From c963abec663feaf1e0132f99ab9d0ebe617ee827 Mon Sep 17 00:00:00 2001 From: Deploy from CI <> Date: Fri, 16 Aug 2024 16:46:44 +0000 Subject: [PATCH] Deploy 2b75dd9551be511605ac09e0046a4e188bc46417 to gh-pages --- .cargo/config.toml | 8 + .github/dependabot.yml | 7 + .github/workflows/book-cd.yml | 40 + .github/workflows/book.yml | 29 + .github/workflows/ci_cuda.yaml | 33 + .github/workflows/maturin.yml | Bin 0 -> 6672 bytes .github/workflows/python.yml | 68 + .github/workflows/rust-ci.yml | 78 + .github/workflows/trufflehog.yml | 15 + .gitignore | 42 + .gitmodules | 3 + .pre-commit-config.yaml | 15 + .vscode/settings.json | 11 + 404.html | 218 ++ FontAwesome/css/font-awesome.css | 4 + FontAwesome/fonts/FontAwesome.ttf | Bin 0 -> 165548 bytes FontAwesome/fonts/fontawesome-webfont.eot | Bin 0 -> 165742 bytes FontAwesome/fonts/fontawesome-webfont.svg | 2671 +++++++++++++++++ FontAwesome/fonts/fontawesome-webfont.ttf | Bin 0 -> 165548 bytes FontAwesome/fonts/fontawesome-webfont.woff | Bin 0 -> 98024 bytes FontAwesome/fonts/fontawesome-webfont.woff2 | Bin 0 -> 77160 bytes ayu-highlight.css | 78 + book.js | 697 +++++ clipboard.min.js | 7 + css/chrome.css | 604 ++++ css/general.css | 232 ++ css/print.css | 50 + css/variables.css | 279 ++ elasticlunr.min.js | 10 + error_manage.html | 254 ++ favicon.png | Bin 0 -> 5679 bytes favicon.svg | 22 + fonts/OPEN-SANS-LICENSE.txt | 202 ++ fonts/SOURCE-CODE-PRO-LICENSE.txt | 93 + fonts/fonts.css | 100 + fonts/open-sans-v17-all-charsets-300.woff2 | Bin 0 -> 44352 bytes ...open-sans-v17-all-charsets-300italic.woff2 | Bin 0 -> 40656 bytes fonts/open-sans-v17-all-charsets-600.woff2 | Bin 0 -> 44936 bytes ...open-sans-v17-all-charsets-600italic.woff2 | Bin 0 -> 42120 bytes fonts/open-sans-v17-all-charsets-700.woff2 | Bin 0 -> 44988 bytes ...open-sans-v17-all-charsets-700italic.woff2 | Bin 0 -> 40800 bytes fonts/open-sans-v17-all-charsets-800.woff2 | Bin 0 -> 44536 bytes ...open-sans-v17-all-charsets-800italic.woff2 | Bin 0 -> 40812 bytes fonts/open-sans-v17-all-charsets-italic.woff2 | Bin 0 -> 41076 bytes .../open-sans-v17-all-charsets-regular.woff2 | Bin 0 -> 43236 bytes ...source-code-pro-v11-all-charsets-500.woff2 | Bin 0 -> 59140 bytes guide/cheatsheet.html | 241 ++ guide/hello_world.html | 394 +++ guide/installation.html | 267 ++ highlight.css | 82 + highlight.js | 54 + index.html | 307 ++ inference/hub.html | 362 +++ inference/inference.html | 231 ++ lib.rs | 199 ++ mark.min.js | 7 + print.html | 954 ++++++ searcher.js | 483 +++ searchindex.js | 1 + searchindex.json | 1 + simplified.rs | 196 ++ tomorrow-night.css | 102 + training/mnist.html | 274 ++ training/simplified.html | 413 +++ training/training.html | 248 ++ 65 files changed, 10686 insertions(+) create mode 100644 .cargo/config.toml create mode 100644 .github/dependabot.yml create mode 100644 .github/workflows/book-cd.yml create mode 100644 .github/workflows/book.yml create mode 100644 .github/workflows/ci_cuda.yaml create mode 100644 .github/workflows/maturin.yml create mode 100644 .github/workflows/python.yml create mode 100644 .github/workflows/rust-ci.yml create mode 100644 .github/workflows/trufflehog.yml create mode 100644 .gitignore create mode 100644 .gitmodules create mode 100644 .pre-commit-config.yaml create mode 100644 .vscode/settings.json create mode 100644 404.html create mode 100644 FontAwesome/css/font-awesome.css create mode 100644 FontAwesome/fonts/FontAwesome.ttf create mode 100644 FontAwesome/fonts/fontawesome-webfont.eot create mode 100644 FontAwesome/fonts/fontawesome-webfont.svg create mode 100644 FontAwesome/fonts/fontawesome-webfont.ttf create mode 100644 FontAwesome/fonts/fontawesome-webfont.woff create mode 100644 FontAwesome/fonts/fontawesome-webfont.woff2 create mode 100644 ayu-highlight.css create mode 100644 book.js create mode 100644 clipboard.min.js create mode 100644 css/chrome.css create mode 100644 css/general.css create mode 100644 css/print.css create mode 100644 css/variables.css create mode 100644 elasticlunr.min.js create mode 100644 error_manage.html create mode 100644 favicon.png create mode 100644 favicon.svg create mode 100644 fonts/OPEN-SANS-LICENSE.txt create mode 100644 fonts/SOURCE-CODE-PRO-LICENSE.txt create mode 100644 fonts/fonts.css create mode 100644 fonts/open-sans-v17-all-charsets-300.woff2 create mode 100644 fonts/open-sans-v17-all-charsets-300italic.woff2 create mode 100644 fonts/open-sans-v17-all-charsets-600.woff2 create mode 100644 fonts/open-sans-v17-all-charsets-600italic.woff2 create mode 100644 fonts/open-sans-v17-all-charsets-700.woff2 create mode 100644 fonts/open-sans-v17-all-charsets-700italic.woff2 create mode 100644 fonts/open-sans-v17-all-charsets-800.woff2 create mode 100644 fonts/open-sans-v17-all-charsets-800italic.woff2 create mode 100644 fonts/open-sans-v17-all-charsets-italic.woff2 create mode 100644 fonts/open-sans-v17-all-charsets-regular.woff2 create mode 100644 fonts/source-code-pro-v11-all-charsets-500.woff2 create mode 100644 guide/cheatsheet.html create mode 100644 guide/hello_world.html create mode 100644 guide/installation.html create mode 100644 highlight.css create mode 100644 highlight.js create mode 100644 index.html create mode 100644 inference/hub.html create mode 100644 inference/inference.html create mode 100644 lib.rs create mode 100644 mark.min.js create mode 100644 print.html create mode 100644 searcher.js create mode 100644 searchindex.js create mode 100644 searchindex.json create mode 100644 simplified.rs create mode 100644 tomorrow-night.css create mode 100644 training/mnist.html create mode 100644 training/simplified.html create mode 100644 training/training.html diff --git a/.cargo/config.toml b/.cargo/config.toml new file mode 100644 index 0000000000..ca9d853b60 --- /dev/null +++ b/.cargo/config.toml @@ -0,0 +1,8 @@ +[build] +rustflags = ["-C", "target-cpu=native"] + +[target.wasm32-unknown-unknown] +rustflags = ["-C", "target-feature=+simd128"] + +[target.x86_64-apple-darwin] +rustflags = ["-C", "target-feature=-avx,-avx2"] \ No newline at end of file diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000000..05bcdac6e8 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,7 @@ +version: 2 +updates: + - package-ecosystem: "cargo" + directory: "/" + schedule: + interval: "weekly" + open-pull-requests-limit: 5 diff --git a/.github/workflows/book-cd.yml b/.github/workflows/book-cd.yml new file mode 100644 index 0000000000..e8149e3832 --- /dev/null +++ b/.github/workflows/book-cd.yml @@ -0,0 +1,40 @@ +name: Deploy Rust book +on: + push: + branches: + - main + +jobs: + deploy: + runs-on: ubuntu-latest + permissions: + contents: write # To push a branch + pull-requests: write # To create a PR from that branch + steps: + - uses: actions/checkout@v3 + with: + fetch-depth: 0 + - name: Install latest mdbook + run: | + tag=$(curl 'https://github.com/repos/rust-lang/mdbook/releases/latest' | jq -r '.tag_name') + url="https://github.com/rust-lang/mdbook/releases/download/${tag}/mdbook-${tag}-x86_64-unknown-linux-gnu.tar.gz" + mkdir mdbook + curl -sSL $url | tar -xz --directory=./mdbook + echo `pwd`/mdbook >> $GITHUB_PATH + - name: Deploy GitHub Pages + run: | + # This assumes your book is in the root of your repository. + # Just add a `cd` here if you need to change to another directory. + cd candle-book + mdbook build + git worktree add gh-pages + git config user.name "Deploy from CI" + git config user.email "" + cd gh-pages + # Delete the ref to avoid keeping history. + git update-ref -d refs/heads/gh-pages + rm -rf * + mv ../book/* . + git add . + git commit -m "Deploy $GITHUB_SHA to gh-pages" + git push --force --set-upstream origin gh-pages diff --git a/.github/workflows/book.yml b/.github/workflows/book.yml new file mode 100644 index 0000000000..bb4d0494fb --- /dev/null +++ b/.github/workflows/book.yml @@ -0,0 +1,29 @@ +name: CI +on: + pull_request: + +jobs: + test: + name: Test candle-book + runs-on: ubuntu-latest + permissions: + contents: write # To push a branch + pull-requests: write # To create a PR from that branch + steps: + - uses: actions/checkout@master + - name: Install Rust + run: | + rustup set profile minimal + rustup toolchain install stable + rustup default stable + - name: Install latest mdbook + run: | + tag=$(curl 'https://github.com/repos/rust-lang/mdbook/releases/latest' | jq -r '.tag_name') + url="https://github.com/rust-lang/mdbook/releases/download/${tag}/mdbook-${tag}-x86_64-unknown-linux-gnu.tar.gz" + mkdir bin + curl -sSL $url | tar -xz --directory=bin + echo "$(pwd)/bin" >> $GITHUB_PATH + - name: Run tests + run: cd candle-book && cargo build && mdbook test -L ../target/debug/deps/ + + diff --git a/.github/workflows/ci_cuda.yaml b/.github/workflows/ci_cuda.yaml new file mode 100644 index 0000000000..0c874e2433 --- /dev/null +++ b/.github/workflows/ci_cuda.yaml @@ -0,0 +1,33 @@ +name: CI / cuda + +on: + workflow_dispatch: + pull_request: + +jobs: + test-cuda: + concurrency: + group: ${{ github.workflow }}-${{ github.job }}-${{ github.head_ref || github.run_id }} + cancel-in-progress: true + runs-on: [single-gpu, nvidia-gpu, t4, ci] + container: + image: nvidia/cuda:12.3.1-devel-ubuntu22.04 + options: --gpus 0 + if: ${{ github.event.pull_request.head.repo.full_name == github.event.pull_request.base.repo.full_name }} + permissions: + contents: write + packages: write + # This is used to complete the identity challenge + # with sigstore/fulcio when running outside of PRs. + id-token: write + security-events: write + steps: + - name: Checkout repository + uses: actions/checkout@v3 + - name: Install dependencies + run: apt-get update && apt install curl build-essential libssl-dev protobuf-compiler pkg-config -y + - name: Install Rust Stable + uses: actions-rust-lang/setup-rust-toolchain@v1 + - uses: Swatinem/rust-cache@v2 + - name: Test (cuda) + run: cargo test --features cuda diff --git a/.github/workflows/maturin.yml b/.github/workflows/maturin.yml new file mode 100644 index 0000000000000000000000000000000000000000..46bdb903da63c434e0e188a438f8a6b6e8478498 GIT binary patch literal 6672 zcmeHLT~8B16ur+T{)ax8G$C#)9}@LRF@QvZL_r^nA*J0yrR=V|TZ=LNb@iN?-tNry zixi85G)*ZxbLYsiCco1UG1fIq4tPVXZLp}7Ni_ZzH4&@u3Lg>p&HvEg7`q({{3u$AwSo+wpg$QJ@ zi(W8V)^A26ffXrY#Q6+=8F$yqrWwg06=5xBD#rc>bZV|R_ucX1P~Ic6NBF+R-XCQf z&mUwD&j)6GRo3LG)Z~d1mO^w8J9XrW&{tmGJX2oO8&Wpm_WhdCqeICq9j`&x;iHfB3m zPm3GafjPFLM0hwvgkp{erOwWXjVeLBrr0eU==u!FXR?VXZOVo`hIaeR{~AV?S=PA> zSbUCXUm3{+Id7UdqV!^fR=oSnEId!o4{QRxm;cX6YHLK zYMP~m`WgQOlB^?E9kWbr8zb7Nu@4(4HJS&t+|?664-v0erzcg|S(8-{kGhujx~#PY z+nC#y+1ckUU8BElw3O}YhL0?U$x4A2M|!zBr+S!OcFcewGO6Wrn;BY8 zru6yPwUgBo0V!^s(|Tesp6H!&BrQFIrDRB2NKBJGS=SYyg1DysGPjp~DC6W99+Q2^ zlf>xtky5TJ)`DsxT2ffpG0Z|+$5$ESGrKNaT+gzQ8nrh~k4)Xfl1#!FX`E;cqsV?# zW|vVW_N8ya#q)8iGN)zLB}Y!@NUcUbX57)nM`D)6sNBCO;*x 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+.hljs-selector-class { + color: #ff7733; +} + +.hljs-number, +.hljs-meta, +.hljs-builtin-name, +.hljs-literal, +.hljs-type, +.hljs-params { + color: #ffee99; +} + +.hljs-string, +.hljs-bullet { + color: #b8cc52; +} + +.hljs-title, +.hljs-built_in, +.hljs-section { + color: #ffb454; +} + +.hljs-keyword, +.hljs-selector-tag, +.hljs-symbol { + color: #ff7733; +} + +.hljs-name { + color: #36a3d9; +} + +.hljs-tag { + color: #00568d; +} + +.hljs-emphasis { + font-style: italic; +} + +.hljs-strong { + font-weight: bold; +} + +.hljs-addition { + color: #91b362; +} + +.hljs-deletion { + color: #d96c75; +} diff --git a/book.js b/book.js new file mode 100644 index 0000000000..aa12e7eccf --- /dev/null +++ b/book.js @@ -0,0 +1,697 @@ +"use strict"; + +// Fix back button cache problem +window.onunload = function () { }; + +// Global variable, shared between modules +function playground_text(playground, hidden = true) { + let code_block = playground.querySelector("code"); + + if (window.ace && code_block.classList.contains("editable")) { + let editor = window.ace.edit(code_block); + return editor.getValue(); + } else if (hidden) { + return code_block.textContent; + } else { + return code_block.innerText; + } +} + +(function codeSnippets() { + function fetch_with_timeout(url, options, timeout = 6000) { + return Promise.race([ + fetch(url, options), + new Promise((_, reject) => setTimeout(() => reject(new Error('timeout')), timeout)) + ]); + } + + var playgrounds = Array.from(document.querySelectorAll(".playground")); + if (playgrounds.length > 0) { + fetch_with_timeout("https://play.rust-lang.org/meta/crates", { + headers: { + 'Content-Type': "application/json", + }, + method: 'POST', + mode: 'cors', + }) + .then(response => response.json()) + .then(response => { + // get list of crates available in the rust playground + let playground_crates = response.crates.map(item => item["id"]); + playgrounds.forEach(block => handle_crate_list_update(block, playground_crates)); + }); + } + + function handle_crate_list_update(playground_block, playground_crates) { + // update the play buttons after receiving the response + update_play_button(playground_block, playground_crates); + + // and install on change listener to dynamically update ACE editors + if (window.ace) { + let code_block = playground_block.querySelector("code"); + if (code_block.classList.contains("editable")) { + let editor = window.ace.edit(code_block); + editor.addEventListener("change", function (e) { + update_play_button(playground_block, playground_crates); + }); + // add Ctrl-Enter command to execute rust code + editor.commands.addCommand({ + name: "run", + bindKey: { + win: "Ctrl-Enter", + mac: "Ctrl-Enter" + }, + exec: _editor => run_rust_code(playground_block) + }); + } + } + } + + // updates the visibility of play button based on `no_run` class and + // used crates vs ones available on https://play.rust-lang.org + function update_play_button(pre_block, playground_crates) { + var play_button = pre_block.querySelector(".play-button"); + + // skip if code is `no_run` + if (pre_block.querySelector('code').classList.contains("no_run")) { + play_button.classList.add("hidden"); + return; + } + + // get list of `extern crate`'s from snippet + var txt = playground_text(pre_block); + var re = /extern\s+crate\s+([a-zA-Z_0-9]+)\s*;/g; + var snippet_crates = []; + var item; + while (item = re.exec(txt)) { + snippet_crates.push(item[1]); + } + + // check if all used crates are available on play.rust-lang.org + var all_available = snippet_crates.every(function (elem) { + return playground_crates.indexOf(elem) > -1; + }); + + if (all_available) { + play_button.classList.remove("hidden"); + } else { + play_button.classList.add("hidden"); + } + } + + function run_rust_code(code_block) { + var result_block = code_block.querySelector(".result"); + if (!result_block) { + result_block = document.createElement('code'); + result_block.className = 'result hljs language-bash'; + + code_block.append(result_block); + } + + let text = playground_text(code_block); + let classes = code_block.querySelector('code').classList; + let edition = "2015"; + if(classes.contains("edition2018")) { + edition = "2018"; + } else if(classes.contains("edition2021")) { + edition = "2021"; + } + var params = { + version: "stable", + optimize: "0", + code: text, + edition: edition + }; + + if (text.indexOf("#![feature") !== -1) { + params.version = "nightly"; + } + + result_block.innerText = "Running..."; + + fetch_with_timeout("https://play.rust-lang.org/evaluate.json", { + headers: { + 'Content-Type': "application/json", + }, + method: 'POST', + mode: 'cors', + body: JSON.stringify(params) + }) + .then(response => response.json()) + .then(response => { + if (response.result.trim() === '') { + result_block.innerText = "No output"; + result_block.classList.add("result-no-output"); + } else { + result_block.innerText = response.result; + result_block.classList.remove("result-no-output"); + } + }) + .catch(error => result_block.innerText = "Playground Communication: " + error.message); + } + + // Syntax highlighting Configuration + hljs.configure({ + tabReplace: ' ', // 4 spaces + languages: [], // Languages used for auto-detection + }); + + let code_nodes = Array + .from(document.querySelectorAll('code')) + // Don't highlight `inline code` blocks in headers. + .filter(function (node) {return !node.parentElement.classList.contains("header"); }); + + if (window.ace) { + // language-rust class needs to be removed for editable + // blocks or highlightjs will capture events + code_nodes + .filter(function (node) {return node.classList.contains("editable"); }) + .forEach(function (block) { block.classList.remove('language-rust'); }); + + code_nodes + .filter(function (node) {return !node.classList.contains("editable"); }) + .forEach(function (block) { hljs.highlightBlock(block); }); + } else { + code_nodes.forEach(function (block) { hljs.highlightBlock(block); }); + } + + // Adding the hljs class gives code blocks the color css + // even if highlighting doesn't apply + code_nodes.forEach(function (block) { block.classList.add('hljs'); }); + + Array.from(document.querySelectorAll("code.hljs")).forEach(function (block) { + + var lines = Array.from(block.querySelectorAll('.boring')); + // If no lines were hidden, return + if (!lines.length) { return; } + block.classList.add("hide-boring"); + + var buttons = document.createElement('div'); + buttons.className = 'buttons'; + buttons.innerHTML = ""; + + // add expand button + var pre_block = block.parentNode; + pre_block.insertBefore(buttons, pre_block.firstChild); + + pre_block.querySelector('.buttons').addEventListener('click', function (e) { + if (e.target.classList.contains('fa-eye')) { + e.target.classList.remove('fa-eye'); + e.target.classList.add('fa-eye-slash'); + e.target.title = 'Hide lines'; + e.target.setAttribute('aria-label', e.target.title); + + block.classList.remove('hide-boring'); + } else if (e.target.classList.contains('fa-eye-slash')) { + e.target.classList.remove('fa-eye-slash'); + e.target.classList.add('fa-eye'); + e.target.title = 'Show hidden lines'; + e.target.setAttribute('aria-label', e.target.title); + + block.classList.add('hide-boring'); + } + }); + }); + + if (window.playground_copyable) { + Array.from(document.querySelectorAll('pre code')).forEach(function (block) { + var pre_block = block.parentNode; + if (!pre_block.classList.contains('playground')) { + var buttons = pre_block.querySelector(".buttons"); + if (!buttons) { + buttons = document.createElement('div'); + buttons.className = 'buttons'; + pre_block.insertBefore(buttons, pre_block.firstChild); + } + + var clipButton = document.createElement('button'); + clipButton.className = 'fa fa-copy clip-button'; + clipButton.title = 'Copy to clipboard'; + clipButton.setAttribute('aria-label', clipButton.title); + clipButton.innerHTML = ''; + + buttons.insertBefore(clipButton, buttons.firstChild); + } + }); + } + + // Process playground code blocks + Array.from(document.querySelectorAll(".playground")).forEach(function (pre_block) { + // Add play button + var buttons = pre_block.querySelector(".buttons"); + if (!buttons) { + buttons = document.createElement('div'); + buttons.className = 'buttons'; + pre_block.insertBefore(buttons, pre_block.firstChild); + } + + var runCodeButton = document.createElement('button'); + runCodeButton.className = 'fa fa-play play-button'; + runCodeButton.hidden = true; + runCodeButton.title = 'Run this code'; + runCodeButton.setAttribute('aria-label', runCodeButton.title); + + buttons.insertBefore(runCodeButton, buttons.firstChild); + runCodeButton.addEventListener('click', function (e) { + run_rust_code(pre_block); + }); + + if (window.playground_copyable) { + var copyCodeClipboardButton = document.createElement('button'); + copyCodeClipboardButton.className = 'fa fa-copy clip-button'; + copyCodeClipboardButton.innerHTML = ''; + copyCodeClipboardButton.title = 'Copy to clipboard'; + copyCodeClipboardButton.setAttribute('aria-label', copyCodeClipboardButton.title); + + buttons.insertBefore(copyCodeClipboardButton, buttons.firstChild); + } + + let code_block = pre_block.querySelector("code"); + if (window.ace && code_block.classList.contains("editable")) { + var undoChangesButton = document.createElement('button'); + undoChangesButton.className = 'fa fa-history reset-button'; + undoChangesButton.title = 'Undo changes'; + undoChangesButton.setAttribute('aria-label', undoChangesButton.title); + + buttons.insertBefore(undoChangesButton, buttons.firstChild); + + undoChangesButton.addEventListener('click', function () { + let editor = window.ace.edit(code_block); + editor.setValue(editor.originalCode); + editor.clearSelection(); + }); + } + }); +})(); + +(function themes() { + var html = document.querySelector('html'); + var themeToggleButton = document.getElementById('theme-toggle'); + var themePopup = document.getElementById('theme-list'); + var themeColorMetaTag = document.querySelector('meta[name="theme-color"]'); + var stylesheets = { + ayuHighlight: document.querySelector("[href$='ayu-highlight.css']"), + tomorrowNight: document.querySelector("[href$='tomorrow-night.css']"), + highlight: document.querySelector("[href$='highlight.css']"), + }; + + function showThemes() { + themePopup.style.display = 'block'; + themeToggleButton.setAttribute('aria-expanded', true); + themePopup.querySelector("button#" + get_theme()).focus(); + } + + function updateThemeSelected() { + themePopup.querySelectorAll('.theme-selected').forEach(function (el) { + el.classList.remove('theme-selected'); + }); + themePopup.querySelector("button#" + get_theme()).classList.add('theme-selected'); + } + + function hideThemes() { + themePopup.style.display = 'none'; + themeToggleButton.setAttribute('aria-expanded', false); + themeToggleButton.focus(); + } + + function get_theme() { + var theme; + try { theme = localStorage.getItem('mdbook-theme'); } catch (e) { } + if (theme === null || theme === undefined) { + return default_theme; + } else { + return theme; + } + } + + function set_theme(theme, store = true) { + let ace_theme; + + if (theme == 'coal' || theme == 'navy') { + stylesheets.ayuHighlight.disabled = true; + stylesheets.tomorrowNight.disabled = false; + stylesheets.highlight.disabled = true; + + ace_theme = "ace/theme/tomorrow_night"; + } else if (theme == 'ayu') { + stylesheets.ayuHighlight.disabled = false; + stylesheets.tomorrowNight.disabled = true; + stylesheets.highlight.disabled = true; + ace_theme = "ace/theme/tomorrow_night"; + } else { + stylesheets.ayuHighlight.disabled = true; + stylesheets.tomorrowNight.disabled = true; + stylesheets.highlight.disabled = false; + ace_theme = "ace/theme/dawn"; + } + + setTimeout(function () { + themeColorMetaTag.content = getComputedStyle(document.documentElement).backgroundColor; + }, 1); + + if (window.ace && window.editors) { + window.editors.forEach(function (editor) { + editor.setTheme(ace_theme); + }); + } + + var previousTheme = get_theme(); + + if (store) { + try { localStorage.setItem('mdbook-theme', theme); } catch (e) { } + } + + html.classList.remove(previousTheme); + html.classList.add(theme); + updateThemeSelected(); + } + + // Set theme + var theme = get_theme(); + + set_theme(theme, false); + + themeToggleButton.addEventListener('click', function () { + if (themePopup.style.display === 'block') { + hideThemes(); + } else { + showThemes(); + } + }); + + themePopup.addEventListener('click', function (e) { + var theme; + if (e.target.className === "theme") { + theme = e.target.id; + } else if (e.target.parentElement.className === "theme") { + theme = e.target.parentElement.id; + } else { + return; + } + set_theme(theme); + }); + + themePopup.addEventListener('focusout', function(e) { + // e.relatedTarget is null in Safari and Firefox on macOS (see workaround below) + if (!!e.relatedTarget && !themeToggleButton.contains(e.relatedTarget) && !themePopup.contains(e.relatedTarget)) { + hideThemes(); + } + }); + + // Should not be needed, but it works around an issue on macOS & iOS: https://github.com/rust-lang/mdBook/issues/628 + document.addEventListener('click', function(e) { + if (themePopup.style.display === 'block' && !themeToggleButton.contains(e.target) && !themePopup.contains(e.target)) { + hideThemes(); + } + }); + + document.addEventListener('keydown', function (e) { + if (e.altKey || e.ctrlKey || e.metaKey || e.shiftKey) { return; } + if (!themePopup.contains(e.target)) { return; } + + switch (e.key) { + case 'Escape': + e.preventDefault(); + hideThemes(); + break; + case 'ArrowUp': + e.preventDefault(); + var li = document.activeElement.parentElement; + if (li && li.previousElementSibling) { + li.previousElementSibling.querySelector('button').focus(); + } + break; + case 'ArrowDown': + e.preventDefault(); + var li = document.activeElement.parentElement; + if (li && li.nextElementSibling) { + li.nextElementSibling.querySelector('button').focus(); + } + break; + case 'Home': + e.preventDefault(); + themePopup.querySelector('li:first-child button').focus(); + break; + case 'End': + e.preventDefault(); + themePopup.querySelector('li:last-child button').focus(); + break; + } + }); +})(); + +(function sidebar() { + var body = document.querySelector("body"); + var sidebar = document.getElementById("sidebar"); + var sidebarLinks = document.querySelectorAll('#sidebar a'); + var sidebarToggleButton = document.getElementById("sidebar-toggle"); + var sidebarResizeHandle = document.getElementById("sidebar-resize-handle"); + var firstContact = null; + + function showSidebar() { + body.classList.remove('sidebar-hidden') + body.classList.add('sidebar-visible'); + Array.from(sidebarLinks).forEach(function (link) { + link.setAttribute('tabIndex', 0); + }); + sidebarToggleButton.setAttribute('aria-expanded', true); + sidebar.setAttribute('aria-hidden', false); + try { localStorage.setItem('mdbook-sidebar', 'visible'); } catch (e) { } + } + + + var sidebarAnchorToggles = document.querySelectorAll('#sidebar a.toggle'); + + function toggleSection(ev) { + ev.currentTarget.parentElement.classList.toggle('expanded'); + } + + Array.from(sidebarAnchorToggles).forEach(function (el) { + el.addEventListener('click', toggleSection); + }); + + function hideSidebar() { + body.classList.remove('sidebar-visible') + body.classList.add('sidebar-hidden'); + Array.from(sidebarLinks).forEach(function (link) { + link.setAttribute('tabIndex', -1); + }); + sidebarToggleButton.setAttribute('aria-expanded', false); + sidebar.setAttribute('aria-hidden', true); + try { localStorage.setItem('mdbook-sidebar', 'hidden'); } catch (e) { } + } + + // Toggle sidebar + sidebarToggleButton.addEventListener('click', function sidebarToggle() { + if (body.classList.contains("sidebar-hidden")) { + var current_width = parseInt( + document.documentElement.style.getPropertyValue('--sidebar-width'), 10); + if (current_width < 150) { + document.documentElement.style.setProperty('--sidebar-width', '150px'); + } + showSidebar(); + } else if (body.classList.contains("sidebar-visible")) { + hideSidebar(); + } else { + if (getComputedStyle(sidebar)['transform'] === 'none') { + hideSidebar(); + } else { + showSidebar(); + } + } + }); + + sidebarResizeHandle.addEventListener('mousedown', initResize, false); + + function initResize(e) { + window.addEventListener('mousemove', resize, false); + window.addEventListener('mouseup', stopResize, false); + body.classList.add('sidebar-resizing'); + } + function resize(e) { + var pos = (e.clientX - sidebar.offsetLeft); + if (pos < 20) { + hideSidebar(); + } else { + if (body.classList.contains("sidebar-hidden")) { + showSidebar(); + } + pos = Math.min(pos, window.innerWidth - 100); + document.documentElement.style.setProperty('--sidebar-width', pos + 'px'); + } + } + //on mouseup remove windows functions mousemove & mouseup + function stopResize(e) { + body.classList.remove('sidebar-resizing'); + window.removeEventListener('mousemove', resize, false); + window.removeEventListener('mouseup', stopResize, false); + } + + document.addEventListener('touchstart', function (e) { + firstContact = { + x: e.touches[0].clientX, + time: Date.now() + }; + }, { passive: true }); + + document.addEventListener('touchmove', function (e) { + if (!firstContact) + return; + + var curX = e.touches[0].clientX; + var xDiff = curX - firstContact.x, + tDiff = Date.now() - firstContact.time; + + if (tDiff < 250 && Math.abs(xDiff) >= 150) { + if (xDiff >= 0 && firstContact.x < Math.min(document.body.clientWidth * 0.25, 300)) + showSidebar(); + else if (xDiff < 0 && curX < 300) + hideSidebar(); + + firstContact = null; + } + }, { passive: true }); +})(); + +(function chapterNavigation() { + document.addEventListener('keydown', function (e) { + if (e.altKey || e.ctrlKey || e.metaKey || e.shiftKey) { return; } + if (window.search && window.search.hasFocus()) { return; } + var html = document.querySelector('html'); + + function next() { + var nextButton = document.querySelector('.nav-chapters.next'); + if (nextButton) { + window.location.href = nextButton.href; + } + } + function prev() { + var previousButton = document.querySelector('.nav-chapters.previous'); + if (previousButton) { + window.location.href = previousButton.href; + } + } + switch (e.key) { + case 'ArrowRight': + e.preventDefault(); + if (html.dir == 'rtl') { + prev(); + } else { + next(); + } + break; + case 'ArrowLeft': + e.preventDefault(); + if (html.dir == 'rtl') { + next(); + } else { + prev(); + } + break; + } + }); +})(); + +(function clipboard() { + var clipButtons = document.querySelectorAll('.clip-button'); + + function hideTooltip(elem) { + elem.firstChild.innerText = ""; + elem.className = 'fa fa-copy clip-button'; + } + + function showTooltip(elem, msg) { + elem.firstChild.innerText = msg; + elem.className = 'fa fa-copy tooltipped'; + } + + var clipboardSnippets = new ClipboardJS('.clip-button', { + text: function (trigger) { + hideTooltip(trigger); + let playground = trigger.closest("pre"); + return playground_text(playground, false); + } + }); + + Array.from(clipButtons).forEach(function (clipButton) { + clipButton.addEventListener('mouseout', function (e) { + hideTooltip(e.currentTarget); + }); + }); + + clipboardSnippets.on('success', function (e) { + e.clearSelection(); + showTooltip(e.trigger, "Copied!"); + }); + + clipboardSnippets.on('error', function (e) { + showTooltip(e.trigger, "Clipboard error!"); + }); +})(); + +(function scrollToTop () { + var menuTitle = document.querySelector('.menu-title'); + + menuTitle.addEventListener('click', function () { + document.scrollingElement.scrollTo({ top: 0, behavior: 'smooth' }); + }); +})(); + +(function controllMenu() { + var menu = document.getElementById('menu-bar'); + + (function controllPosition() { + var scrollTop = document.scrollingElement.scrollTop; + var prevScrollTop = scrollTop; + var minMenuY = -menu.clientHeight - 50; + // When the script loads, the page can be at any scroll (e.g. if you reforesh it). + menu.style.top = scrollTop + 'px'; + // Same as parseInt(menu.style.top.slice(0, -2), but faster + var topCache = menu.style.top.slice(0, -2); + menu.classList.remove('sticky'); + var stickyCache = false; // Same as menu.classList.contains('sticky'), but faster + document.addEventListener('scroll', function () { + scrollTop = Math.max(document.scrollingElement.scrollTop, 0); + // `null` means that it doesn't need to be updated + var nextSticky = null; + var nextTop = null; + var scrollDown = scrollTop > prevScrollTop; + var menuPosAbsoluteY = topCache - scrollTop; + if (scrollDown) { + nextSticky = false; + if (menuPosAbsoluteY > 0) { + nextTop = prevScrollTop; + } + } else { + if (menuPosAbsoluteY > 0) { + nextSticky = true; + } else if (menuPosAbsoluteY < minMenuY) { + nextTop = prevScrollTop + minMenuY; + } + } + if (nextSticky === true && stickyCache === false) { + menu.classList.add('sticky'); + stickyCache = true; + } else if (nextSticky === false && stickyCache === true) { + menu.classList.remove('sticky'); + stickyCache = false; + } + if (nextTop !== null) { + menu.style.top = nextTop + 'px'; + topCache = nextTop; + } + prevScrollTop = scrollTop; + }, { passive: true }); + })(); + (function controllBorder() { + function updateBorder() { + if (menu.offsetTop === 0) { + menu.classList.remove('bordered'); + } else { + menu.classList.add('bordered'); + } + } + updateBorder(); + document.addEventListener('scroll', updateBorder, { passive: true }); + })(); +})(); diff --git a/clipboard.min.js b/clipboard.min.js new file mode 100644 index 0000000000..02c549e35c --- /dev/null +++ b/clipboard.min.js @@ -0,0 +1,7 @@ +/*! + * clipboard.js v2.0.4 + * https://zenorocha.github.io/clipboard.js + * + * Licensed MIT © Zeno Rocha + */ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return function(n){var o={};function r(t){if(o[t])return o[t].exports;var e=o[t]={i:t,l:!1,exports:{}};return n[t].call(e.exports,e,e.exports,r),e.l=!0,e.exports}return r.m=n,r.c=o,r.d=function(t,e,n){r.o(t,e)||Object.defineProperty(t,e,{enumerable:!0,get:n})},r.r=function(t){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(t,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(t,"__esModule",{value:!0})},r.t=function(e,t){if(1&t&&(e=r(e)),8&t)return e;if(4&t&&"object"==typeof e&&e&&e.__esModule)return e;var n=Object.create(null);if(r.r(n),Object.defineProperty(n,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var o in e)r.d(n,o,function(t){return 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Without it, dragging on mobile Safari + will want to reposition the viewport in a weird way. + */ + overflow-x: clip; +} + +/* Menu Bar */ + +#menu-bar, +#menu-bar-hover-placeholder { + z-index: 101; + margin: auto calc(0px - var(--page-padding)); +} +#menu-bar { + position: relative; + display: flex; + flex-wrap: wrap; + background-color: var(--bg); + border-block-end-color: var(--bg); + border-block-end-width: 1px; + border-block-end-style: solid; +} +#menu-bar.sticky, +.js #menu-bar-hover-placeholder:hover + #menu-bar, +.js #menu-bar:hover, +.js.sidebar-visible #menu-bar { + position: -webkit-sticky; + position: sticky; + top: 0 !important; +} +#menu-bar-hover-placeholder { + position: sticky; + position: -webkit-sticky; + top: 0; + height: var(--menu-bar-height); +} +#menu-bar.bordered { + border-block-end-color: var(--table-border-color); +} +#menu-bar i, #menu-bar .icon-button { + position: relative; + padding: 0 8px; + z-index: 10; + line-height: var(--menu-bar-height); + cursor: pointer; + transition: color 0.5s; +} +@media only screen and (max-width: 420px) { + #menu-bar i, #menu-bar .icon-button { + padding: 0 5px; + } +} + +.icon-button { + border: none; + background: none; + padding: 0; + color: inherit; +} +.icon-button i { + margin: 0; +} + +.right-buttons { + margin: 0 15px; +} +.right-buttons a { + text-decoration: none; +} + +.left-buttons { + display: flex; + margin: 0 5px; +} +.no-js .left-buttons button { + display: none; +} + +.menu-title { + display: inline-block; + font-weight: 200; + font-size: 2.4rem; + line-height: var(--menu-bar-height); + text-align: center; + margin: 0; + flex: 1; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; +} +.js .menu-title { + cursor: pointer; +} + +.menu-bar, +.menu-bar:visited, +.nav-chapters, +.nav-chapters:visited, +.mobile-nav-chapters, +.mobile-nav-chapters:visited, +.menu-bar .icon-button, +.menu-bar a i { + color: var(--icons); +} + +.menu-bar i:hover, +.menu-bar .icon-button:hover, +.nav-chapters:hover, +.mobile-nav-chapters i:hover { + color: var(--icons-hover); +} + +/* Nav Icons */ + +.nav-chapters { + font-size: 2.5em; + text-align: center; + text-decoration: none; + + position: fixed; + top: 0; + bottom: 0; + margin: 0; + max-width: 150px; + min-width: 90px; + + display: flex; + justify-content: center; + align-content: center; + flex-direction: column; + + transition: color 0.5s, background-color 0.5s; +} + +.nav-chapters:hover { + text-decoration: none; + background-color: var(--theme-hover); + transition: background-color 0.15s, color 0.15s; +} + +.nav-wrapper { + margin-block-start: 50px; + display: none; +} + +.mobile-nav-chapters { + font-size: 2.5em; + text-align: center; + text-decoration: none; + width: 90px; + border-radius: 5px; + background-color: var(--sidebar-bg); +} + +/* Only Firefox supports flow-relative values */ +.previous { float: left; } +[dir=rtl] .previous { float: right; } + +/* Only Firefox supports flow-relative values */ +.next { + float: right; + right: var(--page-padding); +} +[dir=rtl] .next { + float: left; + right: unset; + left: var(--page-padding); +} + +/* Use the correct buttons for RTL layouts*/ +[dir=rtl] .previous i.fa-angle-left:before {content:"\f105";} +[dir=rtl] .next i.fa-angle-right:before { content:"\f104"; } + +@media only screen and (max-width: 1080px) { + .nav-wide-wrapper { display: none; } + .nav-wrapper { display: block; } +} + +/* sidebar-visible */ +@media only screen and (max-width: 1380px) { + #sidebar-toggle-anchor:checked ~ .page-wrapper .nav-wide-wrapper { display: none; } + #sidebar-toggle-anchor:checked ~ .page-wrapper .nav-wrapper { display: block; } +} + +/* Inline code */ + +:not(pre) > .hljs { + display: inline; + padding: 0.1em 0.3em; + border-radius: 3px; +} + +:not(pre):not(a) > .hljs { + color: var(--inline-code-color); + overflow-x: initial; +} + +a:hover > .hljs { + text-decoration: underline; +} + +pre { + position: relative; +} +pre > .buttons { + position: absolute; + z-index: 100; + right: 0px; + top: 2px; + margin: 0px; + padding: 2px 0px; + + color: var(--sidebar-fg); + cursor: pointer; + visibility: hidden; + opacity: 0; + transition: visibility 0.1s linear, opacity 0.1s linear; +} +pre:hover > .buttons { + visibility: visible; + opacity: 1 +} +pre > .buttons :hover { + color: var(--sidebar-active); + border-color: var(--icons-hover); + background-color: var(--theme-hover); +} +pre > .buttons i { + margin-inline-start: 8px; +} +pre > .buttons button { + cursor: inherit; + margin: 0px 5px; + padding: 3px 5px; + font-size: 14px; + + border-style: solid; + border-width: 1px; + border-radius: 4px; + border-color: var(--icons); + background-color: var(--theme-popup-bg); + transition: 100ms; + transition-property: color,border-color,background-color; + color: var(--icons); +} +@media (pointer: coarse) { + pre > .buttons button { + /* On mobile, make it easier to tap buttons. */ + padding: 0.3rem 1rem; + } + + .sidebar-resize-indicator { + /* Hide resize indicator on devices with limited accuracy */ + display: none; + } +} +pre > code { + display: block; + padding: 1rem; +} + +/* FIXME: ACE editors overlap their buttons because ACE does absolute + positioning within the code block which breaks padding. The only solution I + can think of is to move the padding to the outer pre tag (or insert a div + wrapper), but that would require fixing a whole bunch of CSS rules. +*/ +.hljs.ace_editor { + padding: 0rem 0rem; +} + +pre > .result { + margin-block-start: 10px; +} + +/* Search */ + +#searchresults a { + text-decoration: none; +} + +mark { + border-radius: 2px; + padding-block-start: 0; + padding-block-end: 1px; + padding-inline-start: 3px; + padding-inline-end: 3px; + margin-block-start: 0; + margin-block-end: -1px; + margin-inline-start: -3px; + margin-inline-end: -3px; + background-color: var(--search-mark-bg); + transition: background-color 300ms linear; + cursor: pointer; +} + +mark.fade-out { + background-color: rgba(0,0,0,0) !important; + cursor: auto; +} + +.searchbar-outer { + margin-inline-start: auto; + margin-inline-end: auto; + max-width: var(--content-max-width); +} + +#searchbar { + width: 100%; + margin-block-start: 5px; + margin-block-end: 0; + margin-inline-start: auto; + margin-inline-end: auto; + padding: 10px 16px; + transition: box-shadow 300ms ease-in-out; + border: 1px solid var(--searchbar-border-color); + border-radius: 3px; + background-color: var(--searchbar-bg); + color: var(--searchbar-fg); +} +#searchbar:focus, +#searchbar.active { + box-shadow: 0 0 3px var(--searchbar-shadow-color); +} + +.searchresults-header { + font-weight: bold; + font-size: 1em; + padding-block-start: 18px; + padding-block-end: 0; + padding-inline-start: 5px; + padding-inline-end: 0; + color: var(--searchresults-header-fg); +} + +.searchresults-outer { + margin-inline-start: auto; + margin-inline-end: auto; + max-width: var(--content-max-width); + border-block-end: 1px dashed var(--searchresults-border-color); +} + +ul#searchresults { + list-style: none; + padding-inline-start: 20px; +} +ul#searchresults li { + margin: 10px 0px; + padding: 2px; + border-radius: 2px; +} +ul#searchresults li.focus { + background-color: var(--searchresults-li-bg); +} +ul#searchresults span.teaser { + display: block; + clear: both; + margin-block-start: 5px; + margin-block-end: 0; + margin-inline-start: 20px; + margin-inline-end: 0; + font-size: 0.8em; +} +ul#searchresults span.teaser em { + font-weight: bold; + font-style: normal; +} + +/* Sidebar */ + +.sidebar { + position: fixed; + left: 0; + top: 0; + bottom: 0; + width: var(--sidebar-width); + font-size: 0.875em; + box-sizing: border-box; + -webkit-overflow-scrolling: touch; + overscroll-behavior-y: contain; + background-color: var(--sidebar-bg); + color: var(--sidebar-fg); +} +[dir=rtl] .sidebar { left: unset; right: 0; } +.sidebar-resizing { + -moz-user-select: none; + -webkit-user-select: none; + -ms-user-select: none; + user-select: none; +} +.no-js .sidebar, +.js:not(.sidebar-resizing) .sidebar { + transition: transform 0.3s; /* Animation: slide away */ +} +.sidebar code { + line-height: 2em; +} +.sidebar .sidebar-scrollbox { + overflow-y: auto; + position: absolute; + top: 0; + bottom: 0; + left: 0; + right: 0; + padding: 10px 10px; +} +.sidebar .sidebar-resize-handle { + position: absolute; + cursor: col-resize; + width: 0; + right: calc(var(--sidebar-resize-indicator-width) * -1); + top: 0; + bottom: 0; + display: flex; + align-items: center; +} + +.sidebar-resize-handle .sidebar-resize-indicator { + width: 100%; + height: 12px; + background-color: var(--icons); + margin-inline-start: var(--sidebar-resize-indicator-space); +} + +[dir=rtl] .sidebar .sidebar-resize-handle { + left: calc(var(--sidebar-resize-indicator-width) * -1); + right: unset; +} +.js .sidebar .sidebar-resize-handle { + cursor: col-resize; + width: calc(var(--sidebar-resize-indicator-width) - var(--sidebar-resize-indicator-space)); +} +/* sidebar-hidden */ +#sidebar-toggle-anchor:not(:checked) ~ .sidebar { + transform: translateX(calc(0px - var(--sidebar-width) - var(--sidebar-resize-indicator-width))); + z-index: -1; +} +[dir=rtl] #sidebar-toggle-anchor:not(:checked) ~ .sidebar { + transform: translateX(calc(var(--sidebar-width) + var(--sidebar-resize-indicator-width))); +} +.sidebar::-webkit-scrollbar { + background: var(--sidebar-bg); +} +.sidebar::-webkit-scrollbar-thumb { + background: var(--scrollbar); +} + +/* sidebar-visible */ +#sidebar-toggle-anchor:checked ~ .page-wrapper { + transform: translateX(calc(var(--sidebar-width) + var(--sidebar-resize-indicator-width))); +} +[dir=rtl] #sidebar-toggle-anchor:checked ~ .page-wrapper { + transform: translateX(calc(0px - var(--sidebar-width) - var(--sidebar-resize-indicator-width))); +} +@media only screen and (min-width: 620px) { + #sidebar-toggle-anchor:checked ~ .page-wrapper { + transform: none; + margin-inline-start: calc(var(--sidebar-width) + var(--sidebar-resize-indicator-width)); + } + [dir=rtl] #sidebar-toggle-anchor:checked ~ .page-wrapper { + transform: none; + } +} + +.chapter { + list-style: none outside none; + padding-inline-start: 0; + line-height: 2.2em; +} + +.chapter ol { + width: 100%; +} + +.chapter li { + display: flex; + color: var(--sidebar-non-existant); +} +.chapter li a { + display: block; + padding: 0; + text-decoration: none; + color: var(--sidebar-fg); +} + +.chapter li a:hover { + color: var(--sidebar-active); +} + +.chapter li a.active { + color: var(--sidebar-active); +} + +.chapter li > a.toggle { + cursor: pointer; + display: block; + margin-inline-start: auto; + padding: 0 10px; + user-select: none; + opacity: 0.68; +} + +.chapter li > a.toggle div { + transition: transform 0.5s; +} + +/* collapse the section */ +.chapter li:not(.expanded) + li > ol { + display: none; +} + +.chapter li.chapter-item { + line-height: 1.5em; + margin-block-start: 0.6em; +} + +.chapter li.expanded > a.toggle div { + transform: rotate(90deg); +} + +.spacer { + width: 100%; + height: 3px; + margin: 5px 0px; +} +.chapter .spacer { + background-color: var(--sidebar-spacer); +} + +@media (-moz-touch-enabled: 1), (pointer: coarse) { + .chapter li a { padding: 5px 0; } + .spacer { margin: 10px 0; } +} + +.section { + list-style: none outside none; + padding-inline-start: 20px; + line-height: 1.9em; +} + +/* Theme Menu Popup */ + +.theme-popup { + position: absolute; + left: 10px; + top: var(--menu-bar-height); + z-index: 1000; + border-radius: 4px; + font-size: 0.7em; + color: var(--fg); + background: var(--theme-popup-bg); + border: 1px solid var(--theme-popup-border); + margin: 0; + padding: 0; + list-style: none; + display: none; + /* Don't let the children's background extend past the rounded corners. */ + overflow: hidden; +} +[dir=rtl] .theme-popup { left: unset; right: 10px; } +.theme-popup .default { + color: var(--icons); +} +.theme-popup .theme { + width: 100%; + border: 0; + margin: 0; + padding: 2px 20px; + line-height: 25px; + white-space: nowrap; + text-align: start; + cursor: pointer; + color: inherit; + background: inherit; + font-size: inherit; +} +.theme-popup .theme:hover { + background-color: var(--theme-hover); +} + +.theme-selected::before { + display: inline-block; + content: "✓"; + margin-inline-start: -14px; + width: 14px; +} diff --git a/css/general.css b/css/general.css new file mode 100644 index 0000000000..7670b087d3 --- /dev/null +++ b/css/general.css @@ -0,0 +1,232 @@ +/* Base styles and content styles */ + +:root { + /* Browser default font-size is 16px, this way 1 rem = 10px */ + font-size: 62.5%; + color-scheme: var(--color-scheme); +} + +html { + font-family: "Open Sans", sans-serif; + color: var(--fg); + background-color: var(--bg); + text-size-adjust: none; + -webkit-text-size-adjust: none; +} + +body { + margin: 0; + font-size: 1.6rem; + overflow-x: hidden; +} + +code { + font-family: var(--mono-font) !important; + font-size: var(--code-font-size); + direction: ltr !important; +} + +/* make long words/inline code not x overflow */ +main { + overflow-wrap: break-word; +} + +/* make wide tables scroll if they overflow */ +.table-wrapper { + overflow-x: auto; +} + +/* Don't change font size in headers. */ +h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { + font-size: unset; +} + +.left { float: left; } +.right { float: right; } +.boring { opacity: 0.6; } +.hide-boring .boring { display: none; } +.hidden { display: none !important; } + +h2, h3 { margin-block-start: 2.5em; } +h4, h5 { margin-block-start: 2em; } + +.header + .header h3, +.header + .header h4, +.header + .header h5 { + margin-block-start: 1em; +} + +h1:target::before, +h2:target::before, +h3:target::before, +h4:target::before, +h5:target::before, +h6:target::before { + display: inline-block; + content: "»"; + margin-inline-start: -30px; + width: 30px; +} + +/* This is broken on Safari as of version 14, but is fixed + in Safari Technology Preview 117 which I think will be Safari 14.2. + https://bugs.webkit.org/show_bug.cgi?id=218076 +*/ +:target { + /* Safari does not support logical properties */ + scroll-margin-top: calc(var(--menu-bar-height) + 0.5em); +} + +.page { + outline: 0; + padding: 0 var(--page-padding); + margin-block-start: calc(0px - var(--menu-bar-height)); /* Compensate for the #menu-bar-hover-placeholder */ +} +.page-wrapper { + box-sizing: border-box; + background-color: var(--bg); +} +.no-js .page-wrapper, +.js:not(.sidebar-resizing) .page-wrapper { + transition: margin-left 0.3s ease, transform 0.3s ease; /* Animation: slide away */ +} +[dir=rtl] .js:not(.sidebar-resizing) .page-wrapper { + transition: margin-right 0.3s ease, transform 0.3s ease; /* Animation: slide away */ +} + +.content { + overflow-y: auto; + padding: 0 5px 50px 5px; +} +.content main { + margin-inline-start: auto; + margin-inline-end: auto; + max-width: var(--content-max-width); +} +.content p { line-height: 1.45em; } +.content ol { line-height: 1.45em; } +.content ul { line-height: 1.45em; } +.content a { text-decoration: none; } +.content a:hover { text-decoration: underline; } +.content img, .content video { max-width: 100%; } +.content .header:link, +.content .header:visited { + color: var(--fg); +} +.content .header:link, +.content .header:visited:hover { + text-decoration: none; +} + +table { + margin: 0 auto; + border-collapse: collapse; +} +table td { + padding: 3px 20px; + border: 1px var(--table-border-color) solid; +} +table thead { + background: var(--table-header-bg); +} +table thead td { + font-weight: 700; + border: none; +} +table thead th { + padding: 3px 20px; +} +table thead tr { + border: 1px var(--table-header-bg) solid; +} +/* Alternate background colors for rows */ +table tbody tr:nth-child(2n) { + background: var(--table-alternate-bg); +} + + +blockquote { + margin: 20px 0; + padding: 0 20px; + color: var(--fg); + background-color: var(--quote-bg); + border-block-start: .1em solid var(--quote-border); + border-block-end: .1em solid var(--quote-border); +} + +.warning { + margin: 20px; + padding: 0 20px; + border-inline-start: 2px solid var(--warning-border); +} + +.warning:before { + position: absolute; + width: 3rem; + height: 3rem; + margin-inline-start: calc(-1.5rem - 21px); + content: "ⓘ"; + text-align: center; + background-color: var(--bg); + color: var(--warning-border); + font-weight: bold; + font-size: 2rem; +} + +blockquote .warning:before { + background-color: var(--quote-bg); +} + +kbd { + background-color: var(--table-border-color); + border-radius: 4px; + border: solid 1px var(--theme-popup-border); + box-shadow: inset 0 -1px 0 var(--theme-hover); + display: inline-block; + font-size: var(--code-font-size); + font-family: var(--mono-font); + line-height: 10px; + padding: 4px 5px; + vertical-align: middle; +} + +:not(.footnote-definition) + .footnote-definition, +.footnote-definition + :not(.footnote-definition) { + margin-block-start: 2em; +} +.footnote-definition { + font-size: 0.9em; + margin: 0.5em 0; +} +.footnote-definition p { + display: inline; +} + +.tooltiptext { + position: absolute; + visibility: hidden; + color: #fff; + background-color: #333; + transform: translateX(-50%); /* Center by moving tooltip 50% of its width left */ + left: -8px; /* Half of the width of the icon */ + top: -35px; + font-size: 0.8em; + text-align: center; + border-radius: 6px; + padding: 5px 8px; + margin: 5px; + z-index: 1000; +} +.tooltipped .tooltiptext { + visibility: visible; +} + +.chapter li.part-title { + color: var(--sidebar-fg); + margin: 5px 0px; + font-weight: bold; +} + +.result-no-output { + font-style: italic; +} diff --git a/css/print.css b/css/print.css new file mode 100644 index 0000000000..80ec3a5441 --- /dev/null +++ b/css/print.css @@ -0,0 +1,50 @@ + +#sidebar, +#menu-bar, +.nav-chapters, +.mobile-nav-chapters { + display: none; +} + +#page-wrapper.page-wrapper { + transform: none !important; + margin-inline-start: 0px; + overflow-y: initial; +} + +#content { + max-width: none; + margin: 0; + padding: 0; +} + +.page { + overflow-y: initial; +} + +code { + direction: ltr !important; +} + +pre > .buttons { + z-index: 2; +} + +a, a:visited, a:active, a:hover { + color: #4183c4; + text-decoration: none; +} + +h1, h2, h3, h4, h5, h6 { + page-break-inside: avoid; + page-break-after: avoid; +} + +pre, code { + page-break-inside: avoid; + white-space: pre-wrap; +} + +.fa { + display: none !important; +} diff --git a/css/variables.css b/css/variables.css new file mode 100644 index 0000000000..0da55e8c9a --- /dev/null +++ b/css/variables.css @@ -0,0 +1,279 @@ + +/* Globals */ + +:root { + --sidebar-width: 300px; + --sidebar-resize-indicator-width: 8px; + --sidebar-resize-indicator-space: 2px; + --page-padding: 15px; + --content-max-width: 750px; + --menu-bar-height: 50px; + --mono-font: "Source Code Pro", Consolas, "Ubuntu Mono", Menlo, "DejaVu Sans Mono", monospace, monospace; + --code-font-size: 0.875em /* please adjust the ace font size accordingly in editor.js */ +} + +/* Themes */ + +.ayu { + --bg: hsl(210, 25%, 8%); + --fg: #c5c5c5; + + --sidebar-bg: #14191f; + --sidebar-fg: #c8c9db; + --sidebar-non-existant: #5c6773; + --sidebar-active: #ffb454; + --sidebar-spacer: #2d334f; + + --scrollbar: var(--sidebar-fg); + + --icons: #737480; + --icons-hover: #b7b9cc; + + --links: #0096cf; + + --inline-code-color: #ffb454; + + --theme-popup-bg: #14191f; + --theme-popup-border: #5c6773; + --theme-hover: #191f26; + + --quote-bg: hsl(226, 15%, 17%); + --quote-border: hsl(226, 15%, 22%); + + --warning-border: #ff8e00; + + --table-border-color: hsl(210, 25%, 13%); + --table-header-bg: hsl(210, 25%, 28%); + --table-alternate-bg: hsl(210, 25%, 11%); + + --searchbar-border-color: #848484; + --searchbar-bg: #424242; + --searchbar-fg: #fff; + --searchbar-shadow-color: #d4c89f; + --searchresults-header-fg: #666; + --searchresults-border-color: #888; + --searchresults-li-bg: #252932; + --search-mark-bg: #e3b171; + + --color-scheme: dark; +} + +.coal { + --bg: hsl(200, 7%, 8%); + --fg: #98a3ad; + + --sidebar-bg: #292c2f; + --sidebar-fg: #a1adb8; + --sidebar-non-existant: #505254; + --sidebar-active: #3473ad; + --sidebar-spacer: #393939; + + --scrollbar: var(--sidebar-fg); + + --icons: #43484d; + --icons-hover: #b3c0cc; + + --links: #2b79a2; + + --inline-code-color: #c5c8c6; + + --theme-popup-bg: #141617; + --theme-popup-border: #43484d; + --theme-hover: #1f2124; + + --quote-bg: hsl(234, 21%, 18%); + --quote-border: hsl(234, 21%, 23%); + + --warning-border: #ff8e00; + + --table-border-color: hsl(200, 7%, 13%); + --table-header-bg: hsl(200, 7%, 28%); + --table-alternate-bg: hsl(200, 7%, 11%); + + --searchbar-border-color: #aaa; + --searchbar-bg: #b7b7b7; + --searchbar-fg: #000; + --searchbar-shadow-color: #aaa; + --searchresults-header-fg: #666; + --searchresults-border-color: #98a3ad; + --searchresults-li-bg: #2b2b2f; + --search-mark-bg: #355c7d; + + --color-scheme: dark; +} + +.light { + --bg: hsl(0, 0%, 100%); + --fg: hsl(0, 0%, 0%); + + --sidebar-bg: #fafafa; + --sidebar-fg: hsl(0, 0%, 0%); + --sidebar-non-existant: #aaaaaa; + --sidebar-active: #1f1fff; + --sidebar-spacer: #f4f4f4; + + --scrollbar: #8F8F8F; + + --icons: #747474; + --icons-hover: #000000; + + --links: #20609f; + + --inline-code-color: #301900; + + --theme-popup-bg: #fafafa; + --theme-popup-border: #cccccc; + --theme-hover: #e6e6e6; + + --quote-bg: hsl(197, 37%, 96%); + --quote-border: hsl(197, 37%, 91%); + + --warning-border: #ff8e00; + + --table-border-color: hsl(0, 0%, 95%); + --table-header-bg: hsl(0, 0%, 80%); + --table-alternate-bg: hsl(0, 0%, 97%); + + --searchbar-border-color: #aaa; + --searchbar-bg: #fafafa; + --searchbar-fg: #000; + --searchbar-shadow-color: #aaa; + --searchresults-header-fg: #666; + --searchresults-border-color: #888; + --searchresults-li-bg: #e4f2fe; + --search-mark-bg: #a2cff5; + + --color-scheme: light; +} + +.navy { + --bg: hsl(226, 23%, 11%); + --fg: #bcbdd0; + + --sidebar-bg: #282d3f; + --sidebar-fg: #c8c9db; + --sidebar-non-existant: #505274; + --sidebar-active: #2b79a2; + --sidebar-spacer: #2d334f; + + --scrollbar: var(--sidebar-fg); + + --icons: #737480; + --icons-hover: #b7b9cc; + + --links: #2b79a2; + + --inline-code-color: #c5c8c6; + + --theme-popup-bg: #161923; + --theme-popup-border: #737480; + --theme-hover: #282e40; + + --quote-bg: hsl(226, 15%, 17%); + --quote-border: hsl(226, 15%, 22%); + + --warning-border: #ff8e00; + + --table-border-color: hsl(226, 23%, 16%); + --table-header-bg: hsl(226, 23%, 31%); + --table-alternate-bg: hsl(226, 23%, 14%); + + --searchbar-border-color: #aaa; + --searchbar-bg: #aeaec6; + --searchbar-fg: #000; + --searchbar-shadow-color: #aaa; + --searchresults-header-fg: #5f5f71; + --searchresults-border-color: #5c5c68; + --searchresults-li-bg: #242430; + --search-mark-bg: #a2cff5; + + --color-scheme: dark; +} + +.rust { + --bg: hsl(60, 9%, 87%); + --fg: #262625; + + --sidebar-bg: #3b2e2a; + --sidebar-fg: #c8c9db; + --sidebar-non-existant: #505254; + --sidebar-active: #e69f67; + --sidebar-spacer: #45373a; + + --scrollbar: var(--sidebar-fg); + + --icons: #737480; + --icons-hover: #262625; + + --links: #2b79a2; + + --inline-code-color: #6e6b5e; + + --theme-popup-bg: #e1e1db; + --theme-popup-border: #b38f6b; + --theme-hover: #99908a; + + --quote-bg: hsl(60, 5%, 75%); + --quote-border: hsl(60, 5%, 70%); + + --warning-border: #ff8e00; + + --table-border-color: hsl(60, 9%, 82%); + --table-header-bg: #b3a497; + --table-alternate-bg: hsl(60, 9%, 84%); + + --searchbar-border-color: #aaa; + --searchbar-bg: #fafafa; + --searchbar-fg: #000; + --searchbar-shadow-color: #aaa; + --searchresults-header-fg: #666; + --searchresults-border-color: #888; + --searchresults-li-bg: #dec2a2; + --search-mark-bg: #e69f67; + + --color-scheme: light; +} + +@media (prefers-color-scheme: dark) { + .light.no-js { + --bg: hsl(200, 7%, 8%); + --fg: #98a3ad; + + --sidebar-bg: #292c2f; + --sidebar-fg: #a1adb8; + --sidebar-non-existant: #505254; + --sidebar-active: #3473ad; + --sidebar-spacer: #393939; + + --scrollbar: var(--sidebar-fg); + + --icons: #43484d; + --icons-hover: #b3c0cc; + + --links: #2b79a2; + + --inline-code-color: #c5c8c6; + + --theme-popup-bg: #141617; + --theme-popup-border: #43484d; + --theme-hover: #1f2124; + + --quote-bg: hsl(234, 21%, 18%); + --quote-border: hsl(234, 21%, 23%); + + --warning-border: #ff8e00; + + --table-border-color: hsl(200, 7%, 13%); + --table-header-bg: hsl(200, 7%, 28%); + --table-alternate-bg: hsl(200, 7%, 11%); + + --searchbar-border-color: #aaa; + --searchbar-bg: #b7b7b7; + --searchbar-fg: #000; + --searchbar-shadow-color: #aaa; + --searchresults-header-fg: #666; + --searchresults-border-color: #98a3ad; + --searchresults-li-bg: #2b2b2f; + --search-mark-bg: #355c7d; + } +} diff --git a/elasticlunr.min.js b/elasticlunr.min.js new file mode 100644 index 0000000000..94b20dd2ef --- /dev/null +++ b/elasticlunr.min.js @@ -0,0 +1,10 @@ +/** + * elasticlunr - http://weixsong.github.io + * Lightweight full-text search engine in Javascript for browser search and offline search. - 0.9.5 + * + * Copyright (C) 2017 Oliver Nightingale + * Copyright (C) 2017 Wei Song + * MIT Licensed + * @license + */ +!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. This may cause problems when serialising the index.\n",e)},t.Pipeline.load=function(e){var n=new t.Pipeline;return e.forEach(function(e){var i=t.Pipeline.getRegisteredFunction(e);if(!i)throw new Error("Cannot load un-registered function: "+e);n.add(i)}),n},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(e){t.Pipeline.warnIfFunctionNotRegistered(e),this._queue.push(e)},this)},t.Pipeline.prototype.after=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i+1,0,n)},t.Pipeline.prototype.before=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i,0,n)},t.Pipeline.prototype.remove=function(e){var t=this._queue.indexOf(e);-1!==t&&this._queue.splice(t,1)},t.Pipeline.prototype.run=function(e){for(var t=[],n=e.length,i=this._queue.length,o=0;n>o;o++){for(var r=e[o],s=0;i>s&&(r=this._queue[s](r,o,e),void 0!==r&&null!==r);s++);void 0!==r&&null!==r&&t.push(r)}return t},t.Pipeline.prototype.reset=function(){this._queue=[]},t.Pipeline.prototype.get=function(){return this._queue},t.Pipeline.prototype.toJSON=function(){return this._queue.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})},t.Index=function(){this._fields=[],this._ref="id",this.pipeline=new t.Pipeline,this.documentStore=new t.DocumentStore,this.index={},this.eventEmitter=new t.EventEmitter,this._idfCache={},this.on("add","remove","update",function(){this._idfCache={}}.bind(this))},t.Index.prototype.on=function(){var e=Array.prototype.slice.call(arguments);return this.eventEmitter.addListener.apply(this.eventEmitter,e)},t.Index.prototype.off=function(e,t){return this.eventEmitter.removeListener(e,t)},t.Index.load=function(e){e.version!==t.version&&t.utils.warn("version mismatch: current "+t.version+" importing "+e.version);var n=new this;n._fields=e.fields,n._ref=e.ref,n.documentStore=t.DocumentStore.load(e.documentStore),n.pipeline=t.Pipeline.load(e.pipeline),n.index={};for(var i in e.index)n.index[i]=t.InvertedIndex.load(e.index[i]);return n},t.Index.prototype.addField=function(e){return this._fields.push(e),this.index[e]=new t.InvertedIndex,this},t.Index.prototype.setRef=function(e){return this._ref=e,this},t.Index.prototype.saveDocument=function(e){return this.documentStore=new t.DocumentStore(e),this},t.Index.prototype.addDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.addDoc(i,e),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));this.documentStore.addFieldLength(i,n,o.length);var r={};o.forEach(function(e){e in r?r[e]+=1:r[e]=1},this);for(var s in r){var u=r[s];u=Math.sqrt(u),this.index[n].addToken(s,{ref:i,tf:u})}},this),n&&this.eventEmitter.emit("add",e,this)}},t.Index.prototype.removeDocByRef=function(e){if(e&&this.documentStore.isDocStored()!==!1&&this.documentStore.hasDoc(e)){var t=this.documentStore.getDoc(e);this.removeDoc(t,!1)}},t.Index.prototype.removeDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.hasDoc(i)&&(this.documentStore.removeDoc(i),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));o.forEach(function(e){this.index[n].removeToken(e,i)},this)},this),n&&this.eventEmitter.emit("remove",e,this))}},t.Index.prototype.updateDoc=function(e,t){var t=void 0===t?!0:t;this.removeDocByRef(e[this._ref],!1),this.addDoc(e,!1),t&&this.eventEmitter.emit("update",e,this)},t.Index.prototype.idf=function(e,t){var n="@"+t+"/"+e;if(Object.prototype.hasOwnProperty.call(this._idfCache,n))return this._idfCache[n];var i=this.index[t].getDocFreq(e),o=1+Math.log(this.documentStore.length/(i+1));return this._idfCache[n]=o,o},t.Index.prototype.getFields=function(){return this._fields.slice()},t.Index.prototype.search=function(e,n){if(!e)return[];e="string"==typeof e?{any:e}:JSON.parse(JSON.stringify(e));var i=null;null!=n&&(i=JSON.stringify(n));for(var o=new t.Configuration(i,this.getFields()).get(),r={},s=Object.keys(e),u=0;u0&&t.push(e);for(var i in n)"docs"!==i&&"df"!==i&&this.expandToken(e+i,t,n[i]);return t},t.InvertedIndex.prototype.toJSON=function(){return{root:this.root}},t.Configuration=function(e,n){var e=e||"";if(void 0==n||null==n)throw new Error("fields should not be null");this.config={};var i;try{i=JSON.parse(e),this.buildUserConfig(i,n)}catch(o){t.utils.warn("user configuration parse failed, will use default configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();o + + + + + Error management - Candle Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ + + + + + + + + + + + + + + + + + + +
+ +
+ + + + + + + + +
+
+

Error management

+

You might have seen in the code base a lot of .unwrap() or ?. +If you're unfamiliar with Rust check out the Rust book +for more information.

+

What's important to know though, is that if you want to know where a particular operation failed +You can simply use RUST_BACKTRACE=1 to get the location of where the model actually failed.

+

Let's see on failing code:

+
let x = Tensor::zeros((1, 784), DType::F32, &device)?;
+let y = Tensor::zeros((1, 784), DType::F32, &device)?;
+let z = x.matmul(&y)?;
+

Will print at runtime:

+
Error: ShapeMismatchBinaryOp { lhs: [1, 784], rhs: [1, 784], op: "matmul" }
+
+

After adding RUST_BACKTRACE=1:

+
Error: WithBacktrace { inner: ShapeMismatchBinaryOp { lhs: [1, 784], rhs: [1, 784], op: "matmul" }, backtrace: Backtrace [{ fn: "candle::error::Error::bt", file: "/home/nicolas/.cargo/git/checkouts/candle-5bb8ef7e0626d693/f291065/candle-core/src/error.rs", line: 200 }, { fn: "candle::tensor::Tensor::matmul", file: "/home/nicolas/.cargo/git/checkouts/candle-5bb8ef7e0626d693/f291065/candle-core/src/tensor.rs", line: 816 }, { fn: "myapp::main", file: "./src/main.rs", line: 29 }, { fn: "core::ops::function::FnOnce::call_once", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/core/src/ops/function.rs", line: 250 }, { fn: "std::sys_common::backtrace::__rust_begin_short_backtrace", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/sys_common/backtrace.rs", line: 135 }, { fn: "std::rt::lang_start::{{closure}}", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs", line: 166 }, { fn: "core::ops::function::impls::<impl core::ops::function::FnOnce<A> for &F>::call_once", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/core/src/ops/function.rs", line: 284 }, { fn: "std::panicking::try::do_call", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs", line: 500 }, { fn: "std::panicking::try", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs", line: 464 }, { fn: "std::panic::catch_unwind", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panic.rs", line: 142 }, { fn: "std::rt::lang_start_internal::{{closure}}", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs", line: 148 }, { fn: "std::panicking::try::do_call", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs", line: 500 }, { fn: "std::panicking::try", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs", line: 464 }, { fn: "std::panic::catch_unwind", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panic.rs", line: 142 }, { fn: "std::rt::lang_start_internal", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs", line: 148 }, { fn: "std::rt::lang_start", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs", line: 165 }, { fn: "main" }, { fn: "__libc_start_main" }, { fn: "_start" }] }
+
+

Not super pretty at the moment, but we can see error occurred on { fn: "myapp::main", file: "./src/main.rs", line: 29 }

+

Another thing to note, is that since Rust is compiled it is not necessarily as easy to recover proper stacktraces +especially in release builds. We're using anyhow for that. +The library is still young, please report any issues detecting where an error is coming from.

+

Cuda error management

+

When running a model on Cuda, you might get a stacktrace not really representing the error. +The reason is that CUDA is async by nature, and therefore the error might be caught while you were sending totally different kernels.

+

One way to avoid this is to use CUDA_LAUNCH_BLOCKING=1 as an environment variable. This will force every kernel to be launched sequentially. +You might still however see the error happening on other kernels as the faulty kernel might exit without an error but spoiling some pointer for which the error will happen when dropping the CudaSlice only.

+

If this occurs, you can use compute-sanitizer +This tool is like valgrind but for cuda. It will help locate the errors in the kernels.

+ +
+ + +
+
+ + + +
+ + + + + + + + + + + + + + + + + + +
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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/fonts/SOURCE-CODE-PRO-LICENSE.txt b/fonts/SOURCE-CODE-PRO-LICENSE.txt new file mode 100644 index 0000000000..366206f549 --- /dev/null +++ b/fonts/SOURCE-CODE-PRO-LICENSE.txt @@ -0,0 +1,93 @@ +Copyright 2010, 2012 Adobe Systems Incorporated (http://www.adobe.com/), with Reserved Font Name 'Source'. All Rights Reserved. Source is a trademark of Adobe Systems Incorporated in the United States and/or other countries. + +This Font Software is licensed under the SIL Open Font License, Version 1.1. +This license is copied below, and is also available with a FAQ at: +http://scripts.sil.org/OFL + + +----------------------------------------------------------- +SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 +----------------------------------------------------------- + +PREAMBLE +The goals of the Open Font License (OFL) are to stimulate worldwide +development of collaborative font projects, to support the font creation +efforts of academic and linguistic communities, and to provide a free and +open framework in which fonts may be shared and improved in partnership +with others. + +The OFL allows the licensed fonts to be used, studied, modified and +redistributed freely as long as they are not sold by themselves. The +fonts, including any derivative works, can be bundled, embedded, +redistributed and/or sold with any software provided that any reserved +names are not used by derivative works. The fonts and derivatives, +however, cannot be released under any other type of license. The +requirement for fonts to remain under this license does not apply +to any document created using the fonts or their derivatives. + +DEFINITIONS +"Font Software" refers to the set of files released by the Copyright +Holder(s) under this license and clearly marked as such. This may +include source files, build scripts and documentation. + +"Reserved Font Name" refers to any names specified as such after the +copyright statement(s). + +"Original Version" refers to the collection of Font Software components as +distributed by the Copyright Holder(s). + +"Modified Version" refers to any derivative made by adding to, deleting, +or substituting -- in part or in whole -- any of the components of the +Original Version, by changing formats or by porting the Font Software to a +new environment. + +"Author" refers to any designer, engineer, programmer, technical +writer or other person who contributed to the Font Software. + +PERMISSION & CONDITIONS +Permission is hereby granted, free of charge, to any person obtaining +a copy of the Font Software, to use, study, copy, merge, embed, modify, +redistribute, and sell modified and unmodified copies of the Font +Software, subject to the following conditions: + +1) Neither the Font Software nor any of its individual components, +in Original or Modified Versions, may be sold by itself. + +2) Original or Modified Versions of the Font Software may be bundled, +redistributed and/or sold with any software, provided that each copy +contains the above copyright notice and this license. These can be +included either as stand-alone text files, human-readable headers or +in the appropriate machine-readable metadata fields within text or +binary files as long as those fields can be easily viewed by the user. + +3) No Modified Version of the Font Software may use the Reserved Font +Name(s) unless explicit written permission is granted by the corresponding +Copyright Holder. This restriction only applies to the primary font name as +presented to the users. + +4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font +Software shall not be used to promote, endorse or advertise any +Modified Version, except to acknowledge the contribution(s) of the +Copyright Holder(s) and the Author(s) or with their explicit written +permission. + +5) The Font Software, modified or unmodified, in part or in whole, +must be distributed entirely under this license, and must not be +distributed under any other license. The requirement for fonts to +remain under this license does not apply to any document created +using the Font Software. + +TERMINATION +This license becomes null and void if any of the above conditions are +not met. + +DISCLAIMER +THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT +OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE +COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, +INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL +DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM +OTHER DEALINGS IN THE FONT SOFTWARE. diff --git a/fonts/fonts.css b/fonts/fonts.css new file mode 100644 index 0000000000..858efa5980 --- /dev/null +++ b/fonts/fonts.css @@ -0,0 +1,100 @@ +/* Open Sans is licensed under the Apache License, Version 2.0. See http://www.apache.org/licenses/LICENSE-2.0 */ +/* Source Code Pro is under the Open Font License. See https://scripts.sil.org/cms/scripts/page.php?site_id=nrsi&id=OFL */ + +/* open-sans-300 - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-weight: 300; + src: local('Open Sans Light'), local('OpenSans-Light'), + url('open-sans-v17-all-charsets-300.woff2') format('woff2'); +} + +/* open-sans-300italic - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-weight: 300; + src: local('Open Sans Light Italic'), local('OpenSans-LightItalic'), + url('open-sans-v17-all-charsets-300italic.woff2') format('woff2'); +} + +/* open-sans-regular - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-weight: 400; + src: local('Open Sans Regular'), local('OpenSans-Regular'), + url('open-sans-v17-all-charsets-regular.woff2') format('woff2'); +} + +/* open-sans-italic - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-weight: 400; + src: local('Open Sans Italic'), local('OpenSans-Italic'), + url('open-sans-v17-all-charsets-italic.woff2') format('woff2'); +} + +/* open-sans-600 - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-weight: 600; + src: local('Open Sans SemiBold'), local('OpenSans-SemiBold'), + url('open-sans-v17-all-charsets-600.woff2') format('woff2'); +} + +/* open-sans-600italic - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-weight: 600; + src: local('Open Sans SemiBold Italic'), local('OpenSans-SemiBoldItalic'), + url('open-sans-v17-all-charsets-600italic.woff2') format('woff2'); +} + +/* open-sans-700 - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-weight: 700; + src: local('Open Sans Bold'), local('OpenSans-Bold'), + url('open-sans-v17-all-charsets-700.woff2') format('woff2'); +} + +/* open-sans-700italic - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-weight: 700; + src: local('Open Sans Bold Italic'), local('OpenSans-BoldItalic'), + url('open-sans-v17-all-charsets-700italic.woff2') format('woff2'); +} + +/* open-sans-800 - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-weight: 800; + src: local('Open Sans ExtraBold'), local('OpenSans-ExtraBold'), + url('open-sans-v17-all-charsets-800.woff2') format('woff2'); +} + +/* open-sans-800italic - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */ +@font-face { + font-family: 'Open Sans'; + font-style: italic; + font-weight: 800; + src: local('Open Sans ExtraBold Italic'), local('OpenSans-ExtraBoldItalic'), + url('open-sans-v17-all-charsets-800italic.woff2') format('woff2'); +} + +/* source-code-pro-500 - latin_vietnamese_latin-ext_greek_cyrillic-ext_cyrillic */ +@font-face { + font-family: 'Source Code Pro'; + font-style: normal; + font-weight: 500; + src: url('source-code-pro-v11-all-charsets-500.woff2') format('woff2'); +} diff --git a/fonts/open-sans-v17-all-charsets-300.woff2 b/fonts/open-sans-v17-all-charsets-300.woff2 new file mode 100644 index 0000000000000000000000000000000000000000..9f51be370fa913ce6de2922b580c262c4822b337 GIT binary patch literal 44352 zcmV(;K-<4}Pew8T0RR910Ifg(5dZ)H0f7hr0Ib{q0RR9100000000000000000000 z0000QE*lUWfmQ}!0EY+&fhq})G!YOAg!yQMtSAeP3IG8%0we>R0t6rhgFFYi4Ge-h zTM$x_K;*{m=TyO<(oF`$dr#+gT@uRz<@OP72f1way{Ld4djxa`IG0*=K;{4c|Nq>i zF~-^s+Z{rPn$}0zLzV=G*ddk#MfRZCOSo7Svxg)@O1QTM(GtC{-8am>TkfXJ&}5JO 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-0,0 +1,394 @@ + + + + + + Hello World - MNIST - Candle Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + + + + + +
    +
    +

    Hello world!

    +

    We will now create the hello world of the ML world, building a model capable of solving MNIST dataset.

    +

    Open src/main.rs and fill in this content:

    +
    extern crate candle_core;
+use candle_core::{Device, Result, Tensor};
+
+struct Model {
+    first: Tensor,
+    second: Tensor,
+}
+
+impl Model {
+    fn forward(&self, image: &Tensor) -> Result<Tensor> {
+        let x = image.matmul(&self.first)?;
+        let x = x.relu()?;
+        x.matmul(&self.second)
+    }
+}
+
+fn main() -> Result<()> {
+    // Use Device::new_cuda(0)?; to use the GPU.
+    let device = Device::Cpu;
+
+    let first = Tensor::randn(0f32, 1.0, (784, 100), &device)?;
+    let second = Tensor::randn(0f32, 1.0, (100, 10), &device)?;
+    let model = Model { first, second };
+
+    let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?;
+
+    let digit = model.forward(&dummy_image)?;
+    println!("Digit {digit:?} digit");
+    Ok(())
+}
    +

    Everything should now run with:

    +
    cargo run --release
+
    +

    Using a Linear layer.

    +

    Now that we have this, we might want to complexify things a bit, for instance by adding bias and creating +the classical Linear layer. We can do as such

    +
    #![allow(unused)]
+fn main() {
+extern crate candle_core;
+use candle_core::{Device, Result, Tensor};
+struct Linear{
+    weight: Tensor,
+    bias: Tensor,
+}
+impl Linear{
+    fn forward(&self, x: &Tensor) -> Result<Tensor> {
+        let x = x.matmul(&self.weight)?;
+        x.broadcast_add(&self.bias)
+    }
+}
+
+struct Model {
+    first: Linear,
+    second: Linear,
+}
+
+impl Model {
+    fn forward(&self, image: &Tensor) -> Result<Tensor> {
+        let x = self.first.forward(image)?;
+        let x = x.relu()?;
+        self.second.forward(&x)
+    }
+}
+}
    +

    This will change the model running code into a new function

    +
    extern crate candle_core;
+use candle_core::{Device, Result, Tensor};
+struct Linear{
+    weight: Tensor,
+    bias: Tensor,
+}
+impl Linear{
+    fn forward(&self, x: &Tensor) -> Result<Tensor> {
+        let x = x.matmul(&self.weight)?;
+        x.broadcast_add(&self.bias)
+    }
+}
+
+struct Model {
+    first: Linear,
+    second: Linear,
+}
+
+impl Model {
+    fn forward(&self, image: &Tensor) -> Result<Tensor> {
+        let x = self.first.forward(image)?;
+        let x = x.relu()?;
+        self.second.forward(&x)
+    }
+}
+fn main() -> Result<()> {
+    // Use Device::new_cuda(0)?; to use the GPU.
+    // Use Device::Cpu; to use the CPU.
+    let device = Device::cuda_if_available(0)?;
+
+    // Creating a dummy model
+    let weight = Tensor::randn(0f32, 1.0, (784, 100), &device)?;
+    let bias = Tensor::randn(0f32, 1.0, (100, ), &device)?;
+    let first = Linear{weight, bias};
+    let weight = Tensor::randn(0f32, 1.0, (100, 10), &device)?;
+    let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?;
+    let second = Linear{weight, bias};
+    let model = Model { first, second };
+
+    let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?;
+
+    // Inference on the model
+    let digit = model.forward(&dummy_image)?;
+    println!("Digit {digit:?} digit");
+    Ok(())
+}
    +

    Now it works, it is a great way to create your own layers. +But most of the classical layers are already implemented in candle-nn.

    +

    Using candle_nn.

    +

    For instance Linear is already there. +This Linear is coded with PyTorch layout in mind, to reuse better existing models out there, so it uses the transpose of the weights and not the weights directly.

    +

    So instead we can simplify our example:

    +
    cargo add --git https://github.com/huggingface/candle.git candle-nn
+
    +

    And rewrite our examples using it

    +
    extern crate candle_core;
+extern crate candle_nn;
+use candle_core::{Device, Result, Tensor};
+use candle_nn::{Linear, Module};
+
+struct Model {
+    first: Linear,
+    second: Linear,
+}
+
+impl Model {
+    fn forward(&self, image: &Tensor) -> Result<Tensor> {
+        let x = self.first.forward(image)?;
+        let x = x.relu()?;
+        self.second.forward(&x)
+    }
+}
+
+fn main() -> Result<()> {
+    // Use Device::new_cuda(0)?; to use the GPU.
+    let device = Device::Cpu;
+
+    // This has changed (784, 100) -> (100, 784) !
+    let weight = Tensor::randn(0f32, 1.0, (100, 784), &device)?;
+    let bias = Tensor::randn(0f32, 1.0, (100, ), &device)?;
+    let first = Linear::new(weight, Some(bias));
+    let weight = Tensor::randn(0f32, 1.0, (10, 100), &device)?;
+    let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?;
+    let second = Linear::new(weight, Some(bias));
+    let model = Model { first, second };
+
+    let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?;
+
+    let digit = model.forward(&dummy_image)?;
+    println!("Digit {digit:?} digit");
+    Ok(())
+}
    +

    Feel free to modify this example to use Conv2d to create a classical convnet instead.

    +

    Now that we have the running dummy code we can get to more advanced topics:

    + + +
    + + +
    +
    + + + +
    + + + + + + + + + + + + + + + + + + +
    + + diff --git a/guide/installation.html b/guide/installation.html new file mode 100644 index 0000000000..27b3392587 --- /dev/null +++ b/guide/installation.html @@ -0,0 +1,267 @@ + + + + + + Installation - Candle Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + + + + + +
    +
    +

    Installation

    +

    With Cuda support:

    +
      +
    1. First, make sure that Cuda is correctly installed.
      2. +
    +
      +
    • nvcc --version should print information about your Cuda compiler driver.
      • +
    • nvidia-smi --query-gpu=compute_cap --format=csv should print your GPUs compute capability, e.g. something +like:
      • +
    +
    compute_cap
+8.9
+
    +

    You can also compile the Cuda kernels for a specific compute cap using the +CUDA_COMPUTE_CAP=<compute cap> environment variable.

    +

    If any of the above commands errors out, please make sure to update your Cuda version.

    +
      +
    1. Create a new app and add candle-core with Cuda support.
      2. +
    +

    Start by creating a new cargo:

    +
    cargo new myapp
+cd myapp
+
    +

    Make sure to add the candle-core crate with the cuda feature:

    +
    cargo add --git https://github.com/huggingface/candle.git candle-core --features "cuda"
+
    +

    Run cargo build to make sure everything can be correctly built.

    +
    cargo build
+
    +

    Without Cuda support:

    +

    Create a new app and add candle-core as follows:

    +
    cargo new myapp
+cd myapp
+cargo add --git https://github.com/huggingface/candle.git candle-core
+
    +

    Finally, run cargo build to make sure everything can be correctly built.

    +
    cargo build
+
    +

    With mkl support

    +

    You can also see the mkl feature which could be interesting to get faster inference on CPU. Using mkl

    + +
    + + +
    +
    + + + +
    + + + + + + + + + + + + + + + + + + +
    + + diff --git a/highlight.css b/highlight.css new file mode 100644 index 0000000000..ba57b82b27 --- /dev/null +++ b/highlight.css @@ -0,0 +1,82 @@ +/* + * An increased contrast highlighting scheme loosely based on the + * "Base16 Atelier Dune Light" theme by Bram de Haan + * (http://atelierbram.github.io/syntax-highlighting/atelier-schemes/dune) + * Original Base16 color scheme by Chris Kempson + * (https://github.com/chriskempson/base16) + */ + +/* Comment */ +.hljs-comment, +.hljs-quote { + color: #575757; +} + +/* Red */ +.hljs-variable, +.hljs-template-variable, +.hljs-attribute, +.hljs-tag, +.hljs-name, +.hljs-regexp, +.hljs-link, +.hljs-name, +.hljs-selector-id, +.hljs-selector-class { + color: #d70025; +} + +/* Orange */ +.hljs-number, +.hljs-meta, +.hljs-built_in, +.hljs-builtin-name, +.hljs-literal, +.hljs-type, +.hljs-params { + color: #b21e00; +} + +/* Green */ +.hljs-string, +.hljs-symbol, +.hljs-bullet { + color: #008200; +} + +/* Blue */ +.hljs-title, +.hljs-section 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b/index.html @@ -0,0 +1,307 @@ + + + + + + Introduction - Candle Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + + + + + +
    +
    +

    Introduction

    +

    Features

    +
      +
    • Simple syntax, looks and feels like PyTorch. + +
      • +
    • Backends. +
        +
      • Optimized CPU backend with optional MKL support for x86 and Accelerate for macs.
        • +
      • CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL.
        • +
      • WASM support, run your models in a browser.
        • +
      +
      • +
    • Included models. +
        +
      • Language Models. +
          +
        • LLaMA v1, v2, and v3 with variants such as SOLAR-10.7B.
          • +
        • Falcon.
          • +
        • StarCoder, StarCoder2.
          • +
        • Phi 1, 1.5, 2, and 3.
          • +
        • Mamba, Minimal Mamba
          • +
        • Gemma 2b and 7b.
          • +
        • Mistral 7b v0.1.
          • +
        • Mixtral 8x7b v0.1.
          • +
        • StableLM-3B-4E1T, StableLM-2-1.6B, Stable-Code-3B.
          • +
        • Replit-code-v1.5-3B.
          • +
        • Bert.
          • +
        • Yi-6B and Yi-34B.
          • +
        • Qwen1.5, Qwen1.5 MoE.
          • +
        • RWKV v5 and v6.
          • +
        +
        • +
      • Quantized LLMs. +
          +
        • Llama 7b, 13b, 70b, as well as the chat and code variants.
          • +
        • Mistral 7b, and 7b instruct.
          • +
        • Mixtral 8x7b.
          • +
        • Zephyr 7b a and b (Mistral-7b based).
          • +
        • OpenChat 3.5 (Mistral-7b based).
          • +
        +
        • +
      • Text to text. +
          +
        • T5 and its variants: FlanT5, UL2, MADLAD400 (translation), CoEdit (Grammar correction).
          • +
        • Marian MT (Machine Translation).
          • +
        +
        • +
      • Text to image. +
          +
        • Stable Diffusion v1.5, v2.1, XL v1.0.
          • +
        • Wurstchen v2.
          • +
        +
        • +
      • Image to text. +
          +
        • BLIP.
          • +
        • TrOCR.
          • +
        +
        • +
      • Audio. +
          +
        • Whisper, multi-lingual speech-to-text.
          • +
        • EnCodec, audio compression model.
          • +
        • MetaVoice-1B, text-to-speech model.
          • +
        +
        • +
      • Computer Vision Models. +
          +
        • DINOv2, ConvMixer, EfficientNet, ResNet, ViT, VGG, RepVGG, ConvNeXT, +ConvNeXTv2, MobileOne, EfficientVit (MSRA), MobileNetv4, Hiera.
          • +
        • yolo-v3, yolo-v8.
          • +
        • Segment-Anything Model (SAM).
          • +
        • SegFormer.
          • +
        +
        • +
      +
      • +
    • File formats: load models from safetensors, npz, ggml, or PyTorch files.
      • +
    • Serverless (on CPU), small and fast deployments.
      • +
    • Quantization support using the llama.cpp quantized types.
      • +
    +

    This book will introduce step by step how to use candle.

    + +
    + + +
    +
    + + + +
    + + + + + + + + + + + + + + + + + + +
    + + diff --git a/inference/hub.html b/inference/hub.html new file mode 100644 index 0000000000..b596068b41 --- /dev/null +++ b/inference/hub.html @@ -0,0 +1,362 @@ + + + + + + Using the hub - Candle Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + + + + + +
    +
    +

    Using the hub

    +

    Install the hf-hub crate:

    +
    cargo add hf-hub
+
    +

    Then let's start by downloading the model file.

    +
    #![allow(unused)]
+fn main() {
+extern crate candle_core;
+extern crate hf_hub;
+use hf_hub::api::sync::Api;
+use candle_core::Device;
+
+let api = Api::new().unwrap();
+let repo = api.model("bert-base-uncased".to_string());
+
+let weights = repo.get("model.safetensors").unwrap();
+
+let weights = candle_core::safetensors::load(weights, &Device::Cpu);
+}
    +

    We now have access to all the tensors within the file.

    +

    You can check all the names of the tensors here

    +

    Using async

    +

    hf-hub comes with an async API.

    +
    cargo add hf-hub --features tokio
+
    +
    This is tested directly in examples crate because it needs external dependencies unfortunately:
+See [this](https://github.com/rust-lang/mdBook/issues/706)
+use candle::Device;
+use hf_hub::api::tokio::Api;
+
+let api = Api::new().unwrap();
+let repo = api.model("bert-base-uncased".to_string());
+
+let weights_filename = repo.get("model.safetensors").await.unwrap();
+
+let weights = candle::safetensors::load(weights_filename, &Device::Cpu).unwrap();
    +

    Using in a real model.

    +

    Now that we have our weights, we can use them in our bert architecture:

    +
    #![allow(unused)]
+fn main() {
+extern crate candle_core;
+extern crate candle_nn;
+extern crate hf_hub;
+use hf_hub::api::sync::Api;
+
+let api = Api::new().unwrap();
+let repo = api.model("bert-base-uncased".to_string());
+
+let weights = repo.get("model.safetensors").unwrap();
+use candle_core::{Device, Tensor, DType};
+use candle_nn::{Linear, Module};
+
+let weights = candle_core::safetensors::load(weights, &Device::Cpu).unwrap();
+
+let weight = weights.get("bert.encoder.layer.0.attention.self.query.weight").unwrap();
+let bias = weights.get("bert.encoder.layer.0.attention.self.query.bias").unwrap();
+
+let linear = Linear::new(weight.clone(), Some(bias.clone()));
+
+let input_ids = Tensor::zeros((3, 768), DType::F32, &Device::Cpu).unwrap();
+let output = linear.forward(&input_ids).unwrap();
+}
    +

    For a full reference, you can check out the full bert example.

    +

    Memory mapping

    +

    For more efficient loading, instead of reading the file, you could use memmap2

    +

    Note: Be careful about memory mapping it seems to cause issues on Windows, WSL +and will definitely be slower on network mounted disk, because it will issue more read calls.

    +
    use candle::Device;
+use hf_hub::api::sync::Api;
+use memmap2::Mmap;
+use std::fs;
+
+let api = Api::new().unwrap();
+let repo = api.model("bert-base-uncased".to_string());
+let weights_filename = repo.get("model.safetensors").unwrap();
+
+let file = fs::File::open(weights_filename).unwrap();
+let mmap = unsafe { Mmap::map(&file).unwrap() };
+let weights = candle::safetensors::load_buffer(&mmap[..], &Device::Cpu).unwrap();
    +

    Note: This operation is unsafe. See the safety notice. +In practice model files should never be modified, and the mmaps should be mostly READONLY anyway, so the caveat most likely does not apply, but always keep it in mind.

    +

    Tensor Parallel Sharding

    +

    When using multiple GPUs to use in Tensor Parallel in order to get good latency, you can load only the part of the Tensor you need.

    +

    For that you need to use safetensors directly.

    +
    cargo add safetensors
+
    +
    use candle::{DType, Device, Tensor};
+use hf_hub::api::sync::Api;
+use memmap2::Mmap;
+use safetensors::slice::IndexOp;
+use safetensors::SafeTensors;
+use std::fs;
+
+let api = Api::new().unwrap();
+let repo = api.model("bert-base-uncased".to_string());
+let weights_filename = repo.get("model.safetensors").unwrap();
+
+let file = fs::File::open(weights_filename).unwrap();
+let mmap = unsafe { Mmap::map(&file).unwrap() };
+
+// Use safetensors directly
+let tensors = SafeTensors::deserialize(&mmap[..]).unwrap();
+let view = tensors
+    .tensor("bert.encoder.layer.0.attention.self.query.weight")
+    .unwrap();
+
+// We're going to load shard with rank 1, within a world_size of 4
+// We're going to split along dimension 0 doing VIEW[start..stop, :]
+let rank = 1;
+let world_size = 4;
+let dim = 0;
+let dtype = view.dtype();
+let mut tp_shape = view.shape().to_vec();
+let size = tp_shape[0];
+
+if size % world_size != 0 {
+    panic!("The dimension is not divisible by `world_size`");
+}
+let block_size = size / world_size;
+let start = rank * block_size;
+let stop = (rank + 1) * block_size;
+
+// Everything is expressed in tensor dimension
+// bytes offsets is handled automatically for safetensors.
+
+let iterator = view.slice(start..stop).unwrap();
+
+tp_shape[dim] = block_size;
+
+// Convert safetensors Dtype to candle DType
+let dtype: DType = dtype.try_into().unwrap();
+
+// TODO: Implement from_buffer_iterator so we can skip the extra CPU alloc.
+let raw: Vec<u8> = iterator.into_iter().flatten().cloned().collect();
+let tp_tensor = Tensor::from_raw_buffer(&raw, dtype, &tp_shape, &Device::Cpu).unwrap();
    + +
    + + +
    +
    + + + +
    + + + + + + + + + + + + + + + + + + +
    + + diff --git a/inference/inference.html b/inference/inference.html new file mode 100644 index 0000000000..05a6853c8f --- /dev/null +++ b/inference/inference.html @@ -0,0 +1,231 @@ + + + + + + Running a model - Candle Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + + + + + +
    +
    +

    Running a model

    +

    In order to run an existing model, you will need to download and use existing weights. +Most models are already available on https://huggingface.co/ in safetensors format.

    +

    Let's get started by running an old model : bert-base-uncased.

    + +
    + + +
    +
    + + + +
    + + + + + + + + + + + + + + + + + + +
    + + diff --git a/lib.rs b/lib.rs new file mode 100644 index 0000000000..eeb2c7ba52 --- /dev/null +++ b/lib.rs @@ -0,0 +1,199 @@ +#[cfg(test)] +pub mod simplified; + +#[cfg(test)] +mod tests { + use anyhow::Result; + use candle::{DType, Device, Tensor}; + use parquet::file::reader::SerializedFileReader; + + // NOTE: Waiting on https://github.com/rust-lang/mdBook/pull/1856 + #[rustfmt::skip] + #[tokio::test] + async fn book_hub_1() { +// ANCHOR: book_hub_1 +use candle::Device; +use hf_hub::api::tokio::Api; + +let api = Api::new().unwrap(); +let repo = api.model("bert-base-uncased".to_string()); + +let weights_filename = repo.get("model.safetensors").await.unwrap(); + +let weights = candle::safetensors::load(weights_filename, &Device::Cpu).unwrap(); +// ANCHOR_END: book_hub_1 + assert_eq!(weights.len(), 206); + } + + #[rustfmt::skip] + #[test] + fn book_hub_2() { + { +// ANCHOR: book_hub_2 +use candle::Device; +use hf_hub::api::sync::Api; +use memmap2::Mmap; +use std::fs; + +let api = Api::new().unwrap(); +let repo = api.model("bert-base-uncased".to_string()); +let weights_filename = repo.get("model.safetensors").unwrap(); + +let file = fs::File::open(weights_filename).unwrap(); +let mmap = unsafe { Mmap::map(&file).unwrap() }; +let weights = candle::safetensors::load_buffer(&mmap[..], &Device::Cpu).unwrap(); +// ANCHOR_END: book_hub_2 + assert_eq!(weights.len(), 206); + } + + // #[rustfmt::skip] + // #[test] + // fn book_hub_3() { + { +// ANCHOR: book_hub_3 +use candle::{DType, Device, Tensor}; +use hf_hub::api::sync::Api; +use memmap2::Mmap; +use safetensors::slice::IndexOp; +use safetensors::SafeTensors; +use std::fs; + +let api = Api::new().unwrap(); +let repo = api.model("bert-base-uncased".to_string()); +let weights_filename = repo.get("model.safetensors").unwrap(); + +let file = fs::File::open(weights_filename).unwrap(); +let mmap = unsafe { Mmap::map(&file).unwrap() }; + +// Use safetensors directly +let tensors = SafeTensors::deserialize(&mmap[..]).unwrap(); +let view = tensors + .tensor("bert.encoder.layer.0.attention.self.query.weight") + .unwrap(); + +// We're going to load shard with rank 1, within a world_size of 4 +// We're going to split along dimension 0 doing VIEW[start..stop, :] +let rank = 1; +let world_size = 4; +let dim = 0; +let dtype = view.dtype(); +let mut tp_shape = view.shape().to_vec(); +let size = tp_shape[0]; + +if size % world_size != 0 { + panic!("The dimension is not divisible by `world_size`"); +} +let block_size = size / world_size; +let start = rank * block_size; +let stop = (rank + 1) * block_size; + +// Everything is expressed in tensor dimension +// bytes offsets is handled automatically for safetensors. + +let iterator = view.slice(start..stop).unwrap(); + +tp_shape[dim] = block_size; + +// Convert safetensors Dtype to candle DType +let dtype: DType = dtype.try_into().unwrap(); + +// TODO: Implement from_buffer_iterator so we can skip the extra CPU alloc. +let raw: Vec = iterator.into_iter().flatten().cloned().collect(); +let tp_tensor = Tensor::from_raw_buffer(&raw, dtype, &tp_shape, &Device::Cpu).unwrap(); +// ANCHOR_END: book_hub_3 + assert_eq!(view.shape(), &[768, 768]); + assert_eq!(tp_tensor.dims(), &[192, 768]); + } +} + + #[allow(unused)] + #[rustfmt::skip] + fn book_training_1() -> Result<()>{ +// ANCHOR: book_training_1 +use hf_hub::{api::sync::Api, Repo, RepoType}; + +let dataset_id = "mnist".to_string(); + +let api = Api::new()?; +let repo = Repo::with_revision( + dataset_id, + RepoType::Dataset, + "refs/convert/parquet".to_string(), +); +let repo = api.repo(repo); +let test_parquet_filename = repo.get("mnist/test/0000.parquet")?; +let train_parquet_filename = repo.get("mnist/train/0000.parquet")?; +let test_parquet = SerializedFileReader::new(std::fs::File::open(test_parquet_filename)?)?; +let train_parquet = SerializedFileReader::new(std::fs::File::open(train_parquet_filename)?)?; +// ANCHOR_END: book_training_1 +// Ignore unused +let _train = train_parquet; +// ANCHOR: book_training_2 +for row in test_parquet { + for (idx, (name, field)) in row?.get_column_iter().enumerate() { + println!("Column id {idx}, name {name}, value {field}"); + } +} +// ANCHOR_END: book_training_2 +let test_parquet_filename = repo.get("mnist/test/0000.parquet")?; +let train_parquet_filename = repo.get("mnist/train/0000.parquet")?; +let test_parquet = SerializedFileReader::new(std::fs::File::open(test_parquet_filename)?)?; +let train_parquet = SerializedFileReader::new(std::fs::File::open(train_parquet_filename)?)?; +// ANCHOR: book_training_3 + +let test_samples = 10_000; +let mut test_buffer_images: Vec = Vec::with_capacity(test_samples * 784); +let mut test_buffer_labels: Vec = Vec::with_capacity(test_samples); +for row in test_parquet{ + for (_name, field) in row?.get_column_iter() { + if let parquet::record::Field::Group(subrow) = field { + for (_name, field) in subrow.get_column_iter() { + if let parquet::record::Field::Bytes(value) = field { + let image = image::load_from_memory(value.data()).unwrap(); + test_buffer_images.extend(image.to_luma8().as_raw()); + } + } + }else if let parquet::record::Field::Long(label) = field { + test_buffer_labels.push(*label as u8); + } + } +} +let test_images = (Tensor::from_vec(test_buffer_images, (test_samples, 784), &Device::Cpu)?.to_dtype(DType::F32)? / 255.)?; +let test_labels = Tensor::from_vec(test_buffer_labels, (test_samples, ), &Device::Cpu)?; + +let train_samples = 60_000; +let mut train_buffer_images: Vec = Vec::with_capacity(train_samples * 784); +let mut train_buffer_labels: Vec = Vec::with_capacity(train_samples); +for row in train_parquet{ + for (_name, field) in row?.get_column_iter() { + if let parquet::record::Field::Group(subrow) = field { + for (_name, field) in subrow.get_column_iter() { + if let parquet::record::Field::Bytes(value) = field { + let image = image::load_from_memory(value.data()).unwrap(); + train_buffer_images.extend(image.to_luma8().as_raw()); + } + } + }else if let parquet::record::Field::Long(label) = field { + train_buffer_labels.push(*label as u8); + } + } +} +let train_images = (Tensor::from_vec(train_buffer_images, (train_samples, 784), &Device::Cpu)?.to_dtype(DType::F32)? / 255.)?; +let train_labels = Tensor::from_vec(train_buffer_labels, (train_samples, ), &Device::Cpu)?; + +let mnist = candle_datasets::vision::Dataset { + train_images, + train_labels, + test_images, + test_labels, + labels: 10, +}; + +// ANCHOR_END: book_training_3 +assert_eq!(mnist.test_images.dims(), &[10_000, 784]); +assert_eq!(mnist.test_labels.dims(), &[10_000]); +assert_eq!(mnist.train_images.dims(), &[60_000, 784]); +assert_eq!(mnist.train_labels.dims(), &[60_000]); +Ok(()) + } +} diff --git a/mark.min.js b/mark.min.js new file mode 100644 index 0000000000..1636231883 --- /dev/null +++ b/mark.min.js @@ -0,0 +1,7 @@ +/*!*************************************************** +* mark.js v8.11.1 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r((e=this.wrapRangeInTextNode(e,t,t+n)).previousSibling),e}},{key:"separateGroups",value:function(e,t,n,r,i){for(var o=t.length,a=1;a-1&&r(t[a],e)&&(e=this.wrapGroups(e,s,t[a].length,i))}return e}},{key:"wrapMatches",value:function(e,t,n,r,i){var o=this,a=0===t?0:t+1;this.getTextNodes(function(t){t.nodes.forEach(function(t){t=t.node;for(var i=void 0;null!==(i=e.exec(t.textContent))&&""!==i[a];){if(o.opt.separateGroups)t=o.separateGroups(t,i,a,n,r);else{if(!n(i[a],t))continue;var s=i.index;if(0!==a)for(var c=1;c + + + + + Candle Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + + + + + +
    +
    +

    Introduction

    +

    Features

    +
      +
    • Simple syntax, looks and feels like PyTorch. + +
      • +
    • Backends. +
        +
      • Optimized CPU backend with optional MKL support for x86 and Accelerate for macs.
        • +
      • CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL.
        • +
      • WASM support, run your models in a browser.
        • +
      +
      • +
    • Included models. +
        +
      • Language Models. +
          +
        • LLaMA v1, v2, and v3 with variants such as SOLAR-10.7B.
          • +
        • Falcon.
          • +
        • StarCoder, StarCoder2.
          • +
        • Phi 1, 1.5, 2, and 3.
          • +
        • Mamba, Minimal Mamba
          • +
        • Gemma 2b and 7b.
          • +
        • Mistral 7b v0.1.
          • +
        • Mixtral 8x7b v0.1.
          • +
        • StableLM-3B-4E1T, StableLM-2-1.6B, Stable-Code-3B.
          • +
        • Replit-code-v1.5-3B.
          • +
        • Bert.
          • +
        • Yi-6B and Yi-34B.
          • +
        • Qwen1.5, Qwen1.5 MoE.
          • +
        • RWKV v5 and v6.
          • +
        +
        • +
      • Quantized LLMs. +
          +
        • Llama 7b, 13b, 70b, as well as the chat and code variants.
          • +
        • Mistral 7b, and 7b instruct.
          • +
        • Mixtral 8x7b.
          • +
        • Zephyr 7b a and b (Mistral-7b based).
          • +
        • OpenChat 3.5 (Mistral-7b based).
          • +
        +
        • +
      • Text to text. +
          +
        • T5 and its variants: FlanT5, UL2, MADLAD400 (translation), CoEdit (Grammar correction).
          • +
        • Marian MT (Machine Translation).
          • +
        +
        • +
      • Text to image. +
          +
        • Stable Diffusion v1.5, v2.1, XL v1.0.
          • +
        • Wurstchen v2.
          • +
        +
        • +
      • Image to text. +
          +
        • BLIP.
          • +
        • TrOCR.
          • +
        +
        • +
      • Audio. +
          +
        • Whisper, multi-lingual speech-to-text.
          • +
        • EnCodec, audio compression model.
          • +
        • MetaVoice-1B, text-to-speech model.
          • +
        +
        • +
      • Computer Vision Models. +
          +
        • DINOv2, ConvMixer, EfficientNet, ResNet, ViT, VGG, RepVGG, ConvNeXT, +ConvNeXTv2, MobileOne, EfficientVit (MSRA), MobileNetv4, Hiera.
          • +
        • yolo-v3, yolo-v8.
          • +
        • Segment-Anything Model (SAM).
          • +
        • SegFormer.
          • +
        +
        • +
      +
      • +
    • File formats: load models from safetensors, npz, ggml, or PyTorch files.
      • +
    • Serverless (on CPU), small and fast deployments.
      • +
    • Quantization support using the llama.cpp quantized types.
      • +
    +

    This book will introduce step by step how to use candle.

    +

    Installation

    +

    With Cuda support:

    +
      +
    1. First, make sure that Cuda is correctly installed.
      2. +
    +
      +
    • nvcc --version should print information about your Cuda compiler driver.
      • +
    • nvidia-smi --query-gpu=compute_cap --format=csv should print your GPUs compute capability, e.g. something +like:
      • +
    +
    compute_cap
+8.9
+
    +

    You can also compile the Cuda kernels for a specific compute cap using the +CUDA_COMPUTE_CAP=<compute cap> environment variable.

    +

    If any of the above commands errors out, please make sure to update your Cuda version.

    +
      +
    1. Create a new app and add candle-core with Cuda support.
      2. +
    +

    Start by creating a new cargo:

    +
    cargo new myapp
+cd myapp
+
    +

    Make sure to add the candle-core crate with the cuda feature:

    +
    cargo add --git https://github.com/huggingface/candle.git candle-core --features "cuda"
+
    +

    Run cargo build to make sure everything can be correctly built.

    +
    cargo build
+
    +

    Without Cuda support:

    +

    Create a new app and add candle-core as follows:

    +
    cargo new myapp
+cd myapp
+cargo add --git https://github.com/huggingface/candle.git candle-core
+
    +

    Finally, run cargo build to make sure everything can be correctly built.

    +
    cargo build
+
    +

    With mkl support

    +

    You can also see the mkl feature which could be interesting to get faster inference on CPU. Using mkl

    +

    Hello world!

    +

    We will now create the hello world of the ML world, building a model capable of solving MNIST dataset.

    +

    Open src/main.rs and fill in this content:

    +
    extern crate candle_core;
+use candle_core::{Device, Result, Tensor};
+
+struct Model {
+    first: Tensor,
+    second: Tensor,
+}
+
+impl Model {
+    fn forward(&self, image: &Tensor) -> Result<Tensor> {
+        let x = image.matmul(&self.first)?;
+        let x = x.relu()?;
+        x.matmul(&self.second)
+    }
+}
+
+fn main() -> Result<()> {
+    // Use Device::new_cuda(0)?; to use the GPU.
+    let device = Device::Cpu;
+
+    let first = Tensor::randn(0f32, 1.0, (784, 100), &device)?;
+    let second = Tensor::randn(0f32, 1.0, (100, 10), &device)?;
+    let model = Model { first, second };
+
+    let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?;
+
+    let digit = model.forward(&dummy_image)?;
+    println!("Digit {digit:?} digit");
+    Ok(())
+}
    +

    Everything should now run with:

    +
    cargo run --release
+
    +

    Using a Linear layer.

    +

    Now that we have this, we might want to complexify things a bit, for instance by adding bias and creating +the classical Linear layer. We can do as such

    +
    #![allow(unused)]
+fn main() {
+extern crate candle_core;
+use candle_core::{Device, Result, Tensor};
+struct Linear{
+    weight: Tensor,
+    bias: Tensor,
+}
+impl Linear{
+    fn forward(&self, x: &Tensor) -> Result<Tensor> {
+        let x = x.matmul(&self.weight)?;
+        x.broadcast_add(&self.bias)
+    }
+}
+
+struct Model {
+    first: Linear,
+    second: Linear,
+}
+
+impl Model {
+    fn forward(&self, image: &Tensor) -> Result<Tensor> {
+        let x = self.first.forward(image)?;
+        let x = x.relu()?;
+        self.second.forward(&x)
+    }
+}
+}
    +

    This will change the model running code into a new function

    +
    extern crate candle_core;
+use candle_core::{Device, Result, Tensor};
+struct Linear{
+    weight: Tensor,
+    bias: Tensor,
+}
+impl Linear{
+    fn forward(&self, x: &Tensor) -> Result<Tensor> {
+        let x = x.matmul(&self.weight)?;
+        x.broadcast_add(&self.bias)
+    }
+}
+
+struct Model {
+    first: Linear,
+    second: Linear,
+}
+
+impl Model {
+    fn forward(&self, image: &Tensor) -> Result<Tensor> {
+        let x = self.first.forward(image)?;
+        let x = x.relu()?;
+        self.second.forward(&x)
+    }
+}
+fn main() -> Result<()> {
+    // Use Device::new_cuda(0)?; to use the GPU.
+    // Use Device::Cpu; to use the CPU.
+    let device = Device::cuda_if_available(0)?;
+
+    // Creating a dummy model
+    let weight = Tensor::randn(0f32, 1.0, (784, 100), &device)?;
+    let bias = Tensor::randn(0f32, 1.0, (100, ), &device)?;
+    let first = Linear{weight, bias};
+    let weight = Tensor::randn(0f32, 1.0, (100, 10), &device)?;
+    let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?;
+    let second = Linear{weight, bias};
+    let model = Model { first, second };
+
+    let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?;
+
+    // Inference on the model
+    let digit = model.forward(&dummy_image)?;
+    println!("Digit {digit:?} digit");
+    Ok(())
+}
    +

    Now it works, it is a great way to create your own layers. +But most of the classical layers are already implemented in candle-nn.

    +

    Using candle_nn.

    +

    For instance Linear is already there. +This Linear is coded with PyTorch layout in mind, to reuse better existing models out there, so it uses the transpose of the weights and not the weights directly.

    +

    So instead we can simplify our example:

    +
    cargo add --git https://github.com/huggingface/candle.git candle-nn
+
    +

    And rewrite our examples using it

    +
    extern crate candle_core;
+extern crate candle_nn;
+use candle_core::{Device, Result, Tensor};
+use candle_nn::{Linear, Module};
+
+struct Model {
+    first: Linear,
+    second: Linear,
+}
+
+impl Model {
+    fn forward(&self, image: &Tensor) -> Result<Tensor> {
+        let x = self.first.forward(image)?;
+        let x = x.relu()?;
+        self.second.forward(&x)
+    }
+}
+
+fn main() -> Result<()> {
+    // Use Device::new_cuda(0)?; to use the GPU.
+    let device = Device::Cpu;
+
+    // This has changed (784, 100) -> (100, 784) !
+    let weight = Tensor::randn(0f32, 1.0, (100, 784), &device)?;
+    let bias = Tensor::randn(0f32, 1.0, (100, ), &device)?;
+    let first = Linear::new(weight, Some(bias));
+    let weight = Tensor::randn(0f32, 1.0, (10, 100), &device)?;
+    let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?;
+    let second = Linear::new(weight, Some(bias));
+    let model = Model { first, second };
+
+    let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?;
+
+    let digit = model.forward(&dummy_image)?;
+    println!("Digit {digit:?} digit");
+    Ok(())
+}
    +

    Feel free to modify this example to use Conv2d to create a classical convnet instead.

    +

    Now that we have the running dummy code we can get to more advanced topics:

    + +

    Pytorch cheatsheet

    +

    Cheatsheet:

    +
    + + + + + + + + + + +
    Using PyTorchUsing Candle
    Creationtorch.Tensor([[1, 2], [3, 4]])Tensor::new(&[[1f32, 2.], [3., 4.]], &Device::Cpu)?
    Creationtorch.zeros((2, 2))Tensor::zeros((2, 2), DType::F32, &Device::Cpu)?
    Indexingtensor[:, :4]tensor.i((.., ..4))?
    Operationstensor.view((2, 2))tensor.reshape((2, 2))?
    Operationsa.matmul(b)a.matmul(&b)?
    Arithmetica + b&a + &b
    Devicetensor.to(device="cuda")tensor.to_device(&Device::new_cuda(0)?)?
    Dtypetensor.to(dtype=torch.float16)tensor.to_dtype(&DType::F16)?
    Savingtorch.save({"A": A}, "model.bin")candle::safetensors::save(&HashMap::from([("A", A)]), "model.safetensors")?
    Loadingweights = torch.load("model.bin")candle::safetensors::load("model.safetensors", &device)
    +

    Running a model

    +

    In order to run an existing model, you will need to download and use existing weights. +Most models are already available on https://huggingface.co/ in safetensors format.

    +

    Let's get started by running an old model : bert-base-uncased.

    +

    Using the hub

    +

    Install the hf-hub crate:

    +
    cargo add hf-hub
+
    +

    Then let's start by downloading the model file.

    +
    #![allow(unused)]
+fn main() {
+extern crate candle_core;
+extern crate hf_hub;
+use hf_hub::api::sync::Api;
+use candle_core::Device;
+
+let api = Api::new().unwrap();
+let repo = api.model("bert-base-uncased".to_string());
+
+let weights = repo.get("model.safetensors").unwrap();
+
+let weights = candle_core::safetensors::load(weights, &Device::Cpu);
+}
    +

    We now have access to all the tensors within the file.

    +

    You can check all the names of the tensors here

    +

    Using async

    +

    hf-hub comes with an async API.

    +
    cargo add hf-hub --features tokio
+
    +
    This is tested directly in examples crate because it needs external dependencies unfortunately:
+See [this](https://github.com/rust-lang/mdBook/issues/706)
+use candle::Device;
+use hf_hub::api::tokio::Api;
+
+let api = Api::new().unwrap();
+let repo = api.model("bert-base-uncased".to_string());
+
+let weights_filename = repo.get("model.safetensors").await.unwrap();
+
+let weights = candle::safetensors::load(weights_filename, &Device::Cpu).unwrap();
    +

    Using in a real model.

    +

    Now that we have our weights, we can use them in our bert architecture:

    +
    #![allow(unused)]
+fn main() {
+extern crate candle_core;
+extern crate candle_nn;
+extern crate hf_hub;
+use hf_hub::api::sync::Api;
+
+let api = Api::new().unwrap();
+let repo = api.model("bert-base-uncased".to_string());
+
+let weights = repo.get("model.safetensors").unwrap();
+use candle_core::{Device, Tensor, DType};
+use candle_nn::{Linear, Module};
+
+let weights = candle_core::safetensors::load(weights, &Device::Cpu).unwrap();
+
+let weight = weights.get("bert.encoder.layer.0.attention.self.query.weight").unwrap();
+let bias = weights.get("bert.encoder.layer.0.attention.self.query.bias").unwrap();
+
+let linear = Linear::new(weight.clone(), Some(bias.clone()));
+
+let input_ids = Tensor::zeros((3, 768), DType::F32, &Device::Cpu).unwrap();
+let output = linear.forward(&input_ids).unwrap();
+}
    +

    For a full reference, you can check out the full bert example.

    +

    Memory mapping

    +

    For more efficient loading, instead of reading the file, you could use memmap2

    +

    Note: Be careful about memory mapping it seems to cause issues on Windows, WSL +and will definitely be slower on network mounted disk, because it will issue more read calls.

    +
    use candle::Device;
+use hf_hub::api::sync::Api;
+use memmap2::Mmap;
+use std::fs;
+
+let api = Api::new().unwrap();
+let repo = api.model("bert-base-uncased".to_string());
+let weights_filename = repo.get("model.safetensors").unwrap();
+
+let file = fs::File::open(weights_filename).unwrap();
+let mmap = unsafe { Mmap::map(&file).unwrap() };
+let weights = candle::safetensors::load_buffer(&mmap[..], &Device::Cpu).unwrap();
    +

    Note: This operation is unsafe. See the safety notice. +In practice model files should never be modified, and the mmaps should be mostly READONLY anyway, so the caveat most likely does not apply, but always keep it in mind.

    +

    Tensor Parallel Sharding

    +

    When using multiple GPUs to use in Tensor Parallel in order to get good latency, you can load only the part of the Tensor you need.

    +

    For that you need to use safetensors directly.

    +
    cargo add safetensors
+
    +
    use candle::{DType, Device, Tensor};
+use hf_hub::api::sync::Api;
+use memmap2::Mmap;
+use safetensors::slice::IndexOp;
+use safetensors::SafeTensors;
+use std::fs;
+
+let api = Api::new().unwrap();
+let repo = api.model("bert-base-uncased".to_string());
+let weights_filename = repo.get("model.safetensors").unwrap();
+
+let file = fs::File::open(weights_filename).unwrap();
+let mmap = unsafe { Mmap::map(&file).unwrap() };
+
+// Use safetensors directly
+let tensors = SafeTensors::deserialize(&mmap[..]).unwrap();
+let view = tensors
+    .tensor("bert.encoder.layer.0.attention.self.query.weight")
+    .unwrap();
+
+// We're going to load shard with rank 1, within a world_size of 4
+// We're going to split along dimension 0 doing VIEW[start..stop, :]
+let rank = 1;
+let world_size = 4;
+let dim = 0;
+let dtype = view.dtype();
+let mut tp_shape = view.shape().to_vec();
+let size = tp_shape[0];
+
+if size % world_size != 0 {
+    panic!("The dimension is not divisible by `world_size`");
+}
+let block_size = size / world_size;
+let start = rank * block_size;
+let stop = (rank + 1) * block_size;
+
+// Everything is expressed in tensor dimension
+// bytes offsets is handled automatically for safetensors.
+
+let iterator = view.slice(start..stop).unwrap();
+
+tp_shape[dim] = block_size;
+
+// Convert safetensors Dtype to candle DType
+let dtype: DType = dtype.try_into().unwrap();
+
+// TODO: Implement from_buffer_iterator so we can skip the extra CPU alloc.
+let raw: Vec<u8> = iterator.into_iter().flatten().cloned().collect();
+let tp_tensor = Tensor::from_raw_buffer(&raw, dtype, &tp_shape, &Device::Cpu).unwrap();
    +

    Error management

    +

    You might have seen in the code base a lot of .unwrap() or ?. +If you're unfamiliar with Rust check out the Rust book +for more information.

    +

    What's important to know though, is that if you want to know where a particular operation failed +You can simply use RUST_BACKTRACE=1 to get the location of where the model actually failed.

    +

    Let's see on failing code:

    +
    let x = Tensor::zeros((1, 784), DType::F32, &device)?;
+let y = Tensor::zeros((1, 784), DType::F32, &device)?;
+let z = x.matmul(&y)?;
    +

    Will print at runtime:

    +
    Error: ShapeMismatchBinaryOp { lhs: [1, 784], rhs: [1, 784], op: "matmul" }
+
    +

    After adding RUST_BACKTRACE=1:

    +
    Error: WithBacktrace { inner: ShapeMismatchBinaryOp { lhs: [1, 784], rhs: [1, 784], op: "matmul" }, backtrace: Backtrace [{ fn: "candle::error::Error::bt", file: "/home/nicolas/.cargo/git/checkouts/candle-5bb8ef7e0626d693/f291065/candle-core/src/error.rs", line: 200 }, { fn: "candle::tensor::Tensor::matmul", file: "/home/nicolas/.cargo/git/checkouts/candle-5bb8ef7e0626d693/f291065/candle-core/src/tensor.rs", line: 816 }, { fn: "myapp::main", file: "./src/main.rs", line: 29 }, { fn: "core::ops::function::FnOnce::call_once", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/core/src/ops/function.rs", line: 250 }, { fn: "std::sys_common::backtrace::__rust_begin_short_backtrace", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/sys_common/backtrace.rs", line: 135 }, { fn: "std::rt::lang_start::{{closure}}", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs", line: 166 }, { fn: "core::ops::function::impls::<impl core::ops::function::FnOnce<A> for &F>::call_once", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/core/src/ops/function.rs", line: 284 }, { fn: "std::panicking::try::do_call", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs", line: 500 }, { fn: "std::panicking::try", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs", line: 464 }, { fn: "std::panic::catch_unwind", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panic.rs", line: 142 }, { fn: "std::rt::lang_start_internal::{{closure}}", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs", line: 148 }, { fn: "std::panicking::try::do_call", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs", line: 500 }, { fn: "std::panicking::try", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs", line: 464 }, { fn: "std::panic::catch_unwind", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panic.rs", line: 142 }, { fn: "std::rt::lang_start_internal", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs", line: 148 }, { fn: "std::rt::lang_start", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs", line: 165 }, { fn: "main" }, { fn: "__libc_start_main" }, { fn: "_start" }] }
+
    +

    Not super pretty at the moment, but we can see error occurred on { fn: "myapp::main", file: "./src/main.rs", line: 29 }

    +

    Another thing to note, is that since Rust is compiled it is not necessarily as easy to recover proper stacktraces +especially in release builds. We're using anyhow for that. +The library is still young, please report any issues detecting where an error is coming from.

    +

    Cuda error management

    +

    When running a model on Cuda, you might get a stacktrace not really representing the error. +The reason is that CUDA is async by nature, and therefore the error might be caught while you were sending totally different kernels.

    +

    One way to avoid this is to use CUDA_LAUNCH_BLOCKING=1 as an environment variable. This will force every kernel to be launched sequentially. +You might still however see the error happening on other kernels as the faulty kernel might exit without an error but spoiling some pointer for which the error will happen when dropping the CudaSlice only.

    +

    If this occurs, you can use compute-sanitizer +This tool is like valgrind but for cuda. It will help locate the errors in the kernels.

    +

    Training

    +

    Training starts with data. We're going to use the huggingface hub and +start with the Hello world dataset of machine learning, MNIST.

    +

    Let's start with downloading MNIST from huggingface.

    +

    This requires hf-hub.

    +
    cargo add hf-hub
+
    +

    This is going to be very hands-on for now.

    +
    +

    This uses the standardized parquet files from the refs/convert/parquet branch on every dataset. +Our handles are now [parquet::file::serialized_reader::SerializedFileReader].

    +

    We can inspect the content of the files with:

    +
    +

    You should see something like:

    +
    Column id 1, name label, value 6
+Column id 0, name image, value {bytes: [137, ....]
+Column id 1, name label, value 8
+Column id 0, name image, value {bytes: [137, ....]
+
    +

    So each row contains 2 columns (image, label) with image being saved as bytes. +Let's put them into a useful struct.

    +

    Simplified

    +

    How its works

    +

    This program implements a neural network to predict the winner of the second round of elections based on the results of the first round.

    +

    Basic moments:

    +
      +
    1. A multilayer perceptron with two hidden layers is used. The first hidden layer has 4 neurons, the second has 2 neurons.
      2. +
    2. The input is a vector of 2 numbers - the percentage of votes for the first and second candidates in the first stage.
      3. +
    3. The output is the number 0 or 1, where 1 means that the first candidate will win in the second stage, 0 means that he will lose.
      4. +
    4. For training, samples with real data on the results of the first and second stages of different elections are used.
      5. +
    5. The model is trained by backpropagation using gradient descent and the cross-entropy loss function.
      6. +
    6. Model parameters (weights of neurons) are initialized randomly, then optimized during training.
      7. +
    7. After training, the model is tested on a deferred sample to evaluate the accuracy.
      8. +
    8. If the accuracy on the test set is below 100%, the model is considered underfit and the learning process is repeated.
      9. +
    +

    Thus, this neural network learns to find hidden relationships between the results of the first and second rounds of voting in order to make predictions for new data.

    +
    const VOTE_DIM: usize = 2;
+const RESULTS: usize = 1;
+const EPOCHS: usize = 10;
+const LAYER1_OUT_SIZE: usize = 4;
+const LAYER2_OUT_SIZE: usize = 2;
+const LEARNING_RATE: f64 = 0.05;
+
+#[derive(Clone)]
+pub struct Dataset {
+    pub train_votes: Tensor,
+    pub train_results: Tensor,
+    pub test_votes: Tensor,
+    pub test_results: Tensor,
+}
+
+struct MultiLevelPerceptron {
+    ln1: Linear,
+    ln2: Linear,
+    ln3: Linear,
+}
+
+impl MultiLevelPerceptron {
+    fn new(vs: VarBuilder) -> Result<Self> {
+        let ln1 = candle_nn::linear(VOTE_DIM, LAYER1_OUT_SIZE, vs.pp("ln1"))?;
+        let ln2 = candle_nn::linear(LAYER1_OUT_SIZE, LAYER2_OUT_SIZE, vs.pp("ln2"))?;
+        let ln3 = candle_nn::linear(LAYER2_OUT_SIZE, RESULTS + 1, vs.pp("ln3"))?;
+        Ok(Self { ln1, ln2, ln3 })
+    }
+
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let xs = self.ln1.forward(xs)?;
+        let xs = xs.relu()?;
+        let xs = self.ln2.forward(&xs)?;
+        let xs = xs.relu()?;
+        self.ln3.forward(&xs)
+    }
+}
+
    +
    fn train(m: Dataset, dev: &Device) -> anyhow::Result<MultiLevelPerceptron> {
+    let train_results = m.train_results.to_device(dev)?;
+    let train_votes = m.train_votes.to_device(dev)?;
+    let varmap = VarMap::new();
+    let vs = VarBuilder::from_varmap(&varmap, DType::F32, dev);
+    let model = MultiLevelPerceptron::new(vs.clone())?;
+    let mut sgd = candle_nn::SGD::new(varmap.all_vars(), LEARNING_RATE)?;
+    let test_votes = m.test_votes.to_device(dev)?;
+    let test_results = m.test_results.to_device(dev)?;
+    let mut final_accuracy: f32 = 0.0;
+    for epoch in 1..EPOCHS + 1 {
+        let logits = model.forward(&train_votes)?;
+        let log_sm = ops::log_softmax(&logits, D::Minus1)?;
+        let loss = loss::nll(&log_sm, &train_results)?;
+        sgd.backward_step(&loss)?;
+
+        let test_logits = model.forward(&test_votes)?;
+        let sum_ok = test_logits
+            .argmax(D::Minus1)?
+            .eq(&test_results)?
+            .to_dtype(DType::F32)?
+            .sum_all()?
+            .to_scalar::<f32>()?;
+        let test_accuracy = sum_ok / test_results.dims1()? as f32;
+        final_accuracy = 100. * test_accuracy;
+        println!("Epoch: {epoch:3} Train loss: {:8.5} Test accuracy: {:5.2}%",
+                 loss.to_scalar::<f32>()?,
+                 final_accuracy
+        );
+        if final_accuracy == 100.0 {
+            break;
+        }
+    }
+    if final_accuracy < 100.0 {
+        Err(anyhow::Error::msg("The model is not trained well enough."))
+    } else {
+        Ok(model)
+    }
+}
    +
    #[tokio::test]
+async fn simplified() -> anyhow::Result<()> {
+
+    let dev = Device::cuda_if_available(0)?;
+
+    let train_votes_vec: Vec<u32> = vec![
+        15, 10,
+        10, 15,
+        5, 12,
+        30, 20,
+        16, 12,
+        13, 25,
+        6, 14,
+        31, 21,
+    ];
+    let train_votes_tensor = Tensor::from_vec(train_votes_vec.clone(), (train_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;
+
+    let train_results_vec: Vec<u32> = vec![
+        1,
+        0,
+        0,
+        1,
+        1,
+        0,
+        0,
+        1,
+    ];
+    let train_results_tensor = Tensor::from_vec(train_results_vec, train_votes_vec.len() / VOTE_DIM, &dev)?;
+
+    let test_votes_vec: Vec<u32> = vec![
+        13, 9,
+        8, 14,
+        3, 10,
+    ];
+    let test_votes_tensor = Tensor::from_vec(test_votes_vec.clone(), (test_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;
+
+    let test_results_vec: Vec<u32> = vec![
+        1,
+        0,
+        0,
+    ];
+    let test_results_tensor = Tensor::from_vec(test_results_vec.clone(), test_results_vec.len(), &dev)?;
+
+    let m = Dataset {
+        train_votes: train_votes_tensor,
+        train_results: train_results_tensor,
+        test_votes: test_votes_tensor,
+        test_results: test_results_tensor,
+    };
+
+    let trained_model: MultiLevelPerceptron;
+    loop {
+        println!("Trying to train neural network.");
+        match train(m.clone(), &dev) {
+            Ok(model) => {
+                trained_model = model;
+                break;
+            },
+            Err(e) => {
+                println!("Error: {}", e);
+                continue;
+            }
+        }
+
+    }
+
+    let real_world_votes: Vec<u32> = vec![
+        13, 22,
+    ];
+
+    let tensor_test_votes = Tensor::from_vec(real_world_votes.clone(), (1, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;
+
+    let final_result = trained_model.forward(&tensor_test_votes)?;
+
+    let result = final_result
+        .argmax(D::Minus1)?
+        .to_dtype(DType::F32)?
+        .get(0).map(|x| x.to_scalar::<f32>())??;
+    println!("real_life_votes: {:?}", real_world_votes);
+    println!("neural_network_prediction_result: {:?}", result);
+
+    Ok(())
+
+}
    +

    Example output

    +
    Trying to train neural network.
+Epoch:   1 Train loss:  4.42555 Test accuracy:  0.00%
+Epoch:   2 Train loss:  0.84677 Test accuracy: 33.33%
+Epoch:   3 Train loss:  2.54335 Test accuracy: 33.33%
+Epoch:   4 Train loss:  0.37806 Test accuracy: 33.33%
+Epoch:   5 Train loss:  0.36647 Test accuracy: 100.00%
+real_life_votes: [13, 22]
+neural_network_prediction_result: 0.0
+
    +

    MNIST

    +

    So we now have downloaded the MNIST parquet files, let's put them in a simple struct.

    +
    
+let test_samples = 10_000;
+let mut test_buffer_images: Vec<u8> = Vec::with_capacity(test_samples * 784);
+let mut test_buffer_labels: Vec<u8> = Vec::with_capacity(test_samples);
+for row in test_parquet{
+    for (_name, field) in row?.get_column_iter() {
+        if let parquet::record::Field::Group(subrow) = field {
+            for (_name, field) in subrow.get_column_iter() {
+                if let parquet::record::Field::Bytes(value) = field {
+                    let image = image::load_from_memory(value.data()).unwrap();
+                    test_buffer_images.extend(image.to_luma8().as_raw());
+                }
+            }
+        }else if let parquet::record::Field::Long(label) = field {
+            test_buffer_labels.push(*label as u8);
+        }
+    }
+}
+let test_images = (Tensor::from_vec(test_buffer_images, (test_samples, 784), &Device::Cpu)?.to_dtype(DType::F32)? / 255.)?;
+let test_labels = Tensor::from_vec(test_buffer_labels, (test_samples, ), &Device::Cpu)?;
+
+let train_samples = 60_000;
+let mut train_buffer_images: Vec<u8> = Vec::with_capacity(train_samples * 784);
+let mut train_buffer_labels: Vec<u8> = Vec::with_capacity(train_samples);
+for row in train_parquet{
+    for (_name, field) in row?.get_column_iter() {
+        if let parquet::record::Field::Group(subrow) = field {
+            for (_name, field) in subrow.get_column_iter() {
+                if let parquet::record::Field::Bytes(value) = field {
+                    let image = image::load_from_memory(value.data()).unwrap();
+                    train_buffer_images.extend(image.to_luma8().as_raw());
+                }
+            }
+        }else if let parquet::record::Field::Long(label) = field {
+            train_buffer_labels.push(*label as u8);
+        }
+    }
+}
+let train_images = (Tensor::from_vec(train_buffer_images, (train_samples, 784), &Device::Cpu)?.to_dtype(DType::F32)? / 255.)?;
+let train_labels = Tensor::from_vec(train_buffer_labels, (train_samples, ), &Device::Cpu)?;
+
+let mnist = candle_datasets::vision::Dataset {
+    train_images,
+    train_labels,
+    test_images,
+    test_labels,
+    labels: 10,
+};
+
    +

    The parsing of the file and putting it into single tensors requires the dataset to fit the entire memory. +It is quite rudimentary, but simple enough for a small dataset like MNIST.

    + +
    + + +
    +
    + + + +
    + + + + + + + + + + + + + + + + + + + +
    + + diff --git a/searcher.js b/searcher.js new file mode 100644 index 0000000000..dc03e0a02d --- /dev/null +++ b/searcher.js @@ -0,0 +1,483 @@ +"use strict"; +window.search = window.search || {}; +(function search(search) { + // Search functionality + // + // You can use !hasFocus() to prevent keyhandling in your key + // event handlers while the user is typing their search. + + if (!Mark || !elasticlunr) { + return; + } + + //IE 11 Compatibility from https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/String/startsWith + if (!String.prototype.startsWith) { + String.prototype.startsWith = function(search, pos) { + return this.substr(!pos || pos < 0 ? 0 : +pos, search.length) === search; + }; + } + + var search_wrap = document.getElementById('search-wrapper'), + searchbar = document.getElementById('searchbar'), + searchbar_outer = document.getElementById('searchbar-outer'), + searchresults = document.getElementById('searchresults'), + searchresults_outer = document.getElementById('searchresults-outer'), + searchresults_header = document.getElementById('searchresults-header'), + searchicon = document.getElementById('search-toggle'), + content = document.getElementById('content'), + + searchindex = null, + doc_urls = [], + results_options = { + teaser_word_count: 30, + limit_results: 30, + }, + search_options = { + bool: "AND", + expand: true, + fields: { + title: {boost: 1}, + body: {boost: 1}, + breadcrumbs: {boost: 0} + } + }, + mark_exclude = [], + marker = new Mark(content), + current_searchterm = "", + URL_SEARCH_PARAM = 'search', + URL_MARK_PARAM = 'highlight', + teaser_count = 0, + + SEARCH_HOTKEY_KEYCODE = 83, + ESCAPE_KEYCODE = 27, + DOWN_KEYCODE = 40, + UP_KEYCODE = 38, + SELECT_KEYCODE = 13; + + function hasFocus() { + return searchbar === document.activeElement; + } + + function removeChildren(elem) { + while (elem.firstChild) { + elem.removeChild(elem.firstChild); + } + } + + // Helper to parse a url into its building blocks. + function parseURL(url) { + var a = document.createElement('a'); + a.href = url; + return { + source: url, + protocol: a.protocol.replace(':',''), + host: a.hostname, + port: a.port, + params: (function(){ + var ret = {}; + var seg = a.search.replace(/^\?/,'').split('&'); + var len = seg.length, i = 0, s; + for (;i': '>', + '"': '"', + "'": ''' + }; + var repl = function(c) { return MAP[c]; }; + return function(s) { + return s.replace(/[&<>'"]/g, repl); + }; + })(); + + function formatSearchMetric(count, searchterm) { + if (count == 1) { + return count + " search result for '" + searchterm + "':"; + } else if (count == 0) { + return "No search results for '" + searchterm + "'."; + } else { + return count + " search results for '" + searchterm + "':"; + } + } + + function formatSearchResult(result, searchterms) { + var teaser = makeTeaser(escapeHTML(result.doc.body), searchterms); + teaser_count++; + + // The ?URL_MARK_PARAM= parameter belongs inbetween the page and the #heading-anchor + var url = doc_urls[result.ref].split("#"); + if (url.length == 1) { // no anchor found + url.push(""); + } + + // encodeURIComponent escapes all chars that could allow an XSS except + // for '. Due to that we also manually replace ' with its url-encoded + // representation (%27). + var searchterms = encodeURIComponent(searchterms.join(" ")).replace(/\'/g, "%27"); + + return '' + result.doc.breadcrumbs + '' + + '' + + teaser + ''; + } + + function makeTeaser(body, searchterms) { + // The strategy is as follows: + // First, assign a value to each word in the document: + // Words that correspond to search terms (stemmer aware): 40 + // Normal words: 2 + // First word in a sentence: 8 + // Then use a sliding window with a constant number of words and count the + // sum of the values of the words within the window. Then use the window that got the + // maximum sum. If there are multiple maximas, then get the last one. + // Enclose the terms in . + var stemmed_searchterms = searchterms.map(function(w) { + return elasticlunr.stemmer(w.toLowerCase()); + }); + var searchterm_weight = 40; + var weighted = []; // contains elements of ["word", weight, index_in_document] + // split in sentences, then words + var sentences = body.toLowerCase().split('. '); + var index = 0; + var value = 0; + var searchterm_found = false; + for (var sentenceindex in sentences) { + var words = sentences[sentenceindex].split(' '); + value = 8; + for (var wordindex in words) { + var word = words[wordindex]; + if (word.length > 0) { + for (var searchtermindex in stemmed_searchterms) { + if (elasticlunr.stemmer(word).startsWith(stemmed_searchterms[searchtermindex])) { + value = searchterm_weight; + searchterm_found = true; + } + }; + weighted.push([word, value, index]); + value = 2; + } + index += word.length; + index += 1; // ' ' or '.' if last word in sentence + }; + index += 1; // because we split at a two-char boundary '. ' + }; + + if (weighted.length == 0) { + return body; + } + + var window_weight = []; + var window_size = Math.min(weighted.length, results_options.teaser_word_count); + + var cur_sum = 0; + for (var wordindex = 0; wordindex < window_size; wordindex++) { + cur_sum += weighted[wordindex][1]; + }; + window_weight.push(cur_sum); + for (var wordindex = 0; wordindex < weighted.length - window_size; wordindex++) { + cur_sum -= weighted[wordindex][1]; + cur_sum += weighted[wordindex + window_size][1]; + window_weight.push(cur_sum); + }; + + if (searchterm_found) { + var max_sum = 0; + var max_sum_window_index = 0; + // backwards + for (var i = window_weight.length - 1; i >= 0; i--) { + if (window_weight[i] > max_sum) { + max_sum = window_weight[i]; + max_sum_window_index = i; + } + }; + } else { + max_sum_window_index = 0; + } + + // add around searchterms + var teaser_split = []; + var index = weighted[max_sum_window_index][2]; + for (var i = max_sum_window_index; i < max_sum_window_index+window_size; i++) { + var word = weighted[i]; + if (index < word[2]) { + // missing text from index to start of `word` + teaser_split.push(body.substring(index, word[2])); + index = word[2]; + } + if (word[1] == searchterm_weight) { + teaser_split.push("") + } + index = word[2] + word[0].length; + teaser_split.push(body.substring(word[2], index)); + if (word[1] == searchterm_weight) { + teaser_split.push("") + } + }; + + return teaser_split.join(''); + } + + function init(config) { + results_options = config.results_options; + search_options = config.search_options; + searchbar_outer = config.searchbar_outer; + doc_urls = config.doc_urls; + searchindex = elasticlunr.Index.load(config.index); + + // Set up events + searchicon.addEventListener('click', function(e) { searchIconClickHandler(); }, false); + searchbar.addEventListener('keyup', function(e) { searchbarKeyUpHandler(); }, false); + document.addEventListener('keydown', function(e) { globalKeyHandler(e); }, false); + // If the user uses the browser buttons, do the same as if a reload happened + window.onpopstate = function(e) { doSearchOrMarkFromUrl(); }; + // Suppress "submit" events so the page doesn't reload when the user presses Enter + document.addEventListener('submit', function(e) { e.preventDefault(); }, false); + + // If reloaded, do the search or mark again, depending on the current url parameters + doSearchOrMarkFromUrl(); + } + + function unfocusSearchbar() { + // hacky, but just focusing a div only works once + var tmp = document.createElement('input'); + tmp.setAttribute('style', 'position: absolute; opacity: 0;'); + searchicon.appendChild(tmp); + tmp.focus(); + tmp.remove(); + } + + // On reload or browser history backwards/forwards events, parse the url and do search or mark + function doSearchOrMarkFromUrl() { + // Check current URL for search request + var url = parseURL(window.location.href); + if (url.params.hasOwnProperty(URL_SEARCH_PARAM) + && url.params[URL_SEARCH_PARAM] != "") { + showSearch(true); + searchbar.value = decodeURIComponent( + (url.params[URL_SEARCH_PARAM]+'').replace(/\+/g, '%20')); + searchbarKeyUpHandler(); // -> doSearch() + } else { + showSearch(false); + } + + if (url.params.hasOwnProperty(URL_MARK_PARAM)) { + var words = decodeURIComponent(url.params[URL_MARK_PARAM]).split(' '); + marker.mark(words, { + exclude: mark_exclude + }); + + var markers = document.querySelectorAll("mark"); + function hide() { + for (var i = 0; i < markers.length; i++) { + markers[i].classList.add("fade-out"); + window.setTimeout(function(e) { marker.unmark(); }, 300); + } + } + for (var i = 0; i < markers.length; i++) { + markers[i].addEventListener('click', hide); + } + } + } + + // Eventhandler for keyevents on `document` + function globalKeyHandler(e) { + if (e.altKey || e.ctrlKey || e.metaKey || e.shiftKey || e.target.type === 'textarea' || e.target.type === 'text' || !hasFocus() && /^(?:input|select|textarea)$/i.test(e.target.nodeName)) { return; } + + if (e.keyCode === ESCAPE_KEYCODE) { + e.preventDefault(); + searchbar.classList.remove("active"); + setSearchUrlParameters("", + (searchbar.value.trim() !== "") ? "push" : "replace"); + if (hasFocus()) { + unfocusSearchbar(); + } + showSearch(false); + marker.unmark(); + } else if (!hasFocus() && e.keyCode === SEARCH_HOTKEY_KEYCODE) { + e.preventDefault(); + showSearch(true); + window.scrollTo(0, 0); + searchbar.select(); + } else if (hasFocus() && e.keyCode === DOWN_KEYCODE) { + e.preventDefault(); + unfocusSearchbar(); + searchresults.firstElementChild.classList.add("focus"); + } else if (!hasFocus() && (e.keyCode === DOWN_KEYCODE + || e.keyCode === UP_KEYCODE + || e.keyCode === SELECT_KEYCODE)) { + // not `:focus` because browser does annoying scrolling + var focused = searchresults.querySelector("li.focus"); + if (!focused) return; + e.preventDefault(); + if (e.keyCode === DOWN_KEYCODE) { + var next = focused.nextElementSibling; + if (next) { + focused.classList.remove("focus"); + next.classList.add("focus"); + } + } else if (e.keyCode === UP_KEYCODE) { + focused.classList.remove("focus"); + var prev = focused.previousElementSibling; + if (prev) { + prev.classList.add("focus"); + } else { + searchbar.select(); + } + } else { // SELECT_KEYCODE + window.location.assign(focused.querySelector('a')); + } + } + } + + function showSearch(yes) { + if (yes) { + search_wrap.classList.remove('hidden'); + searchicon.setAttribute('aria-expanded', 'true'); + } else { + search_wrap.classList.add('hidden'); + searchicon.setAttribute('aria-expanded', 'false'); + var results = searchresults.children; + for (var i = 0; i < results.length; i++) { + results[i].classList.remove("focus"); + } + } + } + + function showResults(yes) { + if (yes) { + searchresults_outer.classList.remove('hidden'); + } else { + searchresults_outer.classList.add('hidden'); + } + } + + // Eventhandler for search icon + function searchIconClickHandler() { + if (search_wrap.classList.contains('hidden')) { + showSearch(true); + window.scrollTo(0, 0); + searchbar.select(); + } else { + showSearch(false); + } + } + + // Eventhandler for keyevents while the searchbar is focused + function searchbarKeyUpHandler() { + var searchterm = searchbar.value.trim(); + if (searchterm != "") { + searchbar.classList.add("active"); + doSearch(searchterm); + } else { + searchbar.classList.remove("active"); + showResults(false); + removeChildren(searchresults); + } + + setSearchUrlParameters(searchterm, "push_if_new_search_else_replace"); + + // Remove marks + marker.unmark(); + } + + // Update current url with ?URL_SEARCH_PARAM= parameter, remove ?URL_MARK_PARAM and #heading-anchor . + // `action` can be one of "push", "replace", "push_if_new_search_else_replace" + // and replaces or pushes a new browser history item. + // "push_if_new_search_else_replace" pushes if there is no `?URL_SEARCH_PARAM=abc` yet. + function setSearchUrlParameters(searchterm, action) { + var url = parseURL(window.location.href); + var first_search = ! url.params.hasOwnProperty(URL_SEARCH_PARAM); + if (searchterm != "" || action == "push_if_new_search_else_replace") { + url.params[URL_SEARCH_PARAM] = searchterm; + delete url.params[URL_MARK_PARAM]; + url.hash = ""; + } else { + delete url.params[URL_MARK_PARAM]; + delete url.params[URL_SEARCH_PARAM]; + } + // A new search will also add a new history item, so the user can go back + // to the page prior to searching. A updated search term will only replace + // the url. + if (action == "push" || (action == "push_if_new_search_else_replace" && first_search) ) { + history.pushState({}, document.title, renderURL(url)); + } else if (action == "replace" || (action == "push_if_new_search_else_replace" && !first_search) ) { + history.replaceState({}, document.title, renderURL(url)); + } + } + + function doSearch(searchterm) { + + // Don't search the same twice + if (current_searchterm == searchterm) { return; } + else { current_searchterm = searchterm; } + + if (searchindex == null) { return; } + + // Do the actual search + var results = searchindex.search(searchterm, search_options); + var resultcount = Math.min(results.length, results_options.limit_results); + + // Display search metrics + searchresults_header.innerText = formatSearchMetric(resultcount, searchterm); + + // Clear and insert results + var searchterms = searchterm.split(' '); + removeChildren(searchresults); + for(var i = 0; i < resultcount ; i++){ + var resultElem = document.createElement('li'); + resultElem.innerHTML = formatSearchResult(results[i], searchterms); + searchresults.appendChild(resultElem); + } + + // Display results + showResults(true); + } + + fetch(path_to_root + 'searchindex.json') + .then(response => response.json()) + .then(json => init(json)) + .catch(error => { // Try to load searchindex.js if fetch failed + var script = document.createElement('script'); + script.src = path_to_root + 'searchindex.js'; + script.onload = () => init(window.search); + document.head.appendChild(script); + }); + + // Exported functions + search.hasFocus = hasFocus; +})(window.search); diff --git a/searchindex.js b/searchindex.js new file mode 100644 index 0000000000..cd6c412aef --- /dev/null +++ b/searchindex.js @@ -0,0 +1 @@ +Object.assign(window.search, {"doc_urls":["index.html#introduction","index.html#features","guide/installation.html#installation","guide/hello_world.html#hello-world","guide/hello_world.html#using-a-linear-layer","guide/hello_world.html#using-candle_nn","guide/cheatsheet.html#pytorch-cheatsheet","inference/inference.html#running-a-model","inference/hub.html#using-the-hub","inference/hub.html#using-async","inference/hub.html#using-in-a-real-model","inference/hub.html#memory-mapping","inference/hub.html#tensor-parallel-sharding","error_manage.html#error-management","error_manage.html#cuda-error-management","training/training.html#training","training/simplified.html#simplified","training/simplified.html#how-its-works","training/simplified.html#example-output","training/mnist.html#mnist"],"index":{"documentStore":{"docInfo":{"0":{"body":0,"breadcrumbs":2,"title":1},"1":{"body":219,"breadcrumbs":2,"title":1},"10":{"body":55,"breadcrumbs":7,"title":3},"11":{"body":70,"breadcrumbs":6,"title":2},"12":{"body":139,"breadcrumbs":7,"title":3},"13":{"body":216,"breadcrumbs":4,"title":2},"14":{"body":56,"breadcrumbs":5,"title":3},"15":{"body":91,"breadcrumbs":2,"title":1},"16":{"body":0,"breadcrumbs":3,"title":1},"17":{"body":408,"breadcrumbs":3,"title":1},"18":{"body":49,"breadcrumbs":4,"title":2},"19":{"body":114,"breadcrumbs":3,"title":1},"2":{"body":135,"breadcrumbs":2,"title":1},"3":{"body":84,"breadcrumbs":5,"title":2},"4":{"body":175,"breadcrumbs":6,"title":3},"5":{"body":143,"breadcrumbs":5,"title":2},"6":{"body":54,"breadcrumbs":4,"title":2},"7":{"body":23,"breadcrumbs":4,"title":2},"8":{"body":43,"breadcrumbs":6,"title":2},"9":{"body":37,"breadcrumbs":6,"title":2}},"docs":{"0":{"body":"","breadcrumbs":"Introduction » Introduction","id":"0","title":"Introduction"},"1":{"body":"Simple syntax, looks and feels like PyTorch. Model training. Embed user-defined ops/kernels, such as flash-attention v2 . Backends. Optimized CPU backend with optional MKL support for x86 and Accelerate for macs. CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL. WASM support, run your models in a browser. Included models. Language Models. LLaMA v1, v2, and v3 with variants such as SOLAR-10.7B. Falcon. StarCoder, StarCoder2. Phi 1, 1.5, 2, and 3. Mamba, Minimal Mamba Gemma 2b and 7b. Mistral 7b v0.1. Mixtral 8x7b v0.1. StableLM-3B-4E1T, StableLM-2-1.6B, Stable-Code-3B. Replit-code-v1.5-3B. Bert. Yi-6B and Yi-34B. Qwen1.5, Qwen1.5 MoE. RWKV v5 and v6. Quantized LLMs. Llama 7b, 13b, 70b, as well as the chat and code variants. Mistral 7b, and 7b instruct. Mixtral 8x7b. Zephyr 7b a and b (Mistral-7b based). OpenChat 3.5 (Mistral-7b based). Text to text. T5 and its variants: FlanT5, UL2, MADLAD400 (translation), CoEdit (Grammar correction). Marian MT (Machine Translation). Text to image. Stable Diffusion v1.5, v2.1, XL v1.0. Wurstchen v2. Image to text. BLIP. TrOCR. Audio. Whisper, multi-lingual speech-to-text. EnCodec, audio compression model. MetaVoice-1B, text-to-speech model. Computer Vision Models. DINOv2, ConvMixer, EfficientNet, ResNet, ViT, VGG, RepVGG, ConvNeXT, ConvNeXTv2, MobileOne, EfficientVit (MSRA), MobileNetv4, Hiera. yolo-v3, yolo-v8. Segment-Anything Model (SAM). SegFormer. File formats: load models from safetensors, npz, ggml, or PyTorch files. Serverless (on CPU), small and fast deployments. Quantization support using the llama.cpp quantized types. This book will introduce step by step how to use candle.","breadcrumbs":"Introduction » Features","id":"1","title":"Features"},"10":{"body":"Now that we have our weights, we can use them in our bert architecture: # extern crate candle_core;\n# extern crate candle_nn;\n# extern crate hf_hub;\n# use hf_hub::api::sync::Api;\n# # let api = Api::new().unwrap();\n# let repo = api.model(\"bert-base-uncased\".to_string());\n# # let weights = repo.get(\"model.safetensors\").unwrap();\nuse candle_core::{Device, Tensor, DType};\nuse candle_nn::{Linear, Module}; let weights = candle_core::safetensors::load(weights, &Device::Cpu).unwrap(); let weight = weights.get(\"bert.encoder.layer.0.attention.self.query.weight\").unwrap();\nlet bias = weights.get(\"bert.encoder.layer.0.attention.self.query.bias\").unwrap(); let linear = Linear::new(weight.clone(), Some(bias.clone())); let input_ids = Tensor::zeros((3, 768), DType::F32, &Device::Cpu).unwrap();\nlet output = linear.forward(&input_ids).unwrap(); For a full reference, you can check out the full bert example.","breadcrumbs":"Running a model » Using the hub » Using in a real model.","id":"10","title":"Using in a real model."},"11":{"body":"For more efficient loading, instead of reading the file, you could use memmap2 Note : Be careful about memory mapping it seems to cause issues on Windows, WSL and will definitely be slower on network mounted disk, because it will issue more read calls. use candle::Device;\nuse hf_hub::api::sync::Api;\nuse memmap2::Mmap;\nuse std::fs; let api = Api::new().unwrap();\nlet repo = api.model(\"bert-base-uncased\".to_string());\nlet weights_filename = repo.get(\"model.safetensors\").unwrap(); let file = fs::File::open(weights_filename).unwrap();\nlet mmap = unsafe { Mmap::map(&file).unwrap() };\nlet weights = candle::safetensors::load_buffer(&mmap[..], &Device::Cpu).unwrap(); Note : This operation is unsafe . See the safety notice . In practice model files should never be modified, and the mmaps should be mostly READONLY anyway, so the caveat most likely does not apply, but always keep it in mind.","breadcrumbs":"Running a model » Using the hub » Memory mapping","id":"11","title":"Memory mapping"},"12":{"body":"When using multiple GPUs to use in Tensor Parallel in order to get good latency, you can load only the part of the Tensor you need. For that you need to use safetensors directly. cargo add safetensors use candle::{DType, Device, Tensor};\nuse hf_hub::api::sync::Api;\nuse memmap2::Mmap;\nuse safetensors::slice::IndexOp;\nuse safetensors::SafeTensors;\nuse std::fs; let api = Api::new().unwrap();\nlet repo = api.model(\"bert-base-uncased\".to_string());\nlet weights_filename = repo.get(\"model.safetensors\").unwrap(); let file = fs::File::open(weights_filename).unwrap();\nlet mmap = unsafe { Mmap::map(&file).unwrap() }; // Use safetensors directly\nlet tensors = SafeTensors::deserialize(&mmap[..]).unwrap();\nlet view = tensors .tensor(\"bert.encoder.layer.0.attention.self.query.weight\") .unwrap(); // We're going to load shard with rank 1, within a world_size of 4\n// We're going to split along dimension 0 doing VIEW[start..stop, :]\nlet rank = 1;\nlet world_size = 4;\nlet dim = 0;\nlet dtype = view.dtype();\nlet mut tp_shape = view.shape().to_vec();\nlet size = tp_shape[0]; if size % world_size != 0 { panic!(\"The dimension is not divisible by `world_size`\");\n}\nlet block_size = size / world_size;\nlet start = rank * block_size;\nlet stop = (rank + 1) * block_size; // Everything is expressed in tensor dimension\n// bytes offsets is handled automatically for safetensors. let iterator = view.slice(start..stop).unwrap(); tp_shape[dim] = block_size; // Convert safetensors Dtype to candle DType\nlet dtype: DType = dtype.try_into().unwrap(); // TODO: Implement from_buffer_iterator so we can skip the extra CPU alloc.\nlet raw: Vec = iterator.into_iter().flatten().cloned().collect();\nlet tp_tensor = Tensor::from_raw_buffer(&raw, dtype, &tp_shape, &Device::Cpu).unwrap();","breadcrumbs":"Running a model » Using the hub » Tensor Parallel Sharding","id":"12","title":"Tensor Parallel Sharding"},"13":{"body":"You might have seen in the code base a lot of .unwrap() or ?. If you're unfamiliar with Rust check out the Rust book for more information. What's important to know though, is that if you want to know where a particular operation failed You can simply use RUST_BACKTRACE=1 to get the location of where the model actually failed. Let's see on failing code: let x = Tensor::zeros((1, 784), DType::F32, &device)?;\nlet y = Tensor::zeros((1, 784), DType::F32, &device)?;\nlet z = x.matmul(&y)?; Will print at runtime: Error: ShapeMismatchBinaryOp { lhs: [1, 784], rhs: [1, 784], op: \"matmul\" } After adding RUST_BACKTRACE=1: Error: WithBacktrace { inner: ShapeMismatchBinaryOp { lhs: [1, 784], rhs: [1, 784], op: \"matmul\" }, backtrace: Backtrace [{ fn: \"candle::error::Error::bt\", file: \"/home/nicolas/.cargo/git/checkouts/candle-5bb8ef7e0626d693/f291065/candle-core/src/error.rs\", line: 200 }, { fn: \"candle::tensor::Tensor::matmul\", file: \"/home/nicolas/.cargo/git/checkouts/candle-5bb8ef7e0626d693/f291065/candle-core/src/tensor.rs\", line: 816 }, { fn: \"myapp::main\", file: \"./src/main.rs\", line: 29 }, { fn: \"core::ops::function::FnOnce::call_once\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/core/src/ops/function.rs\", line: 250 }, { fn: \"std::sys_common::backtrace::__rust_begin_short_backtrace\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/sys_common/backtrace.rs\", line: 135 }, { fn: \"std::rt::lang_start::{{closure}}\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs\", line: 166 }, { fn: \"core::ops::function::impls:: for &F>::call_once\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/core/src/ops/function.rs\", line: 284 }, { fn: \"std::panicking::try::do_call\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs\", line: 500 }, { fn: \"std::panicking::try\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs\", line: 464 }, { fn: \"std::panic::catch_unwind\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panic.rs\", line: 142 }, { fn: \"std::rt::lang_start_internal::{{closure}}\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs\", line: 148 }, { fn: \"std::panicking::try::do_call\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs\", line: 500 }, { fn: \"std::panicking::try\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs\", line: 464 }, { fn: \"std::panic::catch_unwind\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panic.rs\", line: 142 }, { fn: \"std::rt::lang_start_internal\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs\", line: 148 }, { fn: \"std::rt::lang_start\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs\", line: 165 }, { fn: \"main\" }, { fn: \"__libc_start_main\" }, { fn: \"_start\" }] } Not super pretty at the moment, but we can see error occurred on { fn: \"myapp::main\", file: \"./src/main.rs\", line: 29 } Another thing to note, is that since Rust is compiled it is not necessarily as easy to recover proper stacktraces especially in release builds. We're using anyhow for that. The library is still young, please report any issues detecting where an error is coming from.","breadcrumbs":"Error management » Error management","id":"13","title":"Error management"},"14":{"body":"When running a model on Cuda, you might get a stacktrace not really representing the error. The reason is that CUDA is async by nature, and therefore the error might be caught while you were sending totally different kernels. One way to avoid this is to use CUDA_LAUNCH_BLOCKING=1 as an environment variable. This will force every kernel to be launched sequentially. You might still however see the error happening on other kernels as the faulty kernel might exit without an error but spoiling some pointer for which the error will happen when dropping the CudaSlice only. If this occurs, you can use compute-sanitizer This tool is like valgrind but for cuda. It will help locate the errors in the kernels.","breadcrumbs":"Error management » Cuda error management","id":"14","title":"Cuda error management"},"15":{"body":"Training starts with data. We're going to use the huggingface hub and start with the Hello world dataset of machine learning, MNIST. Let's start with downloading MNIST from huggingface . This requires hf-hub . cargo add hf-hub This is going to be very hands-on for now. This uses the standardized parquet files from the refs/convert/parquet branch on every dataset. Our handles are now [parquet::file::serialized_reader::SerializedFileReader]. We can inspect the content of the files with: You should see something like: Column id 1, name label, value 6\nColumn id 0, name image, value {bytes: [137, ....]\nColumn id 1, name label, value 8\nColumn id 0, name image, value {bytes: [137, ....] So each row contains 2 columns (image, label) with image being saved as bytes. Let's put them into a useful struct.","breadcrumbs":"Training » Training","id":"15","title":"Training"},"16":{"body":"","breadcrumbs":"Training » Simplified » Simplified","id":"16","title":"Simplified"},"17":{"body":"This program implements a neural network to predict the winner of the second round of elections based on the results of the first round. Basic moments: A multilayer perceptron with two hidden layers is used. The first hidden layer has 4 neurons, the second has 2 neurons. The input is a vector of 2 numbers - the percentage of votes for the first and second candidates in the first stage. The output is the number 0 or 1, where 1 means that the first candidate will win in the second stage, 0 means that he will lose. For training, samples with real data on the results of the first and second stages of different elections are used. The model is trained by backpropagation using gradient descent and the cross-entropy loss function. Model parameters (weights of neurons) are initialized randomly, then optimized during training. After training, the model is tested on a deferred sample to evaluate the accuracy. If the accuracy on the test set is below 100%, the model is considered underfit and the learning process is repeated. Thus, this neural network learns to find hidden relationships between the results of the first and second rounds of voting in order to make predictions for new data. const VOTE_DIM: usize = 2;\nconst RESULTS: usize = 1;\nconst EPOCHS: usize = 10;\nconst LAYER1_OUT_SIZE: usize = 4;\nconst LAYER2_OUT_SIZE: usize = 2;\nconst LEARNING_RATE: f64 = 0.05; #[derive(Clone)]\npub struct Dataset { pub train_votes: Tensor, pub train_results: Tensor, pub test_votes: Tensor, pub test_results: Tensor,\n} struct MultiLevelPerceptron { ln1: Linear, ln2: Linear, ln3: Linear,\n} impl MultiLevelPerceptron { fn new(vs: VarBuilder) -> Result { let ln1 = candle_nn::linear(VOTE_DIM, LAYER1_OUT_SIZE, vs.pp(\"ln1\"))?; let ln2 = candle_nn::linear(LAYER1_OUT_SIZE, LAYER2_OUT_SIZE, vs.pp(\"ln2\"))?; let ln3 = candle_nn::linear(LAYER2_OUT_SIZE, RESULTS + 1, vs.pp(\"ln3\"))?; Ok(Self { ln1, ln2, ln3 }) } fn forward(&self, xs: &Tensor) -> Result { let xs = self.ln1.forward(xs)?; let xs = xs.relu()?; let xs = self.ln2.forward(&xs)?; let xs = xs.relu()?; self.ln3.forward(&xs) }\n} fn train(m: Dataset, dev: &Device) -> anyhow::Result { let train_results = m.train_results.to_device(dev)?; let train_votes = m.train_votes.to_device(dev)?; let varmap = VarMap::new(); let vs = VarBuilder::from_varmap(&varmap, DType::F32, dev); let model = MultiLevelPerceptron::new(vs.clone())?; let mut sgd = candle_nn::SGD::new(varmap.all_vars(), LEARNING_RATE)?; let test_votes = m.test_votes.to_device(dev)?; let test_results = m.test_results.to_device(dev)?; let mut final_accuracy: f32 = 0.0; for epoch in 1..EPOCHS + 1 { let logits = model.forward(&train_votes)?; let log_sm = ops::log_softmax(&logits, D::Minus1)?; let loss = loss::nll(&log_sm, &train_results)?; sgd.backward_step(&loss)?; let test_logits = model.forward(&test_votes)?; let sum_ok = test_logits .argmax(D::Minus1)? .eq(&test_results)? .to_dtype(DType::F32)? .sum_all()? .to_scalar::()?; let test_accuracy = sum_ok / test_results.dims1()? as f32; final_accuracy = 100. * test_accuracy; println!(\"Epoch: {epoch:3} Train loss: {:8.5} Test accuracy: {:5.2}%\", loss.to_scalar::()?, final_accuracy ); if final_accuracy == 100.0 { break; } } if final_accuracy < 100.0 { Err(anyhow::Error::msg(\"The model is not trained well enough.\")) } else { Ok(model) }\n} #[tokio::test]\nasync fn simplified() -> anyhow::Result<()> { let dev = Device::cuda_if_available(0)?; let train_votes_vec: Vec = vec![ 15, 10, 10, 15, 5, 12, 30, 20, 16, 12, 13, 25, 6, 14, 31, 21, ]; let train_votes_tensor = Tensor::from_vec(train_votes_vec.clone(), (train_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?; let train_results_vec: Vec = vec![ 1, 0, 0, 1, 1, 0, 0, 1, ]; let train_results_tensor = Tensor::from_vec(train_results_vec, train_votes_vec.len() / VOTE_DIM, &dev)?; let test_votes_vec: Vec = vec![ 13, 9, 8, 14, 3, 10, ]; let test_votes_tensor = Tensor::from_vec(test_votes_vec.clone(), (test_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?; let test_results_vec: Vec = vec![ 1, 0, 0, ]; let test_results_tensor = Tensor::from_vec(test_results_vec.clone(), test_results_vec.len(), &dev)?; let m = Dataset { train_votes: train_votes_tensor, train_results: train_results_tensor, test_votes: test_votes_tensor, test_results: test_results_tensor, }; let trained_model: MultiLevelPerceptron; loop { println!(\"Trying to train neural network.\"); match train(m.clone(), &dev) { Ok(model) => { trained_model = model; break; }, Err(e) => { println!(\"Error: {}\", e); continue; } } } let real_world_votes: Vec = vec![ 13, 22, ]; let tensor_test_votes = Tensor::from_vec(real_world_votes.clone(), (1, VOTE_DIM), &dev)?.to_dtype(DType::F32)?; let final_result = trained_model.forward(&tensor_test_votes)?; let result = final_result .argmax(D::Minus1)? .to_dtype(DType::F32)? .get(0).map(|x| x.to_scalar::())??; println!(\"real_life_votes: {:?}\", real_world_votes); println!(\"neural_network_prediction_result: {:?}\", result); Ok(()) }","breadcrumbs":"Training » Simplified » How its works","id":"17","title":"How its works"},"18":{"body":"Trying to train neural network.\nEpoch: 1 Train loss: 4.42555 Test accuracy: 0.00%\nEpoch: 2 Train loss: 0.84677 Test accuracy: 33.33%\nEpoch: 3 Train loss: 2.54335 Test accuracy: 33.33%\nEpoch: 4 Train loss: 0.37806 Test accuracy: 33.33%\nEpoch: 5 Train loss: 0.36647 Test accuracy: 100.00%\nreal_life_votes: [13, 22]\nneural_network_prediction_result: 0.0","breadcrumbs":"Training » Simplified » Example output","id":"18","title":"Example output"},"19":{"body":"So we now have downloaded the MNIST parquet files, let's put them in a simple struct. let test_samples = 10_000;\nlet mut test_buffer_images: Vec = Vec::with_capacity(test_samples * 784);\nlet mut test_buffer_labels: Vec = Vec::with_capacity(test_samples);\nfor row in test_parquet{ for (_name, field) in row?.get_column_iter() { if let parquet::record::Field::Group(subrow) = field { for (_name, field) in subrow.get_column_iter() { if let parquet::record::Field::Bytes(value) = field { let image = image::load_from_memory(value.data()).unwrap(); test_buffer_images.extend(image.to_luma8().as_raw()); } } }else if let parquet::record::Field::Long(label) = field { test_buffer_labels.push(*label as u8); } }\n}\nlet test_images = (Tensor::from_vec(test_buffer_images, (test_samples, 784), &Device::Cpu)?.to_dtype(DType::F32)? / 255.)?;\nlet test_labels = Tensor::from_vec(test_buffer_labels, (test_samples, ), &Device::Cpu)?; let train_samples = 60_000;\nlet mut train_buffer_images: Vec = Vec::with_capacity(train_samples * 784);\nlet mut train_buffer_labels: Vec = Vec::with_capacity(train_samples);\nfor row in train_parquet{ for (_name, field) in row?.get_column_iter() { if let parquet::record::Field::Group(subrow) = field { for (_name, field) in subrow.get_column_iter() { if let parquet::record::Field::Bytes(value) = field { let image = image::load_from_memory(value.data()).unwrap(); train_buffer_images.extend(image.to_luma8().as_raw()); } } }else if let parquet::record::Field::Long(label) = field { train_buffer_labels.push(*label as u8); } }\n}\nlet train_images = (Tensor::from_vec(train_buffer_images, (train_samples, 784), &Device::Cpu)?.to_dtype(DType::F32)? / 255.)?;\nlet train_labels = Tensor::from_vec(train_buffer_labels, (train_samples, ), &Device::Cpu)?; let mnist = candle_datasets::vision::Dataset { train_images, train_labels, test_images, test_labels, labels: 10,\n}; The parsing of the file and putting it into single tensors requires the dataset to fit the entire memory. It is quite rudimentary, but simple enough for a small dataset like MNIST.","breadcrumbs":"Training » MNIST » MNIST","id":"19","title":"MNIST"},"2":{"body":"With Cuda support : First, make sure that Cuda is correctly installed. nvcc --version should print information about your Cuda compiler driver. nvidia-smi --query-gpu=compute_cap --format=csv should print your GPUs compute capability, e.g. something like: compute_cap\n8.9 You can also compile the Cuda kernels for a specific compute cap using the CUDA_COMPUTE_CAP= environment variable. If any of the above commands errors out, please make sure to update your Cuda version. Create a new app and add candle-core with Cuda support. Start by creating a new cargo: cargo new myapp\ncd myapp Make sure to add the candle-core crate with the cuda feature: cargo add --git https://github.com/huggingface/candle.git candle-core --features \"cuda\" Run cargo build to make sure everything can be correctly built. cargo build Without Cuda support : Create a new app and add candle-core as follows: cargo new myapp\ncd myapp\ncargo add --git https://github.com/huggingface/candle.git candle-core Finally, run cargo build to make sure everything can be correctly built. cargo build With mkl support You can also see the mkl feature which could be interesting to get faster inference on CPU. Using mkl","breadcrumbs":"Installation » Installation","id":"2","title":"Installation"},"3":{"body":"We will now create the hello world of the ML world, building a model capable of solving MNIST dataset. Open src/main.rs and fill in this content: # extern crate candle_core;\nuse candle_core::{Device, Result, Tensor}; struct Model { first: Tensor, second: Tensor,\n} impl Model { fn forward(&self, image: &Tensor) -> Result { let x = image.matmul(&self.first)?; let x = x.relu()?; x.matmul(&self.second) }\n} fn main() -> Result<()> { // Use Device::new_cuda(0)?; to use the GPU. let device = Device::Cpu; let first = Tensor::randn(0f32, 1.0, (784, 100), &device)?; let second = Tensor::randn(0f32, 1.0, (100, 10), &device)?; let model = Model { first, second }; let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?; let digit = model.forward(&dummy_image)?; println!(\"Digit {digit:?} digit\"); Ok(())\n} Everything should now run with: cargo run --release","breadcrumbs":"Hello World - MNIST » Hello world!","id":"3","title":"Hello world!"},"4":{"body":"Now that we have this, we might want to complexify things a bit, for instance by adding bias and creating the classical Linear layer. We can do as such # extern crate candle_core;\n# use candle_core::{Device, Result, Tensor};\nstruct Linear{ weight: Tensor, bias: Tensor,\n}\nimpl Linear{ fn forward(&self, x: &Tensor) -> Result { let x = x.matmul(&self.weight)?; x.broadcast_add(&self.bias) }\n} struct Model { first: Linear, second: Linear,\n} impl Model { fn forward(&self, image: &Tensor) -> Result { let x = self.first.forward(image)?; let x = x.relu()?; self.second.forward(&x) }\n} This will change the model running code into a new function # extern crate candle_core;\n# use candle_core::{Device, Result, Tensor};\n# struct Linear{\n# weight: Tensor,\n# bias: Tensor,\n# }\n# impl Linear{\n# fn forward(&self, x: &Tensor) -> Result {\n# let x = x.matmul(&self.weight)?;\n# x.broadcast_add(&self.bias)\n# }\n# }\n# # struct Model {\n# first: Linear,\n# second: Linear,\n# }\n# # impl Model {\n# fn forward(&self, image: &Tensor) -> Result {\n# let x = self.first.forward(image)?;\n# let x = x.relu()?;\n# self.second.forward(&x)\n# }\n# }\nfn main() -> Result<()> { // Use Device::new_cuda(0)?; to use the GPU. // Use Device::Cpu; to use the CPU. let device = Device::cuda_if_available(0)?; // Creating a dummy model let weight = Tensor::randn(0f32, 1.0, (784, 100), &device)?; let bias = Tensor::randn(0f32, 1.0, (100, ), &device)?; let first = Linear{weight, bias}; let weight = Tensor::randn(0f32, 1.0, (100, 10), &device)?; let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?; let second = Linear{weight, bias}; let model = Model { first, second }; let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?; // Inference on the model let digit = model.forward(&dummy_image)?; println!(\"Digit {digit:?} digit\"); Ok(())\n} Now it works, it is a great way to create your own layers. But most of the classical layers are already implemented in candle-nn .","breadcrumbs":"Hello World - MNIST » Using a Linear layer.","id":"4","title":"Using a Linear layer."},"5":{"body":"For instance Linear is already there. This Linear is coded with PyTorch layout in mind, to reuse better existing models out there, so it uses the transpose of the weights and not the weights directly. So instead we can simplify our example: cargo add --git https://github.com/huggingface/candle.git candle-nn And rewrite our examples using it # extern crate candle_core;\n# extern crate candle_nn;\nuse candle_core::{Device, Result, Tensor};\nuse candle_nn::{Linear, Module}; struct Model { first: Linear, second: Linear,\n} impl Model { fn forward(&self, image: &Tensor) -> Result { let x = self.first.forward(image)?; let x = x.relu()?; self.second.forward(&x) }\n} fn main() -> Result<()> { // Use Device::new_cuda(0)?; to use the GPU. let device = Device::Cpu; // This has changed (784, 100) -> (100, 784) ! let weight = Tensor::randn(0f32, 1.0, (100, 784), &device)?; let bias = Tensor::randn(0f32, 1.0, (100, ), &device)?; let first = Linear::new(weight, Some(bias)); let weight = Tensor::randn(0f32, 1.0, (10, 100), &device)?; let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?; let second = Linear::new(weight, Some(bias)); let model = Model { first, second }; let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?; let digit = model.forward(&dummy_image)?; println!(\"Digit {digit:?} digit\"); Ok(())\n} Feel free to modify this example to use Conv2d to create a classical convnet instead. Now that we have the running dummy code we can get to more advanced topics: For PyTorch users Running existing models Training models","breadcrumbs":"Hello World - MNIST » Using candle_nn.","id":"5","title":"Using candle_nn."},"6":{"body":"Cheatsheet: Using PyTorch Using Candle Creation torch.Tensor([[1, 2], [3, 4]]) Tensor::new(&[[1f32, 2.], [3., 4.]], &Device::Cpu)? Creation torch.zeros((2, 2)) Tensor::zeros((2, 2), DType::F32, &Device::Cpu)? Indexing tensor[:, :4] tensor.i((.., ..4))? Operations tensor.view((2, 2)) tensor.reshape((2, 2))? Operations a.matmul(b) a.matmul(&b)? Arithmetic a + b &a + &b Device tensor.to(device=\"cuda\") tensor.to_device(&Device::new_cuda(0)?)? Dtype tensor.to(dtype=torch.float16) tensor.to_dtype(&DType::F16)? Saving torch.save({\"A\": A}, \"model.bin\") candle::safetensors::save(&HashMap::from([(\"A\", A)]), \"model.safetensors\")? Loading weights = torch.load(\"model.bin\") candle::safetensors::load(\"model.safetensors\", &device)","breadcrumbs":"PyTorch cheatsheet » Pytorch cheatsheet","id":"6","title":"Pytorch cheatsheet"},"7":{"body":"In order to run an existing model, you will need to download and use existing weights. Most models are already available on https://huggingface.co/ in safetensors format. Let's get started by running an old model : bert-base-uncased.","breadcrumbs":"Running a model » Running a model","id":"7","title":"Running a model"},"8":{"body":"Install the hf-hub crate: cargo add hf-hub Then let's start by downloading the model file . # extern crate candle_core;\n# extern crate hf_hub;\nuse hf_hub::api::sync::Api;\nuse candle_core::Device; let api = Api::new().unwrap();\nlet repo = api.model(\"bert-base-uncased\".to_string()); let weights = repo.get(\"model.safetensors\").unwrap(); let weights = candle_core::safetensors::load(weights, &Device::Cpu); We now have access to all the tensors within the file. You can check all the names of the tensors here","breadcrumbs":"Running a model » Using the hub » Using the hub","id":"8","title":"Using the hub"},"9":{"body":"hf-hub comes with an async API. cargo add hf-hub --features tokio # This is tested directly in examples crate because it needs external dependencies unfortunately:\n# See [this](https://github.com/rust-lang/mdBook/issues/706)\nuse candle::Device;\nuse hf_hub::api::tokio::Api; let api = Api::new().unwrap();\nlet repo = api.model(\"bert-base-uncased\".to_string()); let weights_filename = repo.get(\"model.safetensors\").await.unwrap(); let weights = candle::safetensors::load(weights_filename, &Device::Cpu).unwrap();","breadcrumbs":"Running a model » Using the hub » Using async","id":"9","title":"Using 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Model training. Embed user-defined ops/kernels, such as flash-attention v2 . Backends. Optimized CPU backend with optional MKL support for x86 and Accelerate for macs. CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL. WASM support, run your models in a browser. Included models. Language Models. LLaMA v1, v2, and v3 with variants such as SOLAR-10.7B. Falcon. StarCoder, StarCoder2. Phi 1, 1.5, 2, and 3. Mamba, Minimal Mamba Gemma 2b and 7b. Mistral 7b v0.1. Mixtral 8x7b v0.1. StableLM-3B-4E1T, StableLM-2-1.6B, Stable-Code-3B. Replit-code-v1.5-3B. Bert. Yi-6B and Yi-34B. Qwen1.5, Qwen1.5 MoE. RWKV v5 and v6. Quantized LLMs. Llama 7b, 13b, 70b, as well as the chat and code variants. Mistral 7b, and 7b instruct. Mixtral 8x7b. Zephyr 7b a and b (Mistral-7b based). OpenChat 3.5 (Mistral-7b based). Text to text. T5 and its variants: FlanT5, UL2, MADLAD400 (translation), CoEdit (Grammar correction). Marian MT (Machine Translation). Text to image. Stable Diffusion v1.5, v2.1, XL v1.0. Wurstchen v2. Image to text. BLIP. TrOCR. Audio. Whisper, multi-lingual speech-to-text. EnCodec, audio compression model. MetaVoice-1B, text-to-speech model. Computer Vision Models. DINOv2, ConvMixer, EfficientNet, ResNet, ViT, VGG, RepVGG, ConvNeXT, ConvNeXTv2, MobileOne, EfficientVit (MSRA), MobileNetv4, Hiera. yolo-v3, yolo-v8. Segment-Anything Model (SAM). SegFormer. File formats: load models from safetensors, npz, ggml, or PyTorch files. Serverless (on CPU), small and fast deployments. Quantization support using the llama.cpp quantized types. This book will introduce step by step how to use candle.","breadcrumbs":"Introduction » Features","id":"1","title":"Features"},"10":{"body":"Now that we have our weights, we can use them in our bert architecture: # extern crate candle_core;\n# extern crate candle_nn;\n# extern crate hf_hub;\n# use hf_hub::api::sync::Api;\n# # let api = Api::new().unwrap();\n# let repo = api.model(\"bert-base-uncased\".to_string());\n# # let weights = repo.get(\"model.safetensors\").unwrap();\nuse candle_core::{Device, Tensor, DType};\nuse candle_nn::{Linear, Module}; let weights = candle_core::safetensors::load(weights, &Device::Cpu).unwrap(); let weight = weights.get(\"bert.encoder.layer.0.attention.self.query.weight\").unwrap();\nlet bias = weights.get(\"bert.encoder.layer.0.attention.self.query.bias\").unwrap(); let linear = Linear::new(weight.clone(), Some(bias.clone())); let input_ids = Tensor::zeros((3, 768), DType::F32, &Device::Cpu).unwrap();\nlet output = linear.forward(&input_ids).unwrap(); For a full reference, you can check out the full bert example.","breadcrumbs":"Running a model » Using the hub » Using in a real model.","id":"10","title":"Using in a real model."},"11":{"body":"For more efficient loading, instead of reading the file, you could use memmap2 Note : Be careful about memory mapping it seems to cause issues on Windows, WSL and will definitely be slower on network mounted disk, because it will issue more read calls. use candle::Device;\nuse hf_hub::api::sync::Api;\nuse memmap2::Mmap;\nuse std::fs; let api = Api::new().unwrap();\nlet repo = api.model(\"bert-base-uncased\".to_string());\nlet weights_filename = repo.get(\"model.safetensors\").unwrap(); let file = fs::File::open(weights_filename).unwrap();\nlet mmap = unsafe { Mmap::map(&file).unwrap() };\nlet weights = candle::safetensors::load_buffer(&mmap[..], &Device::Cpu).unwrap(); Note : This operation is unsafe . See the safety notice . In practice model files should never be modified, and the mmaps should be mostly READONLY anyway, so the caveat most likely does not apply, but always keep it in mind.","breadcrumbs":"Running a model » Using the hub » Memory mapping","id":"11","title":"Memory mapping"},"12":{"body":"When using multiple GPUs to use in Tensor Parallel in order to get good latency, you can load only the part of the Tensor you need. For that you need to use safetensors directly. cargo add safetensors use candle::{DType, Device, Tensor};\nuse hf_hub::api::sync::Api;\nuse memmap2::Mmap;\nuse safetensors::slice::IndexOp;\nuse safetensors::SafeTensors;\nuse std::fs; let api = Api::new().unwrap();\nlet repo = api.model(\"bert-base-uncased\".to_string());\nlet weights_filename = repo.get(\"model.safetensors\").unwrap(); let file = fs::File::open(weights_filename).unwrap();\nlet mmap = unsafe { Mmap::map(&file).unwrap() }; // Use safetensors directly\nlet tensors = SafeTensors::deserialize(&mmap[..]).unwrap();\nlet view = tensors .tensor(\"bert.encoder.layer.0.attention.self.query.weight\") .unwrap(); // We're going to load shard with rank 1, within a world_size of 4\n// We're going to split along dimension 0 doing VIEW[start..stop, :]\nlet rank = 1;\nlet world_size = 4;\nlet dim = 0;\nlet dtype = view.dtype();\nlet mut tp_shape = view.shape().to_vec();\nlet size = tp_shape[0]; if size % world_size != 0 { panic!(\"The dimension is not divisible by `world_size`\");\n}\nlet block_size = size / world_size;\nlet start = rank * block_size;\nlet stop = (rank + 1) * block_size; // Everything is expressed in tensor dimension\n// bytes offsets is handled automatically for safetensors. let iterator = view.slice(start..stop).unwrap(); tp_shape[dim] = block_size; // Convert safetensors Dtype to candle DType\nlet dtype: DType = dtype.try_into().unwrap(); // TODO: Implement from_buffer_iterator so we can skip the extra CPU alloc.\nlet raw: Vec = iterator.into_iter().flatten().cloned().collect();\nlet tp_tensor = Tensor::from_raw_buffer(&raw, dtype, &tp_shape, &Device::Cpu).unwrap();","breadcrumbs":"Running a model » Using the hub » Tensor Parallel Sharding","id":"12","title":"Tensor Parallel Sharding"},"13":{"body":"You might have seen in the code base a lot of .unwrap() or ?. If you're unfamiliar with Rust check out the Rust book for more information. What's important to know though, is that if you want to know where a particular operation failed You can simply use RUST_BACKTRACE=1 to get the location of where the model actually failed. Let's see on failing code: let x = Tensor::zeros((1, 784), DType::F32, &device)?;\nlet y = Tensor::zeros((1, 784), DType::F32, &device)?;\nlet z = x.matmul(&y)?; Will print at runtime: Error: ShapeMismatchBinaryOp { lhs: [1, 784], rhs: [1, 784], op: \"matmul\" } After adding RUST_BACKTRACE=1: Error: WithBacktrace { inner: ShapeMismatchBinaryOp { lhs: [1, 784], rhs: [1, 784], op: \"matmul\" }, backtrace: Backtrace [{ fn: \"candle::error::Error::bt\", file: \"/home/nicolas/.cargo/git/checkouts/candle-5bb8ef7e0626d693/f291065/candle-core/src/error.rs\", line: 200 }, { fn: \"candle::tensor::Tensor::matmul\", file: \"/home/nicolas/.cargo/git/checkouts/candle-5bb8ef7e0626d693/f291065/candle-core/src/tensor.rs\", line: 816 }, { fn: \"myapp::main\", file: \"./src/main.rs\", line: 29 }, { fn: \"core::ops::function::FnOnce::call_once\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/core/src/ops/function.rs\", line: 250 }, { fn: \"std::sys_common::backtrace::__rust_begin_short_backtrace\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/sys_common/backtrace.rs\", line: 135 }, { fn: \"std::rt::lang_start::{{closure}}\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs\", line: 166 }, { fn: \"core::ops::function::impls:: for &F>::call_once\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/core/src/ops/function.rs\", line: 284 }, { fn: \"std::panicking::try::do_call\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs\", line: 500 }, { fn: \"std::panicking::try\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs\", line: 464 }, { fn: \"std::panic::catch_unwind\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panic.rs\", line: 142 }, { fn: \"std::rt::lang_start_internal::{{closure}}\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs\", line: 148 }, { fn: \"std::panicking::try::do_call\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs\", line: 500 }, { fn: \"std::panicking::try\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs\", line: 464 }, { fn: \"std::panic::catch_unwind\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panic.rs\", line: 142 }, { fn: \"std::rt::lang_start_internal\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs\", line: 148 }, { fn: \"std::rt::lang_start\", file: \"/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs\", line: 165 }, { fn: \"main\" }, { fn: \"__libc_start_main\" }, { fn: \"_start\" }] } Not super pretty at the moment, but we can see error occurred on { fn: \"myapp::main\", file: \"./src/main.rs\", line: 29 } Another thing to note, is that since Rust is compiled it is not necessarily as easy to recover proper stacktraces especially in release builds. We're using anyhow for that. The library is still young, please report any issues detecting where an error is coming from.","breadcrumbs":"Error management » Error management","id":"13","title":"Error management"},"14":{"body":"When running a model on Cuda, you might get a stacktrace not really representing the error. The reason is that CUDA is async by nature, and therefore the error might be caught while you were sending totally different kernels. One way to avoid this is to use CUDA_LAUNCH_BLOCKING=1 as an environment variable. This will force every kernel to be launched sequentially. You might still however see the error happening on other kernels as the faulty kernel might exit without an error but spoiling some pointer for which the error will happen when dropping the CudaSlice only. If this occurs, you can use compute-sanitizer This tool is like valgrind but for cuda. It will help locate the errors in the kernels.","breadcrumbs":"Error management » Cuda error management","id":"14","title":"Cuda error management"},"15":{"body":"Training starts with data. We're going to use the huggingface hub and start with the Hello world dataset of machine learning, MNIST. Let's start with downloading MNIST from huggingface . This requires hf-hub . cargo add hf-hub This is going to be very hands-on for now. This uses the standardized parquet files from the refs/convert/parquet branch on every dataset. Our handles are now [parquet::file::serialized_reader::SerializedFileReader]. We can inspect the content of the files with: You should see something like: Column id 1, name label, value 6\nColumn id 0, name image, value {bytes: [137, ....]\nColumn id 1, name label, value 8\nColumn id 0, name image, value {bytes: [137, ....] So each row contains 2 columns (image, label) with image being saved as bytes. Let's put them into a useful struct.","breadcrumbs":"Training » Training","id":"15","title":"Training"},"16":{"body":"","breadcrumbs":"Training » Simplified » Simplified","id":"16","title":"Simplified"},"17":{"body":"This program implements a neural network to predict the winner of the second round of elections based on the results of the first round. Basic moments: A multilayer perceptron with two hidden layers is used. The first hidden layer has 4 neurons, the second has 2 neurons. The input is a vector of 2 numbers - the percentage of votes for the first and second candidates in the first stage. The output is the number 0 or 1, where 1 means that the first candidate will win in the second stage, 0 means that he will lose. For training, samples with real data on the results of the first and second stages of different elections are used. The model is trained by backpropagation using gradient descent and the cross-entropy loss function. Model parameters (weights of neurons) are initialized randomly, then optimized during training. After training, the model is tested on a deferred sample to evaluate the accuracy. If the accuracy on the test set is below 100%, the model is considered underfit and the learning process is repeated. Thus, this neural network learns to find hidden relationships between the results of the first and second rounds of voting in order to make predictions for new data. const VOTE_DIM: usize = 2;\nconst RESULTS: usize = 1;\nconst EPOCHS: usize = 10;\nconst LAYER1_OUT_SIZE: usize = 4;\nconst LAYER2_OUT_SIZE: usize = 2;\nconst LEARNING_RATE: f64 = 0.05; #[derive(Clone)]\npub struct Dataset { pub train_votes: Tensor, pub train_results: Tensor, pub test_votes: Tensor, pub test_results: Tensor,\n} struct MultiLevelPerceptron { ln1: Linear, ln2: Linear, ln3: Linear,\n} impl MultiLevelPerceptron { fn new(vs: VarBuilder) -> Result { let ln1 = candle_nn::linear(VOTE_DIM, LAYER1_OUT_SIZE, vs.pp(\"ln1\"))?; let ln2 = candle_nn::linear(LAYER1_OUT_SIZE, LAYER2_OUT_SIZE, vs.pp(\"ln2\"))?; let ln3 = candle_nn::linear(LAYER2_OUT_SIZE, RESULTS + 1, vs.pp(\"ln3\"))?; Ok(Self { ln1, ln2, ln3 }) } fn forward(&self, xs: &Tensor) -> Result { let xs = self.ln1.forward(xs)?; let xs = xs.relu()?; let xs = self.ln2.forward(&xs)?; let xs = xs.relu()?; self.ln3.forward(&xs) }\n} fn train(m: Dataset, dev: &Device) -> anyhow::Result { let train_results = m.train_results.to_device(dev)?; let train_votes = m.train_votes.to_device(dev)?; let varmap = VarMap::new(); let vs = VarBuilder::from_varmap(&varmap, DType::F32, dev); let model = MultiLevelPerceptron::new(vs.clone())?; let mut sgd = candle_nn::SGD::new(varmap.all_vars(), LEARNING_RATE)?; let test_votes = m.test_votes.to_device(dev)?; let test_results = m.test_results.to_device(dev)?; let mut final_accuracy: f32 = 0.0; for epoch in 1..EPOCHS + 1 { let logits = model.forward(&train_votes)?; let log_sm = ops::log_softmax(&logits, D::Minus1)?; let loss = loss::nll(&log_sm, &train_results)?; sgd.backward_step(&loss)?; let test_logits = model.forward(&test_votes)?; let sum_ok = test_logits .argmax(D::Minus1)? .eq(&test_results)? .to_dtype(DType::F32)? .sum_all()? .to_scalar::()?; let test_accuracy = sum_ok / test_results.dims1()? as f32; final_accuracy = 100. * test_accuracy; println!(\"Epoch: {epoch:3} Train loss: {:8.5} Test accuracy: {:5.2}%\", loss.to_scalar::()?, final_accuracy ); if final_accuracy == 100.0 { break; } } if final_accuracy < 100.0 { Err(anyhow::Error::msg(\"The model is not trained well enough.\")) } else { Ok(model) }\n} #[tokio::test]\nasync fn simplified() -> anyhow::Result<()> { let dev = Device::cuda_if_available(0)?; let train_votes_vec: Vec = vec![ 15, 10, 10, 15, 5, 12, 30, 20, 16, 12, 13, 25, 6, 14, 31, 21, ]; let train_votes_tensor = Tensor::from_vec(train_votes_vec.clone(), (train_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?; let train_results_vec: Vec = vec![ 1, 0, 0, 1, 1, 0, 0, 1, ]; let train_results_tensor = Tensor::from_vec(train_results_vec, train_votes_vec.len() / VOTE_DIM, &dev)?; let test_votes_vec: Vec = vec![ 13, 9, 8, 14, 3, 10, ]; let test_votes_tensor = Tensor::from_vec(test_votes_vec.clone(), (test_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?; let test_results_vec: Vec = vec![ 1, 0, 0, ]; let test_results_tensor = Tensor::from_vec(test_results_vec.clone(), test_results_vec.len(), &dev)?; let m = Dataset { train_votes: train_votes_tensor, train_results: train_results_tensor, test_votes: test_votes_tensor, test_results: test_results_tensor, }; let trained_model: MultiLevelPerceptron; loop { println!(\"Trying to train neural network.\"); match train(m.clone(), &dev) { Ok(model) => { trained_model = model; break; }, Err(e) => { println!(\"Error: {}\", e); continue; } } } let real_world_votes: Vec = vec![ 13, 22, ]; let tensor_test_votes = Tensor::from_vec(real_world_votes.clone(), (1, VOTE_DIM), &dev)?.to_dtype(DType::F32)?; let final_result = trained_model.forward(&tensor_test_votes)?; let result = final_result .argmax(D::Minus1)? .to_dtype(DType::F32)? .get(0).map(|x| x.to_scalar::())??; println!(\"real_life_votes: {:?}\", real_world_votes); println!(\"neural_network_prediction_result: {:?}\", result); Ok(()) }","breadcrumbs":"Training » Simplified » How its works","id":"17","title":"How its works"},"18":{"body":"Trying to train neural network.\nEpoch: 1 Train loss: 4.42555 Test accuracy: 0.00%\nEpoch: 2 Train loss: 0.84677 Test accuracy: 33.33%\nEpoch: 3 Train loss: 2.54335 Test accuracy: 33.33%\nEpoch: 4 Train loss: 0.37806 Test accuracy: 33.33%\nEpoch: 5 Train loss: 0.36647 Test accuracy: 100.00%\nreal_life_votes: [13, 22]\nneural_network_prediction_result: 0.0","breadcrumbs":"Training » Simplified » Example output","id":"18","title":"Example output"},"19":{"body":"So we now have downloaded the MNIST parquet files, let's put them in a simple struct. let test_samples = 10_000;\nlet mut test_buffer_images: Vec = Vec::with_capacity(test_samples * 784);\nlet mut test_buffer_labels: Vec = Vec::with_capacity(test_samples);\nfor row in test_parquet{ for (_name, field) in row?.get_column_iter() { if let parquet::record::Field::Group(subrow) = field { for (_name, field) in subrow.get_column_iter() { if let parquet::record::Field::Bytes(value) = field { let image = image::load_from_memory(value.data()).unwrap(); test_buffer_images.extend(image.to_luma8().as_raw()); } } }else if let parquet::record::Field::Long(label) = field { test_buffer_labels.push(*label as u8); } }\n}\nlet test_images = (Tensor::from_vec(test_buffer_images, (test_samples, 784), &Device::Cpu)?.to_dtype(DType::F32)? / 255.)?;\nlet test_labels = Tensor::from_vec(test_buffer_labels, (test_samples, ), &Device::Cpu)?; let train_samples = 60_000;\nlet mut train_buffer_images: Vec = Vec::with_capacity(train_samples * 784);\nlet mut train_buffer_labels: Vec = Vec::with_capacity(train_samples);\nfor row in train_parquet{ for (_name, field) in row?.get_column_iter() { if let parquet::record::Field::Group(subrow) = field { for (_name, field) in subrow.get_column_iter() { if let parquet::record::Field::Bytes(value) = field { let image = image::load_from_memory(value.data()).unwrap(); train_buffer_images.extend(image.to_luma8().as_raw()); } } }else if let parquet::record::Field::Long(label) = field { train_buffer_labels.push(*label as u8); } }\n}\nlet train_images = (Tensor::from_vec(train_buffer_images, (train_samples, 784), &Device::Cpu)?.to_dtype(DType::F32)? / 255.)?;\nlet train_labels = Tensor::from_vec(train_buffer_labels, (train_samples, ), &Device::Cpu)?; let mnist = candle_datasets::vision::Dataset { train_images, train_labels, test_images, test_labels, labels: 10,\n}; The parsing of the file and putting it into single tensors requires the dataset to fit the entire memory. It is quite rudimentary, but simple enough for a small dataset like MNIST.","breadcrumbs":"Training » MNIST » MNIST","id":"19","title":"MNIST"},"2":{"body":"With Cuda support : First, make sure that Cuda is correctly installed. nvcc --version should print information about your Cuda compiler driver. nvidia-smi --query-gpu=compute_cap --format=csv should print your GPUs compute capability, e.g. something like: compute_cap\n8.9 You can also compile the Cuda kernels for a specific compute cap using the CUDA_COMPUTE_CAP= environment variable. If any of the above commands errors out, please make sure to update your Cuda version. Create a new app and add candle-core with Cuda support. Start by creating a new cargo: cargo new myapp\ncd myapp Make sure to add the candle-core crate with the cuda feature: cargo add --git https://github.com/huggingface/candle.git candle-core --features \"cuda\" Run cargo build to make sure everything can be correctly built. cargo build Without Cuda support : Create a new app and add candle-core as follows: cargo new myapp\ncd myapp\ncargo add --git https://github.com/huggingface/candle.git candle-core Finally, run cargo build to make sure everything can be correctly built. cargo build With mkl support You can also see the mkl feature which could be interesting to get faster inference on CPU. Using mkl","breadcrumbs":"Installation » Installation","id":"2","title":"Installation"},"3":{"body":"We will now create the hello world of the ML world, building a model capable of solving MNIST dataset. Open src/main.rs and fill in this content: # extern crate candle_core;\nuse candle_core::{Device, Result, Tensor}; struct Model { first: Tensor, second: Tensor,\n} impl Model { fn forward(&self, image: &Tensor) -> Result { let x = image.matmul(&self.first)?; let x = x.relu()?; x.matmul(&self.second) }\n} fn main() -> Result<()> { // Use Device::new_cuda(0)?; to use the GPU. let device = Device::Cpu; let first = Tensor::randn(0f32, 1.0, (784, 100), &device)?; let second = Tensor::randn(0f32, 1.0, (100, 10), &device)?; let model = Model { first, second }; let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?; let digit = model.forward(&dummy_image)?; println!(\"Digit {digit:?} digit\"); Ok(())\n} Everything should now run with: cargo run --release","breadcrumbs":"Hello World - MNIST » Hello world!","id":"3","title":"Hello world!"},"4":{"body":"Now that we have this, we might want to complexify things a bit, for instance by adding bias and creating the classical Linear layer. 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So instead we can simplify our example: cargo add --git https://github.com/huggingface/candle.git candle-nn And rewrite our examples using it # extern crate candle_core;\n# extern crate candle_nn;\nuse candle_core::{Device, Result, Tensor};\nuse candle_nn::{Linear, Module}; struct Model { first: Linear, second: Linear,\n} impl Model { fn forward(&self, image: &Tensor) -> Result { let x = self.first.forward(image)?; let x = x.relu()?; self.second.forward(&x) }\n} fn main() -> Result<()> { // Use Device::new_cuda(0)?; to use the GPU. let device = Device::Cpu; // This has changed (784, 100) -> (100, 784) ! let weight = Tensor::randn(0f32, 1.0, (100, 784), &device)?; let bias = Tensor::randn(0f32, 1.0, (100, ), &device)?; let first = Linear::new(weight, Some(bias)); let weight = Tensor::randn(0f32, 1.0, (10, 100), &device)?; let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?; let second = Linear::new(weight, Some(bias)); let model = Model { first, second }; let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?; let digit = model.forward(&dummy_image)?; println!(\"Digit {digit:?} digit\"); Ok(())\n} Feel free to modify this example to use Conv2d to create a classical convnet instead. Now that we have the running dummy code we can get to more advanced topics: For PyTorch users Running existing models Training models","breadcrumbs":"Hello World - MNIST » Using candle_nn.","id":"5","title":"Using candle_nn."},"6":{"body":"Cheatsheet: Using PyTorch Using Candle Creation torch.Tensor([[1, 2], [3, 4]]) Tensor::new(&[[1f32, 2.], [3., 4.]], &Device::Cpu)? Creation torch.zeros((2, 2)) Tensor::zeros((2, 2), DType::F32, &Device::Cpu)? Indexing tensor[:, :4] tensor.i((.., ..4))? Operations tensor.view((2, 2)) tensor.reshape((2, 2))? Operations a.matmul(b) a.matmul(&b)? Arithmetic a + b &a + &b Device tensor.to(device=\"cuda\") tensor.to_device(&Device::new_cuda(0)?)? Dtype tensor.to(dtype=torch.float16) tensor.to_dtype(&DType::F16)? Saving torch.save({\"A\": A}, \"model.bin\") candle::safetensors::save(&HashMap::from([(\"A\", A)]), \"model.safetensors\")? 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Let's get started by running an old model : bert-base-uncased.","breadcrumbs":"Running a model » Running a model","id":"7","title":"Running a model"},"8":{"body":"Install the hf-hub crate: cargo add hf-hub Then let's start by downloading the model file . # extern crate candle_core;\n# extern crate hf_hub;\nuse hf_hub::api::sync::Api;\nuse candle_core::Device; let api = Api::new().unwrap();\nlet repo = api.model(\"bert-base-uncased\".to_string()); let weights = repo.get(\"model.safetensors\").unwrap(); let weights = candle_core::safetensors::load(weights, &Device::Cpu); We now have access to all the tensors within the file. You can check all the names of the tensors here","breadcrumbs":"Running a model » Using the hub » Using the hub","id":"8","title":"Using the hub"},"9":{"body":"hf-hub comes with an async API. cargo add hf-hub --features tokio # This is tested directly in examples crate because it needs external dependencies unfortunately:\n# See [this](https://github.com/rust-lang/mdBook/issues/706)\nuse candle::Device;\nuse hf_hub::api::tokio::Api; let api = Api::new().unwrap();\nlet repo = api.model(\"bert-base-uncased\".to_string()); let weights_filename = repo.get(\"model.safetensors\").await.unwrap(); let weights = candle::safetensors::load(weights_filename, &Device::Cpu).unwrap();","breadcrumbs":"Running a model » Using the hub » Using async","id":"9","title":"Using 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Author: Evgeny Igumnov 2023 igumnovnsk@gmail.com +//! This program implements a neural network to predict the winner of the second round of elections based on the results of the first round. +//! +//! ##Basic moments: +//! +//! A multilayer perceptron with two hidden layers is used. The first hidden layer has 4 neurons, the second has 2 neurons. +//! The input is a vector of 2 numbers - the percentage of votes for the first and second candidates in the first stage. +//! The output is the number 0 or 1, where 1 means that the first candidate will win in the second stage, 0 means that he will lose. +//! For training, samples with real data on the results of the first and second stages of different elections are used. +//! The model is trained by backpropagation using gradient descent and the cross-entropy loss function. +//! Model parameters (weights of neurons) are initialized randomly, then optimized during training. +//! After training, the model is tested on a deferred sample to evaluate the accuracy. +//! If the accuracy on the test set is below 100%, the model is considered underfit and the learning process is repeated. +//! Thus, this neural network learns to find hidden relationships between the results of the first and second rounds of voting in order to make predictions for new data. + +#[rustfmt::skip] +mod tests { + +use candle::{DType, Result, Tensor, D, Device}; +use candle_nn::{loss, ops, Linear, Module, VarBuilder, VarMap, Optimizer}; + +// ANCHOR: book_training_simplified1 +const VOTE_DIM: usize = 2; +const RESULTS: usize = 1; +const EPOCHS: usize = 10; +const LAYER1_OUT_SIZE: usize = 4; +const LAYER2_OUT_SIZE: usize = 2; +const LEARNING_RATE: f64 = 0.05; + +#[derive(Clone)] +pub struct Dataset { + pub train_votes: Tensor, + pub train_results: Tensor, + pub test_votes: Tensor, + pub test_results: Tensor, +} + +struct MultiLevelPerceptron { + ln1: Linear, + ln2: Linear, + ln3: Linear, +} + +impl MultiLevelPerceptron { + fn new(vs: VarBuilder) -> Result { + let ln1 = candle_nn::linear(VOTE_DIM, LAYER1_OUT_SIZE, vs.pp("ln1"))?; + let ln2 = candle_nn::linear(LAYER1_OUT_SIZE, LAYER2_OUT_SIZE, vs.pp("ln2"))?; + let ln3 = candle_nn::linear(LAYER2_OUT_SIZE, RESULTS + 1, vs.pp("ln3"))?; + Ok(Self { ln1, ln2, ln3 }) + } + + fn forward(&self, xs: &Tensor) -> Result { + let xs = self.ln1.forward(xs)?; + let xs = xs.relu()?; + let xs = self.ln2.forward(&xs)?; + let xs = xs.relu()?; + self.ln3.forward(&xs) + } +} + +// ANCHOR_END: book_training_simplified1 + + + +// ANCHOR: book_training_simplified3 +#[tokio::test] +async fn simplified() -> anyhow::Result<()> { + + let dev = Device::cuda_if_available(0)?; + + let train_votes_vec: Vec = vec![ + 15, 10, + 10, 15, + 5, 12, + 30, 20, + 16, 12, + 13, 25, + 6, 14, + 31, 21, + ]; + let train_votes_tensor = Tensor::from_vec(train_votes_vec.clone(), (train_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?; + + let train_results_vec: Vec = vec![ + 1, + 0, + 0, + 1, + 1, + 0, + 0, + 1, + ]; + let train_results_tensor = Tensor::from_vec(train_results_vec, train_votes_vec.len() / VOTE_DIM, &dev)?; + + let test_votes_vec: Vec = vec![ + 13, 9, + 8, 14, + 3, 10, + ]; + let test_votes_tensor = Tensor::from_vec(test_votes_vec.clone(), (test_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?; + + let test_results_vec: Vec = vec![ + 1, + 0, + 0, + ]; + let test_results_tensor = Tensor::from_vec(test_results_vec.clone(), test_results_vec.len(), &dev)?; + + let m = Dataset { + train_votes: train_votes_tensor, + train_results: train_results_tensor, + test_votes: test_votes_tensor, + test_results: test_results_tensor, + }; + + let trained_model: MultiLevelPerceptron; + loop { + println!("Trying to train neural network."); + match train(m.clone(), &dev) { + Ok(model) => { + trained_model = model; + break; + }, + Err(e) => { + println!("Error: {}", e); + continue; + } + } + + } + + let real_world_votes: Vec = vec![ + 13, 22, + ]; + + let tensor_test_votes = Tensor::from_vec(real_world_votes.clone(), (1, VOTE_DIM), &dev)?.to_dtype(DType::F32)?; + + let final_result = trained_model.forward(&tensor_test_votes)?; + + let result = final_result + .argmax(D::Minus1)? + .to_dtype(DType::F32)? + .get(0).map(|x| x.to_scalar::())??; + println!("real_life_votes: {:?}", real_world_votes); + println!("neural_network_prediction_result: {:?}", result); + + Ok(()) + +} +// ANCHOR_END: book_training_simplified3 + +// ANCHOR: book_training_simplified2 +fn train(m: Dataset, dev: &Device) -> anyhow::Result { + let train_results = m.train_results.to_device(dev)?; + let train_votes = m.train_votes.to_device(dev)?; + let varmap = VarMap::new(); + let vs = VarBuilder::from_varmap(&varmap, DType::F32, dev); + let model = MultiLevelPerceptron::new(vs.clone())?; + let mut sgd = candle_nn::SGD::new(varmap.all_vars(), LEARNING_RATE)?; + let test_votes = m.test_votes.to_device(dev)?; + let test_results = m.test_results.to_device(dev)?; + let mut final_accuracy: f32 = 0.0; + for epoch in 1..EPOCHS + 1 { + let logits = model.forward(&train_votes)?; + let log_sm = ops::log_softmax(&logits, D::Minus1)?; + let loss = loss::nll(&log_sm, &train_results)?; + sgd.backward_step(&loss)?; + + let test_logits = model.forward(&test_votes)?; + let sum_ok = test_logits + .argmax(D::Minus1)? + .eq(&test_results)? + .to_dtype(DType::F32)? + .sum_all()? + .to_scalar::()?; + let test_accuracy = sum_ok / test_results.dims1()? as f32; + final_accuracy = 100. * test_accuracy; + println!("Epoch: {epoch:3} Train loss: {:8.5} Test accuracy: {:5.2}%", + loss.to_scalar::()?, + final_accuracy + ); + if final_accuracy == 100.0 { + break; + } + } + if final_accuracy < 100.0 { + Err(anyhow::Error::msg("The model is not trained well enough.")) + } else { + Ok(model) + } +} +// ANCHOR_END: book_training_simplified2 + + +} diff --git a/tomorrow-night.css b/tomorrow-night.css new file mode 100644 index 0000000000..81fe276e7f --- /dev/null +++ b/tomorrow-night.css @@ -0,0 +1,102 @@ +/* Tomorrow Night Theme */ +/* https://github.com/jmblog/color-themes-for-highlightjs */ +/* Original theme - https://github.com/chriskempson/tomorrow-theme */ +/* https://github.com/jmblog/color-themes-for-highlightjs */ + +/* Tomorrow Comment */ +.hljs-comment { + color: #969896; +} + +/* Tomorrow Red */ +.hljs-variable, +.hljs-attribute, +.hljs-tag, +.hljs-regexp, +.ruby .hljs-constant, +.xml .hljs-tag .hljs-title, +.xml .hljs-pi, +.xml .hljs-doctype, +.html .hljs-doctype, +.css .hljs-id, +.css .hljs-class, +.css .hljs-pseudo { + color: #cc6666; +} + +/* Tomorrow Orange */ +.hljs-number, +.hljs-preprocessor, +.hljs-pragma, +.hljs-built_in, +.hljs-literal, +.hljs-params, +.hljs-constant { + color: #de935f; +} + +/* Tomorrow Yellow */ +.ruby .hljs-class .hljs-title, +.css .hljs-rule .hljs-attribute { + color: #f0c674; +} + +/* Tomorrow Green */ +.hljs-string, +.hljs-value, +.hljs-inheritance, +.hljs-header, +.hljs-name, +.ruby .hljs-symbol, +.xml .hljs-cdata { + color: #b5bd68; +} + +/* Tomorrow Aqua */ +.hljs-title, +.css .hljs-hexcolor { + color: #8abeb7; +} + +/* Tomorrow Blue */ +.hljs-function, +.python .hljs-decorator, +.python .hljs-title, +.ruby .hljs-function .hljs-title, +.ruby .hljs-title .hljs-keyword, +.perl .hljs-sub, +.javascript .hljs-title, +.coffeescript .hljs-title { + color: #81a2be; +} + +/* Tomorrow Purple */ +.hljs-keyword, +.javascript .hljs-function { + color: #b294bb; +} + +.hljs { + display: block; + overflow-x: auto; + background: #1d1f21; + color: #c5c8c6; +} + +.coffeescript .javascript, +.javascript .xml, +.tex .hljs-formula, +.xml .javascript, +.xml .vbscript, +.xml .css, +.xml .hljs-cdata { + opacity: 0.5; +} + +.hljs-addition { + color: #718c00; +} + +.hljs-deletion { + color: #c82829; +} diff --git a/training/mnist.html b/training/mnist.html new file mode 100644 index 0000000000..fd6f764283 --- /dev/null +++ b/training/mnist.html @@ -0,0 +1,274 @@ + + + + + + MNIST - Candle Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + + + + + +
    +
    +

    MNIST

    +

    So we now have downloaded the MNIST parquet files, let's put them in a simple struct.

    +
    
+let test_samples = 10_000;
+let mut test_buffer_images: Vec<u8> = Vec::with_capacity(test_samples * 784);
+let mut test_buffer_labels: Vec<u8> = Vec::with_capacity(test_samples);
+for row in test_parquet{
+    for (_name, field) in row?.get_column_iter() {
+        if let parquet::record::Field::Group(subrow) = field {
+            for (_name, field) in subrow.get_column_iter() {
+                if let parquet::record::Field::Bytes(value) = field {
+                    let image = image::load_from_memory(value.data()).unwrap();
+                    test_buffer_images.extend(image.to_luma8().as_raw());
+                }
+            }
+        }else if let parquet::record::Field::Long(label) = field {
+            test_buffer_labels.push(*label as u8);
+        }
+    }
+}
+let test_images = (Tensor::from_vec(test_buffer_images, (test_samples, 784), &Device::Cpu)?.to_dtype(DType::F32)? / 255.)?;
+let test_labels = Tensor::from_vec(test_buffer_labels, (test_samples, ), &Device::Cpu)?;
+
+let train_samples = 60_000;
+let mut train_buffer_images: Vec<u8> = Vec::with_capacity(train_samples * 784);
+let mut train_buffer_labels: Vec<u8> = Vec::with_capacity(train_samples);
+for row in train_parquet{
+    for (_name, field) in row?.get_column_iter() {
+        if let parquet::record::Field::Group(subrow) = field {
+            for (_name, field) in subrow.get_column_iter() {
+                if let parquet::record::Field::Bytes(value) = field {
+                    let image = image::load_from_memory(value.data()).unwrap();
+                    train_buffer_images.extend(image.to_luma8().as_raw());
+                }
+            }
+        }else if let parquet::record::Field::Long(label) = field {
+            train_buffer_labels.push(*label as u8);
+        }
+    }
+}
+let train_images = (Tensor::from_vec(train_buffer_images, (train_samples, 784), &Device::Cpu)?.to_dtype(DType::F32)? / 255.)?;
+let train_labels = Tensor::from_vec(train_buffer_labels, (train_samples, ), &Device::Cpu)?;
+
+let mnist = candle_datasets::vision::Dataset {
+    train_images,
+    train_labels,
+    test_images,
+    test_labels,
+    labels: 10,
+};
+
    +

    The parsing of the file and putting it into single tensors requires the dataset to fit the entire memory. +It is quite rudimentary, but simple enough for a small dataset like MNIST.

    + +
    + + +
    +
    + + + +
    + + + + + + + + + + + + + + + + + + +
    + + diff --git a/training/simplified.html b/training/simplified.html new file mode 100644 index 0000000000..9585aa36c6 --- /dev/null +++ b/training/simplified.html @@ -0,0 +1,413 @@ + + + + + + Simplified - Candle Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + + + + + +
    +
    +

    Simplified

    +

    How its works

    +

    This program implements a neural network to predict the winner of the second round of elections based on the results of the first round.

    +

    Basic moments:

    +
      +
    1. A multilayer perceptron with two hidden layers is used. The first hidden layer has 4 neurons, the second has 2 neurons.
      2. +
    2. The input is a vector of 2 numbers - the percentage of votes for the first and second candidates in the first stage.
      3. +
    3. The output is the number 0 or 1, where 1 means that the first candidate will win in the second stage, 0 means that he will lose.
      4. +
    4. For training, samples with real data on the results of the first and second stages of different elections are used.
      5. +
    5. The model is trained by backpropagation using gradient descent and the cross-entropy loss function.
      6. +
    6. Model parameters (weights of neurons) are initialized randomly, then optimized during training.
      7. +
    7. After training, the model is tested on a deferred sample to evaluate the accuracy.
      8. +
    8. If the accuracy on the test set is below 100%, the model is considered underfit and the learning process is repeated.
      9. +
    +

    Thus, this neural network learns to find hidden relationships between the results of the first and second rounds of voting in order to make predictions for new data.

    +
    const VOTE_DIM: usize = 2;
+const RESULTS: usize = 1;
+const EPOCHS: usize = 10;
+const LAYER1_OUT_SIZE: usize = 4;
+const LAYER2_OUT_SIZE: usize = 2;
+const LEARNING_RATE: f64 = 0.05;
+
+#[derive(Clone)]
+pub struct Dataset {
+    pub train_votes: Tensor,
+    pub train_results: Tensor,
+    pub test_votes: Tensor,
+    pub test_results: Tensor,
+}
+
+struct MultiLevelPerceptron {
+    ln1: Linear,
+    ln2: Linear,
+    ln3: Linear,
+}
+
+impl MultiLevelPerceptron {
+    fn new(vs: VarBuilder) -> Result<Self> {
+        let ln1 = candle_nn::linear(VOTE_DIM, LAYER1_OUT_SIZE, vs.pp("ln1"))?;
+        let ln2 = candle_nn::linear(LAYER1_OUT_SIZE, LAYER2_OUT_SIZE, vs.pp("ln2"))?;
+        let ln3 = candle_nn::linear(LAYER2_OUT_SIZE, RESULTS + 1, vs.pp("ln3"))?;
+        Ok(Self { ln1, ln2, ln3 })
+    }
+
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let xs = self.ln1.forward(xs)?;
+        let xs = xs.relu()?;
+        let xs = self.ln2.forward(&xs)?;
+        let xs = xs.relu()?;
+        self.ln3.forward(&xs)
+    }
+}
+
    +
    fn train(m: Dataset, dev: &Device) -> anyhow::Result<MultiLevelPerceptron> {
+    let train_results = m.train_results.to_device(dev)?;
+    let train_votes = m.train_votes.to_device(dev)?;
+    let varmap = VarMap::new();
+    let vs = VarBuilder::from_varmap(&varmap, DType::F32, dev);
+    let model = MultiLevelPerceptron::new(vs.clone())?;
+    let mut sgd = candle_nn::SGD::new(varmap.all_vars(), LEARNING_RATE)?;
+    let test_votes = m.test_votes.to_device(dev)?;
+    let test_results = m.test_results.to_device(dev)?;
+    let mut final_accuracy: f32 = 0.0;
+    for epoch in 1..EPOCHS + 1 {
+        let logits = model.forward(&train_votes)?;
+        let log_sm = ops::log_softmax(&logits, D::Minus1)?;
+        let loss = loss::nll(&log_sm, &train_results)?;
+        sgd.backward_step(&loss)?;
+
+        let test_logits = model.forward(&test_votes)?;
+        let sum_ok = test_logits
+            .argmax(D::Minus1)?
+            .eq(&test_results)?
+            .to_dtype(DType::F32)?
+            .sum_all()?
+            .to_scalar::<f32>()?;
+        let test_accuracy = sum_ok / test_results.dims1()? as f32;
+        final_accuracy = 100. * test_accuracy;
+        println!("Epoch: {epoch:3} Train loss: {:8.5} Test accuracy: {:5.2}%",
+                 loss.to_scalar::<f32>()?,
+                 final_accuracy
+        );
+        if final_accuracy == 100.0 {
+            break;
+        }
+    }
+    if final_accuracy < 100.0 {
+        Err(anyhow::Error::msg("The model is not trained well enough."))
+    } else {
+        Ok(model)
+    }
+}
    +
    #[tokio::test]
+async fn simplified() -> anyhow::Result<()> {
+
+    let dev = Device::cuda_if_available(0)?;
+
+    let train_votes_vec: Vec<u32> = vec![
+        15, 10,
+        10, 15,
+        5, 12,
+        30, 20,
+        16, 12,
+        13, 25,
+        6, 14,
+        31, 21,
+    ];
+    let train_votes_tensor = Tensor::from_vec(train_votes_vec.clone(), (train_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;
+
+    let train_results_vec: Vec<u32> = vec![
+        1,
+        0,
+        0,
+        1,
+        1,
+        0,
+        0,
+        1,
+    ];
+    let train_results_tensor = Tensor::from_vec(train_results_vec, train_votes_vec.len() / VOTE_DIM, &dev)?;
+
+    let test_votes_vec: Vec<u32> = vec![
+        13, 9,
+        8, 14,
+        3, 10,
+    ];
+    let test_votes_tensor = Tensor::from_vec(test_votes_vec.clone(), (test_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;
+
+    let test_results_vec: Vec<u32> = vec![
+        1,
+        0,
+        0,
+    ];
+    let test_results_tensor = Tensor::from_vec(test_results_vec.clone(), test_results_vec.len(), &dev)?;
+
+    let m = Dataset {
+        train_votes: train_votes_tensor,
+        train_results: train_results_tensor,
+        test_votes: test_votes_tensor,
+        test_results: test_results_tensor,
+    };
+
+    let trained_model: MultiLevelPerceptron;
+    loop {
+        println!("Trying to train neural network.");
+        match train(m.clone(), &dev) {
+            Ok(model) => {
+                trained_model = model;
+                break;
+            },
+            Err(e) => {
+                println!("Error: {}", e);
+                continue;
+            }
+        }
+
+    }
+
+    let real_world_votes: Vec<u32> = vec![
+        13, 22,
+    ];
+
+    let tensor_test_votes = Tensor::from_vec(real_world_votes.clone(), (1, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;
+
+    let final_result = trained_model.forward(&tensor_test_votes)?;
+
+    let result = final_result
+        .argmax(D::Minus1)?
+        .to_dtype(DType::F32)?
+        .get(0).map(|x| x.to_scalar::<f32>())??;
+    println!("real_life_votes: {:?}", real_world_votes);
+    println!("neural_network_prediction_result: {:?}", result);
+
+    Ok(())
+
+}
    +

    Example output

    +
    Trying to train neural network.
+Epoch:   1 Train loss:  4.42555 Test accuracy:  0.00%
+Epoch:   2 Train loss:  0.84677 Test accuracy: 33.33%
+Epoch:   3 Train loss:  2.54335 Test accuracy: 33.33%
+Epoch:   4 Train loss:  0.37806 Test accuracy: 33.33%
+Epoch:   5 Train loss:  0.36647 Test accuracy: 100.00%
+real_life_votes: [13, 22]
+neural_network_prediction_result: 0.0
+
    + +
    + + +
    +
    + + + +
    + + + + + + + + + + + + + + + + + + +
    + + diff --git a/training/training.html b/training/training.html new file mode 100644 index 0000000000..0240765c4e --- /dev/null +++ b/training/training.html @@ -0,0 +1,248 @@ + + + + + + Training - Candle Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + + + + + +
    +
    +

    Training

    +

    Training starts with data. We're going to use the huggingface hub and +start with the Hello world dataset of machine learning, MNIST.

    +

    Let's start with downloading MNIST from huggingface.

    +

    This requires hf-hub.

    +
    cargo add hf-hub
+
    +

    This is going to be very hands-on for now.

    +
    +

    This uses the standardized parquet files from the refs/convert/parquet branch on every dataset. +Our handles are now [parquet::file::serialized_reader::SerializedFileReader].

    +

    We can inspect the content of the files with:

    +
    +

    You should see something like:

    +
    Column id 1, name label, value 6
+Column id 0, name image, value {bytes: [137, ....]
+Column id 1, name label, value 8
+Column id 0, name image, value {bytes: [137, ....]
+
    +

    So each row contains 2 columns (image, label) with image being saved as bytes. +Let's put them into a useful struct.

    + +
    + + +
    +
    + + + +
    + + + + + + + + + + + + + + + + + + +
    + +
    + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + + + + + +
    +
    +

    Pytorch cheatsheet

    +

    Cheatsheet:

    +
    + + + + + + + + + + +
    Using PyTorchUsing Candle
    Creationtorch.Tensor([[1, 2], [3, 4]])Tensor::new(&[[1f32, 2.], [3., 4.]], &Device::Cpu)?
    Creationtorch.zeros((2, 2))Tensor::zeros((2, 2), DType::F32, &Device::Cpu)?
    Indexingtensor[:, :4]tensor.i((.., ..4))?
    Operationstensor.view((2, 2))tensor.reshape((2, 2))?
    Operationsa.matmul(b)a.matmul(&b)?
    Arithmetica + b&a + &b
    Devicetensor.to(device="cuda")tensor.to_device(&Device::new_cuda(0)?)?
    Dtypetensor.to(dtype=torch.float16)tensor.to_dtype(&DType::F16)?
    Savingtorch.save({"A": A}, "model.bin")candle::safetensors::save(&HashMap::from([("A", A)]), "model.safetensors")?
    Loadingweights = torch.load("model.bin")candle::safetensors::load("model.safetensors", &device)
    +
    +
    + + +
    +
    + + + +
    + + + + + + + + + + + + + + + + + + +