance with the standard. In order for - # this function to not break when JAX makes the flip, we check for - # both here. - if inspect.ismethod(x.device): - return x.device() - return x.device - if _is_pydata_sparse_array(x): - # `sparse` will gain `.device`, so check for this first. - x_device = getattr(x, "device", None) - if x_device is not None: - return x_device - # Everything but DOK has this attr. - try: - inner = x.data - except AttributeError: - return "cpu" - # Return the device of the constituent array - return device(inner) - return x.device - - -def _is_numpy_array(x: Array) -> bool: - """Return True if `x` is a NumPy array.""" - # Avoid importing NumPy if it isn't already - if "numpy" not in sys.modules: - return False - - import numpy as np # pylint: disable=import-outside-toplevel - - # TODO: Should we reject ndarray subclasses? - return isinstance(x, (np.ndarray, np.generic)) and not _is_jax_zero_gradient_array( - x - ) - - -def _is_dask_array(x: Array) -> bool: - """Return True if `x` is a dask.array Array.""" - # Avoid importing dask if it isn't already - if "dask.array" not in sys.modules: - return False - - # pylint: disable=import-error, import-outside-toplevel - import dask.array # type: ignore[import-not-found] # pyright: ignore[reportMissingImports] - - return isinstance(x, dask.array.Array) - - -def _is_jax_zero_gradient_array(x: Array) -> bool: - """Return True if `x` is a zero-gradient array. - - These arrays are a design quirk of Jax that may one day be removed. - See https://github.com/google/jax/issues/20620. - """ - if "numpy" not in sys.modules or "jax" not in sys.modules: - return False - - # pylint: disable=import-error, import-outside-toplevel - import jax # type: ignore[import-not-found] # pyright: ignore[reportMissingImports] - import numpy as np # pylint: disable=import-outside-toplevel - - return isinstance(x, np.ndarray) and x.dtype == jax.float0 # pyright: ignore[reportUnknownVariableType] - - -def _is_jax_array(x: Array) -> bool: - """Return True if `x` is a JAX array.""" - # Avoid importing jax if it isn't already - if "jax" not in sys.modules: - return False - - # pylint: disable=import-error, import-outside-toplevel - import jax # pyright: ignore[reportMissingImports] - - return isinstance(x, jax.Array) or _is_jax_zero_gradient_array(x) - - -def _is_pydata_sparse_array(x: Array) -> bool: - """Return True if `x` is an array from the `sparse` package.""" - - # Avoid importing jax if it isn't already - if "sparse" not in sys.modules: - return False - - # pylint: disable=import-error, import-outside-toplevel - import sparse # type: ignore[import-not-found] # pyright: ignore[reportMissingImports] - - # TODO: Account for other backends. - return isinstance(x, sparse.SparseArray) diff --git a/src/array_api_extra/_lib/_funcs.py b/src/array_api_extra/_lib/_funcs.py new file mode 100644 index 00000000..05db6251 --- /dev/null +++ b/src/array_api_extra/_lib/_funcs.py @@ -0,0 +1,990 @@ +"""Array-agnostic implementations for the public API.""" + +import math +import warnings +from collections.abc import Callable, Sequence +from types import ModuleType, NoneType +from typing import Literal, cast, overload + +from ._at import at +from ._utils import _compat, _helpers +from ._utils._compat import array_namespace, is_dask_namespace, is_jax_array +from ._utils._helpers import ( + asarrays, + capabilities, + eager_shape, + meta_namespace, + ndindex, +) +from ._utils._typing import Array, Device, DType + +__all__ = [ + "apply_where", + "atleast_nd", + "broadcast_shapes", + "cov", + "create_diagonal", + "expand_dims", + "kron", + "nunique", + "pad", + "setdiff1d", + "sinc", +] + + +@overload +def apply_where( # numpydoc ignore=GL08 + cond: Array, + args: Array | tuple[Array, ...], + f1: Callable[..., Array], + f2: Callable[..., Array], + /, + *, + xp: ModuleType | None = None, +) -> Array: ... + + +@overload +def apply_where( # numpydoc ignore=GL08 + cond: Array, + args: Array | tuple[Array, ...], + f1: Callable[..., Array], + /, + *, + fill_value: Array | complex, + xp: ModuleType | None = None, +) -> Array: ... + + +def apply_where( # numpydoc ignore=PR01,PR02 + cond: Array, + args: Array | tuple[Array, ...], + f1: Callable[..., Array], + f2: Callable[..., Array] | None = None, + /, + *, + fill_value: Array | complex | None = None, + xp: ModuleType | None = None, +) -> Array: + """ + Run one of two elementwise functions depending on a condition. + + Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise + when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``. + + Parameters + ---------- + cond : array + The condition, expressed as a boolean array. + args : Array or tuple of Arrays + Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`. + f1 : callable + Elementwise function of `args`, returning a single array. + Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``. + f2 : callable, optional + Elementwise function of `args`, returning a single array. + Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``. + Mutually exclusive with `fill_value`. + fill_value : Array or scalar, optional + If provided, value with which to fill output array where `cond` is False. + It does not need to be scalar; it needs however to be broadcastable with + `cond` and `args`. + Mutually exclusive with `f2`. You must provide one or the other. + xp : array_namespace, optional + The standard-compatible namespace for `cond` and `args`. Default: infer. + + Returns + ------- + Array + An array with elements from the output of `f1` where `cond` is True and either + the output of `f2` or `fill_value` where `cond` is False. The returned array has + data type determined by type promotion rules between the output of `f1` and + either `fill_value` or the output of `f2`. + + Notes + ----- + ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even + when `cond` is False, and `f2` when cond is True. This function evaluates each + function only for their matching condition, if the backend allows for it. + + On Dask, `f1` and `f2` are applied to the individual chunks and should use functions + from the namespace of the chunks. + + Examples + -------- + >>> import array_api_strict as xp + >>> import array_api_extra as xpx + >>> a = xp.asarray([5, 4, 3]) + >>> b = xp.asarray([0, 2, 2]) + >>> def f(a, b): + ... return a // b + >>> xpx.apply_where(b != 0, (a, b), f, fill_value=xp.nan) + array([ nan, 2., 1.]) + """ + # Parse and normalize arguments + if (f2 is None) == (fill_value is None): + msg = "Exactly one of `fill_value` or `f2` must be given." + raise TypeError(msg) + args_ = list(args) if isinstance(args, tuple) else [args] + del args + + xp = array_namespace(cond, fill_value, *args_) if xp is None else xp + + if isinstance(fill_value, int | float | complex | NoneType): + cond, *args_ = xp.broadcast_arrays(cond, *args_) + else: + cond, fill_value, *args_ = xp.broadcast_arrays(cond, fill_value, *args_) + + if is_dask_namespace(xp): + meta_xp = meta_namespace(cond, fill_value, *args_, xp=xp) + # map_blocks doesn't descend into tuples of Arrays + return xp.map_blocks(_apply_where, cond, f1, f2, fill_value, *args_, xp=meta_xp) + return _apply_where(cond, f1, f2, fill_value, *args_, xp=xp) + + +def _apply_where( # numpydoc ignore=PR01,RT01 + cond: Array, + f1: Callable[..., Array], + f2: Callable[..., Array] | None, + fill_value: Array | int | float | complex | bool | None, + *args: Array, + xp: ModuleType, +) -> Array: + """Helper of `apply_where`. On Dask, this runs on a single chunk.""" + + if not capabilities(xp, device=_compat.device(cond))["boolean indexing"]: + # jax.jit does not support assignment by boolean mask + return xp.where(cond, f1(*args), f2(*args) if f2 is not None else fill_value) + + temp1 = f1(*(arr[cond] for arr in args)) + + if f2 is None: + dtype = xp.result_type(temp1, fill_value) + if isinstance(fill_value, int | float | complex): + out = xp.full_like(cond, dtype=dtype, fill_value=fill_value) + else: + out = xp.astype(fill_value, dtype, copy=True) + else: + ncond = ~cond + temp2 = f2(*(arr[ncond] for arr in args)) + dtype = xp.result_type(temp1, temp2) + out = xp.empty_like(cond, dtype=dtype) + out = at(out, ncond).set(temp2) + + return at(out, cond).set(temp1) + + +def atleast_nd(x: Array, /, *, ndim: int, xp: ModuleType | None = None) -> Array: + """ + Recursively expand the dimension of an array to at least `ndim`. + + Parameters + ---------- + x : array + Input array. + ndim : int + The minimum number of dimensions for the result. + xp : array_namespace, optional + The standard-compatible namespace for `x`. Default: infer. + + Returns + ------- + array + An array with ``res.ndim`` >= `ndim`. + If ``x.ndim`` >= `ndim`, `x` is returned. + If ``x.ndim`` < `ndim`, `x` is expanded by prepending new axes + until ``res.ndim`` equals `ndim`. + + Examples + -------- + >>> import array_api_strict as xp + >>> import array_api_extra as xpx + >>> x = xp.asarray([1]) + >>> xpx.atleast_nd(x, ndim=3, xp=xp) + Array([[[1]]], dtype=array_api_strict.int64) + + >>> x = xp.asarray([[[1, 2], + ... [3, 4]]]) + >>> xpx.atleast_nd(x, ndim=1, xp=xp) is x + True + """ + if xp is None: + xp = array_namespace(x) + + if x.ndim < ndim: + x = xp.expand_dims(x, axis=0) + x = atleast_nd(x, ndim=ndim, xp=xp) + return x + + +# `float` in signature to accept `math.nan` for Dask. +# `int`s are still accepted as `float` is a superclass of `int` in typing +def broadcast_shapes(*shapes: tuple[float | None, ...]) -> tuple[int | None, ...]: + """ + Compute the shape of the broadcasted arrays. + + Duplicates :func:`numpy.broadcast_shapes`, with additional support for + None and NaN sizes. + + This is equivalent to ``xp.broadcast_arrays(arr1, arr2, ...)[0].shape`` + without needing to worry about the backend potentially deep copying + the arrays. + + Parameters + ---------- + *shapes : tuple[int | None, ...] + Shapes of the arrays to broadcast. + + Returns + ------- + tuple[int | None, ...] + The shape of the broadcasted arrays. + + See Also + -------- + numpy.broadcast_shapes : Equivalent NumPy function. + array_api.broadcast_arrays : Function to broadcast actual arrays. + + Notes + ----- + This function accepts the Array API's ``None`` for unknown sizes, + as well as Dask's non-standard ``math.nan``. + Regardless of input, the output always contains ``None`` for unknown sizes. + + Examples + -------- + >>> import array_api_extra as xpx + >>> xpx.broadcast_shapes((2, 3), (2, 1)) + (2, 3) + >>> xpx.broadcast_shapes((4, 2, 3), (2, 1), (1, 3)) + (4, 2, 3) + """ + if not shapes: + return () # Match NumPy output + + ndim = max(len(shape) for shape in shapes) + out: list[int | None] = [] + for axis in range(-ndim, 0): + sizes = {shape[axis] for shape in shapes if axis >= -len(shape)} + # Dask uses NaN for unknown shape, which predates the Array API spec for None + none_size = None in sizes or math.nan in sizes # noqa: PLW0177 + sizes -= {1, None, math.nan} + if len(sizes) > 1: + msg = ( + "shape mismatch: objects cannot be broadcast to a single shape: " + f"{shapes}." + ) + raise ValueError(msg) + out.append(None if none_size else cast(int, sizes.pop()) if sizes else 1) + + return tuple(out) + + +def cov(m: Array, /, *, xp: ModuleType | None = None) -> Array: + """ + Estimate a covariance matrix. + + Covariance indicates the level to which two variables vary together. + If we examine N-dimensional samples, :math:`X = [x_1, x_2, ... x_N]^T`, + then the covariance matrix element :math:`C_{ij}` is the covariance of + :math:`x_i` and :math:`x_j`. The element :math:`C_{ii}` is the variance + of :math:`x_i`. + + This provides a subset of the functionality of ``numpy.cov``. + + Parameters + ---------- + m : array + A 1-D or 2-D array containing multiple variables and observations. + Each row of `m` represents a variable, and each column a single + observation of all those variables. + xp : array_namespace, optional + The standard-compatible namespace for `m`. Default: infer. + + Returns + ------- + array + The covariance matrix of the variables. + + Examples + -------- + >>> import array_api_strict as xp + >>> import array_api_extra as xpx + + Consider two variables, :math:`x_0` and :math:`x_1`, which + correlate perfectly, but in opposite directions: + + >>> x = xp.asarray([[0, 2], [1, 1], [2, 0]]).T + >>> x + Array([[0, 1, 2], + [2, 1, 0]], dtype=array_api_strict.int64) + + Note how :math:`x_0` increases while :math:`x_1` decreases. The covariance + matrix shows this clearly: + + >>> xpx.cov(x, xp=xp) + Array([[ 1., -1.], + [-1., 1.]], dtype=array_api_strict.float64) + + Note that element :math:`C_{0,1}`, which shows the correlation between + :math:`x_0` and :math:`x_1`, is negative. + + Further, note how `x` and `y` are combined: + + >>> x = xp.asarray([-2.1, -1, 4.3]) + >>> y = xp.asarray([3, 1.1, 0.12]) + >>> X = xp.stack((x, y), axis=0) + >>> xpx.cov(X, xp=xp) + Array([[11.71 , -4.286 ], + [-4.286 , 2.14413333]], dtype=array_api_strict.float64) + + >>> xpx.cov(x, xp=xp) + Array(11.71, dtype=array_api_strict.float64) + + >>> xpx.cov(y, xp=xp) + Array(2.14413333, dtype=array_api_strict.float64) + """ + if xp is None: + xp = array_namespace(m) + + m = xp.asarray(m, copy=True) + dtype = ( + xp.float64 if xp.isdtype(m.dtype, "integral") else xp.result_type(m, xp.float64) + ) + + m = atleast_nd(m, ndim=2, xp=xp) + m = xp.astype(m, dtype) + + avg = _helpers.mean(m, axis=1, xp=xp) + + m_shape = eager_shape(m) + fact = m_shape[1] - 1 + + if fact <= 0: + warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning, stacklevel=2) + fact = 0 + + m -= avg[:, None] + m_transpose = m.T + if xp.isdtype(m_transpose.dtype, "complex floating"): + m_transpose = xp.conj(m_transpose) + c = m @ m_transpose + c /= fact + axes = tuple(axis for axis, length in enumerate(c.shape) if length == 1) + return xp.squeeze(c, axis=axes) + + +def one_hot( + x: Array, + /, + num_classes: int, + *, + xp: ModuleType, +) -> Array: # numpydoc ignore=PR01,RT01 + """See docstring in `array_api_extra._delegation.py`.""" + # TODO: Benchmark whether this is faster on the NumPy backend: + # if is_numpy_array(x): + # out = xp.zeros((x.size, num_classes), dtype=dtype) + # out[xp.arange(x.size), xp.reshape(x, (-1,))] = 1 + # return xp.reshape(out, (*x.shape, num_classes)) + range_num_classes = xp.arange(num_classes, dtype=x.dtype, device=_compat.device(x)) + return x[..., xp.newaxis] == range_num_classes + + +def create_diagonal( + x: Array, /, *, offset: int = 0, xp: ModuleType | None = None +) -> Array: + """ + Construct a diagonal array. + + Parameters + ---------- + x : array + An array having shape ``(*batch_dims, k)``. + offset : int, optional + Offset from the leading diagonal (default is ``0``). + Use positive ints for diagonals above the leading diagonal, + and negative ints for diagonals below the leading diagonal. + xp : array_namespace, optional + The standard-compatible namespace for `x`. Default: infer. + + Returns + ------- + array + An array having shape ``(*batch_dims, k+abs(offset), k+abs(offset))`` with `x` + on the diagonal (offset by `offset`). + + Examples + -------- + >>> import array_api_strict as xp + >>> import array_api_extra as xpx + >>> x = xp.asarray([2, 4, 8]) + + >>> xpx.create_diagonal(x, xp=xp) + Array([[2, 0, 0], + [0, 4, 0], + [0, 0, 8]], dtype=array_api_strict.int64) + + >>> xpx.create_diagonal(x, offset=-2, xp=xp) + Array([[0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [2, 0, 0, 0, 0], + [0, 4, 0, 0, 0], + [0, 0, 8, 0, 0]], dtype=array_api_strict.int64) + """ + if xp is None: + xp = array_namespace(x) + + if x.ndim == 0: + err_msg = "`x` must be at least 1-dimensional." + raise ValueError(err_msg) + + x_shape = eager_shape(x) + batch_dims = x_shape[:-1] + n = x_shape[-1] + abs(offset) + diag = xp.zeros((*batch_dims, n**2), dtype=x.dtype, device=_compat.device(x)) + + target_slice = slice( + offset if offset >= 0 else abs(offset) * n, + min(n * (n - offset), diag.shape[-1]), + n + 1, + ) + for index in ndindex(*batch_dims): + diag = at(diag)[(*index, target_slice)].set(x[(*index, slice(None))]) + return xp.reshape(diag, (*batch_dims, n, n)) + + +def default_dtype( + xp: ModuleType, + kind: Literal[ + "real floating", "complex floating", "integral", "indexing" + ] = "real floating", + *, + device: Device | None = None, +) -> DType: + """ + Return the default dtype for the given namespace and device. + + This is a convenience shorthand for + ``xp.__array_namespace_info__().default_dtypes(device=device)[kind]``. + + Parameters + ---------- + xp : array_namespace + The standard-compatible namespace for which to get the default dtype. + kind : {'real floating', 'complex floating', 'integral', 'indexing'}, optional + The kind of dtype to return. Default is 'real floating'. + device : Device, optional + The device for which to get the default dtype. Default: current device. + + Returns + ------- + dtype + The default dtype for the given namespace, kind, and device. + """ + dtypes = xp.__array_namespace_info__().default_dtypes(device=device) + try: + return dtypes[kind] + except KeyError as e: + domain = ("real floating", "complex floating", "integral", "indexing") + assert set(dtypes) == set(domain), f"Non-compliant namespace: {dtypes}" + msg = f"Unknown kind '{kind}'. Expected one of {domain}." + raise ValueError(msg) from e + + +def expand_dims( + a: Array, /, *, axis: int | tuple[int, ...] = (0,), xp: ModuleType | None = None +) -> Array: + """ + Expand the shape of an array. + + Insert (a) new axis/axes that will appear at the position(s) specified by + `axis` in the expanded array shape. + + This is ``xp.expand_dims`` for `axis` an int *or a tuple of ints*. + Roughly equivalent to ``numpy.expand_dims`` for NumPy arrays. + + Parameters + ---------- + a : array + Array to have its shape expanded. + axis : int or tuple of ints, optional + Position(s) in the expanded axes where the new axis (or axes) is/are placed. + If multiple positions are provided, they should be unique (note that a position + given by a positive index could also be referred to by a negative index - + that will also result in an error). + Default: ``(0,)``. + xp : array_namespace, optional + The standard-compatible namespace for `a`. Default: infer. + + Returns + ------- + array + `a` with an expanded shape. + + Examples + -------- + >>> import array_api_strict as xp + >>> import array_api_extra as xpx + >>> x = xp.asarray([1, 2]) + >>> x.shape + (2,) + + The following is equivalent to ``x[xp.newaxis, :]`` or ``x[xp.newaxis]``: + + >>> y = xpx.expand_dims(x, axis=0, xp=xp) + >>> y + Array([[1, 2]], dtype=array_api_strict.int64) + >>> y.shape + (1, 2) + + The following is equivalent to ``x[:, xp.newaxis]``: + + >>> y = xpx.expand_dims(x, axis=1, xp=xp) + >>> y + Array([[1], + [2]], dtype=array_api_strict.int64) + >>> y.shape + (2, 1) + + ``axis`` may also be a tuple: + + >>> y = xpx.expand_dims(x, axis=(0, 1), xp=xp) + >>> y + Array([[[1, 2]]], dtype=array_api_strict.int64) + + >>> y = xpx.expand_dims(x, axis=(2, 0), xp=xp) + >>> y + Array([[[1], + [2]]], dtype=array_api_strict.int64) + """ + if xp is None: + xp = array_namespace(a) + + if not isinstance(axis, tuple): + axis = (axis,) + ndim = a.ndim + len(axis) + if axis != () and (min(axis) < -ndim or max(axis) >= ndim): + err_msg = ( + f"a provided axis position is out of bounds for array of dimension {a.ndim}" + ) + raise IndexError(err_msg) + axis = tuple(dim % ndim for dim in axis) + if len(set(axis)) != len(axis): + err_msg = "Duplicate dimensions specified in `axis`." + raise ValueError(err_msg) + for i in sorted(axis): + a = xp.expand_dims(a, axis=i) + return a + + +def isclose( + a: Array | complex, + b: Array | complex, + *, + rtol: float = 1e-05, + atol: float = 1e-08, + equal_nan: bool = False, + xp: ModuleType, +) -> Array: # numpydoc ignore=PR01,RT01 + """See docstring in array_api_extra._delegation.""" + a, b = asarrays(a, b, xp=xp) + + a_inexact = xp.isdtype(a.dtype, ("real floating", "complex floating")) + b_inexact = xp.isdtype(b.dtype, ("real floating", "complex floating")) + if a_inexact or b_inexact: + # prevent warnings on NumPy and Dask on inf - inf + mxp = meta_namespace(a, b, xp=xp) + out = apply_where( + xp.isinf(a) | xp.isinf(b), + (a, b), + lambda a, b: mxp.isinf(a) & mxp.isinf(b) & (mxp.sign(a) == mxp.sign(b)), # pyright: ignore[reportUnknownArgumentType] + # Note: inf <= inf is True! + lambda a, b: mxp.abs(a - b) <= (atol + rtol * mxp.abs(b)), # pyright: ignore[reportUnknownArgumentType] + xp=xp, + ) + if equal_nan: + out = xp.where(xp.isnan(a) & xp.isnan(b), True, out) + return out + + if xp.isdtype(a.dtype, "bool") or xp.isdtype(b.dtype, "bool"): + if atol >= 1 or rtol >= 1: + return xp.ones_like(a == b) + return a == b + + # integer types + atol = int(atol) + if rtol == 0: + return xp.abs(a - b) <= atol + + # Don't rely on OverflowError, as it is not guaranteed by the Array API. + nrtol = int(1.0 / rtol) + if nrtol > xp.iinfo(b.dtype).max: + # rtol * max_int < 1, so it's inconsequential + return xp.abs(a - b) <= atol + return xp.abs(a - b) <= (atol + xp.abs(b) // nrtol) + + +def kron( + a: Array | complex, + b: Array | complex, + /, + *, + xp: ModuleType | None = None, +) -> Array: + """ + Kronecker product of two arrays. + + Computes the Kronecker product, a composite array made of blocks of the + second array scaled by the first. + + Equivalent to ``numpy.kron`` for NumPy arrays. + + Parameters + ---------- + a, b : Array | int | float | complex + Input arrays or scalars. At least one must be an array. + xp : array_namespace, optional + The standard-compatible namespace for `a` and `b`. Default: infer. + + Returns + ------- + array + The Kronecker product of `a` and `b`. + + Notes + ----- + The function assumes that the number of dimensions of `a` and `b` + are the same, if necessary prepending the smallest with ones. + If ``a.shape = (r0,r1,..,rN)`` and ``b.shape = (s0,s1,...,sN)``, + the Kronecker product has shape ``(r0*s0, r1*s1, ..., rN*SN)``. + The elements are products of elements from `a` and `b`, organized + explicitly by:: + + kron(a,b)[k0,k1,...,kN] = a[i0,i1,...,iN] * b[j0,j1,...,jN] + + where:: + + kt = it * st + jt, t = 0,...,N + + In the common 2-D case (N=1), the block structure can be visualized:: + + [[ a[0,0]*b, a[0,1]*b, ... , a[0,-1]*b ], + [ ... ... ], + [ a[-1,0]*b, a[-1,1]*b, ... , a[-1,-1]*b ]] + + Examples + -------- + >>> import array_api_strict as xp + >>> import array_api_extra as xpx + >>> xpx.kron(xp.asarray([1, 10, 100]), xp.asarray([5, 6, 7]), xp=xp) + Array([ 5, 6, 7, 50, 60, 70, 500, + 600, 700], dtype=array_api_strict.int64) + + >>> xpx.kron(xp.asarray([5, 6, 7]), xp.asarray([1, 10, 100]), xp=xp) + Array([ 5, 50, 500, 6, 60, 600, 7, + 70, 700], dtype=array_api_strict.int64) + + >>> xpx.kron(xp.eye(2), xp.ones((2, 2)), xp=xp) + Array([[1., 1., 0., 0.], + [1., 1., 0., 0.], + [0., 0., 1., 1.], + [0., 0., 1., 1.]], dtype=array_api_strict.float64) + + >>> a = xp.reshape(xp.arange(100), (2, 5, 2, 5)) + >>> b = xp.reshape(xp.arange(24), (2, 3, 4)) + >>> c = xpx.kron(a, b, xp=xp) + >>> c.shape + (2, 10, 6, 20) + >>> I = (1, 3, 0, 2) + >>> J = (0, 2, 1) + >>> J1 = (0,) + J # extend to ndim=4 + >>> S1 = (1,) + b.shape + >>> K = tuple(xp.asarray(I) * xp.asarray(S1) + xp.asarray(J1)) + >>> c[K] == a[I]*b[J] + Array(True, dtype=array_api_strict.bool) + """ + if xp is None: + xp = array_namespace(a, b) + a, b = asarrays(a, b, xp=xp) + + singletons = (1,) * (b.ndim - a.ndim) + a = cast(Array, xp.broadcast_to(a, singletons + a.shape)) + + nd_b, nd_a = b.ndim, a.ndim + nd_max = max(nd_b, nd_a) + if nd_a == 0 or nd_b == 0: + return xp.multiply(a, b) + + a_shape = eager_shape(a) + b_shape = eager_shape(b) + + # Equalise the shapes by prepending smaller one with 1s + a_shape = (1,) * max(0, nd_b - nd_a) + a_shape + b_shape = (1,) * max(0, nd_a - nd_b) + b_shape + + # Insert empty dimensions + a_arr = expand_dims(a, axis=tuple(range(nd_b - nd_a)), xp=xp) + b_arr = expand_dims(b, axis=tuple(range(nd_a - nd_b)), xp=xp) + + # Compute the product + a_arr = expand_dims(a_arr, axis=tuple(range(1, nd_max * 2, 2)), xp=xp) + b_arr = expand_dims(b_arr, axis=tuple(range(0, nd_max * 2, 2)), xp=xp) + result = xp.multiply(a_arr, b_arr) + + # Reshape back and return + res_shape = tuple(a_s * b_s for a_s, b_s in zip(a_shape, b_shape, strict=True)) + return xp.reshape(result, res_shape) + + +def nunique(x: Array, /, *, xp: ModuleType | None = None) -> Array: + """ + Count the number of unique elements in an array. + + Compatible with JAX and Dask, whose laziness would be otherwise + problematic. + + Parameters + ---------- + x : Array + Input array. + xp : array_namespace, optional + The standard-compatible namespace for `x`. Default: infer. + + Returns + ------- + array: 0-dimensional integer array + The number of unique elements in `x`. It can be lazy. + """ + if xp is None: + xp = array_namespace(x) + + if is_jax_array(x): + # size= is JAX-specific + # https://github.com/data-apis/array-api/issues/883 + _, counts = xp.unique_counts(x, size=_compat.size(x)) + return (counts > 0).sum() + + # There are 3 general use cases: + # 1. backend has unique_counts and it returns an array with known shape + # 2. backend has unique_counts and it returns a None-sized array; + # e.g. Dask, ndonnx + # 3. backend does not have unique_counts; e.g. wrapped JAX + if capabilities(xp, device=_compat.device(x))["data-dependent shapes"]: + # xp has unique_counts; O(n) complexity + _, counts = xp.unique_counts(x) + n = _compat.size(counts) + if n is None: + return xp.sum(xp.ones_like(counts)) + return xp.asarray(n, device=_compat.device(x)) + + # xp does not have unique_counts; O(n*logn) complexity + x = xp.reshape(x, (-1,)) + x = xp.sort(x) + mask = x != xp.roll(x, -1) + default_int = default_dtype(xp, "integral", device=_compat.device(x)) + return xp.maximum( + # Special cases: + # - array is size 0 + # - array has all elements equal to each other + xp.astype(xp.any(~mask), default_int), + xp.sum(xp.astype(mask, default_int)), + ) + + +def pad( + x: Array, + pad_width: int | tuple[int, int] | Sequence[tuple[int, int]], + *, + constant_values: complex = 0, + xp: ModuleType, +) -> Array: # numpydoc ignore=PR01,RT01 + """See docstring in `array_api_extra._delegation.py`.""" + # make pad_width a list of length-2 tuples of ints + if isinstance(pad_width, int): + pad_width_seq = [(pad_width, pad_width)] * x.ndim + elif ( + isinstance(pad_width, tuple) + and len(pad_width) == 2 + and all(isinstance(i, int) for i in pad_width) + ): + pad_width_seq = [cast(tuple[int, int], pad_width)] * x.ndim + else: + pad_width_seq = cast(list[tuple[int, int]], list(pad_width)) + + slices: list[slice] = [] + newshape: list[int] = [] + for ax, w_tpl in enumerate(pad_width_seq): + if len(w_tpl) != 2: + msg = f"expect a 2-tuple (before, after), got {w_tpl}." + raise ValueError(msg) + + sh = eager_shape(x)[ax] + + if w_tpl[0] == 0 and w_tpl[1] == 0: + sl = slice(None, None, None) + else: + stop: int | None + start, stop = w_tpl + stop = None if stop == 0 else -stop + + sl = slice(start, stop, None) + sh += w_tpl[0] + w_tpl[1] + + newshape.append(sh) + slices.append(sl) + + padded = xp.full( + tuple(newshape), + fill_value=constant_values, + dtype=x.dtype, + device=_compat.device(x), + ) + return at(padded, tuple(slices)).set(x) + + +def setdiff1d( + x1: Array | complex, + x2: Array | complex, + /, + *, + assume_unique: bool = False, + xp: ModuleType | None = None, +) -> Array: + """ + Find the set difference of two arrays. + + Return the unique values in `x1` that are not in `x2`. + + Parameters + ---------- + x1 : array | int | float | complex | bool + Input array. + x2 : array + Input comparison array. + assume_unique : bool + If ``True``, the input arrays are both assumed to be unique, which + can speed up the calculation. Default is ``False``. + xp : array_namespace, optional + The standard-compatible namespace for `x1` and `x2`. Default: infer. + + Returns + ------- + array + 1D array of values in `x1` that are not in `x2`. The result + is sorted when `assume_unique` is ``False``, but otherwise only sorted + if the input is sorted. + + Examples + -------- + >>> import array_api_strict as xp + >>> import array_api_extra as xpx + + >>> x1 = xp.asarray([1, 2, 3, 2, 4, 1]) + >>> x2 = xp.asarray([3, 4, 5, 6]) + >>> xpx.setdiff1d(x1, x2, xp=xp) + Array([1, 2], dtype=array_api_strict.int64) + """ + if xp is None: + xp = array_namespace(x1, x2) + # https://github.com/microsoft/pyright/issues/10103 + x1_, x2_ = asarrays(x1, x2, xp=xp) + + if assume_unique: + x1_ = xp.reshape(x1_, (-1,)) + x2_ = xp.reshape(x2_, (-1,)) + else: + x1_ = xp.unique_values(x1_) + x2_ = xp.unique_values(x2_) + + return x1_[_helpers.in1d(x1_, x2_, assume_unique=True, invert=True, xp=xp)] + + +def sinc(x: Array, /, *, xp: ModuleType | None = None) -> Array: + r""" + Return the normalized sinc function. + + The sinc function is equal to :math:`\sin(\pi x)/(\pi x)` for any argument + :math:`x\ne 0`. ``sinc(0)`` takes the limit value 1, making ``sinc`` not + only everywhere continuous but also infinitely differentiable. + + .. note:: + + Note the normalization factor of ``pi`` used in the definition. + This is the most commonly used definition in signal processing. + Use ``sinc(x / xp.pi)`` to obtain the unnormalized sinc function + :math:`\sin(x)/x` that is more common in mathematics. + + Parameters + ---------- + x : array + Array (possibly multi-dimensional) of values for which to calculate + ``sinc(x)``. Must have a real floating point dtype. + xp : array_namespace, optional + The standard-compatible namespace for `x`. Default: infer. + + Returns + ------- + array + ``sinc(x)`` calculated elementwise, which has the same shape as the input. + + Notes + ----- + The name sinc is short for "sine cardinal" or "sinus cardinalis". + + The sinc function is used in various signal processing applications, + including in anti-aliasing, in the construction of a Lanczos resampling + filter, and in interpolation. + + For bandlimited interpolation of discrete-time signals, the ideal + interpolation kernel is proportional to the sinc function. + + References + ---------- + #. Weisstein, Eric W. "Sinc Function." From MathWorld--A Wolfram Web + Resource. https://mathworld.wolfram.com/SincFunction.html + #. Wikipedia, "Sinc function", + https://en.wikipedia.org/wiki/Sinc_function + + Examples + -------- + >>> import array_api_strict as xp + >>> import array_api_extra as xpx + >>> x = xp.linspace(-4, 4, 41) + >>> xpx.sinc(x, xp=xp) + Array([-3.89817183e-17, -4.92362781e-02, + -8.40918587e-02, -8.90384387e-02, + -5.84680802e-02, 3.89817183e-17, + 6.68206631e-02, 1.16434881e-01, + 1.26137788e-01, 8.50444803e-02, + -3.89817183e-17, -1.03943254e-01, + -1.89206682e-01, -2.16236208e-01, + -1.55914881e-01, 3.89817183e-17, + 2.33872321e-01, 5.04551152e-01, + 7.56826729e-01, 9.35489284e-01, + 1.00000000e+00, 9.35489284e-01, + 7.56826729e-01, 5.04551152e-01, + 2.33872321e-01, 3.89817183e-17, + -1.55914881e-01, -2.16236208e-01, + -1.89206682e-01, -1.03943254e-01, + -3.89817183e-17, 8.50444803e-02, + 1.26137788e-01, 1.16434881e-01, + 6.68206631e-02, 3.89817183e-17, + -5.84680802e-02, -8.90384387e-02, + -8.40918587e-02, -4.92362781e-02, + -3.89817183e-17], dtype=array_api_strict.float64) + """ + if xp is None: + xp = array_namespace(x) + + if not xp.isdtype(x.dtype, "real floating"): + err_msg = "`x` must have a real floating data type." + raise ValueError(err_msg) + # no scalars in `where` - array-api#807 + y = xp.pi * xp.where( + xp.astype(x, xp.bool), + x, + xp.asarray(xp.finfo(x.dtype).eps, dtype=x.dtype, device=_compat.device(x)), + ) + return xp.sin(y) / y diff --git a/src/array_api_extra/_lib/_lazy.py b/src/array_api_extra/_lib/_lazy.py new file mode 100644 index 00000000..d5095001 --- /dev/null +++ b/src/array_api_extra/_lib/_lazy.py @@ -0,0 +1,357 @@ +"""Public API Functions.""" + +from __future__ import annotations + +import math +from collections.abc import Callable, Sequence +from functools import partial, wraps +from types import ModuleType +from typing import TYPE_CHECKING, Any, ParamSpec, TypeAlias, cast, overload + +from ._funcs import broadcast_shapes +from ._utils import _compat +from ._utils._compat import ( + array_namespace, + is_dask_namespace, + is_jax_namespace, +) +from ._utils._helpers import is_python_scalar +from ._utils._typing import Array, DType + +if TYPE_CHECKING: # pragma: no cover + import numpy as np + from numpy.typing import ArrayLike + + NumPyObject: TypeAlias = np.ndarray[Any, Any] | np.generic +else: + # Sphinx hack + NumPyObject = Any + +P = ParamSpec("P") + + +@overload +def lazy_apply( # type: ignore[valid-type] + func: Callable[P, Array | ArrayLike], + *args: Array | complex | None, + shape: tuple[int | None, ...] | None = None, + dtype: DType | None = None, + as_numpy: bool = False, + xp: ModuleType | None = None, + **kwargs: P.kwargs, # pyright: ignore[reportGeneralTypeIssues] +) -> Array: ... # numpydoc ignore=GL08 + + +@overload +def lazy_apply( # type: ignore[valid-type] + func: Callable[P, Sequence[Array | ArrayLike]], + *args: Array | complex | None, + shape: Sequence[tuple[int | None, ...]], + dtype: Sequence[DType] | None = None, + as_numpy: bool = False, + xp: ModuleType | None = None, + **kwargs: P.kwargs, # pyright: ignore[reportGeneralTypeIssues] +) -> tuple[Array, ...]: ... # numpydoc ignore=GL08 + + +def lazy_apply( # type: ignore[valid-type] # numpydoc ignore=GL07,SA04 + func: Callable[P, Array | ArrayLike | Sequence[Array | ArrayLike]], + *args: Array | complex | None, + shape: tuple[int | None, ...] | Sequence[tuple[int | None, ...]] | None = None, + dtype: DType | Sequence[DType] | None = None, + as_numpy: bool = False, + xp: ModuleType | None = None, + **kwargs: P.kwargs, # pyright: ignore[reportGeneralTypeIssues] +) -> Array | tuple[Array, ...]: + """ + Lazily apply an eager function. + + If the backend of the input arrays is lazy, e.g. Dask or jitted JAX, the execution + of the function is delayed until the graph is materialized; if it's eager, the + function is executed immediately. + + Parameters + ---------- + func : callable + The function to apply. + + It must accept one or more array API compliant arrays as positional arguments. + If `as_numpy=True`, inputs are converted to NumPy before they are passed to + `func`. + It must return either a single array-like or a sequence of array-likes. + + `func` must be a pure function, i.e. without side effects, as depending on the + backend it may be executed more than once or never. + *args : Array | int | float | complex | bool | None + One or more Array API compliant arrays, Python scalars, or None's. + + If `as_numpy=True`, you need to be able to apply :func:`numpy.asarray` to + non-None args to convert them to NumPy; read notes below about specific + backends. + shape : tuple[int | None, ...] | Sequence[tuple[int | None, ...]], optional + Output shape or sequence of output shapes, one for each output of `func`. + Default: assume single output and broadcast shapes of the input arrays. + dtype : DType | Sequence[DType], optional + Output dtype or sequence of output dtypes, one for each output of `func`. + dtype(s) must belong to the same array namespace as the input arrays. + Default: infer the result type(s) from the input arrays. + as_numpy : bool, optional + If True, convert the input arrays to NumPy before passing them to `func`. + This is particularly useful to make NumPy-only functions, e.g. written in Cython + or Numba, work transparently with array API-compliant arrays. + Default: False. + xp : array_namespace, optional + The standard-compatible namespace for `args`. Default: infer. + **kwargs : Any, optional + Additional keyword arguments to pass verbatim to `func`. + They cannot contain Array objects. + + Returns + ------- + Array | tuple[Array, ...] + The result(s) of `func` applied to the input arrays, wrapped in the same + array namespace as the inputs. + If shape is omitted or a single `tuple[int | None, ...]`, return a single array. + Otherwise, return a tuple of arrays. + + Notes + ----- + JAX + This allows applying eager functions to jitted JAX arrays, which are lazy. + The function won't be applied until the JAX array is materialized. + When running inside ``jax.jit``, `shape` must be fully known, i.e. it cannot + contain any `None` elements. + + .. warning:: + + `func` must never raise inside ``jax.jit``, as the resulting behavior is + undefined. + + Using this with `as_numpy=False` is particularly useful to apply non-jittable + JAX functions to arrays on GPU devices. + If ``as_numpy=True``, the :doc:`jax:transfer_guard` may prevent arrays on a GPU + device from being transferred back to CPU. This is treated as an implicit + transfer. + + PyTorch, CuPy + If ``as_numpy=True``, these backends raise by default if you attempt to convert + arrays on a GPU device to NumPy. + + Sparse + If ``as_numpy=True``, by default sparse prevents implicit densification through + :func:`numpy.asarray`. `This safety mechanism can be disabled + `_. + + Dask + This allows applying eager functions to Dask arrays. + The Dask graph won't be computed until the user calls ``compute()`` or + ``persist()`` down the line. + + The function name will be prominently visible on the user-facing Dask + dashboard and on Prometheus metrics, so it is recommended for it to be + meaningful. + + `lazy_apply` doesn't know if `func` reduces along any axes; also, shape + changes are non-trivial in chunked Dask arrays. For these reasons, all inputs + will be rechunked into a single chunk. + + .. warning:: + + The whole operation needs to fit in memory all at once on a single worker. + + The outputs will also be returned as a single chunk and you should consider + rechunking them into smaller chunks afterwards. + + If you want to distribute the calculation across multiple workers, you + should use :func:`dask.array.map_blocks`, :func:`dask.array.map_overlap`, + :func:`dask.array.blockwise`, or a native Dask wrapper instead of + `lazy_apply`. + + Dask wrapping around other backends + If ``as_numpy=False``, `func` will receive in input eager arrays of the meta + namespace, as defined by the ``._meta`` attribute of the input Dask arrays. + The outputs of `func` will be wrapped by the meta namespace, and then wrapped + again by Dask. + + Raises + ------ + ValueError + When ``xp=jax.numpy``, the output `shape` is unknown (it contains ``None`` on + one or more axes) and this function was called inside ``jax.jit``. + RuntimeError + When ``xp=sparse`` and auto-densification is disabled. + Exception (backend-specific) + When the backend disallows implicit device to host transfers and the input + arrays are on a non-CPU device, e.g. on GPU. + + See Also + -------- + jax.transfer_guard + jax.pure_callback + dask.array.map_blocks + dask.array.map_overlap + dask.array.blockwise + """ + args_not_none = [arg for arg in args if arg is not None] + array_args = [arg for arg in args_not_none if not is_python_scalar(arg)] + if not array_args: + msg = "Must have at least one argument array" + raise ValueError(msg) + if xp is None: + xp = array_namespace(*args) + + # Normalize and validate shape and dtype + shapes: list[tuple[int | None, ...]] + dtypes: list[DType] + multi_output = False + + if shape is None: + shapes = [broadcast_shapes(*(arg.shape for arg in array_args))] + elif all(isinstance(s, int | None) for s in shape): + # Do not test for shape to be a tuple + # https://github.com/data-apis/array-api/issues/891#issuecomment-2637430522 + shapes = [cast(tuple[int | None, ...], shape)] + else: + shapes = list(shape) # type: ignore[arg-type] # pyright: ignore[reportAssignmentType] + multi_output = True + + if dtype is None: + dtypes = [xp.result_type(*args_not_none)] * len(shapes) + elif multi_output: + if not isinstance(dtype, Sequence): + msg = "Got multiple shapes but only one dtype" + raise ValueError(msg) + dtypes = list(dtype) # pyright: ignore[reportUnknownArgumentType] + else: + if isinstance(dtype, Sequence): + msg = "Got single shape but multiple dtypes" + raise ValueError(msg) + + dtypes = [dtype] + + if len(shapes) != len(dtypes): + msg = f"Got {len(shapes)} shapes and {len(dtypes)} dtypes" + raise ValueError(msg) + del shape + del dtype + # End of shape and dtype parsing + + # Backend-specific branches + if is_dask_namespace(xp): + import dask + + metas: list[Array] = [arg._meta for arg in array_args] # pylint: disable=protected-access # pyright: ignore[reportAttributeAccessIssue] + meta_xp = array_namespace(*metas) + + wrapped = dask.delayed( # type: ignore[attr-defined] # pyright: ignore[reportPrivateImportUsage] + _lazy_apply_wrapper(func, as_numpy, multi_output, meta_xp), + pure=True, + ) + # This finalizes each arg, which is the same as arg.rechunk(-1). + # Please read docstring above for why we're not using + # dask.array.map_blocks or dask.array.blockwise! + delayed_out = wrapped(*args, **kwargs) + + out = tuple( + xp.from_delayed( + delayed_out[i], # pyright: ignore[reportIndexIssue] + # Dask's unknown shapes diverge from the Array API specification + shape=tuple(math.nan if s is None else s for s in shape), + dtype=dtype, + meta=metas[0], + ) + for i, (shape, dtype) in enumerate(zip(shapes, dtypes, strict=True)) + ) + + elif is_jax_namespace(xp) and _is_jax_jit_enabled(xp): + # Delay calling func with jax.pure_callback, which will forward to func eager + # JAX arrays. Do not use jax.pure_callback when running outside of the JIT, + # as it does not support raising exceptions: + # https://github.com/jax-ml/jax/issues/26102 + import jax + + if any(None in shape for shape in shapes): + msg = "Output shape must be fully known when running inside jax.jit" + raise ValueError(msg) + + # Shield kwargs from being coerced into JAX arrays. + # jax.pure_callback calls jax.jit under the hood, but without the chance of + # passing static_argnames / static_argnums. + wrapped = _lazy_apply_wrapper( + partial(func, **kwargs), as_numpy, multi_output, xp + ) + + # suppress unused-ignore to run mypy in -e lint as well as -e dev + out = cast( # type: ignore[bad-cast,unused-ignore] + tuple[Array, ...], + jax.pure_callback( + wrapped, + tuple( + jax.ShapeDtypeStruct(shape, dtype) # pyright: ignore[reportUnknownArgumentType] + for shape, dtype in zip(shapes, dtypes, strict=True) + ), + *args, + ), + ) + + else: + # Eager backends, including non-jitted JAX + wrapped = _lazy_apply_wrapper(func, as_numpy, multi_output, xp) + out = wrapped(*args, **kwargs) + + return out if multi_output else out[0] + + +def _is_jax_jit_enabled(xp: ModuleType) -> bool: # numpydoc ignore=PR01,RT01 + """Return True if this function is being called inside ``jax.jit``.""" + import jax # pylint: disable=import-outside-toplevel + + x = xp.asarray(False) + try: + return bool(x) + except jax.errors.TracerBoolConversionError: + return True + + +def _lazy_apply_wrapper( # numpydoc ignore=PR01,RT01 + func: Callable[..., Array | ArrayLike | Sequence[Array | ArrayLike]], + as_numpy: bool, + multi_output: bool, + xp: ModuleType, +) -> Callable[..., tuple[Array, ...]]: + """ + Helper of `lazy_apply`. + + Given a function that accepts one or more arrays as positional arguments and returns + a single array-like or a sequence of array-likes, return a function that accepts the + same number of Array API arrays and always returns a tuple of Array API array. + + Any keyword arguments are passed through verbatim to the wrapped function. + """ + + # On Dask, @wraps causes the graph key to contain the wrapped function's name + @wraps(func) + def wrapper( + *args: Array | complex | None, **kwargs: Any + ) -> tuple[Array, ...]: # numpydoc ignore=GL08 + args_list = [] + device = None + for arg in args: + if arg is not None and not is_python_scalar(arg): + if device is None: + device = _compat.device(arg) + if as_numpy: + import numpy as np + + arg = cast(Array, np.asarray(arg)) # pyright: ignore[reportInvalidCast] # noqa: PLW2901 + args_list.append(arg) + assert device is not None + + out = func(*args_list, **kwargs) + + if multi_output: + assert isinstance(out, Sequence) + return tuple(xp.asarray(o, device=device) for o in out) + return (xp.asarray(out, device=device),) + + return wrapper diff --git a/src/array_api_extra/_lib/_testing.py b/src/array_api_extra/_lib/_testing.py new file mode 100644 index 00000000..30e2f1ef --- /dev/null +++ b/src/array_api_extra/_lib/_testing.py @@ -0,0 +1,304 @@ +""" +Testing utilities. + +Note that this is private API; don't expect it to be stable. +See also ..testing for public testing utilities. +""" + +from __future__ import annotations + +import math +from types import ModuleType +from typing import Any, cast + +import numpy as np +import pytest + +from ._utils._compat import ( + array_namespace, + is_array_api_strict_namespace, + is_cupy_namespace, + is_dask_namespace, + is_jax_namespace, + is_numpy_namespace, + is_pydata_sparse_namespace, + is_torch_array, + is_torch_namespace, + to_device, +) +from ._utils._typing import Array, Device + +__all__ = ["as_numpy_array", "xp_assert_close", "xp_assert_equal", "xp_assert_less"] + + +def _check_ns_shape_dtype( + actual: Array, + desired: Array, + check_dtype: bool, + check_shape: bool, + check_scalar: bool, +) -> ModuleType: # numpydoc ignore=RT03 + """ + Assert that namespace, shape and dtype of the two arrays match. + + Parameters + ---------- + actual : Array + The array produced by the tested function. + desired : Array + The expected array (typically hardcoded). + check_dtype, check_shape : bool, default: True + Whether to check agreement between actual and desired dtypes and shapes + check_scalar : bool, default: False + NumPy only: whether to check agreement between actual and desired types - + 0d array vs scalar. + + Returns + ------- + Arrays namespace. + """ + actual_xp = array_namespace(actual) # Raises on scalars and lists + desired_xp = array_namespace(desired) + + msg = f"namespaces do not match: {actual_xp} != f{desired_xp}" + assert actual_xp == desired_xp, msg + + # Dask uses nan instead of None for unknown shapes + actual_shape = cast(tuple[float, ...], actual.shape) + desired_shape = cast(tuple[float, ...], desired.shape) + assert None not in actual_shape # Requires explicit support + assert None not in desired_shape + if is_dask_namespace(desired_xp): + if any(math.isnan(i) for i in actual_shape): + actual_shape = actual.compute().shape # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] + if any(math.isnan(i) for i in desired_shape): + desired_shape = desired.compute().shape # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] + + if check_shape: + msg = f"shapes do not match: {actual_shape} != f{desired_shape}" + assert actual_shape == desired_shape, msg + else: + # Ignore shape, but check flattened size. This is normally done by + # np.testing.assert_array_equal etc even when strict=False, but not for + # non-materializable arrays. + actual_size = math.prod(actual_shape) # pyright: ignore[reportUnknownArgumentType] + desired_size = math.prod(desired_shape) # pyright: ignore[reportUnknownArgumentType] + msg = f"sizes do not match: {actual_size} != f{desired_size}" + assert actual_size == desired_size, msg + + if check_dtype: + msg = f"dtypes do not match: {actual.dtype} != {desired.dtype}" + assert actual.dtype == desired.dtype, msg + + if is_numpy_namespace(actual_xp) and check_scalar: + # only NumPy distinguishes between scalars and arrays; we do if check_scalar. + _msg = ( + "array-ness does not match:\n Actual: " + f"{type(actual)}\n Desired: {type(desired)}" + ) + assert np.isscalar(actual) == np.isscalar(desired), _msg + + return desired_xp + + +def _is_materializable(x: Array) -> bool: + """ + Return True if you can call `as_numpy_array(x)`; False otherwise. + """ + # Important: here we assume that we're not tracing - + # e.g. we're not inside `jax.jit`` nor `cupy.cuda.Stream.begin_capture`. + return not is_torch_array(x) or x.device.type != "meta" # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] + + +def as_numpy_array(array: Array, *, xp: ModuleType) -> np.typing.NDArray[Any]: + """ + Convert array to NumPy, bypassing GPU-CPU transfer guards and densification guards. + """ + if is_cupy_namespace(xp): + return xp.asnumpy(array) + if is_pydata_sparse_namespace(xp): + return array.todense() # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] + + if is_torch_namespace(xp): + array = to_device(array, "cpu") + if is_array_api_strict_namespace(xp): + cpu: Device = xp.Device("CPU_DEVICE") + array = to_device(array, cpu) + if is_jax_namespace(xp): + import jax + + # Note: only needed if the transfer guard is enabled + cpu = cast(Device, jax.devices("cpu")[0]) + array = to_device(array, cpu) + + return np.asarray(array) + + +def xp_assert_equal( + actual: Array, + desired: Array, + *, + err_msg: str = "", + check_dtype: bool = True, + check_shape: bool = True, + check_scalar: bool = False, +) -> None: + """ + Array-API compatible version of `np.testing.assert_array_equal`. + + Parameters + ---------- + actual : Array + The array produced by the tested function. + desired : Array + The expected array (typically hardcoded). + err_msg : str, optional + Error message to display on failure. + check_dtype, check_shape : bool, default: True + Whether to check agreement between actual and desired dtypes and shapes + check_scalar : bool, default: False + NumPy only: whether to check agreement between actual and desired types - + 0d array vs scalar. + + See Also + -------- + xp_assert_close : Similar function for inexact equality checks. + numpy.testing.assert_array_equal : Similar function for NumPy arrays. + """ + xp = _check_ns_shape_dtype(actual, desired, check_dtype, check_shape, check_scalar) + if not _is_materializable(actual): + return + actual_np = as_numpy_array(actual, xp=xp) + desired_np = as_numpy_array(desired, xp=xp) + np.testing.assert_array_equal(actual_np, desired_np, err_msg=err_msg) + + +def xp_assert_less( + x: Array, + y: Array, + *, + err_msg: str = "", + check_dtype: bool = True, + check_shape: bool = True, + check_scalar: bool = False, +) -> None: + """ + Array-API compatible version of `np.testing.assert_array_less`. + + Parameters + ---------- + x, y : Array + The arrays to compare according to ``x < y`` (elementwise). + err_msg : str, optional + Error message to display on failure. + check_dtype, check_shape : bool, default: True + Whether to check agreement between actual and desired dtypes and shapes + check_scalar : bool, default: False + NumPy only: whether to check agreement between actual and desired types - + 0d array vs scalar. + + See Also + -------- + xp_assert_close : Similar function for inexact equality checks. + numpy.testing.assert_array_equal : Similar function for NumPy arrays. + """ + xp = _check_ns_shape_dtype(x, y, check_dtype, check_shape, check_scalar) + if not _is_materializable(x): + return + x_np = as_numpy_array(x, xp=xp) + y_np = as_numpy_array(y, xp=xp) + np.testing.assert_array_less(x_np, y_np, err_msg=err_msg) + + +def xp_assert_close( + actual: Array, + desired: Array, + *, + rtol: float | None = None, + atol: float = 0, + err_msg: str = "", + check_dtype: bool = True, + check_shape: bool = True, + check_scalar: bool = False, +) -> None: + """ + Array-API compatible version of `np.testing.assert_allclose`. + + Parameters + ---------- + actual : Array + The array produced by the tested function. + desired : Array + The expected array (typically hardcoded). + rtol : float, optional + Relative tolerance. Default: dtype-dependent. + atol : float, optional + Absolute tolerance. Default: 0. + err_msg : str, optional + Error message to display on failure. + check_dtype, check_shape : bool, default: True + Whether to check agreement between actual and desired dtypes and shapes + check_scalar : bool, default: False + NumPy only: whether to check agreement between actual and desired types - + 0d array vs scalar. + + See Also + -------- + xp_assert_equal : Similar function for exact equality checks. + isclose : Public function for checking closeness. + numpy.testing.assert_allclose : Similar function for NumPy arrays. + + Notes + ----- + The default `atol` and `rtol` differ from `xp.all(xpx.isclose(a, b))`. + """ + xp = _check_ns_shape_dtype(actual, desired, check_dtype, check_shape, check_scalar) + if not _is_materializable(actual): + return + + if rtol is None: + if xp.isdtype(actual.dtype, ("real floating", "complex floating")): + # multiplier of 4 is used as for `np.float64` this puts the default `rtol` + # roughly half way between sqrt(eps) and the default for + # `numpy.testing.assert_allclose`, 1e-7 + rtol = xp.finfo(actual.dtype).eps ** 0.5 * 4 + else: + rtol = 1e-7 + + actual_np = as_numpy_array(actual, xp=xp) + desired_np = as_numpy_array(desired, xp=xp) + np.testing.assert_allclose( # pyright: ignore[reportCallIssue] + actual_np, + desired_np, + rtol=rtol, # pyright: ignore[reportArgumentType] + atol=atol, + err_msg=err_msg, + ) + + +def xfail( + request: pytest.FixtureRequest, *, reason: str, strict: bool | None = None +) -> None: + """ + XFAIL the currently running test. + + Unlike ``pytest.xfail``, allow rest of test to execute instead of immediately + halting it, so that it may result in a XPASS. + xref https://github.com/pandas-dev/pandas/issues/38902 + + Parameters + ---------- + request : pytest.FixtureRequest + ``request`` argument of the test function. + reason : str + Reason for the expected failure. + strict: bool, optional + If True, the test will be marked as failed if it passes. + If False, the test will be marked as passed if it fails. + Default: ``xfail_strict`` value in ``pyproject.toml``, or False if absent. + """ + if strict is not None: + marker = pytest.mark.xfail(reason=reason, strict=strict) + else: + marker = pytest.mark.xfail(reason=reason) + request.node.add_marker(marker) diff --git a/src/array_api_extra/_lib/_typing.py b/src/array_api_extra/_lib/_typing.py deleted file mode 100644 index 13079807..00000000 --- a/src/array_api_extra/_lib/_typing.py +++ /dev/null @@ -1,10 +0,0 @@ -from __future__ import annotations # https://github.com/pylint-dev/pylint/pull/9990 - -from types import ModuleType -from typing import Any - -# To be changed to a Protocol later (see data-apis/array-api#589) -Array = Any # type: ignore[no-any-explicit] -Device = Any # type: ignore[no-any-explicit] - -__all__ = ["Array", "Device", "ModuleType"] diff --git a/src/array_api_extra/_lib/_utils.py b/src/array_api_extra/_lib/_utils.py deleted file mode 100644 index bf65340e..00000000 --- a/src/array_api_extra/_lib/_utils.py +++ /dev/null @@ -1,65 +0,0 @@ -from __future__ import annotations # https://github.com/pylint-dev/pylint/pull/9990 - -import typing - -if typing.TYPE_CHECKING: - from ._typing import Array, ModuleType - -from . import _compat - -__all__ = ["in1d"] - - -def in1d( - x1: Array, - x2: Array, - /, - *, - assume_unique: bool = False, - invert: bool = False, - xp: ModuleType, -) -> Array: - """Checks whether each element of an array is also present in a - second array. - - Returns a boolean array the same length as `x1` that is True - where an element of `x1` is in `x2` and False otherwise. - - This function has been adapted using the original implementation - present in numpy: - https://github.com/numpy/numpy/blob/v1.26.0/numpy/lib/arraysetops.py#L524-L758 - """ - - # This code is run to make the code significantly faster - if x2.shape[0] < 10 * x1.shape[0] ** 0.145: - if invert: - mask = xp.ones(x1.shape[0], dtype=xp.bool, device=x1.device) - for a in x2: - mask &= x1 != a - else: - mask = xp.zeros(x1.shape[0], dtype=xp.bool, device=x1.device) - for a in x2: - mask |= x1 == a - return mask - - rev_idx = xp.empty(0) # placeholder - if not assume_unique: - x1, rev_idx = xp.unique_inverse(x1) - x2 = xp.unique_values(x2) - - ar = xp.concat((x1, x2)) - device_ = _compat.device(ar) - # We need this to be a stable sort. - order = xp.argsort(ar, stable=True) - reverse_order = xp.argsort(order, stable=True) - sar = xp.take(ar, order, axis=0) - if sar.size >= 1: - bool_ar = sar[1:] != sar[:-1] if invert else sar[1:] == sar[:-1] - else: - bool_ar = xp.asarray([False]) if invert else xp.asarray([True]) - flag = xp.concat((bool_ar, xp.asarray([invert], device=device_))) - ret = xp.take(flag, reverse_order, axis=0) - - if assume_unique: - return ret[: x1.shape[0]] - return xp.take(ret, rev_idx, axis=0) diff --git a/src/array_api_extra/_lib/_utils/__init__.py b/src/array_api_extra/_lib/_utils/__init__.py new file mode 100644 index 00000000..3628c45f --- /dev/null +++ b/src/array_api_extra/_lib/_utils/__init__.py @@ -0,0 +1 @@ +"""Modules housing private utility functions.""" diff --git a/src/array_api_extra/_lib/_utils/_compat.py b/src/array_api_extra/_lib/_utils/_compat.py new file mode 100644 index 00000000..82ce76b8 --- /dev/null +++ b/src/array_api_extra/_lib/_utils/_compat.py @@ -0,0 +1,74 @@ +"""Acquire helpers from array-api-compat.""" +# Allow packages that vendor both `array-api-extra` and +# `array-api-compat` to override the import location + +# pylint: disable=duplicate-code +try: + from ...._array_api_compat_vendor import ( + array_namespace, + device, + is_array_api_obj, + is_array_api_strict_namespace, + is_cupy_array, + is_cupy_namespace, + is_dask_array, + is_dask_namespace, + is_jax_array, + is_jax_namespace, + is_lazy_array, + is_numpy_array, + is_numpy_namespace, + is_pydata_sparse_array, + is_pydata_sparse_namespace, + is_torch_array, + is_torch_namespace, + is_writeable_array, + size, + to_device, + ) +except ImportError: + from array_api_compat import ( + array_namespace, + device, + is_array_api_obj, + is_array_api_strict_namespace, + is_cupy_array, + is_cupy_namespace, + is_dask_array, + is_dask_namespace, + is_jax_array, + is_jax_namespace, + is_lazy_array, + is_numpy_array, + is_numpy_namespace, + is_pydata_sparse_array, + is_pydata_sparse_namespace, + is_torch_array, + is_torch_namespace, + is_writeable_array, + size, + to_device, + ) + +__all__ = [ + "array_namespace", + "device", + "is_array_api_obj", + "is_array_api_strict_namespace", + "is_cupy_array", + "is_cupy_namespace", + "is_dask_array", + "is_dask_namespace", + "is_jax_array", + "is_jax_namespace", + "is_lazy_array", + "is_numpy_array", + "is_numpy_namespace", + "is_pydata_sparse_array", + "is_pydata_sparse_namespace", + "is_torch_array", + "is_torch_namespace", + "is_writeable_array", + "size", + "to_device", +] diff --git a/src/array_api_extra/_lib/_utils/_compat.pyi b/src/array_api_extra/_lib/_utils/_compat.pyi new file mode 100644 index 00000000..95c6bc8a --- /dev/null +++ b/src/array_api_extra/_lib/_utils/_compat.pyi @@ -0,0 +1,45 @@ +"""Static type stubs for `_compat.py`.""" + +# https://github.com/scikit-learn/scikit-learn/pull/27910#issuecomment-2568023972 +from __future__ import annotations + +from types import ModuleType +from typing import Any, TypeGuard + +# TODO import from typing (requires Python >=3.13) +from typing_extensions import TypeIs + +from ._typing import Array, Device + +# pylint: disable=missing-class-docstring,unused-argument + +def array_namespace( + *xs: Array | complex | None, + api_version: str | None = None, + use_compat: bool | None = None, +) -> ModuleType: ... +def device(x: Array, /) -> Device: ... +def is_array_api_obj(x: object, /) -> TypeIs[Array]: ... +def is_array_api_strict_namespace(xp: ModuleType, /) -> bool: ... +def is_cupy_namespace(xp: ModuleType, /) -> bool: ... +def is_dask_namespace(xp: ModuleType, /) -> bool: ... +def is_jax_namespace(xp: ModuleType, /) -> bool: ... +def is_numpy_namespace(xp: ModuleType, /) -> bool: ... +def is_pydata_sparse_namespace(xp: ModuleType, /) -> bool: ... +def is_torch_namespace(xp: ModuleType, /) -> bool: ... +def is_cupy_array(x: object, /) -> TypeGuard[Array]: ... +def is_dask_array(x: object, /) -> TypeGuard[Array]: ... +def is_jax_array(x: object, /) -> TypeGuard[Array]: ... +def is_numpy_array(x: object, /) -> TypeGuard[Array]: ... +def is_pydata_sparse_array(x: object, /) -> TypeGuard[Array]: ... +def is_torch_array(x: object, /) -> TypeGuard[Array]: ... +def is_lazy_array(x: object, /) -> TypeGuard[Array]: ... +def is_writeable_array(x: object, /) -> TypeGuard[Array]: ... +def size(x: Array, /) -> int | None: ... +def to_device( + x: Array, + device: Device, # pylint: disable=redefined-outer-name + /, + *, + stream: int | Any | None = None, +) -> Array: ... diff --git a/src/array_api_extra/_lib/_utils/_helpers.py b/src/array_api_extra/_lib/_utils/_helpers.py new file mode 100644 index 00000000..d177b376 --- /dev/null +++ b/src/array_api_extra/_lib/_utils/_helpers.py @@ -0,0 +1,598 @@ +"""Helper functions used by `array_api_extra/_funcs.py`.""" + +from __future__ import annotations + +import io +import math +import pickle +import types +from collections.abc import Callable, Generator, Iterable +from functools import wraps +from types import ModuleType +from typing import ( + TYPE_CHECKING, + Any, + ClassVar, + Generic, + Literal, + ParamSpec, + TypeAlias, + TypeVar, + cast, +) + +from . import _compat +from ._compat import ( + array_namespace, + is_array_api_obj, + is_dask_namespace, + is_jax_namespace, + is_numpy_array, + is_pydata_sparse_namespace, + is_torch_namespace, +) +from ._typing import Array, Device + +if TYPE_CHECKING: # pragma: no cover + # TODO import from typing (requires Python >=3.12 and >=3.13) + from typing_extensions import TypeIs, override +else: + + def override(func): + return func + + +P = ParamSpec("P") +T = TypeVar("T") + + +__all__ = [ + "asarrays", + "capabilities", + "eager_shape", + "in1d", + "is_python_scalar", + "jax_autojit", + "mean", + "meta_namespace", + "pickle_flatten", + "pickle_unflatten", +] + + +def in1d( + x1: Array, + x2: Array, + /, + *, + assume_unique: bool = False, + invert: bool = False, + xp: ModuleType | None = None, +) -> Array: # numpydoc ignore=PR01,RT01 + """ + Check whether each element of an array is also present in a second array. + + Returns a boolean array the same length as `x1` that is True + where an element of `x1` is in `x2` and False otherwise. + + This function has been adapted using the original implementation + present in numpy: + https://github.com/numpy/numpy/blob/v1.26.0/numpy/lib/arraysetops.py#L524-L758 + """ + if xp is None: + xp = array_namespace(x1, x2) + + x1_shape = eager_shape(x1) + x2_shape = eager_shape(x2) + + # This code is run to make the code significantly faster + if x2_shape[0] < 10 * x1_shape[0] ** 0.145 and isinstance(x2, Iterable): + if invert: + mask = xp.ones(x1_shape[0], dtype=xp.bool, device=_compat.device(x1)) + for a in x2: + mask &= x1 != a + else: + mask = xp.zeros(x1_shape[0], dtype=xp.bool, device=_compat.device(x1)) + for a in x2: + mask |= x1 == a + return mask + + rev_idx = xp.empty(0) # placeholder + if not assume_unique: + x1, rev_idx = xp.unique_inverse(x1) + x2 = xp.unique_values(x2) + + ar = xp.concat((x1, x2)) + device_ = _compat.device(ar) + # We need this to be a stable sort. + order = xp.argsort(ar, stable=True) + reverse_order = xp.argsort(order, stable=True) + sar = xp.take(ar, order, axis=0) + ar_size = _compat.size(sar) + assert ar_size is not None, "xp.unique*() on lazy backends raises" + if ar_size >= 1: + bool_ar = sar[1:] != sar[:-1] if invert else sar[1:] == sar[:-1] + else: + bool_ar = xp.asarray([False]) if invert else xp.asarray([True]) + flag = xp.concat((bool_ar, xp.asarray([invert], device=device_))) + ret = xp.take(flag, reverse_order, axis=0) + + if assume_unique: + return ret[: x1.shape[0]] + return xp.take(ret, rev_idx, axis=0) + + +def mean( + x: Array, + /, + *, + axis: int | tuple[int, ...] | None = None, + keepdims: bool = False, + xp: ModuleType | None = None, +) -> Array: # numpydoc ignore=PR01,RT01 + """ + Complex mean, https://github.com/data-apis/array-api/issues/846. + """ + if xp is None: + xp = array_namespace(x) + + if xp.isdtype(x.dtype, "complex floating"): + x_real = xp.real(x) + x_imag = xp.imag(x) + mean_real = xp.mean(x_real, axis=axis, keepdims=keepdims) + mean_imag = xp.mean(x_imag, axis=axis, keepdims=keepdims) + return mean_real + (mean_imag * xp.asarray(1j)) + return xp.mean(x, axis=axis, keepdims=keepdims) + + +def is_python_scalar(x: object) -> TypeIs[complex]: # numpydoc ignore=PR01,RT01 + """Return True if `x` is a Python scalar, False otherwise.""" + # isinstance(x, float) returns True for np.float64 + # isinstance(x, complex) returns True for np.complex128 + # bool is a subclass of int + return isinstance(x, int | float | complex) and not is_numpy_array(x) + + +def asarrays( + a: Array | complex, + b: Array | complex, + xp: ModuleType, +) -> tuple[Array, Array]: + """ + Ensure both `a` and `b` are arrays. + + If `b` is a python scalar, it is converted to the same dtype as `a`, and vice versa. + + Behavior is not specified when mixing a Python ``float`` and an array with an + integer data type; this may give ``float32``, ``float64``, or raise an exception. + Behavior is implementation-specific. + + Similarly, behavior is not specified when mixing a Python ``complex`` and an array + with a real-valued data type; this may give ``complex64``, ``complex128``, or raise + an exception. Behavior is implementation-specific. + + Parameters + ---------- + a, b : Array | int | float | complex | bool + Input arrays or scalars. At least one must be an array. + xp : array_namespace, optional + The standard-compatible namespace for `x`. Default: infer. + + Returns + ------- + Array, Array + The input arrays, possibly converted to arrays if they were scalars. + + See Also + -------- + mixing-arrays-with-python-scalars : Array API specification for the behavior. + """ + a_scalar = is_python_scalar(a) + b_scalar = is_python_scalar(b) + if not a_scalar and not b_scalar: + # This includes misc. malformed input e.g. str + return a, b # type: ignore[return-value] + + swap = False + if a_scalar: + swap = True + b, a = a, b + + if is_array_api_obj(a): + # a is an Array API object + # b is a int | float | complex | bool + xa = a + + # https://data-apis.org/array-api/draft/API_specification/type_promotion.html#mixing-arrays-with-python-scalars + same_dtype = { + bool: "bool", + int: ("integral", "real floating", "complex floating"), + float: ("real floating", "complex floating"), + complex: "complex floating", + } + kind = same_dtype[type(cast(complex, b))] + if xp.isdtype(a.dtype, kind): + xb = xp.asarray(b, dtype=a.dtype) + else: + # Undefined behaviour. Let the function deal with it, if it can. + xb = xp.asarray(b) + + else: + # Neither a nor b are Array API objects. + # Note: we can only reach this point when one explicitly passes + # xp=xp to the calling function; otherwise we fail earlier on + # array_namespace(a, b). + xa, xb = xp.asarray(a), xp.asarray(b) + + return (xb, xa) if swap else (xa, xb) + + +def ndindex(*x: int) -> Generator[tuple[int, ...]]: + """ + Generate all N-dimensional indices for a given array shape. + + Given the shape of an array, an ndindex instance iterates over the N-dimensional + index of the array. At each iteration a tuple of indices is returned, the last + dimension is iterated over first. + + This has an identical API to numpy.ndindex. + + Parameters + ---------- + *x : int + The shape of the array. + """ + if not x: + yield () + return + for i in ndindex(*x[:-1]): + for j in range(x[-1]): + yield *i, j + + +def eager_shape(x: Array, /) -> tuple[int, ...]: + """ + Return shape of an array. Raise if shape is not fully defined. + + Parameters + ---------- + x : Array + Input array. + + Returns + ------- + tuple[int, ...] + Shape of the array. + """ + shape = x.shape + # Dask arrays uses non-standard NaN instead of None + if any(s is None or math.isnan(s) for s in shape): + msg = "Unsupported lazy shape" + raise TypeError(msg) + return cast(tuple[int, ...], shape) + + +def meta_namespace( + *arrays: Array | complex | None, xp: ModuleType | None = None +) -> ModuleType: + """ + Get the namespace of Dask chunks. + + On all other backends, just return the namespace of the arrays. + + Parameters + ---------- + *arrays : Array | int | float | complex | bool | None + Input arrays. + xp : array_namespace, optional + The standard-compatible namespace for the input arrays. Default: infer. + + Returns + ------- + array_namespace + If xp is Dask, the namespace of the Dask chunks; + otherwise, the namespace of the arrays. + """ + xp = array_namespace(*arrays) if xp is None else xp + if not is_dask_namespace(xp): + return xp + # Quietly skip scalars and None's + metas = [cast(Array | None, getattr(a, "_meta", None)) for a in arrays] + return array_namespace(*metas) + + +def capabilities( + xp: ModuleType, *, device: Device | None = None +) -> dict[str, int | None]: + """ + Return patched ``xp.__array_namespace_info__().capabilities()``. + + TODO this helper should be eventually removed once all the special cases + it handles are fixed in the respective backends. + + Parameters + ---------- + xp : array_namespace + The standard-compatible namespace. + device : Device, optional + The device to use. + + Returns + ------- + dict + Capabilities of the namespace. + """ + out = xp.__array_namespace_info__().capabilities() + if is_pydata_sparse_namespace(xp): + if out["boolean indexing"]: + # FIXME https://github.com/pydata/sparse/issues/876 + # boolean indexing is supported, but not when the index is a sparse array. + # boolean indexing by list or numpy array is not part of the Array API. + out = out.copy() + out["boolean indexing"] = False + elif is_jax_namespace(xp): + if out["boolean indexing"]: # pragma: no cover + # Backwards compatibility with jax <0.6.0 + # https://github.com/jax-ml/jax/issues/27418 + out = out.copy() + out["boolean indexing"] = False + elif is_torch_namespace(xp): + # FIXME https://github.com/data-apis/array-api/issues/945 + device = xp.get_default_device() if device is None else xp.device(device) + if device.type == "meta": # type: ignore[union-attr] # pyright: ignore[reportAttributeAccessIssue,reportOptionalMemberAccess] + out = out.copy() + out["boolean indexing"] = False + out["data-dependent shapes"] = False + + return out + + +_BASIC_PICKLED_TYPES = frozenset(( + bool, int, float, complex, str, bytes, bytearray, + list, tuple, dict, set, frozenset, range, slice, + types.NoneType, types.EllipsisType, +)) # fmt: skip +_BASIC_REST_TYPES = frozenset(( + type, types.BuiltinFunctionType, types.FunctionType, types.ModuleType +)) # fmt: skip + +FlattenRest: TypeAlias = tuple[object, ...] + + +def pickle_flatten( + obj: object, cls: type[T] | tuple[type[T], ...] +) -> tuple[list[T], FlattenRest]: + """ + Use the pickle machinery to extract objects out of an arbitrary container. + + Unlike regular ``pickle.dumps``, this function always succeeds. + + Parameters + ---------- + obj : object + The object to pickle. + cls : type | tuple[type, ...] + One or multiple classes to extract from the object. + The instances of these classes inside ``obj`` will not be pickled. + + Returns + ------- + instances : list[cls] + All instances of ``cls`` found inside ``obj`` (not pickled). + rest + Opaque object containing the pickled bytes plus all other objects where + ``__reduce__`` / ``__reduce_ex__`` is either not implemented or raised. + These are unpickleable objects, types, modules, and functions. + + This object is *typically* hashable save for fairly exotic objects + that are neither pickleable nor hashable. + + This object is pickleable if everything except ``instances`` was pickleable + in the input object. + + See Also + -------- + pickle_unflatten : Reverse function. + + Examples + -------- + >>> class A: + ... def __repr__(self): + ... return "" + >>> class NS: + ... def __repr__(self): + ... return "" + ... def __reduce__(self): + ... assert False, "not serializable" + >>> obj = {1: A(), 2: [A(), NS(), A()]} + >>> instances, rest = pickle_flatten(obj, A) + >>> instances + [, , ] + >>> pickle_unflatten(instances, rest) + {1: , 2: [, , ]} + + This can be also used to swap inner objects; the only constraint is that + the number of objects in and out must be the same: + + >>> pickle_unflatten(["foo", "bar", "baz"], rest) + {1: "foo", 2: ["bar", , "baz"]} + """ + instances: list[T] = [] + rest: list[object] = [] + + class Pickler(pickle.Pickler): # numpydoc ignore=GL08 + """ + Use the `pickle.Pickler.persistent_id` hook to extract objects. + """ + + @override + def persistent_id( + self, obj: object + ) -> Literal[0, 1, None]: # numpydoc ignore=GL08 + if isinstance(obj, cls): + instances.append(obj) # type: ignore[arg-type] + return 0 + + typ_ = type(obj) + if typ_ in _BASIC_PICKLED_TYPES: # No subclasses! + # If obj is a collection, recursively descend inside it + return None + if typ_ in _BASIC_REST_TYPES: + rest.append(obj) + return 1 + + try: + # Note: a class that defines __slots__ without defining __getstate__ + # cannot be pickled with __reduce__(), but can with __reduce_ex__(5) + _ = obj.__reduce_ex__(pickle.HIGHEST_PROTOCOL) + except Exception: # pylint: disable=broad-exception-caught + rest.append(obj) + return 1 + + # Object can be pickled. Let the Pickler recursively descend inside it. + return None + + f = io.BytesIO() + p = Pickler(f, protocol=pickle.HIGHEST_PROTOCOL) + p.dump(obj) + return instances, (f.getvalue(), *rest) + + +def pickle_unflatten(instances: Iterable[object], rest: FlattenRest) -> Any: + """ + Reverse of ``pickle_flatten``. + + Parameters + ---------- + instances : Iterable + Inner objects to be reinserted into the flattened container. + rest : FlattenRest + Extra bits, as returned by ``pickle_flatten``. + + Returns + ------- + object + The outer object originally passed to ``pickle_flatten`` after a + pickle->unpickle round-trip. + + See Also + -------- + pickle_flatten : Serializing function. + pickle.loads : Standard unpickle function. + + Notes + ----- + The `instances` iterable must yield at least the same number of elements as the ones + returned by ``pickle_flatten``, but the elements do not need to be the same objects + or even the same types of objects. Excess elements, if any, will be left untouched. + """ + iters = iter(instances), iter(rest) + pik = cast(bytes, next(iters[1])) + + class Unpickler(pickle.Unpickler): # numpydoc ignore=GL08 + """Mirror of the overridden Pickler in pickle_flatten.""" + + @override + def persistent_load(self, pid: Literal[0, 1]) -> object: # numpydoc ignore=GL08 + try: + return next(iters[pid]) + except StopIteration as e: + msg = "Not enough objects to unpickle" + raise ValueError(msg) from e + + f = io.BytesIO(pik) + return Unpickler(f).load() + + +class _AutoJITWrapper(Generic[T]): # numpydoc ignore=PR01 + """ + Helper of :func:`jax_autojit`. + + Wrap arbitrary inputs and outputs of the jitted function and + convert them to/from PyTrees. + """ + + obj: T + _registered: ClassVar[bool] = False + __slots__: tuple[str, ...] = ("obj",) + + def __init__(self, obj: T) -> None: # numpydoc ignore=GL08 + self._register() + self.obj = obj + + @classmethod + def _register(cls) -> None: # numpydoc ignore=SS06 + """ + Register upon first use instead of at import time, to avoid + globally importing JAX. + """ + if not cls._registered: + import jax + + jax.tree_util.register_pytree_node( + cls, + lambda obj: pickle_flatten(obj, jax.Array), # pyright: ignore[reportUnknownArgumentType] + lambda aux_data, children: pickle_unflatten(children, aux_data), # pyright: ignore[reportUnknownArgumentType] + ) + cls._registered = True + + +def jax_autojit( + func: Callable[P, T], +) -> Callable[P, T]: # numpydoc ignore=PR01,RT01,SS03 + """ + Wrap `func` with ``jax.jit``, with the following differences: + + - Python scalar arguments and return values are not automatically converted to + ``jax.Array`` objects. + - All non-array arguments are automatically treated as static. + Unlike ``jax.jit``, static arguments must be either hashable or serializable with + ``pickle``. + - Unlike ``jax.jit``, non-array arguments and return values are not limited to + tuple/list/dict, but can be any object serializable with ``pickle``. + - Automatically descend into non-array arguments and find ``jax.Array`` objects + inside them, then rebuild the arguments when entering `func`, swapping the JAX + concrete arrays with tracer objects. + - Automatically descend into non-array return values and find ``jax.Array`` objects + inside them, then rebuild them downstream of exiting the JIT, swapping the JAX + tracer objects with concrete arrays. + + See Also + -------- + jax.jit : JAX JIT compilation function. + + Notes + ----- + These are useful choices *for testing purposes only*, which is how this function is + intended to be used. The output of ``jax.jit`` is a C++ level callable, that + directly dispatches to the compiled kernel after the initial call. In comparison, + ``jax_autojit`` incurs a much higher dispatch time. + + Additionally, consider:: + + def f(x: Array, y: float, plus: bool) -> Array: + return x + y if plus else x - y + + j1 = jax.jit(f, static_argnames="plus") + j2 = jax_autojit(f) + + In the above example, ``j2`` requires a lot less setup to be tested effectively than + ``j1``, but on the flip side it means that it will be re-traced for every different + value of ``y``, which likely makes it not fit for purpose in production. + """ + import jax + + @jax.jit # type: ignore[misc] # pyright: ignore[reportUntypedFunctionDecorator] + def inner( # numpydoc ignore=GL08 + wargs: _AutoJITWrapper[Any], + ) -> _AutoJITWrapper[T]: + args, kwargs = wargs.obj + res = func(*args, **kwargs) # pyright: ignore[reportCallIssue] + return _AutoJITWrapper(res) + + @wraps(func) + def outer(*args: P.args, **kwargs: P.kwargs) -> T: # numpydoc ignore=GL08 + wargs = _AutoJITWrapper((args, kwargs)) + return inner(wargs).obj + + return outer diff --git a/src/array_api_extra/_lib/_utils/_typing.py b/src/array_api_extra/_lib/_utils/_typing.py new file mode 100644 index 00000000..8204be47 --- /dev/null +++ b/src/array_api_extra/_lib/_utils/_typing.py @@ -0,0 +1,10 @@ +# numpydoc ignore=GL08 +# pylint: disable=missing-module-docstring,duplicate-code + +Array = object +DType = object +Device = object +GetIndex = object +SetIndex = object + +__all__ = ["Array", "DType", "Device", "GetIndex", "SetIndex"] diff --git a/src/array_api_extra/_lib/_utils/_typing.pyi b/src/array_api_extra/_lib/_utils/_typing.pyi new file mode 100644 index 00000000..35c255fc --- /dev/null +++ b/src/array_api_extra/_lib/_utils/_typing.pyi @@ -0,0 +1,105 @@ +"""Static typing helpers.""" + +from __future__ import annotations + +from types import EllipsisType +from typing import Protocol, TypeAlias + +# TODO import from typing (requires Python >=3.12) +from typing_extensions import override + +# TODO: use array-api-typing once it is available + +class Array(Protocol): # pylint: disable=missing-class-docstring + # Unary operations + def __abs__(self) -> Array: ... + def __pos__(self) -> Array: ... + def __neg__(self) -> Array: ... + def __invert__(self) -> Array: ... + # Binary operations + def __add__(self, other: Array | complex, /) -> Array: ... + def __sub__(self, other: Array | complex, /) -> Array: ... + def __mul__(self, other: Array | complex, /) -> Array: ... + def __truediv__(self, other: Array | complex, /) -> Array: ... + def __floordiv__(self, other: Array | complex, /) -> Array: ... + def __mod__(self, other: Array | complex, /) -> Array: ... + def __pow__(self, other: Array | complex, /) -> Array: ... + def __matmul__(self, other: Array, /) -> Array: ... + def __and__(self, other: Array | int, /) -> Array: ... + def __or__(self, other: Array | int, /) -> Array: ... + def __xor__(self, other: Array | int, /) -> Array: ... + def __lshift__(self, other: Array | int, /) -> Array: ... + def __rshift__(self, other: Array | int, /) -> Array: ... + def __lt__(self, other: Array | complex, /) -> Array: ... + def __le__(self, other: Array | complex, /) -> Array: ... + def __gt__(self, other: Array | complex, /) -> Array: ... + def __ge__(self, other: Array | complex, /) -> Array: ... + @override + def __eq__(self, other: Array | complex, /) -> Array: ... # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride] + @override + def __ne__(self, other: Array | complex, /) -> Array: ... # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride] + # Reflected operations + def __radd__(self, other: Array | complex, /) -> Array: ... + def __rsub__(self, other: Array | complex, /) -> Array: ... + def __rmul__(self, other: Array | complex, /) -> Array: ... + def __rtruediv__(self, other: Array | complex, /) -> Array: ... + def __rfloordiv__(self, other: Array | complex, /) -> Array: ... + def __rmod__(self, other: Array | complex, /) -> Array: ... + def __rpow__(self, other: Array | complex, /) -> Array: ... + def __rmatmul__(self, other: Array, /) -> Array: ... + def __rand__(self, other: Array | int, /) -> Array: ... + def __ror__(self, other: Array | int, /) -> Array: ... + def __rxor__(self, other: Array | int, /) -> Array: ... + def __rlshift__(self, other: Array | int, /) -> Array: ... + def __rrshift__(self, other: Array | int, /) -> Array: ... + # Attributes + @property + def dtype(self) -> DType: ... + @property + def device(self) -> Device: ... + @property + def mT(self) -> Array: ... # pylint: disable=invalid-name + @property + def ndim(self) -> int: ... + @property + def shape(self) -> tuple[int | None, ...]: ... + @property + def size(self) -> int | None: ... + @property + def T(self) -> Array: ... # pylint: disable=invalid-name + # Collection operations (note: an Array does not have to be Sized or Iterable) + def __getitem__(self, key: GetIndex, /) -> Array: ... + def __setitem__(self, key: SetIndex, value: Array | complex, /) -> None: ... + # Materialization methods (may raise on lazy arrays) + def __bool__(self) -> bool: ... + def __complex__(self) -> complex: ... + def __float__(self) -> float: ... + def __index__(self) -> int: ... + def __int__(self) -> int: ... + + # Misc methods (frequently not implemented in Arrays wrapped by array-api-compat) + # def __array_namespace__(*, api_version: str | None) -> ModuleType: ... + # def __dlpack__( + # *, + # stream: int | Any | None = None, + # max_version: tuple[int, int] | None = None, + # dl_device: tuple[int, int] | None = None, # tuple[Enum, int] + # copy: bool | None = None, + # ) -> Any: ... + # def __dlpack_device__() -> tuple[int, int]: ... # tuple[Enum, int] + # def to_device(device: Device, /, *, stream: int | Any | None = None) -> Array: ... + +class DType(Protocol): # pylint: disable=missing-class-docstring + pass + +class Device(Protocol): # pylint: disable=missing-class-docstring + pass + +SetIndex: TypeAlias = ( + int | slice | EllipsisType | Array | tuple[int | slice | EllipsisType | Array, ...] +) +GetIndex: TypeAlias = ( + SetIndex | None | tuple[int | slice | EllipsisType | None | Array, ...] +) + +__all__ = ["Array", "DType", "Device", "GetIndex", "SetIndex"] diff --git a/src/array_api_extra/testing.py b/src/array_api_extra/testing.py new file mode 100644 index 00000000..d40fea1a --- /dev/null +++ b/src/array_api_extra/testing.py @@ -0,0 +1,415 @@ +""" +Public testing utilities. + +See also _lib._testing for additional private testing utilities. +""" + +from __future__ import annotations + +import contextlib +import enum +import warnings +from collections.abc import Callable, Generator, Iterator, Sequence +from functools import wraps +from types import ModuleType +from typing import TYPE_CHECKING, Any, ParamSpec, TypeVar, cast + +from ._lib._utils._compat import is_dask_namespace, is_jax_namespace +from ._lib._utils._helpers import jax_autojit, pickle_flatten, pickle_unflatten + +__all__ = ["lazy_xp_function", "patch_lazy_xp_functions"] + +if TYPE_CHECKING: # pragma: no cover + # TODO import override from typing (requires Python >=3.12) + import pytest + from dask.typing import Graph, Key, SchedulerGetCallable + from typing_extensions import override + +else: + # Sphinx hacks + SchedulerGetCallable = object + + def override(func): + return func + + +P = ParamSpec("P") +T = TypeVar("T") + +_ufuncs_tags: dict[object, dict[str, Any]] = {} + + +class Deprecated(enum.Enum): + """Unique type for deprecated parameters.""" + + DEPRECATED = 1 + + +DEPRECATED = Deprecated.DEPRECATED + + +def lazy_xp_function( + func: Callable[..., Any], + *, + allow_dask_compute: bool | int = False, + jax_jit: bool = True, + static_argnums: Deprecated = DEPRECATED, + static_argnames: Deprecated = DEPRECATED, +) -> None: # numpydoc ignore=GL07 + """ + Tag a function to be tested on lazy backends. + + Tag a function so that when any tests are executed with ``xp=jax.numpy`` the + function is replaced with a jitted version of itself, and when it is executed with + ``xp=dask.array`` the function will raise if it attempts to materialize the graph. + This will be later expanded to provide test coverage for other lazy backends. + + In order for the tag to be effective, the test or a fixture must call + :func:`patch_lazy_xp_functions`. + + Parameters + ---------- + func : callable + Function to be tested. + allow_dask_compute : bool | int, optional + Whether `func` is allowed to internally materialize the Dask graph, or maximum + number of times it is allowed to do so. This is typically triggered by + ``bool()``, ``float()``, or ``np.asarray()``. + + Set to 1 if you are aware that `func` converts the input parameters to NumPy and + want to let it do so at least for the time being, knowing that it is going to be + extremely detrimental for performance. + + If a test needs values higher than 1 to pass, it is a canary that the conversion + to NumPy/bool/float is happening multiple times, which translates to multiple + computations of the whole graph. Short of making the function fully lazy, you + should at least add explicit calls to ``np.asarray()`` early in the function. + *Note:* the counter of `allow_dask_compute` resets after each call to `func`, so + a test function that invokes `func` multiple times should still work with this + parameter set to 1. + + Set to True to allow `func` to materialize the graph an unlimited number + of times. + + Default: False, meaning that `func` must be fully lazy and never materialize the + graph. + jax_jit : bool, optional + Set to True to replace `func` with a smart variant of ``jax.jit(func)`` after + calling the :func:`patch_lazy_xp_functions` test helper with ``xp=jax.numpy``. + This is the default behaviour. + Set to False if `func` is only compatible with eager (non-jitted) JAX. + + Unlike with vanilla ``jax.jit``, all arguments and return types that are not JAX + arrays are treated as static; the function can accept and return arbitrary + wrappers around JAX arrays. This difference is because, in real life, most users + won't wrap the function directly with ``jax.jit`` but rather they will use it + within their own code, which is itself then wrapped by ``jax.jit``, and + internally consume the function's outputs. + + In other words, the pattern that is being tested is:: + + >>> @jax.jit + ... def user_func(x): + ... y = user_prepares_inputs(x) + ... z = func(y, some_static_arg=True) + ... return user_consumes(z) + + Default: True. + static_argnums : + Deprecated; ignored + static_argnames : + Deprecated; ignored + + See Also + -------- + patch_lazy_xp_functions : Companion function to call from the test or fixture. + jax.jit : JAX function to compile a function for performance. + + Examples + -------- + In ``test_mymodule.py``:: + + from array_api_extra.testing import lazy_xp_function from mymodule import myfunc + + lazy_xp_function(myfunc) + + def test_myfunc(xp): + a = xp.asarray([1, 2]) + # When xp=jax.numpy, this is similar to `b = jax.jit(myfunc)(a)` + # When xp=dask.array, crash on compute() or persist() + b = myfunc(a) + + Notes + ----- + In order for this tag to be effective, the test function must be imported into the + test module globals without its namespace; alternatively its namespace must be + declared in a ``lazy_xp_modules`` list in the test module globals. + + Example 1:: + + from mymodule import myfunc + + lazy_xp_function(myfunc) + + def test_myfunc(xp): + x = myfunc(xp.asarray([1, 2])) + + Example 2:: + + import mymodule + + lazy_xp_modules = [mymodule] + lazy_xp_function(mymodule.myfunc) + + def test_myfunc(xp): + x = mymodule.myfunc(xp.asarray([1, 2])) + + A test function can circumvent this monkey-patching system by using a namespace + outside of the two above patterns. You need to sanitize your code to make sure this + only happens intentionally. + + Example 1:: + + import mymodule + from mymodule import myfunc + + lazy_xp_function(myfunc) + + def test_myfunc(xp): + a = xp.asarray([1, 2]) + b = myfunc(a) # This is wrapped when xp=jax.numpy or xp=dask.array + c = mymodule.myfunc(a) # This is not + + Example 2:: + + import mymodule + + class naked: + myfunc = mymodule.myfunc + + lazy_xp_modules = [mymodule] + lazy_xp_function(mymodule.myfunc) + + def test_myfunc(xp): + a = xp.asarray([1, 2]) + b = mymodule.myfunc(a) # This is wrapped when xp=jax.numpy or xp=dask.array + c = naked.myfunc(a) # This is not + """ + if static_argnums is not DEPRECATED or static_argnames is not DEPRECATED: + warnings.warn( + ( + "The `static_argnums` and `static_argnames` parameters are deprecated " + "and ignored. They will be removed in a future version." + ), + DeprecationWarning, + stacklevel=2, + ) + tags = { + "allow_dask_compute": allow_dask_compute, + "jax_jit": jax_jit, + } + + try: + func._lazy_xp_function = tags # type: ignore[attr-defined] # pylint: disable=protected-access # pyright: ignore[reportFunctionMemberAccess] + except AttributeError: # @cython.vectorize + _ufuncs_tags[func] = tags + + +def patch_lazy_xp_functions( + request: pytest.FixtureRequest, + monkeypatch: pytest.MonkeyPatch | None = None, + *, + xp: ModuleType, +) -> contextlib.AbstractContextManager[None]: + """ + Test lazy execution of functions tagged with :func:`lazy_xp_function`. + + If ``xp==jax.numpy``, search for all functions which have been tagged with + :func:`lazy_xp_function` in the globals of the module that defines the current test, + as well as in the ``lazy_xp_modules`` list in the globals of the same module, + and wrap them with :func:`jax.jit`. Unwrap them at the end of the test. + + If ``xp==dask.array``, wrap the functions with a decorator that disables + ``compute()`` and ``persist()`` and ensures that exceptions and warnings are raised + eagerly. + + This function should be typically called by your library's `xp` fixture that runs + tests on multiple backends:: + + @pytest.fixture(params=[ + numpy, + array_api_strict, + pytest.param(jax.numpy, marks=pytest.mark.thread_unsafe), + pytest.param(dask.array, marks=pytest.mark.thread_unsafe), + ]) + def xp(request): + with patch_lazy_xp_functions(request, xp=request.param): + yield request.param + + but it can be otherwise be called by the test itself too. + + Parameters + ---------- + request : pytest.FixtureRequest + Pytest fixture, as acquired by the test itself or by one of its fixtures. + monkeypatch : pytest.MonkeyPatch + Deprecated + xp : array_namespace + Array namespace to be tested. + + See Also + -------- + lazy_xp_function : Tag a function to be tested on lazy backends. + pytest.FixtureRequest : `request` test function parameter. + + Notes + ----- + This context manager monkey-patches modules and as such is thread unsafe + on Dask and JAX. If you run your test suite with + `pytest-run-parallel `_, + you should mark these backends with ``@pytest.mark.thread_unsafe``, as shown in + the example above. + """ + mod = cast(ModuleType, request.module) + mods = [mod, *cast(list[ModuleType], getattr(mod, "lazy_xp_modules", []))] + + to_revert: list[tuple[ModuleType, str, object]] = [] + + def temp_setattr(mod: ModuleType, name: str, func: object) -> None: + """ + Variant of monkeypatch.setattr, which allows monkey-patching only selected + parameters of a test so that pytest-run-parallel can run on the remainder. + """ + assert hasattr(mod, name) + to_revert.append((mod, name, getattr(mod, name))) + setattr(mod, name, func) + + if monkeypatch is not None: + warnings.warn( + ( + "The `monkeypatch` parameter is deprecated and will be removed in a " + "future version. " + "Use `patch_lazy_xp_function` as a context manager instead." + ), + DeprecationWarning, + stacklevel=2, + ) + # Enable using patch_lazy_xp_function not as a context manager + temp_setattr = monkeypatch.setattr # type: ignore[assignment] # pyright: ignore[reportAssignmentType] + + def iter_tagged() -> Iterator[ + tuple[ModuleType, str, Callable[..., Any], dict[str, Any]] + ]: + for mod in mods: + for name, func in mod.__dict__.items(): + tags: dict[str, Any] | None = None + with contextlib.suppress(AttributeError): + tags = func._lazy_xp_function # pylint: disable=protected-access + if tags is None: + with contextlib.suppress(KeyError, TypeError): + tags = _ufuncs_tags[func] + if tags is not None: + yield mod, name, func, tags + + if is_dask_namespace(xp): + for mod, name, func, tags in iter_tagged(): + n = tags["allow_dask_compute"] + if n is True: + n = 1_000_000 + elif n is False: + n = 0 + wrapped = _dask_wrap(func, n) + temp_setattr(mod, name, wrapped) + + elif is_jax_namespace(xp): + for mod, name, func, tags in iter_tagged(): + if tags["jax_jit"]: + wrapped = jax_autojit(func) + temp_setattr(mod, name, wrapped) + + # We can't just decorate patch_lazy_xp_functions with + # @contextlib.contextmanager because it would not work with the + # deprecated monkeypatch when not used as a context manager. + @contextlib.contextmanager + def revert_on_exit() -> Generator[None]: + try: + yield + finally: + for mod, name, orig_func in to_revert: + setattr(mod, name, orig_func) + + return revert_on_exit() + + +class CountingDaskScheduler(SchedulerGetCallable): + """ + Dask scheduler that counts how many times `dask.compute` is called. + + If the number of times exceeds 'max_count', it raises an error. + This is a wrapper around Dask's own 'synchronous' scheduler. + + Parameters + ---------- + max_count : int + Maximum number of allowed calls to `dask.compute`. + msg : str + Assertion to raise when the count exceeds `max_count`. + """ + + count: int + max_count: int + msg: str + + def __init__(self, max_count: int, msg: str): # numpydoc ignore=GL08 + self.count = 0 + self.max_count = max_count + self.msg = msg + + @override + def __call__( + self, dsk: Graph, keys: Sequence[Key] | Key, **kwargs: Any + ) -> Any: # numpydoc ignore=GL08 + import dask + + self.count += 1 + # This should yield a nice traceback to the + # offending line in the user's code + assert self.count <= self.max_count, self.msg + + return dask.get(dsk, keys, **kwargs) # type: ignore[attr-defined] # pyright: ignore[reportPrivateImportUsage] + + +def _dask_wrap( + func: Callable[P, T], n: int +) -> Callable[P, T]: # numpydoc ignore=PR01,RT01 + """ + Wrap `func` to raise if it attempts to call `dask.compute` more than `n` times. + + After the function returns, materialize the graph in order to re-raise exceptions. + """ + import dask + import dask.array as da + + func_name = getattr(func, "__name__", str(func)) + n_str = f"only up to {n}" if n else "no" + msg = ( + f"Called `dask.compute()` or `dask.persist()` {n + 1} times, " + f"but {n_str} calls are allowed. Set " + f"`lazy_xp_function({func_name}, allow_dask_compute={n + 1})` " + "to allow for more (but note that this will harm performance). " + ) + + @wraps(func) + def wrapper(*args: P.args, **kwargs: P.kwargs) -> T: # numpydoc ignore=GL08 + scheduler = CountingDaskScheduler(n, msg) + with dask.config.set({"scheduler": scheduler}): # pyright: ignore[reportPrivateImportUsage] + out = func(*args, **kwargs) + + # Block until the graph materializes and reraise exceptions. This allows + # `pytest.raises` and `pytest.warns` to work as expected. Note that this would + # not work on scheduler='distributed', as it would not block. + arrays, rest = pickle_flatten(out, da.Array) + arrays = dask.persist(arrays, scheduler="threads")[0] # type: ignore[attr-defined,no-untyped-call] # pyright: ignore[reportPrivateImportUsage] + return pickle_unflatten(arrays, rest) # pyright: ignore[reportUnknownArgumentType] + + return wrapper diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 00000000..3ccaf52b --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1 @@ +"""Needed to import .conftest from the test modules.""" diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 00000000..76fb7650 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,234 @@ +"""Pytest fixtures.""" + +from collections.abc import Callable, Generator +from functools import partial, wraps +from types import ModuleType +from typing import ParamSpec, TypeVar, cast + +import numpy as np +import pytest + +from array_api_extra._lib._backends import Backend +from array_api_extra._lib._testing import xfail +from array_api_extra._lib._utils._compat import array_namespace +from array_api_extra._lib._utils._compat import device as get_device +from array_api_extra._lib._utils._typing import Device +from array_api_extra.testing import patch_lazy_xp_functions + +T = TypeVar("T") +P = ParamSpec("P") + +np_compat = array_namespace(np.empty(0)) # type: ignore[arg-type] # pyright: ignore[reportArgumentType] + + +@pytest.fixture(params=[b.pytest_param() for b in Backend]) +def library(request: pytest.FixtureRequest) -> Backend: # numpydoc ignore=PR01,RT03 + """ + Parameterized fixture that iterates on all libraries. + + Returns + ------- + The current Backend enum. + """ + elem = cast(Backend, request.param) + + for marker_name, skip_or_xfail, allow_kwargs in ( + ("skip_xp_backend", pytest.skip, {"reason"}), + ("xfail_xp_backend", partial(xfail, request), {"reason", "strict"}), + ): + for marker in request.node.iter_markers(marker_name): + if len(marker.args) != 1: + msg = f"Expected exactly one positional argument; got {marker.args}" + raise TypeError(msg) + if not isinstance(marker.args[0], Backend): + msg = f"Argument of {marker_name} must be a Backend enum" + raise TypeError(msg) + if invalid_kwargs := set(marker.kwargs) - allow_kwargs: + msg = f"Unexpected kwarg(s): {invalid_kwargs}" + raise TypeError(msg) + + library: Backend = marker.args[0] + reason: str | None = marker.kwargs.get("reason", None) + strict: bool | None = marker.kwargs.get("strict", None) + + if library == elem: + reason = f"{library}: {reason}" if reason else str(library) + kwargs = {"strict": strict} if strict is not None else {} + skip_or_xfail(reason=reason, **kwargs) + + return elem + + +class NumPyReadOnly: + """ + Variant of array_api_compat.numpy producing read-only arrays. + + Read-only NumPy arrays fail on `__iadd__` etc., whereas read-only libraries such as + JAX and Sparse simply don't define those methods, which makes calls to `+=` fall + back to `__add__`. + + Note that this is not a full read-only Array API library. Notably, + `array_namespace(x)` returns array_api_compat.numpy. This is actually the desired + behaviour, so that when a tested function internally calls `xp = + array_namespace(*args) or xp`, it will internally create writeable arrays. + For this reason, tests that explicitly pass xp=xp to the tested functions may + misbehave and should be skipped for NUMPY_READONLY. + """ + + def __getattr__(self, name: str) -> object: # numpydoc ignore=PR01,RT01 + """Wrap all functions that return arrays to make their output read-only.""" + func = getattr(np_compat, name) + if not callable(func) or isinstance(func, type): + return func + return self._wrap(func) + + @staticmethod + def _wrap(func: Callable[P, T]) -> Callable[P, T]: # numpydoc ignore=PR01,RT01 + """Wrap func to make all np.ndarrays it returns read-only.""" + + def as_readonly(o: T) -> T: # numpydoc ignore=PR01,RT01 + """Unset the writeable flag in o.""" + try: + # Don't use is_numpy_array(o), as it includes np.generic + if isinstance(o, np.ndarray): + o.flags.writeable = False + except TypeError: + # Cannot interpret as a data type + return o + + # This works with namedtuples too + if isinstance(o, tuple | list): + return type(o)(*(as_readonly(i) for i in o)) # type: ignore[arg-type,return-value] # pyright: ignore[reportArgumentType] + + return o + + @wraps(func) + def wrapper(*args: P.args, **kwargs: P.kwargs) -> T: # numpydoc ignore=GL08 + return as_readonly(func(*args, **kwargs)) + + return wrapper + + +@pytest.fixture +def xp( + library: Backend, request: pytest.FixtureRequest +) -> Generator[ModuleType]: # numpydoc ignore=PR01,RT03 + """ + Parameterized fixture that iterates on all libraries. + + Returns + ------- + The current array namespace. + """ + if library == Backend.NUMPY_READONLY: + yield NumPyReadOnly() # type: ignore[misc] # pyright: ignore[reportReturnType] + return + + xp = pytest.importorskip(library.modname) + # Possibly wrap module with array_api_compat + xp = array_namespace(xp.empty(0)) + + if library == Backend.ARRAY_API_STRICTEST: + with xp.ArrayAPIStrictFlags( + boolean_indexing=False, + data_dependent_shapes=False, + # writeable=False, # TODO implement in array-api-strict + # lazy=True, # TODO implement in array-api-strict + enabled_extensions=(), + ): + yield xp + return + + if library.like(Backend.JAX): + _setup_jax(library) + elif library.like(Backend.TORCH): + _setup_torch(library) + + # On Dask and JAX, monkey-patch all functions tagged by `lazy_xp_function` + # in the global scope of the module containing the test function. + with patch_lazy_xp_functions(request, xp=xp): + yield xp + + +def _setup_jax(library: Backend) -> None: + import jax + + # suppress unused-ignore to run mypy in -e lint as well as -e dev + jax.config.update("jax_enable_x64", True) # type: ignore[no-untyped-call,unused-ignore] + + if library == Backend.JAX_GPU: + try: + device = jax.devices("cuda")[0] + except RuntimeError: + pytest.skip("no CUDA device available") + else: + device = jax.devices("cpu")[0] + jax.config.update("jax_default_device", device) + + +def _setup_torch(library: Backend) -> None: + import torch + + # This is already the default, but some tests or env variables may change it. + # TODO test both float32 and float64, like in scipy. + torch.set_default_dtype(torch.float32) + + if library == Backend.TORCH_GPU: + import torch.cuda + + if not torch.cuda.is_available(): + pytest.skip("no CUDA device available") + torch.set_default_device("cuda") + else: + assert library == Backend.TORCH + torch.set_default_device("cpu") + + +# Can select the test with `pytest -k dask` +@pytest.fixture(params=[Backend.DASK.pytest_param()]) +def da( + request: pytest.FixtureRequest, +) -> Generator[ModuleType]: # numpydoc ignore=PR01,RT01 + """Variant of the `xp` fixture that only yields dask.array.""" + xp = pytest.importorskip("dask.array") + xp = array_namespace(xp.empty(0)) + with patch_lazy_xp_functions(request, xp=xp): + yield xp + + +@pytest.fixture(params=[Backend.JAX.pytest_param(), Backend.JAX_GPU.pytest_param()]) +def jnp( + request: pytest.FixtureRequest, +) -> Generator[ModuleType]: # numpydoc ignore=PR01,RT01 + """Variant of the `xp` fixture that only yields jax.numpy.""" + xp = pytest.importorskip("jax.numpy") + _setup_jax(request.param) + with patch_lazy_xp_functions(request, xp=xp): + yield xp + + +@pytest.fixture(params=[Backend.TORCH, Backend.TORCH_GPU]) +def torch(request: pytest.FixtureRequest) -> ModuleType: # numpydoc ignore=PR01,RT01 + """Variant of the `xp` fixture that only yields torch.""" + xp = pytest.importorskip("torch") + xp = array_namespace(xp.empty(0)) + _setup_torch(request.param) + return xp + + +@pytest.fixture +def device( + library: Backend, xp: ModuleType +) -> Device: # numpydoc ignore=PR01,RT01,RT03 + """ + Return a valid device for the backend. + + Where possible, return a device that is not the default one. + """ + if library == Backend.ARRAY_API_STRICT: + return xp.Device("device1") + if library == Backend.TORCH: + return xp.device("meta") + if library == Backend.TORCH_GPU: + return xp.device("cpu") + return get_device(xp.empty(0)) diff --git a/tests/test_at.py b/tests/test_at.py new file mode 100644 index 00000000..d9e1bdf9 --- /dev/null +++ b/tests/test_at.py @@ -0,0 +1,335 @@ +import math +from collections.abc import Callable, Generator +from contextlib import contextmanager +from types import ModuleType +from typing import cast + +import numpy as np +import pytest + +from array_api_extra import at +from array_api_extra._lib._at import _AtOp +from array_api_extra._lib._backends import Backend +from array_api_extra._lib._testing import xp_assert_equal +from array_api_extra._lib._utils._compat import array_namespace, is_writeable_array +from array_api_extra._lib._utils._compat import device as get_device +from array_api_extra._lib._utils._typing import Array, Device, SetIndex +from array_api_extra.testing import lazy_xp_function + +pytestmark = [ + pytest.mark.skip_xp_backend( + Backend.SPARSE, reason="read-only backend without .at support" + ), + pytest.mark.skip_xp_backend(Backend.ARRAY_API_STRICTEST, reason="boolean indexing"), +] + + +def at_op( + x: Array, + idx: SetIndex, + op: _AtOp, + y: Array | object, + copy: bool | None = None, + xp: ModuleType | None = None, +) -> Array: + """ + Wrapper around at(x, idx).op(y, copy=copy, xp=xp). + + This is a hack to allow wrapping `at()` with `lazy_xp_function`. + For clarity, at() itself works inside jax.jit without hacks; this is + just a workaround for when one wants to apply jax.jit to `at()` directly, + which is not a common use case. + """ + meth = cast(Callable[..., Array], getattr(at(x, idx), op.value)) + return meth(y, copy=copy, xp=xp) + + +lazy_xp_function(at_op) + + +@contextmanager +def assert_copy( + array: Array, copy: bool | None, expect_copy: bool | None = None +) -> Generator[None, None, None]: + if copy is False and not is_writeable_array(array): + with pytest.raises((TypeError, ValueError)): + yield + return + + xp = array_namespace(array) + array_orig = xp.asarray(array, copy=True) + yield + + if expect_copy is None: + expect_copy = copy + + if expect_copy: + # Original has not been modified + xp_assert_equal(array, array_orig) + elif expect_copy is False: + # Original has been modified + with pytest.raises(AssertionError): + xp_assert_equal(array, array_orig) + # Test nothing for copy=None. Dask changes behaviour depending on + # whether it's a special case of a bool mask with scalar RHS or not. + + +@pytest.mark.parametrize("copy", [False, True, None]) +@pytest.mark.parametrize( + ("op", "y", "expect_list"), + [ + (_AtOp.SET, 40.0, [10.0, 40.0, 40.0]), + (_AtOp.ADD, 40.0, [10.0, 60.0, 70.0]), + (_AtOp.SUBTRACT, 100.0, [10.0, -80.0, -70.0]), + (_AtOp.MULTIPLY, 2.0, [10.0, 40.0, 60.0]), + (_AtOp.DIVIDE, 2.0, [10.0, 10.0, 15.0]), + (_AtOp.POWER, 2.0, [10.0, 400.0, 900.0]), + (_AtOp.MIN, 25.0, [10.0, 20.0, 25.0]), + (_AtOp.MAX, 25.0, [10.0, 25.0, 30.0]), + ], +) +@pytest.mark.parametrize( + ("bool_mask", "x_ndim", "y_ndim"), + [ + (False, 1, 0), + (False, 1, 1), + (True, 1, 0), # Uses xp.where(idx, y, x) on JAX and Dask + pytest.param( + *(True, 1, 1), + marks=( + pytest.mark.xfail_xp_backend( + Backend.JAX, + reason="bool mask update with shaped rhs", + strict=False, # test passes when copy=False + ), + pytest.mark.xfail_xp_backend( + Backend.JAX_GPU, + reason="bool mask update with shaped rhs", + strict=False, # test passes when copy=False + ), + pytest.mark.xfail_xp_backend( + Backend.DASK, reason="bool mask update with shaped rhs" + ), + ), + ), + (False, 0, 0), + (True, 0, 0), + ], +) +def test_update_ops( + xp: ModuleType, + copy: bool | None, + op: _AtOp, + y: float, + expect_list: list[float], + bool_mask: bool, + x_ndim: int, + y_ndim: int, +): + if x_ndim == 1: + x = xp.asarray([10.0, 20.0, 30.0]) + idx = xp.asarray([False, True, True]) if bool_mask else slice(1, None) + expect: list[float] | float = expect_list + else: + idx = xp.asarray(True) if bool_mask else () + # Pick an element that does change with the operation + if op is _AtOp.MIN: + x = xp.asarray(30.0) + expect = expect_list[2] + else: + x = xp.asarray(20.0) + expect = expect_list[1] + + if y_ndim == 1: + y = xp.asarray([y, y]) + + with assert_copy(x, copy): + z = at_op(x, idx, op, y, copy=copy) + assert isinstance(z, type(x)) + xp_assert_equal(z, xp.asarray(expect)) + + +@pytest.mark.parametrize("op", list(_AtOp)) +def test_copy_default(xp: ModuleType, library: Backend, op: _AtOp): + """ + Test that the default copy behaviour is False for writeable arrays + and True for read-only ones. + """ + x = xp.asarray([1.0, 10.0, 20.0]) + expect_copy = not is_writeable_array(x) + meth = cast(Callable[..., Array], getattr(at(x)[:2], op.value)) + with assert_copy(x, None, expect_copy): + _ = meth(2.0) + + x = xp.asarray([1.0, 10.0, 20.0]) + # Dask's default copy value is True for bool masks, + # even if the arrays are writeable. + expect_copy = not is_writeable_array(x) or library is Backend.DASK + idx = xp.asarray([True, True, False]) + meth = cast(Callable[..., Array], getattr(at(x, idx), op.value)) + with assert_copy(x, None, expect_copy): + _ = meth(2.0) + + +def test_copy_invalid(): + a = np.asarray([1, 2, 3]) + with pytest.raises(ValueError, match="copy"): + _ = at(a, 0).set(4, copy="invalid") # type: ignore[arg-type] # pyright: ignore[reportArgumentType] + + +def test_xp(): + a = cast(Array, np.asarray([1, 2, 3])) # pyright: ignore[reportInvalidCast] + _ = at(a, 0).set(4, xp=np) + _ = at(a, 0).add(4, xp=np) + _ = at(a, 0).subtract(4, xp=np) + _ = at(a, 0).multiply(4, xp=np) + _ = at(a, 0).divide(4, xp=np) + _ = at(a, 0).power(4, xp=np) + _ = at(a, 0).min(4, xp=np) + _ = at(a, 0).max(4, xp=np) + + +def test_alternate_index_syntax(): + xp = cast(ModuleType, np) # type: ignore[redundant-cast] # pyright: ignore[reportInvalidCast] + a = cast(Array, xp.asarray([1, 2, 3])) + xp_assert_equal(at(a, 0).set(4, copy=True), xp.asarray([4, 2, 3])) + xp_assert_equal(at(a)[0].set(4, copy=True), xp.asarray([4, 2, 3])) + + a_at = at(a) + xp_assert_equal(a_at[0].add(1, copy=True), xp.asarray([2, 2, 3])) + xp_assert_equal(a_at[1].add(2, copy=True), xp.asarray([1, 4, 3])) + + with pytest.raises(ValueError, match="Index"): + _ = at(a).set(4) + with pytest.raises(ValueError, match="Index"): + _ = at(a, 0)[0].set(4) + + +@pytest.mark.parametrize("copy", [True, None]) +@pytest.mark.parametrize("bool_mask", [False, True]) +@pytest.mark.parametrize("op", list(_AtOp)) +def test_incompatible_dtype( + xp: ModuleType, + library: Backend, + op: _AtOp, + copy: bool | None, + bool_mask: bool, +): + """Test that at() replicates the backend's behaviour for + in-place operations with incompatible dtypes. + + Behavior is backend-specific, but only two behaviors are allowed: + 1. raise an exception, or + 2. return the same dtype as x, disregarding y.dtype (no broadcasting). + + Note that __i__ and ____ behave differently, and we want to + replicate the behavior of __i__: + + >>> a = np.asarray([1, 2, 3]) + >>> a / 1.5 + array([0. , 0.66666667, 1.33333333]) + >>> a /= 1.5 + UFuncTypeError: Cannot cast ufunc 'divide' output from dtype('float64') + to dtype('int64') with casting rule 'same_kind' + """ + x = xp.asarray([2, 4]) + idx = xp.asarray([True, False]) if bool_mask else slice(None) + z = None + + if library.like(Backend.JAX): + if bool_mask: + z = at_op(x, idx, op, 1.1, copy=copy) + else: + with pytest.warns(FutureWarning, match="cannot safely cast"): + z = at_op(x, idx, op, 1.1, copy=copy) + + elif library.like(Backend.DASK): + z = at_op(x, idx, op, 1.1, copy=copy) + + elif library.like(Backend.ARRAY_API_STRICT) and op is not _AtOp.SET: + with pytest.raises(Exception, match=r"cast|promote|dtype"): + _ = at_op(x, idx, op, 1.1, copy=copy) + + elif op in (_AtOp.SET, _AtOp.MIN, _AtOp.MAX): + # There is no __i__ version of these operations + z = at_op(x, idx, op, 1.1, copy=copy) + + else: + with pytest.raises(Exception, match=r"cast|promote|dtype"): + _ = at_op(x, idx, op, 1.1, copy=copy) + + assert z is None or z.dtype == x.dtype + + +def test_bool_mask_nd(xp: ModuleType): + x = xp.asarray([[1, 2, 3], [4, 5, 6]]) + idx = xp.asarray([[True, False, False], [False, True, True]]) + z = at_op(x, idx, _AtOp.SET, 0) + xp_assert_equal(z, xp.asarray([[0, 2, 3], [4, 0, 0]])) + + +@pytest.mark.parametrize("bool_mask", [False, True]) +def test_no_inf_warnings(xp: ModuleType, bool_mask: bool): + x = xp.asarray([math.inf, 1.0, 2.0]) + idx = ~xp.isinf(x) if bool_mask else slice(1, None) + # inf - inf -> nan with a warning + z = at_op(x, idx, _AtOp.SUBTRACT, math.inf) + xp_assert_equal(z, xp.asarray([math.inf, -math.inf, -math.inf])) + + +@pytest.mark.parametrize( + "copy", + [ + None, + pytest.param( + False, + marks=[ + pytest.mark.skip_xp_backend( + Backend.NUMPY, reason="np.generic is read-only" + ), + pytest.mark.skip_xp_backend( + Backend.NUMPY_READONLY, reason="read-only backend" + ), + pytest.mark.skip_xp_backend(Backend.JAX, reason="read-only backend"), + pytest.mark.skip_xp_backend( + Backend.JAX_GPU, reason="read-only backend" + ), + pytest.mark.skip_xp_backend(Backend.SPARSE, reason="read-only backend"), + ], + ), + ], +) +@pytest.mark.parametrize("bool_mask", [False, True]) +def test_gh134(xp: ModuleType, bool_mask: bool, copy: bool | None): + """ + Test that xpx.at doesn't encroach in a bug of dask.array.Array.__setitem__, which + blindly assumes that chunk contents are writeable np.ndarray objects: + + https://github.com/dask/dask/issues/11722 + + In other words: when special-casing bool masks for Dask, unless the user explicitly + asks for copy=False, do not needlessly write back to the input. + """ + x = xp.zeros(1) + + # In NumPy, we have a writeable np.ndarray in input and a read-only np.generic in + # output. As both are Arrays, this behaviour is Array API compliant. + # In Dask, we have a writeable da.Array on both sides, and if you call __setitem__ + # on it all seems fine, but when you compute() your graph is corrupted. + y = x[0] + + idx = xp.asarray(True) if bool_mask else () + z = at_op(y, idx, _AtOp.SET, 1, copy=copy) + xp_assert_equal(z, xp.asarray(1, dtype=x.dtype)) + + +def test_device(xp: ModuleType, device: Device): + x = xp.asarray([1, 2, 3], device=device) + + y = xp.asarray([4, 5], device=device) + z = at(x)[:2].set(y) + assert get_device(z) == get_device(x) + + idx = xp.asarray([True, False, True], device=device) + z = at(x)[idx].set(4) + assert get_device(z) == get_device(x) diff --git a/tests/test_funcs.py b/tests/test_funcs.py index 36411958..769b4119 100644 --- a/tests/test_funcs.py +++ b/tests/test_funcs.py @@ -1,198 +1,892 @@ -from __future__ import annotations # https://github.com/pylint-dev/pylint/pull/9990 - -import contextlib -import typing +import math import warnings +from types import ModuleType +from typing import Any, cast -# data-apis/array-api-strict#6 -import array_api_strict as xp # type: ignore[import-untyped] # pyright: ignore[reportMissingTypeStubs] +import hypothesis +import hypothesis.extra.numpy as npst import numpy as np import pytest -from numpy.testing import assert_allclose, assert_array_equal, assert_equal +from hypothesis import given +from hypothesis import strategies as st from array_api_extra import ( + apply_where, + at, atleast_nd, + broadcast_shapes, cov, create_diagonal, + default_dtype, expand_dims, + isclose, kron, + nunique, + one_hot, + pad, setdiff1d, sinc, ) +from array_api_extra._lib._backends import NUMPY_VERSION, Backend +from array_api_extra._lib._testing import xp_assert_close, xp_assert_equal +from array_api_extra._lib._utils._compat import device as get_device +from array_api_extra._lib._utils._helpers import eager_shape, ndindex +from array_api_extra._lib._utils._typing import Array, Device +from array_api_extra.testing import lazy_xp_function + +lazy_xp_function(apply_where) +lazy_xp_function(atleast_nd) +lazy_xp_function(cov) +lazy_xp_function(create_diagonal) +lazy_xp_function(expand_dims) +lazy_xp_function(kron) +lazy_xp_function(nunique) +lazy_xp_function(one_hot) +lazy_xp_function(pad) +# FIXME calls in1d which calls xp.unique_values without size +lazy_xp_function(setdiff1d, jax_jit=False) +lazy_xp_function(sinc) + + +class TestApplyWhere: + @staticmethod + def f1(x: Array, y: Array | int = 10) -> Array: + return x + y + + @staticmethod + def f2(x: Array, y: Array | int = 10) -> Array: + return x - y + + def test_f1_f2(self, xp: ModuleType): + x = xp.asarray([1, 2, 3, 4]) + cond = x % 2 == 0 + actual = apply_where(cond, x, self.f1, self.f2) + expect = xp.where(cond, self.f1(x), self.f2(x)) + xp_assert_equal(actual, expect) + + def test_fill_value(self, xp: ModuleType): + x = xp.asarray([1, 2, 3, 4]) + cond = x % 2 == 0 + actual = apply_where(x % 2 == 0, x, self.f1, fill_value=0) + expect = xp.where(cond, self.f1(x), xp.asarray(0)) + xp_assert_equal(actual, expect) + + actual = apply_where(x % 2 == 0, x, self.f1, fill_value=xp.asarray(0)) + xp_assert_equal(actual, expect) + + def test_args_tuple(self, xp: ModuleType): + x = xp.asarray([1, 2, 3, 4]) + y = xp.asarray([10, 20, 30, 40]) + cond = x % 2 == 0 + actual = apply_where(cond, (x, y), self.f1, self.f2) + expect = xp.where(cond, self.f1(x, y), self.f2(x, y)) + xp_assert_equal(actual, expect) + + def test_broadcast(self, xp: ModuleType): + x = xp.asarray([1, 2]) + y = xp.asarray([[10], [20], [30]]) + cond = xp.broadcast_to(xp.asarray(True), (4, 1, 1)) + + actual = apply_where(cond, (x, y), self.f1, self.f2) + expect = xp.where(cond, self.f1(x, y), self.f2(x, y)) + xp_assert_equal(actual, expect) + + actual = apply_where( + cond, + (x, y), + lambda x, _: x, + lambda _, y: y, + ) + expect = xp.where(cond, x, y) + xp_assert_equal(actual, expect) + + # Shaped fill_value + actual = apply_where(cond, x, self.f1, fill_value=y) + expect = xp.where(cond, self.f1(x), y) + xp_assert_equal(actual, expect) + + def test_dtype_propagation(self, xp: ModuleType, library: Backend): + x = xp.asarray([1, 2], dtype=xp.int8) + y = xp.asarray([3, 4], dtype=xp.int16) + cond = x % 2 == 0 + + mxp = np if library is Backend.DASK else xp + actual = apply_where( + cond, + (x, y), + self.f1, + lambda x, y: mxp.astype(x - y, xp.int64), # pyright: ignore[reportArgumentType] + ) + assert actual.dtype == xp.int64 + + actual = apply_where(cond, y, self.f1, fill_value=5) + assert actual.dtype == xp.int16 + + @pytest.mark.parametrize("fill_value_raw", [3, [3, 4]]) + @pytest.mark.parametrize( + ("fill_value_dtype", "expect_dtype"), [("int32", "int32"), ("int8", "int16")] + ) + def test_dtype_propagation_fill_value( + self, + xp: ModuleType, + fill_value_raw: int | list[int], + fill_value_dtype: str, + expect_dtype: str, + ): + x = xp.asarray([1, 2], dtype=xp.int16) + cond = x % 2 == 0 + fill_value = xp.asarray(fill_value_raw, dtype=getattr(xp, fill_value_dtype)) + + actual = apply_where(cond, x, self.f1, fill_value=fill_value) + assert actual.dtype == getattr(xp, expect_dtype) + + def test_dont_overwrite_fill_value(self, xp: ModuleType): + x = xp.asarray([1, 2]) + fill_value = xp.asarray([100, 200]) + actual = apply_where(x % 2 == 0, x, self.f1, fill_value=fill_value) + xp_assert_equal(actual, xp.asarray([100, 12])) + xp_assert_equal(fill_value, xp.asarray([100, 200])) + + @pytest.mark.skip_xp_backend( + Backend.ARRAY_API_STRICTEST, + reason="no boolean indexing -> run everywhere", + ) + @pytest.mark.skip_xp_backend( + Backend.SPARSE, + reason="no indexing by sparse array -> run everywhere", + ) + def test_dont_run_on_false(self, xp: ModuleType): + x = xp.asarray([1.0, 2.0, 0.0]) + y = xp.asarray([0.0, 3.0, 4.0]) + # On NumPy, division by zero will trigger warnings + actual = apply_where( + x == 0, + (x, y), + lambda x, y: x / y, + lambda x, y: y / x, + ) + xp_assert_equal(actual, xp.asarray([0.0, 1.5, 0.0])) -if typing.TYPE_CHECKING: - from array_api_extra._lib._typing import Array + def test_bad_args(self, xp: ModuleType): + x = xp.asarray([1, 2, 3, 4]) + cond = x % 2 == 0 + # Neither f2 nor fill_value + with pytest.raises(TypeError, match="Exactly one of"): + apply_where(cond, x, self.f1) # type: ignore[call-overload] # pyright: ignore[reportCallIssue] + # Both f2 and fill_value + with pytest.raises(TypeError, match="Exactly one of"): + apply_where(cond, x, self.f1, self.f2, fill_value=0) # type: ignore[call-overload] # pyright: ignore[reportCallIssue] + + @pytest.mark.skip_xp_backend(Backend.NUMPY_READONLY, reason="xp=xp") + def test_xp(self, xp: ModuleType): + x = xp.asarray([1, 2, 3, 4]) + cond = x % 2 == 0 + actual = apply_where(cond, x, self.f1, self.f2, xp=xp) + expect = xp.where(cond, self.f1(x), self.f2(x)) + xp_assert_equal(actual, expect) + + def test_device(self, xp: ModuleType, device: Device): + x = xp.asarray([1, 2, 3, 4], device=device) + y = apply_where(x % 2 == 0, x, self.f1, self.f2) + assert get_device(y) == device + y = apply_where(x % 2 == 0, x, self.f1, fill_value=0) + assert get_device(y) == device + y = apply_where(x % 2 == 0, x, self.f1, fill_value=x) + assert get_device(y) == device + + @pytest.mark.filterwarnings("ignore::RuntimeWarning") # overflows, etc. + @hypothesis.settings( + # The xp and library fixtures are not regenerated between hypothesis iterations + suppress_health_check=[hypothesis.HealthCheck.function_scoped_fixture], + # JAX can take a long time to initialize on the first call + deadline=None, + ) + @given( + n_arrays=st.integers(min_value=1, max_value=3), + rng_seed=st.integers(min_value=1000000000, max_value=9999999999), + dtype=st.sampled_from((np.float32, np.float64)), + p=st.floats(min_value=0, max_value=1), + data=st.data(), + ) + def test_hypothesis( + self, + n_arrays: int, + rng_seed: int, + dtype: np.dtype[Any], + p: float, + data: st.DataObject, + xp: ModuleType, + library: Backend, + ): + if ( + library.like(Backend.NUMPY) + and NUMPY_VERSION < (2, 0) + and dtype is np.float32 + ): + pytest.xfail(reason="NumPy 1.x dtype promotion for scalars") + + mbs = npst.mutually_broadcastable_shapes(num_shapes=n_arrays + 1, min_side=0) + input_shapes, _ = data.draw(mbs) + cond_shape, *shapes = input_shapes + + # cupy/cupy#8382 + # https://github.com/jax-ml/jax/issues/26658 + elements = {"allow_subnormal": not library.like(Backend.CUPY, Backend.JAX)} + + fill_value = xp.asarray( + data.draw(npst.arrays(dtype=dtype, shape=(), elements=elements)) + ) + float_fill_value = float(fill_value) + if library is Backend.CUPY and dtype is np.float32: + # Avoid data-dependent dtype promotion when encountering subnormals + # close to the max float32 value + float_fill_value = float(np.clip(float_fill_value, -1e38, 1e38)) + + arrays = tuple( + xp.asarray( + data.draw(npst.arrays(dtype=dtype, shape=shape, elements=elements)) + ) + for shape in shapes + ) + + def f1(*args: Array) -> Array: + return cast(Array, sum(args)) + + def f2(*args: Array) -> Array: + return cast(Array, sum(args) / 2) + + rng = np.random.default_rng(rng_seed) + cond = xp.asarray(rng.random(size=cond_shape) > p) + + res1 = apply_where(cond, arrays, f1, fill_value=fill_value) + res2 = apply_where(cond, arrays, f1, f2) + res3 = apply_where(cond, arrays, f1, fill_value=float_fill_value) + + ref1 = xp.where(cond, f1(*arrays), fill_value) + ref2 = xp.where(cond, f1(*arrays), f2(*arrays)) + ref3 = xp.where(cond, f1(*arrays), float_fill_value) + + xp_assert_close(res1, ref1, rtol=2e-16) + xp_assert_equal(res2, ref2) + xp_assert_equal(res3, ref3) class TestAtLeastND: - def test_0D(self): - x = xp.asarray(1) + def test_0D(self, xp: ModuleType): + x = xp.asarray(1.0) - y = atleast_nd(x, ndim=0, xp=xp) - assert_array_equal(y, x) + y = atleast_nd(x, ndim=0) + xp_assert_equal(y, x) - y = atleast_nd(x, ndim=1, xp=xp) - assert_array_equal(y, xp.ones((1,))) + y = atleast_nd(x, ndim=1) + xp_assert_equal(y, xp.ones((1,))) - y = atleast_nd(x, ndim=5, xp=xp) - assert_array_equal(y, xp.ones((1, 1, 1, 1, 1))) + y = atleast_nd(x, ndim=5) + xp_assert_equal(y, xp.ones((1, 1, 1, 1, 1))) - def test_1D(self): + def test_1D(self, xp: ModuleType): x = xp.asarray([0, 1]) - y = atleast_nd(x, ndim=0, xp=xp) - assert_array_equal(y, x) + y = atleast_nd(x, ndim=0) + xp_assert_equal(y, x) - y = atleast_nd(x, ndim=1, xp=xp) - assert_array_equal(y, x) + y = atleast_nd(x, ndim=1) + xp_assert_equal(y, x) - y = atleast_nd(x, ndim=2, xp=xp) - assert_array_equal(y, xp.asarray([[0, 1]])) + y = atleast_nd(x, ndim=2) + xp_assert_equal(y, xp.asarray([[0, 1]])) - y = atleast_nd(x, ndim=5, xp=xp) - assert_array_equal(y, xp.reshape(xp.arange(2), (1, 1, 1, 1, 2))) + y = atleast_nd(x, ndim=5) + xp_assert_equal(y, xp.asarray([[[[[0, 1]]]]])) - def test_2D(self): - x = xp.asarray([[3]]) + def test_2D(self, xp: ModuleType): + x = xp.asarray([[3.0]]) - y = atleast_nd(x, ndim=0, xp=xp) - assert_array_equal(y, x) + y = atleast_nd(x, ndim=0) + xp_assert_equal(y, x) - y = atleast_nd(x, ndim=2, xp=xp) - assert_array_equal(y, x) + y = atleast_nd(x, ndim=2) + xp_assert_equal(y, x) - y = atleast_nd(x, ndim=3, xp=xp) - assert_array_equal(y, 3 * xp.ones((1, 1, 1))) + y = atleast_nd(x, ndim=3) + xp_assert_equal(y, 3 * xp.ones((1, 1, 1))) - y = atleast_nd(x, ndim=5, xp=xp) - assert_array_equal(y, 3 * xp.ones((1, 1, 1, 1, 1))) + y = atleast_nd(x, ndim=5) + xp_assert_equal(y, 3 * xp.ones((1, 1, 1, 1, 1))) - def test_5D(self): + def test_5D(self, xp: ModuleType): x = xp.ones((1, 1, 1, 1, 1)) - y = atleast_nd(x, ndim=0, xp=xp) - assert_array_equal(y, x) + y = atleast_nd(x, ndim=0) + xp_assert_equal(y, x) - y = atleast_nd(x, ndim=4, xp=xp) - assert_array_equal(y, x) + y = atleast_nd(x, ndim=4) + xp_assert_equal(y, x) - y = atleast_nd(x, ndim=5, xp=xp) - assert_array_equal(y, x) + y = atleast_nd(x, ndim=5) + xp_assert_equal(y, x) - y = atleast_nd(x, ndim=6, xp=xp) - assert_array_equal(y, xp.ones((1, 1, 1, 1, 1, 1))) + y = atleast_nd(x, ndim=6) + xp_assert_equal(y, xp.ones((1, 1, 1, 1, 1, 1))) - y = atleast_nd(x, ndim=9, xp=xp) - assert_array_equal(y, xp.ones((1, 1, 1, 1, 1, 1, 1, 1, 1))) + y = atleast_nd(x, ndim=9) + xp_assert_equal(y, xp.ones((1, 1, 1, 1, 1, 1, 1, 1, 1))) + + def test_device(self, xp: ModuleType, device: Device): + x = xp.asarray([1, 2, 3], device=device) + assert get_device(atleast_nd(x, ndim=2)) == device + + def test_xp(self, xp: ModuleType): + x = xp.asarray(1.0) + y = atleast_nd(x, ndim=1, xp=xp) + xp_assert_equal(y, xp.ones((1,))) + + +class TestBroadcastShapes: + @pytest.mark.parametrize( + "args", + [ + (), + ((),), + ((), ()), + ((1,),), + ((1,), (1,)), + ((2,), (1,)), + ((3, 1, 4), (2, 1)), + ((1, 1, 4), (2, 1)), + ((1,), ()), + ((), (2,), ()), + ((0,),), + ((0,), (1,)), + ((2, 0), (1, 1)), + ((2, 0, 3), (2, 1, 1)), + ], + ) + def test_simple(self, args: tuple[tuple[int, ...], ...]): + expect = np.broadcast_shapes(*args) + actual = broadcast_shapes(*args) + assert actual == expect + + @pytest.mark.parametrize( + "args", + [ + ((2,), (3,)), + ((2, 3), (1, 2)), + ((2,), (0,)), + ((2, 0, 2), (1, 3, 1)), + ], + ) + def test_fail(self, args: tuple[tuple[int, ...], ...]): + match = "cannot be broadcast to a single shape" + with pytest.raises(ValueError, match=match): + _ = np.broadcast_shapes(*args) + with pytest.raises(ValueError, match=match): + _ = broadcast_shapes(*args) + + @pytest.mark.parametrize( + "args", + [ + ((None,), (None,)), + ((math.nan,), (None,)), + ((1, None, 2, 4), (2, 3, None, 1), (2, None, None, 4)), + ((1, math.nan, 2), (4, 2, 3, math.nan), (4, 2, None, None)), + ((math.nan, 1), (None, 2), (None, 2)), + ], + ) + def test_none(self, args: tuple[tuple[float | None, ...], ...]): + expect = args[-1] + actual = broadcast_shapes(*args[:-1]) + assert actual == expect class TestCov: - def test_basic(self): - assert_allclose( - cov(xp.asarray([[0, 2], [1, 1], [2, 0]]).T, xp=xp), - xp.asarray([[1.0, -1.0], [-1.0, 1.0]]), + def test_basic(self, xp: ModuleType): + xp_assert_close( + cov(xp.asarray([[0, 2], [1, 1], [2, 0]]).T), + xp.asarray([[1.0, -1.0], [-1.0, 1.0]], dtype=xp.float64), ) - def test_complex(self): - x = xp.asarray([[1, 2, 3], [1j, 2j, 3j]]) - res = xp.asarray([[1.0, -1.0j], [1.0j, 1.0]]) - assert_allclose(cov(x, xp=xp), res) + def test_complex(self, xp: ModuleType): + actual = cov(xp.asarray([[1, 2, 3], [1j, 2j, 3j]])) + expect = xp.asarray([[1.0, -1.0j], [1.0j, 1.0]], dtype=xp.complex128) + xp_assert_close(actual, expect) - def test_empty(self): + @pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="sparse#877") + def test_empty(self, xp: ModuleType): with warnings.catch_warnings(record=True): warnings.simplefilter("always", RuntimeWarning) - assert_array_equal(cov(xp.asarray([]), xp=xp), xp.nan) - assert_array_equal( - cov(xp.reshape(xp.asarray([]), (0, 2)), xp=xp), - xp.reshape(xp.asarray([]), (0, 0)), + xp_assert_equal(cov(xp.asarray([])), xp.asarray(xp.nan, dtype=xp.float64)) + xp_assert_equal( + cov(xp.reshape(xp.asarray([]), (0, 2))), + xp.reshape(xp.asarray([], dtype=xp.float64), (0, 0)), ) - assert_array_equal( - cov(xp.reshape(xp.asarray([]), (2, 0)), xp=xp), - xp.asarray([[xp.nan, xp.nan], [xp.nan, xp.nan]]), + xp_assert_equal( + cov(xp.reshape(xp.asarray([]), (2, 0))), + xp.asarray([[xp.nan, xp.nan], [xp.nan, xp.nan]], dtype=xp.float64), ) - def test_combination(self): + def test_combination(self, xp: ModuleType): x = xp.asarray([-2.1, -1, 4.3]) y = xp.asarray([3, 1.1, 0.12]) X = xp.stack((x, y), axis=0) - desired = xp.asarray([[11.71, -4.286], [-4.286, 2.144133]]) - assert_allclose(cov(X, xp=xp), desired, rtol=1e-6) - assert_allclose(cov(x, xp=xp), xp.asarray(11.71)) - assert_allclose(cov(y, xp=xp), xp.asarray(2.144133), rtol=1e-6) + desired = xp.asarray([[11.71, -4.286], [-4.286, 2.144133]], dtype=xp.float64) + xp_assert_close(cov(X), desired, rtol=1e-6) + xp_assert_close(cov(x), xp.asarray(11.71, dtype=xp.float64)) + xp_assert_close(cov(y), xp.asarray(2.144133, dtype=xp.float64), rtol=1e-6) + + def test_device(self, xp: ModuleType, device: Device): + x = xp.asarray([1, 2, 3], device=device) + assert get_device(cov(x)) == device + @pytest.mark.skip_xp_backend(Backend.NUMPY_READONLY, reason="xp=xp") + def test_xp(self, xp: ModuleType): + xp_assert_close( + cov(xp.asarray([[0.0, 2.0], [1.0, 1.0], [2.0, 0.0]]).T, xp=xp), + xp.asarray([[1.0, -1.0], [-1.0, 1.0]], dtype=xp.float64), + ) + +@pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="no arange", strict=False) +class TestOneHot: + @pytest.mark.parametrize("n_dim", range(4)) + @pytest.mark.parametrize("num_classes", [1, 3, 10]) + def test_dims_and_classes(self, xp: ModuleType, n_dim: int, num_classes: int): + shape = tuple(range(2, 2 + n_dim)) + rng = np.random.default_rng(2347823) + np_x = rng.integers(num_classes, size=shape) + x = xp.asarray(np_x) + y = one_hot(x, num_classes) + assert y.shape == (*x.shape, num_classes) + for *i_list, j in ndindex(*shape, num_classes): + i = tuple(i_list) + assert float(y[(*i, j)]) == (int(x[i]) == j) + + def test_basic(self, xp: ModuleType): + actual = one_hot(xp.asarray([0, 1, 2]), 3) + expected = xp.asarray([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]) + xp_assert_equal(actual, expected) + + actual = one_hot(xp.asarray([1, 2, 0]), 3) + expected = xp.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]]) + xp_assert_equal(actual, expected) + + def test_2d(self, xp: ModuleType): + actual = one_hot(xp.asarray([[2, 1, 0], [1, 0, 2]]), 3, axis=1) + expected = xp.asarray( + [ + [[0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]], + [[0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 1.0]], + ] + ) + xp_assert_equal(actual, expected) + + @pytest.mark.skip_xp_backend( + Backend.ARRAY_API_STRICTEST, reason="backend doesn't support Boolean indexing" + ) + def test_abstract_size(self, xp: ModuleType): + x = xp.arange(5) + x = x[x > 2] + actual = one_hot(x, 5) + expected = xp.asarray([[0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0]]) + xp_assert_equal(actual, expected) + + @pytest.mark.skip_xp_backend( + Backend.TORCH_GPU, reason="Puts Pytorch into a bad state." + ) + def test_out_of_bound(self, xp: ModuleType): + # Undefined behavior. Either return zero, or raise. + try: + actual = one_hot(xp.asarray([-1, 3]), 3) + except IndexError: + return + expected = xp.asarray([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]) + xp_assert_equal(actual, expected) + + @pytest.mark.parametrize( + "int_dtype", + ["int8", "int16", "int32", "int64", "uint8", "uint16", "uint32", "uint64"], + ) + def test_int_types(self, xp: ModuleType, int_dtype: str): + dtype = getattr(xp, int_dtype) + x = xp.asarray([0, 1, 2], dtype=dtype) + actual = one_hot(x, 3) + expected = xp.asarray([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]) + xp_assert_equal(actual, expected) + + def test_custom_dtype(self, xp: ModuleType): + actual = one_hot(xp.asarray([0, 1, 2], dtype=xp.int32), 3, dtype=xp.bool) + expected = xp.asarray( + [[True, False, False], [False, True, False], [False, False, True]] + ) + xp_assert_equal(actual, expected) + + def test_axis(self, xp: ModuleType): + expected = xp.asarray([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0]]).T + actual = one_hot(xp.asarray([1, 2, 0]), 3, axis=0) + xp_assert_equal(actual, expected) + + actual = one_hot(xp.asarray([1, 2, 0]), 3, axis=-2) + xp_assert_equal(actual, expected) + + def test_non_integer(self, xp: ModuleType): + with pytest.raises(TypeError): + _ = one_hot(xp.asarray([1.0]), 3) + + def test_device(self, xp: ModuleType, device: Device): + x = xp.asarray([0, 1, 2], device=device) + y = one_hot(x, 3) + assert get_device(y) == device + + +@pytest.mark.skip_xp_backend( + Backend.SPARSE, reason="read-only backend without .at support" +) class TestCreateDiagonal: - def test_1d(self): + def test_1d_from_numpy(self, xp: ModuleType): # from np.diag tests vals = 100 * xp.arange(5, dtype=xp.float64) - b = xp.zeros((5, 5)) + b = xp.zeros((5, 5), dtype=xp.float64) for k in range(5): - b[k, k] = vals[k] - assert_array_equal(create_diagonal(vals, xp=xp), b) - b = xp.zeros((7, 7)) + b = at(b)[k, k].set(vals[k]) + xp_assert_equal(create_diagonal(vals), b) + b = xp.zeros((7, 7), dtype=xp.float64) c = xp.asarray(b, copy=True) for k in range(5): - b[k, k + 2] = vals[k] - c[k + 2, k] = vals[k] - assert_array_equal(create_diagonal(vals, offset=2, xp=xp), b) - assert_array_equal(create_diagonal(vals, offset=-2, xp=xp), c) + b = at(b)[k, k + 2].set(vals[k]) + c = at(c)[k + 2, k].set(vals[k]) + xp_assert_equal(create_diagonal(vals, offset=2), b) + xp_assert_equal(create_diagonal(vals, offset=-2), c) @pytest.mark.parametrize("n", range(1, 10)) @pytest.mark.parametrize("offset", range(1, 10)) - def test_create_diagonal(self, n: int, offset: int): + def test_1d_from_scipy(self, xp: ModuleType, n: int, offset: int): # from scipy._lib tests rng = np.random.default_rng(2347823) one = xp.asarray(1.0) x = rng.random(n) - A = create_diagonal(xp.asarray(x, dtype=one.dtype), offset=offset, xp=xp) + A = create_diagonal(xp.asarray(x, dtype=one.dtype), offset=offset) B = xp.asarray(np.diag(x, offset), dtype=one.dtype) - assert_array_equal(A, B) + xp_assert_equal(A, B) - def test_0d(self): + def test_0d_raises(self, xp: ModuleType): with pytest.raises(ValueError, match="1-dimensional"): - create_diagonal(xp.asarray(1), xp=xp) + _ = create_diagonal(xp.asarray(1)) + + @pytest.mark.parametrize( + "shape", + [ + (0,), + (10,), + (0, 1), + (1, 0), + (0, 0), + (4, 2, 1), + (1, 1, 7), + (0, 0, 1), + (3, 2, 4, 5), + ], + ) + def test_nd(self, xp: ModuleType, shape: tuple[int, ...]): + rng = np.random.default_rng(2347823) + b = xp.asarray( + rng.integers((1 << 64) - 1, size=shape, dtype=np.uint64), dtype=xp.uint64 + ) + c = create_diagonal(b) + zero = xp.zeros((), dtype=xp.uint64) + assert c.shape == (*b.shape, b.shape[-1]) + for i in ndindex(*eager_shape(c)): + xp_assert_equal(c[i], b[i[:-1]] if i[-2] == i[-1] else zero) + + def test_device(self, xp: ModuleType, device: Device): + x = xp.asarray([1, 2, 3], device=device) + assert get_device(create_diagonal(x)) == device + + def test_xp(self, xp: ModuleType): + x = xp.asarray([1, 2]) + y = create_diagonal(x, xp=xp) + xp_assert_equal(y, xp.asarray([[1, 0], [0, 2]])) + + +class TestDefaultDType: + def test_basic(self, xp: ModuleType): + assert default_dtype(xp) == xp.empty(0).dtype + + def test_kind(self, xp: ModuleType): + assert default_dtype(xp, "real floating") == xp.empty(0).dtype + assert default_dtype(xp, "complex floating") == (xp.empty(0) * 1j).dtype + assert default_dtype(xp, "integral") == xp.int64 + assert default_dtype(xp, "indexing") == xp.int64 + + with pytest.raises(ValueError, match="Unknown kind"): + _ = default_dtype(xp, "foo") # type: ignore[arg-type] # pyright: ignore[reportArgumentType] + + def test_device(self, xp: ModuleType, device: Device): + # Note: at the moment there are no known namespaces with + # device-specific default dtypes. + assert default_dtype(xp, device=None) == xp.empty(0).dtype + assert default_dtype(xp, device=device) == xp.empty(0).dtype + + def test_torch(self, torch: ModuleType): + xp = torch + xp.set_default_dtype(xp.float64) + assert default_dtype(xp) == xp.float64 + assert default_dtype(xp, "real floating") == xp.float64 + assert default_dtype(xp, "complex floating") == xp.complex128 + + xp.set_default_dtype(xp.float32) + assert default_dtype(xp) == xp.float32 + assert default_dtype(xp, "real floating") == xp.float32 + assert default_dtype(xp, "complex floating") == xp.complex64 - def test_2d(self): - with pytest.raises(ValueError, match="1-dimensional"): - create_diagonal(xp.asarray([[1]]), xp=xp) + +class TestExpandDims: + def test_single_axis(self, xp: ModuleType): + """Trivial case where xpx.expand_dims doesn't add anything to xp.expand_dims""" + a = xp.asarray(np.reshape(np.arange(2 * 3 * 4 * 5), (2, 3, 4, 5))) + for axis in range(-5, 4): + b = expand_dims(a, axis=axis) + xp_assert_equal(b, xp.expand_dims(a, axis=axis)) + + def test_axis_tuple(self, xp: ModuleType): + a = xp.empty((3, 3, 3)) + assert expand_dims(a, axis=(0, 1, 2)).shape == (1, 1, 1, 3, 3, 3) + assert expand_dims(a, axis=(0, -1, -2)).shape == (1, 3, 3, 3, 1, 1) + assert expand_dims(a, axis=(0, 3, 5)).shape == (1, 3, 3, 1, 3, 1) + assert expand_dims(a, axis=(0, -3, -5)).shape == (1, 1, 3, 1, 3, 3) + + def test_axis_out_of_range(self, xp: ModuleType): + a = xp.empty((2, 3, 4, 5)) + with pytest.raises(IndexError, match="out of bounds"): + _ = expand_dims(a, axis=-6) + with pytest.raises(IndexError, match="out of bounds"): + _ = expand_dims(a, axis=5) + + a = xp.empty((3, 3, 3)) + with pytest.raises(IndexError, match="out of bounds"): + _ = expand_dims(a, axis=(0, -6)) + with pytest.raises(IndexError, match="out of bounds"): + _ = expand_dims(a, axis=(0, 5)) + + def test_repeated_axis(self, xp: ModuleType): + a = xp.empty((3, 3, 3)) + with pytest.raises(ValueError, match="Duplicate dimensions"): + _ = expand_dims(a, axis=(1, 1)) + + def test_positive_negative_repeated(self, xp: ModuleType): + # https://github.com/data-apis/array-api/issues/760#issuecomment-1989449817 + a = xp.empty((2, 3, 4, 5)) + with pytest.raises(ValueError, match="Duplicate dimensions"): + _ = expand_dims(a, axis=(3, -3)) + + def test_device(self, xp: ModuleType, device: Device): + x = xp.asarray([1, 2, 3], device=device) + assert get_device(expand_dims(x, axis=0)) == device + + def test_xp(self, xp: ModuleType): + x = xp.asarray([1, 2, 3]) + y = expand_dims(x, axis=(0, 1, 2), xp=xp) + assert y.shape == (1, 1, 1, 3) + + +@pytest.mark.filterwarnings( # array_api_strictest + "ignore:invalid value encountered:RuntimeWarning:array_api_strict" +) +@pytest.mark.filterwarnings( # sparse + "ignore:invalid value encountered:RuntimeWarning:sparse" +) +class TestIsClose: + @pytest.mark.parametrize("swap", [False, True]) + @pytest.mark.parametrize( + ("a", "b"), + [ + (0.0, 0.0), + (1.0, 1.0), + (1.0, 2.0), + (1.0, -1.0), + (100.0, 101.0), + (0, 0), + (1, 1), + (1, 2), + (1, -1), + (1.0 + 1j, 1.0 + 1j), + (1.0 + 1j, 1.0 - 1j), + (float("inf"), float("inf")), + (float("inf"), 100.0), + (float("inf"), float("-inf")), + (float("-inf"), float("-inf")), + (float("nan"), float("nan")), + (float("nan"), 100.0), + (1e6, 1e6 + 1), # True - within rtol + (1e6, 1e6 + 100), # False - outside rtol + (1e-6, 1.1e-6), # False - outside atol + (1e-7, 1.1e-7), # True - outside atol + (1e6 + 0j, 1e6 + 1j), # True - within rtol + (1e6 + 0j, 1e6 + 100j), # False - outside rtol + ], + ) + def test_basic(self, a: float, b: float, swap: bool, xp: ModuleType): + if swap: + b, a = a, b + a_xp = xp.asarray(a) + b_xp = xp.asarray(b) + + xp_assert_equal(isclose(a_xp, b_xp), xp.asarray(np.isclose(a, b))) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + ar_np = a * np.arange(10) + br_np = b * np.arange(10) + ar_xp = xp.asarray(ar_np) + br_xp = xp.asarray(br_np) + + xp_assert_equal(isclose(ar_xp, br_xp), xp.asarray(np.isclose(ar_np, br_np))) + + @pytest.mark.parametrize("dtype", ["float32", "int32"]) + def test_broadcast(self, dtype: str, xp: ModuleType): + dtype = getattr(xp, dtype) + a = xp.asarray([1, 2, 3], dtype=dtype) + b = xp.asarray([[1], [5]], dtype=dtype) + actual = isclose(a, b) + expect = xp.asarray( + [[True, False, False], [False, False, False]], dtype=xp.bool + ) + + xp_assert_equal(actual, expect) + + def test_some_inf(self, xp: ModuleType): + a = xp.asarray([0.0, 1.0, xp.inf, xp.inf, xp.inf]) + b = xp.asarray([1e-9, 1.0, xp.inf, -xp.inf, 2.0]) + actual = isclose(a, b) + xp_assert_equal(actual, xp.asarray([True, True, True, False, False])) + + def test_equal_nan(self, xp: ModuleType): + a = xp.asarray([xp.nan, xp.nan, 1.0]) + b = xp.asarray([xp.nan, 1.0, xp.nan]) + xp_assert_equal(isclose(a, b), xp.asarray([False, False, False])) + xp_assert_equal(isclose(a, b, equal_nan=True), xp.asarray([True, False, False])) + + @pytest.mark.parametrize("dtype", ["float32", "complex64", "int32"]) + def test_tolerance(self, dtype: str, xp: ModuleType): + dtype = getattr(xp, dtype) + a = xp.asarray([100, 100], dtype=dtype) + b = xp.asarray([101, 102], dtype=dtype) + xp_assert_equal(isclose(a, b), xp.asarray([False, False])) + xp_assert_equal(isclose(a, b, atol=1), xp.asarray([True, False])) + xp_assert_equal(isclose(a, b, rtol=0.01), xp.asarray([True, False])) + + # Attempt to trigger division by 0 in rtol on int dtype + xp_assert_equal(isclose(a, b, rtol=0), xp.asarray([False, False])) + xp_assert_equal(isclose(a, b, atol=1, rtol=0), xp.asarray([True, False])) + + @pytest.mark.parametrize("dtype", ["int8", "uint8"]) + def test_tolerance_integer_overflow(self, dtype: str, xp: ModuleType): + """1/rtol is too large for dtype""" + a = xp.asarray([100, 100], dtype=getattr(xp, dtype)) + b = xp.asarray([100, 101], dtype=getattr(xp, dtype)) + xp_assert_equal(isclose(a, b), xp.asarray([True, False])) + + def test_very_small_numbers(self, xp: ModuleType): + a = xp.asarray([1e-9, 1e-9]) + b = xp.asarray([1.0001e-9, 1.00001e-9]) + # Difference is below default atol + xp_assert_equal(isclose(a, b), xp.asarray([True, True])) + # Use only rtol + xp_assert_equal(isclose(a, b, atol=0), xp.asarray([False, True])) + xp_assert_equal(isclose(a, b, atol=0, rtol=0), xp.asarray([False, False])) + + def test_bool_dtype(self, xp: ModuleType): + a = xp.asarray([False, True, False]) + b = xp.asarray([True, True, False]) + xp_assert_equal(isclose(a, b), xp.asarray([False, True, True])) + xp_assert_equal(isclose(a, b, atol=1), xp.asarray([True, True, True])) + xp_assert_equal(isclose(a, b, atol=2), xp.asarray([True, True, True])) + xp_assert_equal(isclose(a, b, rtol=1), xp.asarray([True, True, True])) + xp_assert_equal(isclose(a, b, rtol=2), xp.asarray([True, True, True])) + + # Test broadcasting + xp_assert_equal( + isclose(a, xp.asarray(True), atol=1), xp.asarray([True, True, True]) + ) + xp_assert_equal( + isclose(xp.asarray(True), b, atol=1), xp.asarray([True, True, True]) + ) + + @pytest.mark.skip_xp_backend(Backend.SPARSE, reason="index by sparse array") + @pytest.mark.skip_xp_backend(Backend.ARRAY_API_STRICTEST, reason="unknown shape") + def test_none_shape(self, xp: ModuleType): + a = xp.asarray([1, 5, 0]) + b = xp.asarray([1, 4, 2]) + b = b[a < 5] + a = a[a < 5] + xp_assert_equal(isclose(a, b), xp.asarray([True, False])) + + @pytest.mark.skip_xp_backend(Backend.SPARSE, reason="index by sparse array") + @pytest.mark.skip_xp_backend(Backend.ARRAY_API_STRICTEST, reason="unknown shape") + def test_none_shape_bool(self, xp: ModuleType): + a = xp.asarray([True, True, False]) + b = xp.asarray([True, False, True]) + b = b[a] + a = a[a] + xp_assert_equal(isclose(a, b), xp.asarray([True, False])) + + @pytest.mark.skip_xp_backend(Backend.NUMPY_READONLY, reason="xp=xp") + def test_python_scalar(self, xp: ModuleType): + a = xp.asarray([0.0, 0.1], dtype=xp.float32) + xp_assert_equal(isclose(a, 0.0), xp.asarray([True, False])) + xp_assert_equal(isclose(0.0, a), xp.asarray([True, False])) + + a = xp.asarray([0, 1], dtype=xp.int16) + xp_assert_equal(isclose(a, 0), xp.asarray([True, False])) + xp_assert_equal(isclose(0, a), xp.asarray([True, False])) + + xp_assert_equal(isclose(0, 0, xp=xp), xp.asarray(True)) + xp_assert_equal(isclose(0, 1, xp=xp), xp.asarray(False)) + + def test_all_python_scalars(self): + with pytest.raises(TypeError, match="Unrecognized"): + _ = isclose(0, 0) + + def test_xp(self, xp: ModuleType): + a = xp.asarray([0.0, 0.0]) + b = xp.asarray([1e-9, 1e-4]) + xp_assert_equal(isclose(a, b, xp=xp), xp.asarray([True, False])) + + @pytest.mark.parametrize("equal_nan", [True, False]) + def test_device(self, xp: ModuleType, device: Device, equal_nan: bool): + a = xp.asarray([0.0, 0.0, xp.nan], device=device) + b = xp.asarray([1e-9, 1e-4, xp.nan], device=device) + res = isclose(a, b, equal_nan=equal_nan) + assert get_device(res) == device class TestKron: - def test_basic(self): + def test_basic(self, xp: ModuleType): # Using 0-dimensional array a = xp.asarray(1) b = xp.asarray([[1, 2], [3, 4]]) - k = xp.asarray([[1, 2], [3, 4]]) - assert_array_equal(kron(a, b, xp=xp), k) - a = xp.asarray([[1, 2], [3, 4]]) - b = xp.asarray(1) - assert_array_equal(kron(a, b, xp=xp), k) + xp_assert_equal(kron(a, b), b) + xp_assert_equal(kron(b, a), b) # Using 1-dimensional array a = xp.asarray([3]) b = xp.asarray([[1, 2], [3, 4]]) k = xp.asarray([[3, 6], [9, 12]]) - assert_array_equal(kron(a, b, xp=xp), k) - a = xp.asarray([[1, 2], [3, 4]]) - b = xp.asarray([3]) - assert_array_equal(kron(a, b, xp=xp), k) + xp_assert_equal(kron(a, b), k) + xp_assert_equal(kron(b, a), k) # Using 3-dimensional array a = xp.asarray([[[1]], [[2]]]) b = xp.asarray([[1, 2], [3, 4]]) k = xp.asarray([[[1, 2], [3, 4]], [[2, 4], [6, 8]]]) - assert_array_equal(kron(a, b, xp=xp), k) - a = xp.asarray([[1, 2], [3, 4]]) - b = xp.asarray([[[1]], [[2]]]) - k = xp.asarray([[[1, 2], [3, 4]], [[2, 4], [6, 8]]]) - assert_array_equal(kron(a, b, xp=xp), k) + xp_assert_equal(kron(a, b), k) + xp_assert_equal(kron(b, a), k) - def test_kron_smoke(self): + def test_kron_smoke(self, xp: ModuleType): a = xp.ones((3, 3)) b = xp.ones((3, 3)) k = xp.ones((9, 9)) - assert_array_equal(kron(a, b, xp=xp), k) + xp_assert_equal(kron(a, b), k) @pytest.mark.parametrize( ("shape_a", "shape_b"), @@ -205,7 +899,9 @@ def test_kron_smoke(self): ((2, 0, 0, 2), (2, 0, 2)), ], ) - def test_kron_shape(self, shape_a: tuple[int, ...], shape_b: tuple[int, ...]): + def test_kron_shape( + self, xp: ModuleType, shape_a: tuple[int, ...], shape_b: tuple[int, ...] + ): a = xp.ones(shape_a) b = xp.ones(shape_b) normalised_shape_a = xp.asarray( @@ -218,101 +914,251 @@ def test_kron_shape(self, shape_a: tuple[int, ...], shape_b: tuple[int, ...]): int(dim) for dim in xp.multiply(normalised_shape_a, normalised_shape_b) ) - k = kron(a, b, xp=xp) - assert_equal(k.shape, expected_shape, err_msg="Unexpected shape from kron") + k = kron(a, b) + assert k.shape == expected_shape + def test_python_scalar(self, xp: ModuleType): + a = 1 + # Test no dtype promotion to xp.asarray(a); use b.dtype + b = xp.asarray([[1, 2], [3, 4]], dtype=xp.int16) + xp_assert_equal(kron(a, b), b) + xp_assert_equal(kron(b, a), b) + xp_assert_equal(kron(1, 1, xp=xp), xp.asarray(1)) -class TestExpandDims: - def test_functionality(self): - def _squeeze_all(b: Array) -> Array: - """Mimics `np.squeeze(b)`. `xpx.squeeze`?""" - for axis in range(b.ndim): - with contextlib.suppress(ValueError): - b = xp.squeeze(b, axis=axis) - return b - - s = (2, 3, 4, 5) - a = xp.empty(s) - for axis in range(-5, 4): - b = expand_dims(a, axis=axis, xp=xp) - assert b.shape[axis] == 1 - assert _squeeze_all(b).shape == s + def test_all_python_scalars(self): + with pytest.raises(TypeError, match="Unrecognized"): + _ = kron(1, 1) - def test_axis_tuple(self): - a = xp.empty((3, 3, 3)) - assert expand_dims(a, axis=(0, 1, 2), xp=xp).shape == (1, 1, 1, 3, 3, 3) - assert expand_dims(a, axis=(0, -1, -2), xp=xp).shape == (1, 3, 3, 3, 1, 1) - assert expand_dims(a, axis=(0, 3, 5), xp=xp).shape == (1, 3, 3, 1, 3, 1) - assert expand_dims(a, axis=(0, -3, -5), xp=xp).shape == (1, 1, 3, 1, 3, 3) - - def test_axis_out_of_range(self): - s = (2, 3, 4, 5) - a = xp.empty(s) - with pytest.raises(IndexError, match="out of bounds"): - expand_dims(a, axis=-6, xp=xp) - with pytest.raises(IndexError, match="out of bounds"): - expand_dims(a, axis=5, xp=xp) + def test_device(self, xp: ModuleType, device: Device): + x1 = xp.asarray([1, 2, 3], device=device) + x2 = xp.asarray([4, 5], device=device) + assert get_device(kron(x1, x2)) == device - a = xp.empty((3, 3, 3)) - with pytest.raises(IndexError, match="out of bounds"): - expand_dims(a, axis=(0, -6), xp=xp) - with pytest.raises(IndexError, match="out of bounds"): - expand_dims(a, axis=(0, 5), xp=xp) + def test_xp(self, xp: ModuleType): + a = xp.ones((3, 3)) + b = xp.ones((3, 3)) + k = xp.ones((9, 9)) + xp_assert_equal(kron(a, b, xp=xp), k) - def test_repeated_axis(self): - a = xp.empty((3, 3, 3)) - with pytest.raises(ValueError, match="Duplicate dimensions"): - expand_dims(a, axis=(1, 1), xp=xp) - def test_positive_negative_repeated(self): - # https://github.com/data-apis/array-api/issues/760#issuecomment-1989449817 - a = xp.empty((2, 3, 4, 5)) - with pytest.raises(ValueError, match="Duplicate dimensions"): - expand_dims(a, axis=(3, -3), xp=xp) +class TestNUnique: + def test_simple(self, xp: ModuleType): + a = xp.asarray([[1, 1], [0, 2], [2, 2]]) + xp_assert_equal(nunique(a), xp.asarray(3)) + + def test_empty(self, xp: ModuleType): + a = xp.asarray([]) + xp_assert_equal(nunique(a), xp.asarray(0)) + + def test_size1(self, xp: ModuleType): + a = xp.asarray([123]) + xp_assert_equal(nunique(a), xp.asarray(1)) + + def test_all_equal(self, xp: ModuleType): + a = xp.asarray([123, 123, 123]) + xp_assert_equal(nunique(a), xp.asarray(1)) + + @pytest.mark.xfail_xp_backend(Backend.DASK, reason="No equal_nan kwarg in unique") + @pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="sparse#855") + def test_nan(self, xp: ModuleType, library: Backend): + if library.like(Backend.NUMPY) and NUMPY_VERSION < (1, 24): + pytest.xfail("NumPy <1.24 has no equal_nan kwarg in unique") + # Each NaN is counted separately + a = xp.asarray([xp.nan, 123.0, xp.nan]) + xp_assert_equal(nunique(a), xp.asarray(3)) + + @pytest.mark.parametrize("size", [0, 1, 2]) + def test_device(self, xp: ModuleType, device: Device, size: int): + a = xp.asarray([0.0] * size, device=device) + assert get_device(nunique(a)) == device + + def test_xp(self, xp: ModuleType): + a = xp.asarray([[1, 1], [0, 2], [2, 2]]) + xp_assert_equal(nunique(a, xp=xp), xp.asarray(3)) + + +class TestPad: + def test_simple(self, xp: ModuleType): + a = xp.asarray([1, 2, 3]) + padded = pad(a, 2) + xp_assert_equal(padded, xp.asarray([0, 0, 1, 2, 3, 0, 0])) + + @pytest.mark.xfail_xp_backend( + Backend.SPARSE, reason="constant_values can only be equal to fill value" + ) + def test_fill_value(self, xp: ModuleType): + a = xp.asarray([1, 2, 3]) + padded = pad(a, 2, constant_values=42) + xp_assert_equal(padded, xp.asarray([42, 42, 1, 2, 3, 42, 42])) + def test_ndim(self, xp: ModuleType): + a = xp.asarray(np.reshape(np.arange(2 * 3 * 4), (2, 3, 4))) + padded = pad(a, 2) + assert padded.shape == (6, 7, 8) + + def test_mode_not_implemented(self, xp: ModuleType): + a = xp.asarray([1, 2, 3]) + with pytest.raises(NotImplementedError, match="Only `'constant'`"): + _ = pad(a, 2, mode="edge") # type: ignore[arg-type] # pyright: ignore[reportArgumentType] + + def test_device(self, xp: ModuleType, device: Device): + a = xp.asarray(0.0, device=device) + assert get_device(pad(a, 2)) == device + + def test_xp(self, xp: ModuleType): + padded = pad(xp.asarray(0), 1, xp=xp) + xp_assert_equal(padded, xp.asarray(0)) + + def test_tuple_width(self, xp: ModuleType): + a = xp.asarray(np.reshape(np.arange(12), (3, 4))) + padded = pad(a, (1, 0)) + assert padded.shape == (4, 5) + + padded = pad(a, (1, 2)) + assert padded.shape == (6, 7) + + with pytest.raises((ValueError, RuntimeError)): + _ = pad(a, [(1, 2, 3)]) # type: ignore[list-item] # pyright: ignore[reportArgumentType] + + def test_sequence_of_tuples_width(self, xp: ModuleType): + a = xp.asarray(np.reshape(np.arange(12), (3, 4))) + + padded = pad(a, ((1, 0), (0, 2))) + assert padded.shape == (4, 6) + padded = pad(a, ((1, 0), (0, 0))) + assert padded.shape == (4, 4) + + +assume_unique = pytest.mark.parametrize( + "assume_unique", + [ + True, + pytest.param( + False, + marks=pytest.mark.xfail_xp_backend( + Backend.DASK, reason="NaN-shaped arrays" + ), + ), + ], +) + + +@pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="no argsort") +@pytest.mark.skip_xp_backend(Backend.ARRAY_API_STRICTEST, reason="no unique_values") class TestSetDiff1D: - def test_setdiff1d(self): + @pytest.mark.xfail_xp_backend(Backend.DASK, reason="NaN-shaped arrays") + @pytest.mark.xfail_xp_backend( + Backend.TORCH, reason="index_select not implemented for uint32" + ) + @pytest.mark.xfail_xp_backend( + Backend.TORCH_GPU, reason="index_select not implemented for uint32" + ) + def test_setdiff1d(self, xp: ModuleType): x1 = xp.asarray([6, 5, 4, 7, 1, 2, 7, 4]) x2 = xp.asarray([2, 4, 3, 3, 2, 1, 5]) expected = xp.asarray([6, 7]) - actual = setdiff1d(x1, x2, xp=xp) - assert_array_equal(actual, expected) + actual = setdiff1d(x1, x2) + xp_assert_equal(actual, expected) x1 = xp.arange(21) x2 = xp.arange(19) expected = xp.asarray([19, 20]) - actual = setdiff1d(x1, x2, xp=xp) - assert_array_equal(actual, expected) + actual = setdiff1d(x1, x2) + xp_assert_equal(actual, expected) - assert_array_equal(setdiff1d(xp.empty(0), xp.empty(0), xp=xp), xp.empty(0)) + xp_assert_equal(setdiff1d(xp.empty(0), xp.empty(0)), xp.empty(0)) x1 = xp.empty(0, dtype=xp.uint32) x2 = x1 - assert_equal(setdiff1d(x1, x2, xp=xp).dtype, xp.uint32) + assert xp.isdtype(setdiff1d(x1, x2).dtype, xp.uint32) - def test_assume_unique(self): + def test_assume_unique(self, xp: ModuleType): x1 = xp.asarray([3, 2, 1]) x2 = xp.asarray([7, 5, 2]) expected = xp.asarray([3, 1]) + actual = setdiff1d(x1, x2, assume_unique=True) + xp_assert_equal(actual, expected) + + @assume_unique + @pytest.mark.parametrize("shape1", [(), (1,), (1, 1)]) + @pytest.mark.parametrize("shape2", [(), (1,), (1, 1)]) + def test_shapes( + self, + assume_unique: bool, + shape1: tuple[int, ...], + shape2: tuple[int, ...], + xp: ModuleType, + ): + x1 = xp.zeros(shape1) + x2 = xp.zeros(shape2) + actual = setdiff1d(x1, x2, assume_unique=assume_unique) + xp_assert_equal(actual, xp.empty((0,))) + + @assume_unique + @pytest.mark.skip_xp_backend(Backend.NUMPY_READONLY, reason="xp=xp") + def test_python_scalar(self, xp: ModuleType, assume_unique: bool): + # Test no dtype promotion to xp.asarray(x2); use x1.dtype + x1 = xp.asarray([3, 1, 2], dtype=xp.int16) + x2 = 3 + actual = setdiff1d(x1, x2, assume_unique=assume_unique) + xp_assert_equal(actual, xp.asarray([1, 2], dtype=xp.int16)) + + actual = setdiff1d(x2, x1, assume_unique=assume_unique) + xp_assert_equal(actual, xp.asarray([], dtype=xp.int16)) + + xp_assert_equal( + setdiff1d(0, 0, assume_unique=assume_unique, xp=xp), + xp.asarray([0])[:0], # Default int dtype for backend + ) + + @pytest.mark.parametrize("assume_unique", [True, False]) + def test_all_python_scalars(self, assume_unique: bool): + with pytest.raises(TypeError, match="Unrecognized"): + _ = setdiff1d(0, 0, assume_unique=assume_unique) + + @assume_unique + @pytest.mark.skip_xp_backend( + Backend.TORCH, reason="device='meta' does not support unknown shapes" + ) + def test_device(self, xp: ModuleType, device: Device, assume_unique: bool): + x1 = xp.asarray([3, 8, 20], device=device) + x2 = xp.asarray([2, 3, 4], device=device) + assert get_device(setdiff1d(x1, x2, assume_unique=assume_unique)) == device + + @pytest.mark.skip_xp_backend(Backend.NUMPY_READONLY, reason="xp=xp") + def test_xp(self, xp: ModuleType): + x1 = xp.asarray([3, 8, 20]) + x2 = xp.asarray([2, 3, 4]) + expected = xp.asarray([8, 20]) actual = setdiff1d(x1, x2, assume_unique=True, xp=xp) - assert_array_equal(actual, expected) + xp_assert_equal(actual, expected) class TestSinc: - def test_simple(self): - assert_array_equal(sinc(xp.asarray(0.0), xp=xp), xp.asarray(1.0)) - w = sinc(xp.linspace(-1, 1, 100), xp=xp) + def test_simple(self, xp: ModuleType): + xp_assert_equal(sinc(xp.asarray(0.0)), xp.asarray(1.0)) + x = xp.asarray(np.linspace(-1, 1, 100)) + w = sinc(x) # check symmetry - assert_allclose(w, xp.flip(w, axis=0)) + xp_assert_close(w, xp.flip(w, axis=0)) @pytest.mark.parametrize("x", [0, 1 + 3j]) - def test_dtype(self, x: int | complex): + def test_dtype(self, xp: ModuleType, x: int | complex): with pytest.raises(ValueError, match="real floating data type"): - sinc(xp.asarray(x), xp=xp) + _ = sinc(xp.asarray(x)) + + def test_3d(self, xp: ModuleType): + x = np.arange(18, dtype=np.float64).reshape((3, 3, 2)) + expected = np.zeros_like(x) + expected[0, 0, 0] = 1 + x = xp.asarray(x) + expected = xp.asarray(expected) + xp_assert_close(sinc(x), expected, atol=1e-15) + + def test_device(self, xp: ModuleType, device: Device): + x = xp.asarray(0.0, device=device) + assert get_device(sinc(x)) == device - def test_3d(self): - x = xp.reshape(xp.arange(18, dtype=xp.float64), (3, 3, 2)) - expected = xp.zeros((3, 3, 2)) - expected[0, 0, 0] = 1.0 - assert_allclose(sinc(x, xp=xp), expected, atol=1e-15) + def test_xp(self, xp: ModuleType): + xp_assert_equal(sinc(xp.asarray(0.0), xp=xp), xp.asarray(1.0)) diff --git a/tests/test_helpers.py b/tests/test_helpers.py new file mode 100644 index 00000000..9f5c924d --- /dev/null +++ b/tests/test_helpers.py @@ -0,0 +1,419 @@ +from types import ModuleType +from typing import TYPE_CHECKING, Generic, TypeVar, cast + +import numpy as np +import pytest + +from array_api_extra._lib._backends import Backend +from array_api_extra._lib._testing import xp_assert_equal +from array_api_extra._lib._utils._compat import array_namespace +from array_api_extra._lib._utils._compat import device as get_device +from array_api_extra._lib._utils._helpers import ( + asarrays, + capabilities, + eager_shape, + in1d, + jax_autojit, + meta_namespace, + ndindex, + pickle_flatten, + pickle_unflatten, +) +from array_api_extra._lib._utils._typing import Array, Device, DType +from array_api_extra.testing import lazy_xp_function + +from .conftest import np_compat + +if TYPE_CHECKING: # pragma: no cover + # TODO import from typing (requires Python >=3.12) + from typing_extensions import override +else: + + def override(func): + return func + + +T = TypeVar("T") + +# FIXME calls xp.unique_values without size +lazy_xp_function(in1d, jax_jit=False) + + +@pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="no unique_inverse") +@pytest.mark.skip_xp_backend(Backend.ARRAY_API_STRICTEST, reason="no unique_inverse") +class TestIn1D: + # cover both code paths + @pytest.mark.parametrize( + "n", + [ + pytest.param(9, id="fast path"), + pytest.param( + 15, + id="slow path", + marks=pytest.mark.xfail_xp_backend( + Backend.DASK, reason="NaN-shaped array" + ), + ), + ], + ) + def test_no_invert_assume_unique(self, xp: ModuleType, n: int): + x1 = xp.asarray([3, 8, 20]) + x2 = xp.arange(n) + expected = xp.asarray([True, True, False]) + actual = in1d(x1, x2) + xp_assert_equal(actual, expected) + + def test_device(self, xp: ModuleType, device: Device): + x1 = xp.asarray([3, 8, 20], device=device) + x2 = xp.asarray([2, 3, 4], device=device) + assert get_device(in1d(x1, x2)) == device + + @pytest.mark.skip_xp_backend(Backend.NUMPY_READONLY, reason="xp=xp") + def test_xp(self, xp: ModuleType): + x1 = xp.asarray([1, 6]) + x2 = xp.asarray([0, 1, 2, 3, 4]) + expected = xp.asarray([True, False]) + actual = in1d(x1, x2, xp=xp) + xp_assert_equal(actual, expected) + + +class TestAsArrays: + @pytest.mark.parametrize( + ("dtype", "b", "defined"), + [ + # Well-defined cases of dtype promotion from Python scalar to Array + # bool vs. bool + ("bool", True, True), + # int vs. xp.*int*, xp.float*, xp.complex* + ("int16", 1, True), + ("uint8", 1, True), + ("float32", 1, True), + ("float64", 1, True), + ("complex64", 1, True), + ("complex128", 1, True), + # float vs. xp.float, xp.complex + ("float32", 1.0, True), + ("float64", 1.0, True), + ("complex64", 1.0, True), + ("complex128", 1.0, True), + # complex vs. xp.complex + ("complex64", 1.0j, True), + ("complex128", 1.0j, True), + # Undefined cases + ("bool", 1, False), + ("int64", 1.0, False), + ("float64", 1.0j, False), + ], + ) + def test_array_vs_scalar( + self, dtype: str, b: complex, defined: bool, xp: ModuleType + ): + a = xp.asarray(1, dtype=getattr(xp, dtype)) + + xa, xb = asarrays(a, b, xp) + assert xa.dtype == a.dtype + if defined: + assert xb.dtype == a.dtype + else: + assert xb.dtype == xp.asarray(b).dtype + + xbr, xar = asarrays(b, a, xp) + assert xar.dtype == xa.dtype + assert xbr.dtype == xb.dtype + + def test_scalar_vs_scalar(self, xp: ModuleType): + a, b = asarrays(1, 2.2, xp=xp) + assert a.dtype == xp.asarray(1).dtype # Default dtype + assert b.dtype == xp.asarray(2.2).dtype # Default dtype; not broadcasted + + ALL_TYPES: tuple[str, ...] = ( + "int8", + "int16", + "int32", + "int64", + "uint8", + "uint16", + "uint32", + "uint64", + "float32", + "float64", + "complex64", + "complex128", + "bool", + ) + + @pytest.mark.parametrize("a_type", ALL_TYPES) + @pytest.mark.parametrize("b_type", ALL_TYPES) + def test_array_vs_array(self, a_type: str, b_type: str, xp: ModuleType): + """ + Test that when both inputs of asarray are already Array API objects, + they are returned unchanged. + """ + a = xp.asarray(1, dtype=getattr(xp, a_type)) + b = xp.asarray(1, dtype=getattr(xp, b_type)) + xa, xb = asarrays(a, b, xp) + assert xa.dtype == a.dtype + assert xb.dtype == b.dtype + + @pytest.mark.parametrize("dtype", [np.float64, np.complex128]) + def test_numpy_generics(self, dtype: DType): + """ + Test special case of np.float64 and np.complex128, + which are subclasses of float and complex. + """ + a = cast(Array, dtype(0)) # type: ignore[operator] # pyright: ignore[reportCallIssue] + xa, xb = asarrays(a, 0, xp=np_compat) + assert xa.dtype == dtype + assert xb.dtype == dtype + + +@pytest.mark.parametrize( + "shape", [(), (1,), (5,), (2, 3), (5, 3, 8), (0,), (3, 0), (0, 0, 1)] +) +def test_ndindex(shape: tuple[int, ...]): + assert tuple(ndindex(*shape)) == tuple(np.ndindex(*shape)) + + +@pytest.mark.skip_xp_backend(Backend.SPARSE, reason="index by sparse array") +@pytest.mark.skip_xp_backend(Backend.ARRAY_API_STRICTEST, reason="boolean indexing") +def test_eager_shape(xp: ModuleType, library: Backend): + a = xp.asarray([1, 2, 3]) + # Lazy arrays, like Dask, have an eager shape until you slice them with + # a lazy boolean mask + assert eager_shape(a) == a.shape == (3,) + + b = a[a > 2] + if library is Backend.DASK: + with pytest.raises(TypeError, match="Unsupported lazy shape"): + _ = eager_shape(b) + # FIXME can't test use case for None in the shape until we add support for + # other lazy backends + else: + assert eager_shape(b) == b.shape == (1,) + + +class TestMetaNamespace: + @pytest.mark.skip_xp_backend(Backend.NUMPY_READONLY, reason="namespace tests") + def test_basic(self, xp: ModuleType, library: Backend): + args = None, xp.asarray(0), 1 + expect = np_compat if library is Backend.DASK else xp + assert meta_namespace(*args) is expect + + def test_dask_metas(self, da: ModuleType): + cp = pytest.importorskip("cupy") + cp_compat = array_namespace(cp.empty(0)) + args = None, da.from_array(cp.asarray(0)), 1 + assert meta_namespace(*args) is cp_compat + + def test_xp(self, xp: ModuleType): + args = None, xp.asarray(0), 1 + assert meta_namespace(*args, xp=xp) in (xp, np_compat) + + +class TestCapabilities: + def test_basic(self, xp: ModuleType): + expect = {"boolean indexing", "data-dependent shapes"} + if xp.__array_api_version__ >= "2024.12": + expect.add("max dimensions") + assert capabilities(xp).keys() == expect + + def test_device(self, xp: ModuleType, library: Backend, device: Device): + expect_keys = {"boolean indexing", "data-dependent shapes"} + if xp.__array_api_version__ >= "2024.12": + expect_keys.add("max dimensions") + assert capabilities(xp, device=device).keys() == expect_keys + + if library.like(Backend.TORCH): + # The output of capabilities is device-specific. + + # Test that device=None gets the current default device. + expect = capabilities(xp, device=device) + with xp.device(device): + actual = capabilities(xp) + assert actual == expect + + # Test that we're accepting anything that is accepted by the + # device= parameter in other functions + actual = capabilities(xp, device=device.type) # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] + + +class Wrapper(Generic[T]): # noqa: PLW1641 + """Trivial opaque wrapper. Must be pickleable.""" + + x: T + # __slots__ make this object serializable with __reduce_ex__(5), + # but not with __reduce__ + __slots__: tuple[str, ...] = ("x",) + + def __init__(self, x: T): + self.x = x + + # Note: this makes the object not hashable + @override + def __eq__(self, other: object) -> bool: + return isinstance(other, Wrapper) and self.x == other.x + + +class TestPickleFlatten: + def test_roundtrip(self): + class NotSerializable: + @override + def __reduce__(self) -> tuple[object, ...]: + raise NotImplementedError() + + # Note: NotHashable() instances can be reduced to an + # unserializable local class + class NotHashable: # noqa: PLW1641 + @override + def __eq__(self, other: object) -> bool: + return isinstance(other, type(self)) and other.__dict__ == self.__dict__ + + with pytest.raises(TypeError): + _ = hash(NotHashable()) + + # Extracted objects need be neither pickleable nor serializable + class C(NotSerializable, NotHashable): + x: int + + def __init__(self, x: int): + self.x = x + + class D(C): + pass + + c1 = C(1) + c2 = C(2) + d3 = D(3) + + # An assorted bunch of opaque containers, standard containers, + # non-serializable objects, and non-hashable objects (but not at the same time) + obj = Wrapper([1, c1, {2: (c2, {NotSerializable()})}, NotHashable(), d3]) + instances, rest = pickle_flatten(obj, C) + + assert instances == [c1, c2, d3] + obj2 = pickle_unflatten(instances, rest) + assert obj2 == obj + + def test_swap_objects(self): + class C: + pass + + obj = [1, C(), {2: (C(), {C()})}] + _, rest = pickle_flatten(obj, C) + obj2 = pickle_unflatten(["foo", "bar", "baz"], rest) + assert obj2 == [1, "foo", {2: ("bar", {"baz"})}] + + def test_multi_class(self): + class C: + pass + + class D: + pass + + c, d = C(), D() + instances, _ = pickle_flatten([c, d], (C, D)) + assert len(instances) == 2 + assert instances[0] is c + assert instances[1] is d + + def test_no_class(self): + obj = {1: "foo", 2: (3, 4)} + instances, rest = pickle_flatten(obj, ()) # type: ignore[var-annotated] + assert instances == [] + obj2 = pickle_unflatten([], rest) + assert obj2 == obj + + def test_flattened_stream(self): + """ + Test that multiple calls to flatten() can feed into the same stream of instances + """ + obj1 = Wrapper(1) + obj2 = [Wrapper(2), Wrapper(3)] + instances1, rest1 = pickle_flatten(obj1, Wrapper) + instances2, rest2 = pickle_flatten(obj2, Wrapper) + it = iter(instances1 + instances2 + [Wrapper(4)]) + assert pickle_unflatten(it, rest1) == obj1 + assert pickle_unflatten(it, rest2) == obj2 + assert list(it) == [Wrapper(4)] + + def test_too_short(self): + obj = [Wrapper(1), Wrapper(2)] + instances, rest = pickle_flatten(obj, Wrapper) + with pytest.raises(ValueError, match="Not enough"): + pickle_unflatten(instances[:1], rest) + + def test_recursion(self): + obj: list[object] = [Wrapper(1)] + obj.append(obj) + + instances, rest = pickle_flatten(obj, Wrapper) + assert instances == [Wrapper(1)] + + obj2 = pickle_unflatten(instances, rest) + assert len(obj2) == 2 + assert obj2[0] is obj[0] + assert obj2[1] is obj2 + + +class TestJAXAutoJIT: + def test_basic(self, jnp: ModuleType): + @jax_autojit + def f(x: Array, k: object = False) -> Array: + return x + 1 if k else x - 1 + + # Basic recognition of static_argnames + xp_assert_equal(f(jnp.asarray([1, 2])), jnp.asarray([0, 1])) + xp_assert_equal(f(jnp.asarray([1, 2]), False), jnp.asarray([0, 1])) + xp_assert_equal(f(jnp.asarray([1, 2]), True), jnp.asarray([2, 3])) + xp_assert_equal(f(jnp.asarray([1, 2]), 1), jnp.asarray([2, 3])) + + # static argument is not an ArrayLike + xp_assert_equal(f(jnp.asarray([1, 2]), "foo"), jnp.asarray([2, 3])) + + # static argument is not hashable, but serializable + xp_assert_equal(f(jnp.asarray([1, 2]), ["foo"]), jnp.asarray([2, 3])) + + def test_wrapper(self, jnp: ModuleType): + @jax_autojit + def f(w: Wrapper[Array]) -> Wrapper[Array]: + return Wrapper(w.x + 1) + + inp = Wrapper(jnp.asarray([1, 2])) + out = f(inp).x + xp_assert_equal(out, jnp.asarray([2, 3])) + + def test_static_hashable(self, jnp: ModuleType): + """Static argument/return value is hashable, but not serializable""" + + class C: + def __reduce__(self) -> object: # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride,reportImplicitOverride] + raise Exception() + + @jax_autojit + def f(x: object) -> object: + return x + + inp = C() + out = f(inp) + assert out is inp + + # Serializable opaque input contains non-serializable object plus array + winp = Wrapper((C(), jnp.asarray([1, 2]))) + out = f(winp) + assert isinstance(out, Wrapper) + assert out.x[0] is winp.x[0] + assert out.x[1] is not winp.x[1] + xp_assert_equal(out.x[1], winp.x[1]) + + def test_arraylikes_are_static(self): + pytest.importorskip("jax") + + @jax_autojit + def f(x: list[int]) -> list[int]: + assert isinstance(x, list) + assert x == [1, 2] + return [3, 4] + + out = f([1, 2]) + assert isinstance(out, list) + assert out == [3, 4] diff --git a/tests/test_lazy.py b/tests/test_lazy.py new file mode 100644 index 00000000..07904a33 --- /dev/null +++ b/tests/test_lazy.py @@ -0,0 +1,454 @@ +import contextlib +from types import ModuleType +from typing import cast + +import numpy as np +import pytest + +import array_api_extra as xpx # Let some tests bypass lazy_xp_function +from array_api_extra import lazy_apply +from array_api_extra._lib._backends import Backend +from array_api_extra._lib._testing import xp_assert_equal +from array_api_extra._lib._utils import _compat +from array_api_extra._lib._utils._compat import array_namespace, is_dask_array +from array_api_extra._lib._utils._helpers import eager_shape +from array_api_extra._lib._utils._typing import Array, Device +from array_api_extra.testing import lazy_xp_function + +lazy_xp_function(lazy_apply) + +as_numpy = pytest.mark.parametrize( + "as_numpy", + [ + False, + pytest.param( + True, + marks=[ + pytest.mark.skip_xp_backend(Backend.CUPY, reason="device->host copy"), + pytest.mark.skip_xp_backend( + Backend.TORCH_GPU, reason="device->host copy" + ), + pytest.mark.skip_xp_backend(Backend.SPARSE, reason="densification"), + ], + ), + ], +) + + +@as_numpy +@pytest.mark.parametrize("shape", [(2,), (3, 2)]) +@pytest.mark.parametrize("dtype", ["int32", "float64"]) +def test_lazy_apply_simple( + xp: ModuleType, library: Backend, shape: tuple[int, ...], dtype: str, as_numpy: bool +): + def f(x: Array) -> Array: + xp2 = array_namespace(x) + if as_numpy or library in (Backend.NUMPY_READONLY, Backend.DASK): + assert isinstance(x, np.ndarray) + else: + assert xp2 is xp + + y = xp2.broadcast_to(xp2.astype(x + 1, getattr(xp2, dtype)), shape) + return xp2.asarray(y, copy=True) # PyTorch: ensure writeable NumPy array + + x = xp.asarray([1, 2], dtype=xp.int16) + expect = xp.broadcast_to(xp.astype(x + 1, getattr(xp, dtype)), shape) + actual = lazy_apply(f, x, shape=shape, dtype=getattr(xp, dtype), as_numpy=as_numpy) + xp_assert_equal(actual, expect) + + +@as_numpy +def test_lazy_apply_broadcast(xp: ModuleType, as_numpy: bool): + """Test that default shape and dtype are broadcasted from the inputs.""" + + def f(x: Array, y: Array) -> Array: + return x + y + + x = xp.asarray([1, 2], dtype=xp.int16) + y = xp.asarray([[4], [5], [6]], dtype=xp.int32) + z = lazy_apply(f, x, y, as_numpy=as_numpy) + xp_assert_equal(z, x + y) + + +@as_numpy +def test_lazy_apply_multi_output(xp: ModuleType, as_numpy: bool): + def f(x: Array) -> tuple[Array, Array]: + xp2 = array_namespace(x) + y = x + xp2.asarray(2, dtype=xp2.int8) # Sparse: bad dtype propagation + z = xp2.broadcast_to(xp2.astype(x + 1, xp2.int16), (3, 2)) + z = xp2.asarray(z, copy=True) # PyTorch: ensure writeable NumPy array + return y, z + + x = xp.asarray([1, 2], dtype=xp.int8) + expect = ( + xp.asarray([3, 4], dtype=xp.int8), + xp.asarray([[2, 3], [2, 3], [2, 3]], dtype=xp.int16), + ) + actual = lazy_apply( + f, x, shape=((2,), (3, 2)), dtype=(xp.int8, xp.int16), as_numpy=as_numpy + ) + assert isinstance(actual, tuple) + assert len(actual) == 2 + xp_assert_equal(actual[0], expect[0]) + xp_assert_equal(actual[1], expect[1]) + + +@pytest.mark.parametrize( + "as_numpy", + [ + False, + pytest.param( + True, + marks=[ + pytest.mark.skip_xp_backend(Backend.CUPY, reason="device->host copy"), + pytest.mark.skip_xp_backend( + Backend.TORCH_GPU, reason="device->host copy" + ), + pytest.mark.skip_xp_backend(Backend.SPARSE, reason="densification"), + ], + ), + ], +) +def test_lazy_apply_multi_output_broadcast_dtype(xp: ModuleType, as_numpy: bool): + """ + If dtype is omitted and there are multiple shapes, use the same + dtype for all output arrays, broadcasted from the inputs + """ + + def f(x: Array, y: Array) -> tuple[Array, Array]: + return x + y, x - y + + x = xp.asarray([1, 2], dtype=xp.float32) + y = xp.asarray([3], dtype=xp.float64) + expect = ( + xp.asarray([4, 5], dtype=xp.float64), + xp.asarray([-2, -1], dtype=xp.float64), + ) + actual = lazy_apply(f, x, y, shape=((2,), (2,)), as_numpy=as_numpy) + assert isinstance(actual, tuple) + assert len(actual) == 2 + xp_assert_equal(actual[0], expect[0]) + xp_assert_equal(actual[1], expect[1]) + + +def test_lazy_apply_core_indices(da: ModuleType): + """ + Test that a function that performs reductions along axes does so + globally and not locally to each Dask chunk. + """ + + def f(x: Array) -> Array: + xp = array_namespace(x) + return xp.sum(x, axis=0) + x + + x_np = cast(Array, np.arange(15).reshape(5, 3)) # pyright: ignore[reportInvalidCast] + expect = da.asarray(f(x_np)) + x_da = da.asarray(x_np).rechunk(3) + + # A naive map_blocks fails because it applies f to each chunk separately, + # but f needs to reduce along axis 0 which is broken into multiple chunks. + # axis 0 is a "core axis" or "core index" (from xarray.apply_ufunc's + # "core dimension"). + with pytest.raises(AssertionError): + xp_assert_equal(da.map_blocks(f, x_da), expect) + + xp_assert_equal(lazy_apply(f, x_da), expect) + + +def test_lazy_apply_dont_run_on_meta(da: ModuleType): + """Test that Dask won't try running func on the meta array, + as it may have minimum size requirements. + """ + + def f(x: Array) -> Array: + assert x.size + return x + 1 + + x = da.arange(10) + assert not x._meta.size + y = lazy_apply(f, x) + xp_assert_equal(y, x + 1) + + +def test_lazy_apply_dask_non_numpy_meta(da: ModuleType): + """Test Dask wrapping around a meta-namespace other than numpy.""" + # At the moment of writing, of all Array API namespaces CuPy is + # the only one that Dask supports. + # For this reason, we can only test as_numpy=False since + # np.asarray(cp.Array) is blocked by the transfer guard. + + cp = pytest.importorskip("cupy") + cp = array_namespace(cp.empty(0)) + x_cp = cp.asarray([1, 2, 3]) + x_da = da.asarray([1, 2, 3]).map_blocks(cp.asarray) + assert array_namespace(x_da._meta) is cp + + def f(x: Array) -> Array: + return x + 1 + + y = lazy_apply(f, x_da) + assert array_namespace(y._meta) is cp # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] + xp_assert_equal(y.compute(), x_cp + 1) # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] + + +def test_dask_key(da: ModuleType): + """Test that the function name is visible on the Dask dashboard and in metrics.""" + + def helloworld(x: Array) -> Array: + return x + 1 + + x = da.asarray([1, 2]) + # Use full namespace to bypass monkey-patching by lazy_xp_function, + # which calls persist() to materialize exceptions and warnings and in + # doing so squashes the graph. + y = xpx.lazy_apply(helloworld, x) + + prefixes = set() + for key in y.__dask_graph__(): # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] + name = key[0] if isinstance(key, tuple) else key + assert isinstance(name, str) + prefixes.add(name.split("-")[0]) + + assert "helloworld" in prefixes + + +def test_lazy_apply_none_shape_in_args(xp: ModuleType, library: Backend): + x = xp.asarray([1, 1, 2, 2, 2]) + + # TODO mxp = meta_namespace(x, xp=xp) + mxp = np if library is Backend.DASK else xp + int_type = xp.asarray(0).dtype + + ctx: contextlib.AbstractContextManager[object] + if library.like(Backend.JAX): + ctx = pytest.raises(ValueError, match="Output shape must be fully known") + elif library is Backend.ARRAY_API_STRICTEST: + ctx = pytest.raises(RuntimeError, match="data-dependent shapes") + else: + ctx = contextlib.nullcontext() + + # Single output + with ctx: + values = lazy_apply(mxp.unique_values, x, shape=(None,)) + xp_assert_equal(xp.sort(values), xp.asarray([1, 2])) + + with ctx: + # Multi output + values, counts = lazy_apply( + mxp.unique_counts, + x, + shape=((None,), (None,)), + dtype=(x.dtype, int_type), + ) + xp_assert_equal(values, xp.asarray([1, 2])) + xp_assert_equal(counts, xp.asarray([2, 3])) + + +def check_lazy_apply_none_shape_broadcast(x: Array) -> Array: + def f(x: Array) -> Array: + return x + + x = x[x > 1] + # Use explicit namespace to bypass monkey-patching by lazy_xp_function + return xpx.lazy_apply(f, x) + + +lazy_xp_function(check_lazy_apply_none_shape_broadcast) + + +@pytest.mark.skip_xp_backend(Backend.SPARSE, reason="index by sparse array") +@pytest.mark.skip_xp_backend(Backend.JAX, reason="boolean indexing") +@pytest.mark.skip_xp_backend(Backend.JAX_GPU, reason="boolean indexing") +@pytest.mark.skip_xp_backend(Backend.ARRAY_API_STRICTEST, reason="boolean indexing") +def test_lazy_apply_none_shape_broadcast(xp: ModuleType): + """Broadcast from input array with unknown shape""" + x = xp.asarray([1, 2, 2]) + actual = check_lazy_apply_none_shape_broadcast(x) + xp_assert_equal(actual, xp.asarray([2, 2])) + + +@pytest.mark.parametrize( + "as_numpy", + [ + False, + pytest.param( + True, + marks=[ + pytest.mark.skip_xp_backend( + Backend.ARRAY_API_STRICT, reason="device->host copy" + ), + pytest.mark.skip_xp_backend(Backend.CUPY, reason="device->host copy"), + pytest.mark.skip_xp_backend( + Backend.TORCH, reason="materialize 'meta' device" + ), + pytest.mark.skip_xp_backend( + Backend.TORCH_GPU, reason="device->host copy" + ), + pytest.mark.skip_xp_backend(Backend.SPARSE, reason="densification"), + ], + ), + ], +) +def test_lazy_apply_device(xp: ModuleType, as_numpy: bool, device: Device): + def f(x: Array) -> Array: + xp2 = array_namespace(x) + # Deliberately forgetting to add device here to test that the + # output is transferred to the right device. This is necessary when + # as_numpy=True anyway. + return xp2.zeros(x.shape, dtype=x.dtype) + + x = xp.asarray([1, 2], device=device) + y = lazy_apply(f, x, as_numpy=as_numpy) + assert _compat.device(y) == device + + +def test_lazy_apply_arraylike(xp: ModuleType): + """Wrapped func returns an array-like""" + x = xp.asarray([1, 2, 3]) + + # Single output + def f(x: Array) -> int: + shape = eager_shape(x) + return shape[0] + + expect = xp.asarray(3) + actual = lazy_apply(f, x, shape=(), dtype=expect.dtype) + xp_assert_equal(actual, expect) + + # Multi output + def g(x: Array) -> tuple[int, list[int]]: + shape = eager_shape(x) + return shape[0], list(shape) + + actual2 = lazy_apply(g, x, shape=((), (1,)), dtype=(expect.dtype, expect.dtype)) + xp_assert_equal(actual2[0], xp.asarray(3)) + xp_assert_equal(actual2[1], xp.asarray([3])) + + +def test_lazy_apply_scalars_and_nones(xp: ModuleType, library: Backend): + def f(x: Array, y: None, z: int | Array) -> Array: + mxp = array_namespace(x, y, z) + mtyp = type(mxp.asarray(0)) + assert isinstance(x, mtyp) + assert y is None + # jax.pure_callback wraps scalar args + assert isinstance(z, mtyp if library.like(Backend.JAX) else int) + return x + z + + x = xp.asarray([1, 2]) + w = lazy_apply(f, x, None, 3) + xp_assert_equal(w, x + 3) + + +def check_lazy_apply_kwargs(x: Array, expect_cls: type, as_numpy: bool) -> Array: + is_dask = is_dask_array(x) + recursive: list[object] = [] + if not is_dask: # dask.delayed crashes on recursion + recursive.append(recursive) + + def eager( + x: Array, + z: dict[int, list[int]], + msg: str, + msgs: list[str], + scalar: int, + recursive: list[list[object]], + ) -> Array: + assert isinstance(x, expect_cls) + # JAX will crash if x isn't material + assert int(x) == 0 + # Did we re-wrap the namedtuple correctly, or did it get + # accidentally changed to a basic tuple? + assert z == {0: [1, 2]} + assert msg == "Hello World" # must be hidden from JAX + assert msgs[0] == "Hello World" # must be hidden from JAX + assert isinstance(msg, str) + assert isinstance(msgs[0], str) + assert scalar == 1 # must be hidden from JAX + assert isinstance(scalar, int) + assert isinstance(recursive, list) + if not is_dask: + assert recursive[0][0] is recursive[0] + return x + 1 + + # Use explicit namespace to bypass monkey-patching by lazy_xp_function + return xpx.lazy_apply( # pyright: ignore[reportCallIssue] + eager, + x, + z={0: [1, 2]}, + msg="Hello World", + msgs=["Hello World"], + # This will be automatically cast to jax.Array if we don't wrap it + scalar=1, + recursive=recursive, + shape=x.shape, + dtype=x.dtype, + as_numpy=as_numpy, + ) + + +lazy_xp_function(check_lazy_apply_kwargs) + + +@as_numpy +def test_lazy_apply_kwargs(xp: ModuleType, library: Backend, as_numpy: bool): + """When as_numpy=True, search and replace arrays in the (nested) keywords arguments + with numpy arrays, and leave the rest untouched.""" + x = xp.asarray(0) + expect_cls = np.ndarray if as_numpy or library is Backend.DASK else type(x) + actual = check_lazy_apply_kwargs(x, expect_cls, as_numpy) + xp_assert_equal(actual, x + 1) + + +class CustomError(Exception): + pass + + +def raises(x: Array) -> Array: + def eager(_: Array) -> Array: + msg = "Hello World" + raise CustomError(msg) + + # Use explicit namespace to bypass monkey-patching by lazy_xp_function + return xpx.lazy_apply(eager, x, shape=x.shape, dtype=x.dtype) + + +# jax.pure_callback does not support raising +# https://github.com/jax-ml/jax/issues/26102 +lazy_xp_function(raises, jax_jit=False) + + +def test_lazy_apply_raises(xp: ModuleType): + """ + See Also + -------- + test_testing.py::test_lazy_xp_function_eagerly_raises + """ + x = xp.asarray(0) + + with pytest.raises(CustomError, match="Hello World"): + # Here we are disregarding the return value, which would + # normally cause the graph not to materialize and the + # exception not to be raised. + # However, lazy_xp_function will do it for us on function exit. + _ = raises(x) + + +def test_invalid_args(): + def f(x: Array) -> Array: + return x + + x = np.asarray(1) + + with pytest.raises(ValueError, match="at least one argument array"): + _ = lazy_apply(f, shape=(1,), dtype=np.int32, xp=np) + with pytest.raises(ValueError, match="at least one argument array"): + _ = lazy_apply(f, 1, shape=(1,), dtype=np.int32, xp=np) + with pytest.raises(ValueError, match="at least one argument array"): + _ = lazy_apply(f, shape=(1,), dtype=np.int32) + with pytest.raises(ValueError, match="multiple shapes but only one dtype"): + _ = lazy_apply(f, x, shape=[(1,), (2,)], dtype=np.int32) # type: ignore[call-overload] # pyright: ignore[reportCallIssue,reportArgumentType] + with pytest.raises(ValueError, match="single shape but multiple dtypes"): + _ = lazy_apply(f, x, shape=(1,), dtype=[np.int32, np.int64]) # pyright: ignore[reportCallIssue,reportArgumentType] + with pytest.raises(ValueError, match="2 shapes and 1 dtypes"): + _ = lazy_apply(f, x, shape=[(1,), (2,)], dtype=[np.int32]) # type: ignore[arg-type] # pyright: ignore[reportCallIssue,reportArgumentType] diff --git a/tests/test_testing.py b/tests/test_testing.py new file mode 100644 index 00000000..3a93e287 --- /dev/null +++ b/tests/test_testing.py @@ -0,0 +1,470 @@ +from collections.abc import Callable +from types import ModuleType +from typing import cast + +import numpy as np +import pytest + +from array_api_extra._lib._backends import Backend +from array_api_extra._lib._testing import ( + as_numpy_array, + xp_assert_close, + xp_assert_equal, + xp_assert_less, +) +from array_api_extra._lib._utils._compat import ( + array_namespace, + is_dask_namespace, + is_jax_namespace, +) +from array_api_extra._lib._utils._typing import Array, Device +from array_api_extra.testing import lazy_xp_function, patch_lazy_xp_functions + +# pyright: reportUnknownParameterType=false,reportMissingParameterType=false + + +class TestAsNumPyArray: + def test_basic(self, xp: ModuleType): + x = xp.asarray([1, 2, 3]) + y = as_numpy_array(x, xp=xp) + xp_assert_equal(y, np.asarray([1, 2, 3])) # type: ignore[arg-type] # pyright: ignore[reportArgumentType] + + @pytest.mark.skip_xp_backend(Backend.TORCH, reason="materialize 'meta' device") + def test_device(self, xp: ModuleType, device: Device): + x = xp.asarray([1, 2, 3], device=device) + y = as_numpy_array(x, xp=xp) + xp_assert_equal(y, np.asarray([1, 2, 3])) # type: ignore[arg-type] # pyright: ignore[reportArgumentType] + + +class TestAssertEqualCloseLess: + @pytest.mark.parametrize("func", [xp_assert_equal, xp_assert_close]) + def test_assert_equal_close_basic(self, xp: ModuleType, func: Callable[..., None]): + func(xp.asarray(0), xp.asarray(0)) + func(xp.asarray([1, 2]), xp.asarray([1, 2])) + + with pytest.raises(AssertionError, match="Mismatched elements"): + func(xp.asarray([1, 2]), xp.asarray([2, 1])) + + with pytest.raises(AssertionError, match="hello"): + func(xp.asarray([1, 2]), xp.asarray([2, 1]), err_msg="hello") + + @pytest.mark.parametrize("func", [xp_assert_equal, xp_assert_close, xp_assert_less]) + def test_shape_dtype(self, xp: ModuleType, func: Callable[..., None]): + with pytest.raises(AssertionError, match="shapes do not match"): + func(xp.asarray([0]), xp.asarray([[0]])) + + with pytest.raises(AssertionError, match="dtypes do not match"): + func(xp.asarray(0, dtype=xp.float32), xp.asarray(0, dtype=xp.float64)) + + @pytest.mark.skip_xp_backend(Backend.NUMPY, reason="test other ns vs. numpy") + @pytest.mark.skip_xp_backend( + Backend.NUMPY_READONLY, reason="test other ns vs. numpy" + ) + @pytest.mark.parametrize("func", [xp_assert_equal, xp_assert_close, xp_assert_less]) + def test_namespace(self, xp: ModuleType, func: Callable[..., None]): + with pytest.raises(AssertionError, match="namespaces do not match"): + func(xp.asarray(0), np.asarray(0)) + with pytest.raises(TypeError, match="Unrecognized array input"): + func(xp.asarray(0), 0) + with pytest.raises(TypeError, match="list is not a supported array type"): + func(xp.asarray([0]), [0]) + + @pytest.mark.parametrize("func", [xp_assert_equal, xp_assert_close, xp_assert_less]) + def test_check_shape(self, xp: ModuleType, func: Callable[..., None]): + a = xp.asarray([1] if func is xp_assert_less else [2]) + b = xp.asarray(2) + c = xp.asarray(0) + d = xp.asarray([2, 2]) + + with pytest.raises(AssertionError, match="shapes do not match"): + func(a, b) + func(a, b, check_shape=False) + with pytest.raises(AssertionError, match="Mismatched elements"): + func(a, c, check_shape=False) + with pytest.raises(AssertionError, match="sizes do not match"): + func(a, d, check_shape=False) + + @pytest.mark.parametrize("func", [xp_assert_equal, xp_assert_close, xp_assert_less]) + def test_check_dtype(self, xp: ModuleType, func: Callable[..., None]): + a = xp.asarray(1 if func is xp_assert_less else 2) + b = xp.asarray(2, dtype=xp.int16) + c = xp.asarray(0, dtype=xp.int16) + + with pytest.raises(AssertionError, match="dtypes do not match"): + func(a, b) + func(a, b, check_dtype=False) + with pytest.raises(AssertionError, match="Mismatched elements"): + func(a, c, check_dtype=False) + + @pytest.mark.parametrize("func", [xp_assert_equal, xp_assert_close, xp_assert_less]) + @pytest.mark.xfail_xp_backend( + Backend.SPARSE, reason="sparse [()] returns np.generic" + ) + def test_check_scalar( + self, xp: ModuleType, library: Backend, func: Callable[..., None] + ): + a = xp.asarray(1 if func is xp_assert_less else 2) + b = xp.asarray(2)[()] # Note: only makes a difference on NumPy + c = xp.asarray(0) + + func(a, b) + if library.like(Backend.NUMPY): + with pytest.raises(AssertionError, match="array-ness does not match"): + func(a, b, check_scalar=True) + else: + func(a, b, check_scalar=True) + with pytest.raises(AssertionError, match="Mismatched elements"): + func(a, c, check_scalar=True) + + @pytest.mark.parametrize("dtype", ["int64", "float64"]) + def test_assert_close_tolerance(self, dtype: str, xp: ModuleType): + a = xp.asarray([100], dtype=getattr(xp, dtype)) + b = xp.asarray([102], dtype=getattr(xp, dtype)) + + with pytest.raises(AssertionError, match="Mismatched elements"): + xp_assert_close(a, b) + + xp_assert_close(a, b, rtol=0.03) + with pytest.raises(AssertionError, match="Mismatched elements"): + xp_assert_close(a, b, rtol=0.01) + + xp_assert_close(a, b, atol=3) + with pytest.raises(AssertionError, match="Mismatched elements"): + xp_assert_close(a, b, atol=1) + + def test_assert_less(self, xp: ModuleType): + xp_assert_less(xp.asarray(-1), xp.asarray(0)) + xp_assert_less(xp.asarray([1, 2]), xp.asarray([2, 3])) + with pytest.raises(AssertionError, match="Mismatched elements"): + xp_assert_less(xp.asarray([1, 1]), xp.asarray([2, 1])) + + @pytest.mark.parametrize("func", [xp_assert_equal, xp_assert_close, xp_assert_less]) + @pytest.mark.skip_xp_backend(Backend.SPARSE, reason="index by sparse array") + @pytest.mark.skip_xp_backend(Backend.ARRAY_API_STRICTEST, reason="boolean indexing") + def test_none_shape(self, xp: ModuleType, func: Callable[..., None]): + """On Dask and other lazy backends, test that a shape with NaN's or None's + can be compared to a real shape. + """ + # actual has shape=(None, ) + a = xp.asarray([1] if func is xp_assert_less else [2]) + a = a[a > 0] + + func(a, xp.asarray([2])) + with pytest.raises(AssertionError, match="shapes do not match"): + func(a, xp.asarray(2)) + with pytest.raises(AssertionError, match="shapes do not match"): + func(a, xp.asarray([2, 3])) + with pytest.raises(AssertionError, match="Mismatched elements"): + func(a, xp.asarray([0])) + + # desired has shape=(None, ) + a = xp.asarray([3] if func is xp_assert_less else [2]) + a = a[a > 0] + + func(xp.asarray([2]), a) + with pytest.raises(AssertionError, match="shapes do not match"): + func(xp.asarray(2), a) + with pytest.raises(AssertionError, match="shapes do not match"): + func(xp.asarray([2, 3]), a) + with pytest.raises(AssertionError, match="Mismatched elements"): + func(xp.asarray([4]), a) + + @pytest.mark.parametrize("func", [xp_assert_equal, xp_assert_close, xp_assert_less]) + def test_device(self, xp: ModuleType, device: Device, func: Callable[..., None]): + a = xp.asarray([1] if func is xp_assert_less else [2], device=device) + b = xp.asarray([2], device=device) + c = xp.asarray([2, 2], device=device) + + func(a, b) + with pytest.raises(AssertionError, match="shapes do not match"): + func(a, c) + # This is normally performed by np.testing.assert_array_equal etc. + # but in case of torch device='meta' we have to do it manually + with pytest.raises(AssertionError, match="sizes do not match"): + func(a, c, check_shape=False) + + +def good_lazy(x: Array) -> Array: + """A function that behaves well in Dask and jax.jit""" + return x * 2.0 + + +def non_materializable(x: Array) -> Array: + """ + This function materializes the input array, so it will fail when wrapped in jax.jit + and it will trigger an expensive computation in Dask. + """ + xp = array_namespace(x) + # Crashes inside jax.jit + # On Dask, this triggers two computations of the whole graph + if xp.any(x < 0.0) or xp.any(x > 10.0): + msg = "Values must be in the [0, 10] range" + raise ValueError(msg) + return x + + +def non_materializable2(x: Array) -> Array: + return non_materializable(x) + + +def non_materializable3(x: Array) -> Array: + return non_materializable(x) + + +def non_materializable4(x: Array) -> Array: + return non_materializable(x) + + +def non_materializable5(x: Array) -> Array: + return non_materializable(x) + + +lazy_xp_function(good_lazy) +# Works on JAX and Dask +lazy_xp_function(non_materializable2, jax_jit=False, allow_dask_compute=2) +lazy_xp_function(non_materializable3, jax_jit=False, allow_dask_compute=True) +# Works on JAX, but not Dask +lazy_xp_function(non_materializable4, jax_jit=False, allow_dask_compute=1) +# Works neither on Dask nor JAX +lazy_xp_function(non_materializable5) + + +def test_lazy_xp_function(xp: ModuleType): + x = xp.asarray([1.0, 2.0]) + + xp_assert_equal(good_lazy(x), xp.asarray([2.0, 4.0])) + # Not wrapped + xp_assert_equal(non_materializable(x), xp.asarray([1.0, 2.0])) + # Wrapping explicitly disabled + xp_assert_equal(non_materializable2(x), xp.asarray([1.0, 2.0])) + xp_assert_equal(non_materializable3(x), xp.asarray([1.0, 2.0])) + + if is_jax_namespace(xp): + xp_assert_equal(non_materializable4(x), xp.asarray([1.0, 2.0])) + with pytest.raises( + TypeError, match="Attempted boolean conversion of traced array" + ): + _ = non_materializable5(x) # Wrapped + + elif is_dask_namespace(xp): + with pytest.raises( + AssertionError, + match=r"dask\.compute.* 2 times, but only up to 1 calls are allowed", + ): + _ = non_materializable4(x) + with pytest.raises( + AssertionError, + match=r"dask\.compute.* 1 times, but no calls are allowed", + ): + _ = non_materializable5(x) + + else: + xp_assert_equal(non_materializable4(x), xp.asarray([1.0, 2.0])) + xp_assert_equal(non_materializable5(x), xp.asarray([1.0, 2.0])) + + +def static_params(x: Array, n: int, flag: bool = False) -> Array: + """Function with static parameters that must not be jitted""" + if flag and n > 0: # This fails if n or flag are jitted arrays + return x * 2.0 + return x * 3.0 + + +lazy_xp_function(static_params) + + +def test_lazy_xp_function_static_params(xp: ModuleType): + x = xp.asarray([1.0, 2.0]) + xp_assert_equal(static_params(x, 1), xp.asarray([3.0, 6.0])) + xp_assert_equal(static_params(x, 1, True), xp.asarray([2.0, 4.0])) + xp_assert_equal(static_params(x, 1, False), xp.asarray([3.0, 6.0])) + xp_assert_equal(static_params(x, 0, False), xp.asarray([3.0, 6.0])) + xp_assert_equal(static_params(x, 1, flag=True), xp.asarray([2.0, 4.0])) + xp_assert_equal(static_params(x, n=1, flag=True), xp.asarray([2.0, 4.0])) + + +def test_lazy_xp_function_deprecated_static_argnames(): + with pytest.warns(DeprecationWarning, match="static_argnames"): + lazy_xp_function(static_params, static_argnames=["flag"]) # type: ignore[arg-type] # pyright: ignore[reportArgumentType] + with pytest.warns(DeprecationWarning, match="static_argnums"): + lazy_xp_function(static_params, static_argnums=[1]) # type: ignore[arg-type] # pyright: ignore[reportArgumentType] + + +try: + # Test an arbitrary Cython ufunc (@cython.vectorize). + # When SCIPY_ARRAY_API is not set, this is the same as + # scipy.special.erf. + from scipy.special._ufuncs import erf # type: ignore[import-untyped] + + lazy_xp_function(erf) +except ImportError: + erf = None + + +@pytest.mark.skip_xp_backend(Backend.TORCH_GPU, reason="device->host copy") +@pytest.mark.filterwarnings("ignore:__array_wrap__:DeprecationWarning") # PyTorch +def test_lazy_xp_function_cython_ufuncs(xp: ModuleType, library: Backend): + pytest.importorskip("scipy") + assert erf is not None + x = xp.asarray([6.0, 7.0]) + if library.like(Backend.ARRAY_API_STRICT, Backend.JAX): + # array-api-strict arrays are auto-converted to NumPy + # which results in an assertion error for mismatched namespaces + # eager JAX arrays are auto-converted to NumPy in eager JAX + # and fail in jax.jit (which lazy_xp_function tests here) + with pytest.raises((TypeError, AssertionError)): + xp_assert_equal(cast(Array, erf(x)), xp.asarray([1.0, 1.0])) + else: + # CuPy, Dask and sparse define __array_ufunc__ and dispatch accordingly + # note that when sparse reduces to scalar it returns a np.generic, which + # would make xp_assert_equal fail. + xp_assert_equal(cast(Array, erf(x)), xp.asarray([1.0, 1.0])) + + +def dask_raises(x: Array) -> Array: + def _raises(x: Array) -> Array: + # Test that map_blocks doesn't eagerly call the function; + # dtype and meta should be sufficient to skip the trial run. + assert x.shape == (3,) + msg = "Hello world" + raise ValueError(msg) + + return x.map_blocks(_raises, dtype=x.dtype, meta=x._meta) # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] + + +lazy_xp_function(dask_raises) + + +def test_lazy_xp_function_eagerly_raises(da: ModuleType): + """Test that the pattern:: + + with pytest.raises(Exception): + func(x) + + works with Dask, even though it normally wouldn't as we're disregarding the func + output so the graph would not be ordinarily materialized. + lazy_xp_function contains ad-hoc code to materialize and reraise exceptions. + """ + x = da.arange(3) + with pytest.raises(ValueError, match="Hello world"): + _ = dask_raises(x) + + +class Wrapper: + """Trivial opaque wrapper. Must be pickleable.""" + + x: Array + + def __init__(self, x: Array): + self.x = x + + +def check_opaque_wrapper(w: Wrapper, xp: ModuleType) -> Wrapper: + assert isinstance(w, Wrapper) + assert array_namespace(w.x) == xp + return Wrapper(w.x + 1) + + +lazy_xp_function(check_opaque_wrapper) + + +def test_lazy_xp_function_opaque_wrappers(xp: ModuleType): + """ + Test that function input and output can be wrapped into arbitrary + serializable Python objects, even if jax.jit does not support them. + """ + x = xp.asarray([1, 2]) + xp2 = array_namespace(x) # Revert NUMPY_READONLY to array_api_compat.numpy + res = check_opaque_wrapper(Wrapper(x), xp2) + xp_assert_equal(res.x, xp.asarray([2, 3])) + + +def test_lazy_xp_function_opaque_wrappers_eagerly_raise(da: ModuleType): + """ + Like `test_lazy_xp_function_eagerly_raises`, but the returned object is + wrapped in an opaque wrapper. + """ + x = da.arange(3) + with pytest.raises(ValueError, match="Hello world"): + _ = Wrapper(dask_raises(x)) + + +def check_recursive(x: list[object]) -> list[object]: + assert isinstance(x, list) + assert x[1] is x + y: list[object] = [cast(Array, x[0]) + 1] + y.append(y) + return y + + +lazy_xp_function(check_recursive) + + +def test_lazy_xp_function_recursive(xp: ModuleType): + """Test that inputs and outputs can be recursive data structures.""" + x: list[object] = [xp.asarray([1, 2])] + x.append(x) + y = check_recursive(x) + assert isinstance(y, list) + xp_assert_equal(cast(Array, y[0]), xp.asarray([2, 3])) + assert y[1] is y + + +wrapped = ModuleType("wrapped") +naked = ModuleType("naked") + + +def f(x: Array) -> Array: + xp = array_namespace(x) + # Crash in jax.jit and trigger compute() on Dask + if not xp.all(x): + msg = "Values must be non-zero" + raise ValueError(msg) + return x + + +wrapped.f = f # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] +naked.f = f # type: ignore[attr-defined] # pyright: ignore[reportAttributeAccessIssue] +del f + + +lazy_xp_function(wrapped.f) +lazy_xp_modules = [wrapped] + + +def test_lazy_xp_modules(xp: ModuleType, library: Backend): + x = xp.asarray([1.0, 2.0]) + y = naked.f(x) + xp_assert_equal(y, x) + + if library.like(Backend.JAX): + with pytest.raises( + TypeError, match="Attempted boolean conversion of traced array" + ): + wrapped.f(x) + elif library.like(Backend.DASK): + with pytest.raises(AssertionError, match=r"dask\.compute"): + wrapped.f(x) + else: + y = wrapped.f(x) + xp_assert_equal(y, x) + + +def test_patch_lazy_xp_functions_deprecated_monkeypatch( + request: pytest.FixtureRequest, monkeypatch: pytest.MonkeyPatch +): + xp = pytest.importorskip("dask.array") + + x = xp.asarray([1.0, 2.0]) + y = non_materializable5(x) + xp_assert_equal(y, x) + + with pytest.warns(DeprecationWarning, match="`monkeypatch` parameter"): + _ = patch_lazy_xp_functions(request, monkeypatch, xp=xp) + + with pytest.raises(AssertionError, match=r"dask\.compute.* 1 times"): + _ = non_materializable5(x) + + monkeypatch.undo() + y = non_materializable5(x) + xp_assert_equal(y, x) diff --git a/tests/test_utils.py b/tests/test_utils.py deleted file mode 100644 index a34ec56f..00000000 --- a/tests/test_utils.py +++ /dev/null @@ -1,24 +0,0 @@ -from __future__ import annotations # https://github.com/pylint-dev/pylint/pull/9990 - -import typing - -# data-apis/array-api-strict#6 -import array_api_strict as xp # type: ignore[import-untyped] # pyright: ignore[reportMissingTypeStubs] -import pytest -from numpy.testing import assert_array_equal - -from array_api_extra._lib._utils import in1d - -if typing.TYPE_CHECKING: - from array_api_extra._lib._typing import Array - - -# some test coverage already provided by TestSetDiff1D -class TestIn1D: - # cover both code paths - @pytest.mark.parametrize("x2", [xp.arange(9), xp.arange(15)]) - def test_no_invert_assume_unique(self, x2: Array): - x1 = xp.asarray([3, 8, 20]) - expected = xp.asarray([True, True, False]) - actual = in1d(x1, x2, xp=xp) - assert_array_equal(actual, expected) diff --git a/tests/test_version.py b/tests/test_version.py index 1b20232f..b9717a50 100644 --- a/tests/test_version.py +++ b/tests/test_version.py @@ -1,5 +1,3 @@ -from __future__ import annotations # https://github.com/pylint-dev/pylint/pull/9990 - import importlib.metadata import array_api_extra as xpx diff --git a/typos.toml b/typos.toml new file mode 100644 index 00000000..34233590 --- /dev/null +++ b/typos.toml @@ -0,0 +1,8 @@ +[files] +extend-exclude = [".git/", ".pixi/"] +ignore-hidden = false + +[default.extend-words] +nd = "nd" +typ = "typ" +cardinalis = "cardinalis" diff --git a/vendor_tests/__init__.py b/vendor_tests/__init__.py new file mode 100644 index 00000000..9fd27fde --- /dev/null +++ b/vendor_tests/__init__.py @@ -0,0 +1 @@ +"""Allow for relative imports in `test_vendor.py`.""" diff --git a/vendor_tests/_array_api_compat_vendor.py b/vendor_tests/_array_api_compat_vendor.py new file mode 100644 index 00000000..9bc129b5 --- /dev/null +++ b/vendor_tests/_array_api_compat_vendor.py @@ -0,0 +1,11 @@ +"""This file is a hook imported by `src/array_api_extra/_lib/_compat.py`.""" +# pyright: reportUnknownParameterType=false, reportMissingParameterType=false + +from .array_api_compat import * # noqa: F403 +from .array_api_compat import array_namespace as array_namespace_compat + + +# Let unit tests check with `is` that we are picking up the function from this module +# and not from the original array_api_compat module. +def array_namespace(*xs, **kwargs): # numpydoc ignore=GL08 + return array_namespace_compat(*xs, **kwargs) diff --git a/vendor_tests/test_vendor.py b/vendor_tests/test_vendor.py new file mode 100644 index 00000000..e43c82fb --- /dev/null +++ b/vendor_tests/test_vendor.py @@ -0,0 +1,76 @@ +# pyright: reportAttributeAccessIssue=false + +from typing import Any + +import array_api_strict as xp +from numpy.testing import assert_array_equal + + +def test_vendor_compat(): + from ._array_api_compat_vendor import ( # type: ignore[attr-defined] + array_namespace, + device, + is_array_api_obj, + is_array_api_strict_namespace, + is_cupy_array, + is_cupy_namespace, + is_dask_array, + is_dask_namespace, + is_jax_array, + is_jax_namespace, + is_lazy_array, + is_numpy_array, + is_numpy_namespace, + is_pydata_sparse_array, + is_pydata_sparse_namespace, + is_torch_array, + is_torch_namespace, + is_writeable_array, + size, + to_device, + ) + + x = xp.asarray([1, 2, 3]) + assert array_namespace(x) is xp + to_device(x, device(x)) + assert is_array_api_obj(x) + assert is_array_api_strict_namespace(xp) + assert not is_cupy_array(x) + assert not is_cupy_namespace(xp) + assert not is_dask_array(x) + assert not is_dask_namespace(xp) + assert not is_jax_array(x) + assert not is_jax_namespace(xp) + assert not is_lazy_array(x) + assert not is_numpy_array(x) + assert not is_numpy_namespace(xp) + assert not is_pydata_sparse_array(x) + assert not is_pydata_sparse_namespace(xp) + assert not is_torch_array(x) + assert not is_torch_namespace(xp) + assert is_writeable_array(x) + assert size(x) == 3 + + +def test_vendor_extra(): + from .array_api_extra import atleast_nd + + x = xp.asarray(1) + y = atleast_nd(x, ndim=0) + assert_array_equal(y, x) # pyright: ignore[reportUnknownArgumentType] + + +def test_vendor_extra_testing(): + from .array_api_extra.testing import lazy_xp_function + + def f(x: Any) -> Any: + return x + + lazy_xp_function(f) + + +def test_vendor_extra_uses_vendor_compat(): + from ._array_api_compat_vendor import array_namespace as n1 + from .array_api_extra._lib._utils._compat import array_namespace as n2 + + assert n1 is n2