mirror of
https://github.com/huggingface/candle.git
synced 2025-06-16 02:38:10 +00:00
Deploy f3a73f80d1 to gh-pages
This commit is contained in:
Deploy from CI
8
.cargo/config.toml
Normal file
8
.cargo/config.toml
Normal file
@ -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"]
|
||||
7
.github/dependabot.yml
vendored
Normal file
7
.github/dependabot.yml
vendored
Normal file
@ -0,0 +1,7 @@
|
||||
version: 2
|
||||
updates:
|
||||
- package-ecosystem: "cargo"
|
||||
directory: "/"
|
||||
schedule:
|
||||
interval: "weekly"
|
||||
open-pull-requests-limit: 5
|
||||
40
.github/workflows/book-cd.yml
vendored
Normal file
40
.github/workflows/book-cd.yml
vendored
Normal file
@ -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=$(curl 'https://api.github.com/repos/rust-lang/mdbook/releases/latest' | jq -r '.tag_name')
|
||||
url="https://github.com/rust-lang/mdbook/releases/download/${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 $GITHUB_SHA to gh-pages"
|
||||
git push --force --set-upstream origin gh-pages
|
||||
29
.github/workflows/book.yml
vendored
Normal file
29
.github/workflows/book.yml
vendored
Normal file
@ -0,0 +1,29 @@
|
||||
name: CI
|
||||
on:
|
||||
pull_request:
|
||||
|
||||
jobs:
|
||||
test:
|
||||
name: Test candle-book
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
contents: write # To push a branch
|
||||
pull-requests: write # To create a PR from that branch
|
||||
steps:
|
||||
- uses: actions/checkout@master
|
||||
- name: Install Rust
|
||||
run: |
|
||||
rustup set profile minimal
|
||||
rustup toolchain install stable
|
||||
rustup default stable
|
||||
- name: Install latest mdbook
|
||||
run: |
|
||||
tag=$(curl 'https://api.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/
|
||||
|
||||
|
||||
34
.github/workflows/ci_cuda.yaml
vendored
Normal file
34
.github/workflows/ci_cuda.yaml
vendored
Normal file
@ -0,0 +1,34 @@
|
||||
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:
|
||||
group: aws-g4dn-2xlarge
|
||||
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
|
||||
BIN
.github/workflows/maturin.yml
vendored
Normal file
BIN
.github/workflows/maturin.yml
vendored
Normal file
Binary file not shown.
68
.github/workflows/python.yml
vendored
Normal file
68
.github/workflows/python.yml
vendored
Normal file
@ -0,0 +1,68 @@
|
||||
name: PyO3-CI
|
||||
|
||||
on:
|
||||
workflow_dispatch:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
paths:
|
||||
- candle-pyo3/**
|
||||
pull_request:
|
||||
paths:
|
||||
- candle-pyo3/**
|
||||
|
||||
jobs:
|
||||
build_and_test:
|
||||
name: Check everything builds & tests
|
||||
runs-on: ${{ matrix.os }}
|
||||
strategy:
|
||||
matrix:
|
||||
os: [ubuntu-latest] # For now, only test on Linux
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Install Rust
|
||||
uses: actions-rs/toolchain@v1
|
||||
with:
|
||||
toolchain: stable
|
||||
|
||||
- name: Install Python
|
||||
uses: actions/setup-python@v4
|
||||
with:
|
||||
python-version: 3.11
|
||||
architecture: "x64"
|
||||
|
||||
- name: Cache Cargo Registry
|
||||
uses: actions/cache@v1
|
||||
with:
|
||||
path: ~/.cargo/registry
|
||||
key: ${{ runner.os }}-cargo-registry-${{ hashFiles('**/Cargo.lock') }}
|
||||
|
||||
- name: Install Protoc
|
||||
uses: arduino/setup-protoc@v2
|
||||
with:
|
||||
version: "25.0"
|
||||
repo-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
|
||||
- name: Install
|
||||
working-directory: ./candle-pyo3
|
||||
run: |
|
||||
python -m venv .env
|
||||
source .env/bin/activate
|
||||
pip install -U pip
|
||||
pip install pytest maturin black
|
||||
python -m maturin develop -r --features onnx
|
||||
|
||||
- name: Check style
|
||||
working-directory: ./candle-pyo3
|
||||
run: |
|
||||
source .env/bin/activate
|
||||
python stub.py --check
|
||||
black --check .
|
||||
|
||||
- name: Run tests
|
||||
working-directory: ./candle-pyo3
|
||||
run: |
|
||||
source .env/bin/activate
|
||||
python -m pytest -s -v tests
|
||||
87
.github/workflows/rust-ci.yml
vendored
Normal file
87
.github/workflows/rust-ci.yml
vendored
Normal file
@ -0,0 +1,87 @@
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
pull_request:
|
||||
|
||||
name: Continuous integration
|
||||
|
||||
jobs:
|
||||
check:
|
||||
name: Check
|
||||
runs-on: ${{ matrix.os }}
|
||||
strategy:
|
||||
matrix:
|
||||
os: [ubuntu-latest, windows-latest, macOS-latest]
|
||||
rust: [stable]
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.11"
|
||||
- uses: actions-rs/toolchain@v1
|
||||
with:
|
||||
profile: minimal
|
||||
toolchain: ${{ matrix.rust }}
|
||||
override: true
|
||||
- uses: actions-rs/cargo@v1
|
||||
with:
|
||||
command: check
|
||||
args: --workspace
|
||||
|
||||
test:
|
||||
name: Test Suite
|
||||
runs-on: ${{ matrix.os }}
|
||||
strategy:
|
||||
matrix:
|
||||
os: [ubuntu-latest, windows-latest, macOS-latest]
|
||||
rust: [stable]
|
||||
steps:
|
||||
- name: Delete huge unnecessary tools folder
|
||||
if: runner.os == 'Linux'
|
||||
run: rm -rf /opt/hostedtoolcache
|
||||
- uses: actions/checkout@v4
|
||||
- uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.11"
|
||||
- uses: actions-rs/toolchain@v1
|
||||
with:
|
||||
profile: minimal
|
||||
toolchain: ${{ matrix.rust }}
|
||||
override: true
|
||||
- uses: actions-rs/cargo@v1
|
||||
with:
|
||||
command: test
|
||||
args: --workspace
|
||||
|
||||
fmt:
|
||||
name: Rustfmt
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: actions-rs/toolchain@v1
|
||||
with:
|
||||
profile: minimal
|
||||
toolchain: stable
|
||||
override: true
|
||||
- run: rustup component add rustfmt
|
||||
- uses: actions-rs/cargo@v1
|
||||
with:
|
||||
command: fmt
|
||||
args: --all -- --check
|
||||
|
||||
clippy:
|
||||
name: Clippy
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: actions-rs/toolchain@v1
|
||||
with:
|
||||
profile: minimal
|
||||
toolchain: stable
|
||||
override: true
|
||||
- run: rustup component add clippy
|
||||
- uses: actions-rs/cargo@v1
|
||||
with:
|
||||
command: clippy
|
||||
args: --workspace --tests --examples -- -D warnings
|
||||
15
.github/workflows/trufflehog.yml
vendored
Normal file
15
.github/workflows/trufflehog.yml
vendored
Normal file
@ -0,0 +1,15 @@
|
||||
on:
|
||||
push:
|
||||
|
||||
name: Secret Leaks
|
||||
|
||||
jobs:
|
||||
trufflehog:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
- name: Secret Scanning
|
||||
uses: trufflesecurity/trufflehog@main
|
||||
48
.gitignore
vendored
Normal file
48
.gitignore
vendored
Normal file
@ -0,0 +1,48 @@
|
||||
# Generated by Cargo
|
||||
# will have compiled files and executables
|
||||
debug/
|
||||
data/
|
||||
dist/
|
||||
target/
|
||||
|
||||
# Remove Cargo.lock from gitignore if creating an executable, leave it for libraries
|
||||
# More information here https://doc.rust-lang.org/cargo/guide/cargo-toml-vs-cargo-lock.html
|
||||
Cargo.lock
|
||||
|
||||
# editor config
|
||||
.helix
|
||||
.vscode
|
||||
|
||||
# These are backup files generated by rustfmt
|
||||
**/*.rs.bk
|
||||
|
||||
# MSVC Windows builds of rustc generate these, which store debugging information
|
||||
*.pdb
|
||||
|
||||
*tokenizer*.json
|
||||
*.npz
|
||||
|
||||
perf.data
|
||||
flamegraph.svg
|
||||
*.dylib
|
||||
*.so
|
||||
*.swp
|
||||
*.swo
|
||||
trace-*.json
|
||||
|
||||
candle-wasm-examples/*/build
|
||||
candle-wasm-examples/*/*.bin
|
||||
candle-wasm-examples/*/*.jpeg
|
||||
candle-wasm-examples/*/audios/*.wav
|
||||
candle-wasm-examples/**/*.safetensors
|
||||
candle-wasm-examples/**/*.gguf
|
||||
candle-wasm-examples/*/package-lock.json
|
||||
candle-wasm-examples/**/config*.json
|
||||
.DS_Store
|
||||
.idea/*
|
||||
__pycache__
|
||||
out.safetensors
|
||||
out.wav
|
||||
bria.mp3
|
||||
bria.safetensors
|
||||
bria.wav
|
||||
3
.gitmodules
vendored
Normal file
3
.gitmodules
vendored
Normal file
@ -0,0 +1,3 @@
|
||||
[submodule "candle-examples/examples/flash-attn/cutlass"]
|
||||
path = candle-flash-attn/cutlass
|
||||
url = https://github.com/NVIDIA/cutlass.git
|
||||
15
.pre-commit-config.yaml
Normal file
15
.pre-commit-config.yaml
Normal file
@ -0,0 +1,15 @@
|
||||
repos:
|
||||
- repo: https://github.com/Narsil/pre-commit-rust
|
||||
rev: 2eed6366172ef2a5186e8785ec0e67243d7d73d0
|
||||
hooks:
|
||||
- id: fmt
|
||||
name: "Rust (fmt)"
|
||||
- id: clippy
|
||||
name: "Rust (clippy)"
|
||||
args:
|
||||
[
|
||||
"--tests",
|
||||
"--examples",
|
||||
"--",
|
||||
"-Dwarnings",
|
||||
]
|
||||
11
.vscode/settings.json
vendored
Normal file
11
.vscode/settings.json
vendored
Normal file
@ -0,0 +1,11 @@
|
||||
{
|
||||
"[python]": {
|
||||
"editor.defaultFormatter": "ms-python.black-formatter"
|
||||
},
|
||||
"python.formatting.provider": "none",
|
||||
"python.testing.pytestArgs": [
|
||||
"candle-pyo3"
|
||||
],
|
||||
"python.testing.unittestEnabled": false,
|
||||
"python.testing.pytestEnabled": true
|
||||
}
|
||||
200
404.html
Normal file
200
404.html
Normal file
@ -0,0 +1,200 @@
|
||||
<!DOCTYPE HTML>
|
||||
<html lang="en" class="light sidebar-visible" dir="ltr">
|
||||
<head>
|
||||
<!-- Book generated using mdBook -->
|
||||
<meta charset="UTF-8">
|
||||
<title>Page not found - Candle Documentation</title>
|
||||
<base href="/">
|
||||
|
||||
|
||||
<!-- Custom HTML head -->
|
||||
|
||||
<meta name="description" content="">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1">
|
||||
<meta name="theme-color" content="#ffffff">
|
||||
|
||||
<link rel="icon" href="favicon.svg">
|
||||
<link rel="shortcut icon" href="favicon.png">
|
||||
<link rel="stylesheet" href="css/variables.css">
|
||||
<link rel="stylesheet" href="css/general.css">
|
||||
<link rel="stylesheet" href="css/chrome.css">
|
||||
<link rel="stylesheet" href="css/print.css" media="print">
|
||||
|
||||
<!-- Fonts -->
|
||||
<link rel="stylesheet" href="FontAwesome/css/font-awesome.css">
|
||||
<link rel="stylesheet" href="fonts/fonts.css">
|
||||
|
||||
<!-- Highlight.js Stylesheets -->
|
||||
<link rel="stylesheet" id="highlight-css" href="highlight.css">
|
||||
<link rel="stylesheet" id="tomorrow-night-css" href="tomorrow-night.css">
|
||||
<link rel="stylesheet" id="ayu-highlight-css" href="ayu-highlight.css">
|
||||
|
||||
<!-- Custom theme stylesheets -->
|
||||
|
||||
|
||||
<!-- Provide site root and default themes to javascript -->
|
||||
<script>
|
||||
const path_to_root = "";
|
||||
const default_light_theme = "light";
|
||||
const default_dark_theme = "navy";
|
||||
</script>
|
||||
<!-- Start loading toc.js asap -->
|
||||
<script src="toc.js"></script>
|
||||
</head>
|
||||
<body>
|
||||
<div id="body-container">
|
||||
<!-- Work around some values being stored in localStorage wrapped in quotes -->
|
||||
<script>
|
||||
try {
|
||||
let theme = localStorage.getItem('mdbook-theme');
|
||||
let sidebar = localStorage.getItem('mdbook-sidebar');
|
||||
|
||||
if (theme.startsWith('"') && theme.endsWith('"')) {
|
||||
localStorage.setItem('mdbook-theme', theme.slice(1, theme.length - 1));
|
||||
}
|
||||
|
||||
if (sidebar.startsWith('"') && sidebar.endsWith('"')) {
|
||||
localStorage.setItem('mdbook-sidebar', sidebar.slice(1, sidebar.length - 1));
|
||||
}
|
||||
} catch (e) { }
|
||||
</script>
|
||||
|
||||
<!-- Set the theme before any content is loaded, prevents flash -->
|
||||
<script>
|
||||
const default_theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? default_dark_theme : default_light_theme;
|
||||
let theme;
|
||||
try { theme = localStorage.getItem('mdbook-theme'); } catch(e) { }
|
||||
if (theme === null || theme === undefined) { theme = default_theme; }
|
||||
const html = document.documentElement;
|
||||
html.classList.remove('light')
|
||||
html.classList.add(theme);
|
||||
html.classList.add("js");
|
||||
</script>
|
||||
|
||||
<input type="checkbox" id="sidebar-toggle-anchor" class="hidden">
|
||||
|
||||
<!-- Hide / unhide sidebar before it is displayed -->
|
||||
<script>
|
||||
let sidebar = null;
|
||||
const sidebar_toggle = document.getElementById("sidebar-toggle-anchor");
|
||||
if (document.body.clientWidth >= 1080) {
|
||||
try { sidebar = localStorage.getItem('mdbook-sidebar'); } catch(e) { }
|
||||
sidebar = sidebar || 'visible';
|
||||
} else {
|
||||
sidebar = 'hidden';
|
||||
}
|
||||
sidebar_toggle.checked = sidebar === 'visible';
|
||||
html.classList.remove('sidebar-visible');
|
||||
html.classList.add("sidebar-" + sidebar);
|
||||
</script>
|
||||
|
||||
<nav id="sidebar" class="sidebar" aria-label="Table of contents">
|
||||
<!-- populated by js -->
|
||||
<mdbook-sidebar-scrollbox class="sidebar-scrollbox"></mdbook-sidebar-scrollbox>
|
||||
<noscript>
|
||||
<iframe class="sidebar-iframe-outer" src="toc.html"></iframe>
|
||||
</noscript>
|
||||
<div id="sidebar-resize-handle" class="sidebar-resize-handle">
|
||||
<div class="sidebar-resize-indicator"></div>
|
||||
</div>
|
||||
</nav>
|
||||
|
||||
<div id="page-wrapper" class="page-wrapper">
|
||||
|
||||
<div class="page">
|
||||
<div id="menu-bar-hover-placeholder"></div>
|
||||
<div id="menu-bar" class="menu-bar sticky">
|
||||
<div class="left-buttons">
|
||||
<label id="sidebar-toggle" class="icon-button" for="sidebar-toggle-anchor" title="Toggle Table of Contents" aria-label="Toggle Table of Contents" aria-controls="sidebar">
|
||||
<i class="fa fa-bars"></i>
|
||||
</label>
|
||||
<button id="theme-toggle" class="icon-button" type="button" title="Change theme" aria-label="Change theme" aria-haspopup="true" aria-expanded="false" aria-controls="theme-list">
|
||||
<i class="fa fa-paint-brush"></i>
|
||||
</button>
|
||||
<ul id="theme-list" class="theme-popup" aria-label="Themes" role="menu">
|
||||
<li role="none"><button role="menuitem" class="theme" id="default_theme">Auto</button></li>
|
||||
<li role="none"><button role="menuitem" class="theme" id="light">Light</button></li>
|
||||
<li role="none"><button role="menuitem" class="theme" id="rust">Rust</button></li>
|
||||
<li role="none"><button role="menuitem" class="theme" id="coal">Coal</button></li>
|
||||
<li role="none"><button role="menuitem" class="theme" id="navy">Navy</button></li>
|
||||
<li role="none"><button role="menuitem" class="theme" id="ayu">Ayu</button></li>
|
||||
</ul>
|
||||
<button id="search-toggle" class="icon-button" type="button" title="Search. (Shortkey: s)" aria-label="Toggle Searchbar" aria-expanded="false" aria-keyshortcuts="S" aria-controls="searchbar">
|
||||
<i class="fa fa-search"></i>
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<h1 class="menu-title">Candle Documentation</h1>
|
||||
|
||||
<div class="right-buttons">
|
||||
<a href="print.html" title="Print this book" aria-label="Print this book">
|
||||
<i id="print-button" class="fa fa-print"></i>
|
||||
</a>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="search-wrapper" class="hidden">
|
||||
<form id="searchbar-outer" class="searchbar-outer">
|
||||
<input type="search" id="searchbar" name="searchbar" placeholder="Search this book ..." aria-controls="searchresults-outer" aria-describedby="searchresults-header">
|
||||
</form>
|
||||
<div id="searchresults-outer" class="searchresults-outer hidden">
|
||||
<div id="searchresults-header" class="searchresults-header"></div>
|
||||
<ul id="searchresults">
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Apply ARIA attributes after the sidebar and the sidebar toggle button are added to the DOM -->
|
||||
<script>
|
||||
document.getElementById('sidebar-toggle').setAttribute('aria-expanded', sidebar === 'visible');
|
||||
document.getElementById('sidebar').setAttribute('aria-hidden', sidebar !== 'visible');
|
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Array.from(document.querySelectorAll('#sidebar a')).forEach(function(link) {
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link.setAttribute('tabIndex', sidebar === 'visible' ? 0 : -1);
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});
|
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</script>
|
||||
|
||||
<div id="content" class="content">
|
||||
<main>
|
||||
<h1 id="document-not-found-404"><a class="header" href="#document-not-found-404">Document not found (404)</a></h1>
|
||||
<p>This URL is invalid, sorry. Please use the navigation bar or search to continue.</p>
|
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</main>
|
||||
|
||||
<nav class="nav-wrapper" aria-label="Page navigation">
|
||||
<!-- Mobile navigation buttons -->
|
||||
|
||||
|
||||
<div style="clear: both"></div>
|
||||
</nav>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<nav class="nav-wide-wrapper" aria-label="Page navigation">
|
||||
|
||||
</nav>
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
<script>
|
||||
window.playground_copyable = true;
|
||||
</script>
|
||||
|
||||
|
||||
<script src="elasticlunr.min.js"></script>
|
||||
<script src="mark.min.js"></script>
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<script src="searcher.js"></script>
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||||
<script src="clipboard.min.js"></script>
|
||||
<script src="highlight.js"></script>
|
||||
<script src="book.js"></script>
|
||||
|
||||
<!-- Custom JS scripts -->
|
||||
|
||||
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
4
FontAwesome/css/font-awesome.css
vendored
Normal file
4
FontAwesome/css/font-awesome.css
vendored
Normal file
File diff suppressed because one or more lines are too long
BIN
FontAwesome/fonts/FontAwesome.ttf
Normal file
BIN
FontAwesome/fonts/FontAwesome.ttf
Normal file
Binary file not shown.
BIN
FontAwesome/fonts/fontawesome-webfont.eot
Normal file
BIN
FontAwesome/fonts/fontawesome-webfont.eot
Normal file
Binary file not shown.
2671
FontAwesome/fonts/fontawesome-webfont.svg
Normal file
2671
FontAwesome/fonts/fontawesome-webfont.svg
Normal file
File diff suppressed because it is too large
Load Diff
|
After Width: | Height: | Size: 434 KiB |
BIN
FontAwesome/fonts/fontawesome-webfont.ttf
Normal file
BIN
FontAwesome/fonts/fontawesome-webfont.ttf
Normal file
Binary file not shown.
BIN
FontAwesome/fonts/fontawesome-webfont.woff
Normal file
BIN
FontAwesome/fonts/fontawesome-webfont.woff
Normal file
Binary file not shown.
BIN
FontAwesome/fonts/fontawesome-webfont.woff2
Normal file
BIN
FontAwesome/fonts/fontawesome-webfont.woff2
Normal file
Binary file not shown.
78
ayu-highlight.css
Normal file
78
ayu-highlight.css
Normal file
@ -0,0 +1,78 @@
|
||||
/*
|
||||
Based off of the Ayu theme
|
||||
Original by Dempfi (https://github.com/dempfi/ayu)
|
||||
*/
|
||||
|
||||
.hljs {
|
||||
display: block;
|
||||
overflow-x: auto;
|
||||
background: #191f26;
|
||||
color: #e6e1cf;
|
||||
}
|
||||
|
||||
.hljs-comment,
|
||||
.hljs-quote {
|
||||
color: #5c6773;
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
.hljs-variable,
|
||||
.hljs-template-variable,
|
||||
.hljs-attribute,
|
||||
.hljs-attr,
|
||||
.hljs-regexp,
|
||||
.hljs-link,
|
||||
.hljs-selector-id,
|
||||
.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;
|
||||
}
|
||||
769
book.js
Normal file
769
book.js
Normal file
@ -0,0 +1,769 @@
|
||||
'use strict';
|
||||
|
||||
/* global default_theme, default_dark_theme, default_light_theme, hljs, ClipboardJS */
|
||||
|
||||
// Fix back button cache problem
|
||||
window.onunload = function() { };
|
||||
|
||||
// Global variable, shared between modules
|
||||
function playground_text(playground, hidden = true) {
|
||||
const code_block = playground.querySelector('code');
|
||||
|
||||
if (window.ace && code_block.classList.contains('editable')) {
|
||||
const 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)),
|
||||
]);
|
||||
}
|
||||
|
||||
const 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
|
||||
const 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) {
|
||||
const code_block = playground_block.querySelector('code');
|
||||
if (code_block.classList.contains('editable')) {
|
||||
const editor = window.ace.edit(code_block);
|
||||
editor.addEventListener('change', () => {
|
||||
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) {
|
||||
const 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
|
||||
const txt = playground_text(pre_block);
|
||||
const re = /extern\s+crate\s+([a-zA-Z_0-9]+)\s*;/g;
|
||||
const snippet_crates = [];
|
||||
let item;
|
||||
// eslint-disable-next-line no-cond-assign
|
||||
while (item = re.exec(txt)) {
|
||||
snippet_crates.push(item[1]);
|
||||
}
|
||||
|
||||
// check if all used crates are available on play.rust-lang.org
|
||||
const 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) {
|
||||
let 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);
|
||||
}
|
||||
|
||||
const text = playground_text(code_block);
|
||||
const classes = code_block.querySelector('code').classList;
|
||||
let edition = '2015';
|
||||
classes.forEach(className => {
|
||||
if (className.startsWith('edition')) {
|
||||
edition = className.slice(7);
|
||||
}
|
||||
});
|
||||
const 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
|
||||
});
|
||||
|
||||
const 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) {
|
||||
|
||||
const lines = Array.from(block.querySelectorAll('.boring'));
|
||||
// If no lines were hidden, return
|
||||
if (!lines.length) {
|
||||
return;
|
||||
}
|
||||
block.classList.add('hide-boring');
|
||||
|
||||
const buttons = document.createElement('div');
|
||||
buttons.className = 'buttons';
|
||||
buttons.innerHTML = '<button class="fa fa-eye" title="Show hidden lines" \
|
||||
aria-label="Show hidden lines"></button>';
|
||||
|
||||
// add expand button
|
||||
const 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) {
|
||||
const pre_block = block.parentNode;
|
||||
if (!pre_block.classList.contains('playground')) {
|
||||
let buttons = pre_block.querySelector('.buttons');
|
||||
if (!buttons) {
|
||||
buttons = document.createElement('div');
|
||||
buttons.className = 'buttons';
|
||||
pre_block.insertBefore(buttons, pre_block.firstChild);
|
||||
}
|
||||
|
||||
const clipButton = document.createElement('button');
|
||||
clipButton.className = 'clip-button';
|
||||
clipButton.title = 'Copy to clipboard';
|
||||
clipButton.setAttribute('aria-label', clipButton.title);
|
||||
clipButton.innerHTML = '<i class="tooltiptext"></i>';
|
||||
|
||||
buttons.insertBefore(clipButton, buttons.firstChild);
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
// Process playground code blocks
|
||||
Array.from(document.querySelectorAll('.playground')).forEach(function(pre_block) {
|
||||
// Add play button
|
||||
let buttons = pre_block.querySelector('.buttons');
|
||||
if (!buttons) {
|
||||
buttons = document.createElement('div');
|
||||
buttons.className = 'buttons';
|
||||
pre_block.insertBefore(buttons, pre_block.firstChild);
|
||||
}
|
||||
|
||||
const 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', () => {
|
||||
run_rust_code(pre_block);
|
||||
});
|
||||
|
||||
if (window.playground_copyable) {
|
||||
const copyCodeClipboardButton = document.createElement('button');
|
||||
copyCodeClipboardButton.className = 'clip-button';
|
||||
copyCodeClipboardButton.innerHTML = '<i class="tooltiptext"></i>';
|
||||
copyCodeClipboardButton.title = 'Copy to clipboard';
|
||||
copyCodeClipboardButton.setAttribute('aria-label', copyCodeClipboardButton.title);
|
||||
|
||||
buttons.insertBefore(copyCodeClipboardButton, buttons.firstChild);
|
||||
}
|
||||
|
||||
const code_block = pre_block.querySelector('code');
|
||||
if (window.ace && code_block.classList.contains('editable')) {
|
||||
const 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() {
|
||||
const editor = window.ace.edit(code_block);
|
||||
editor.setValue(editor.originalCode);
|
||||
editor.clearSelection();
|
||||
});
|
||||
}
|
||||
});
|
||||
})();
|
||||
|
||||
(function themes() {
|
||||
const html = document.querySelector('html');
|
||||
const themeToggleButton = document.getElementById('theme-toggle');
|
||||
const themePopup = document.getElementById('theme-list');
|
||||
const themeColorMetaTag = document.querySelector('meta[name="theme-color"]');
|
||||
const themeIds = [];
|
||||
themePopup.querySelectorAll('button.theme').forEach(function(el) {
|
||||
themeIds.push(el.id);
|
||||
});
|
||||
const stylesheets = {
|
||||
ayuHighlight: document.querySelector('#ayu-highlight-css'),
|
||||
tomorrowNight: document.querySelector('#tomorrow-night-css'),
|
||||
highlight: document.querySelector('#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');
|
||||
});
|
||||
const selected = get_saved_theme() ?? 'default_theme';
|
||||
let element = themePopup.querySelector('button#' + selected);
|
||||
if (element === null) {
|
||||
// Fall back in case there is no "Default" item.
|
||||
element = themePopup.querySelector('button#' + get_theme());
|
||||
}
|
||||
element.classList.add('theme-selected');
|
||||
}
|
||||
|
||||
function hideThemes() {
|
||||
themePopup.style.display = 'none';
|
||||
themeToggleButton.setAttribute('aria-expanded', false);
|
||||
themeToggleButton.focus();
|
||||
}
|
||||
|
||||
function get_saved_theme() {
|
||||
let theme = null;
|
||||
try {
|
||||
theme = localStorage.getItem('mdbook-theme');
|
||||
} catch (e) {
|
||||
// ignore error.
|
||||
}
|
||||
return theme;
|
||||
}
|
||||
|
||||
function delete_saved_theme() {
|
||||
localStorage.removeItem('mdbook-theme');
|
||||
}
|
||||
|
||||
function get_theme() {
|
||||
const theme = get_saved_theme();
|
||||
if (theme === null || theme === undefined || !themeIds.includes(theme)) {
|
||||
if (typeof default_dark_theme === 'undefined') {
|
||||
// A customized index.hbs might not define this, so fall back to
|
||||
// old behavior of determining the default on page load.
|
||||
return default_theme;
|
||||
}
|
||||
return window.matchMedia('(prefers-color-scheme: dark)').matches
|
||||
? default_dark_theme
|
||||
: default_light_theme;
|
||||
} else {
|
||||
return theme;
|
||||
}
|
||||
}
|
||||
|
||||
let previousTheme = default_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);
|
||||
});
|
||||
}
|
||||
|
||||
if (store) {
|
||||
try {
|
||||
localStorage.setItem('mdbook-theme', theme);
|
||||
} catch (e) {
|
||||
// ignore error.
|
||||
}
|
||||
}
|
||||
|
||||
html.classList.remove(previousTheme);
|
||||
html.classList.add(theme);
|
||||
previousTheme = theme;
|
||||
updateThemeSelected();
|
||||
}
|
||||
|
||||
const query = window.matchMedia('(prefers-color-scheme: dark)');
|
||||
query.onchange = function() {
|
||||
set_theme(get_theme(), false);
|
||||
};
|
||||
|
||||
// Set theme.
|
||||
set_theme(get_theme(), false);
|
||||
|
||||
themeToggleButton.addEventListener('click', function() {
|
||||
if (themePopup.style.display === 'block') {
|
||||
hideThemes();
|
||||
} else {
|
||||
showThemes();
|
||||
}
|
||||
});
|
||||
|
||||
themePopup.addEventListener('click', function(e) {
|
||||
let theme;
|
||||
if (e.target.className === 'theme') {
|
||||
theme = e.target.id;
|
||||
} else if (e.target.parentElement.className === 'theme') {
|
||||
theme = e.target.parentElement.id;
|
||||
} else {
|
||||
return;
|
||||
}
|
||||
if (theme === 'default_theme' || theme === null) {
|
||||
delete_saved_theme();
|
||||
set_theme(get_theme(), false);
|
||||
} else {
|
||||
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;
|
||||
}
|
||||
|
||||
let li;
|
||||
switch (e.key) {
|
||||
case 'Escape':
|
||||
e.preventDefault();
|
||||
hideThemes();
|
||||
break;
|
||||
case 'ArrowUp':
|
||||
e.preventDefault();
|
||||
li = document.activeElement.parentElement;
|
||||
if (li && li.previousElementSibling) {
|
||||
li.previousElementSibling.querySelector('button').focus();
|
||||
}
|
||||
break;
|
||||
case 'ArrowDown':
|
||||
e.preventDefault();
|
||||
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() {
|
||||
const body = document.querySelector('body');
|
||||
const sidebar = document.getElementById('sidebar');
|
||||
const sidebarLinks = document.querySelectorAll('#sidebar a');
|
||||
const sidebarToggleButton = document.getElementById('sidebar-toggle');
|
||||
const sidebarToggleAnchor = document.getElementById('sidebar-toggle-anchor');
|
||||
const sidebarResizeHandle = document.getElementById('sidebar-resize-handle');
|
||||
let 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) {
|
||||
// Ignore error.
|
||||
}
|
||||
}
|
||||
|
||||
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) {
|
||||
// Ignore error.
|
||||
}
|
||||
}
|
||||
|
||||
// Toggle sidebar
|
||||
sidebarToggleAnchor.addEventListener('change', function sidebarToggle() {
|
||||
if (sidebarToggleAnchor.checked) {
|
||||
const current_width = parseInt(
|
||||
document.documentElement.style.getPropertyValue('--sidebar-width'), 10);
|
||||
if (current_width < 150) {
|
||||
document.documentElement.style.setProperty('--sidebar-width', '150px');
|
||||
}
|
||||
showSidebar();
|
||||
} else {
|
||||
hideSidebar();
|
||||
}
|
||||
});
|
||||
|
||||
sidebarResizeHandle.addEventListener('mousedown', initResize, false);
|
||||
|
||||
function initResize() {
|
||||
window.addEventListener('mousemove', resize, false);
|
||||
window.addEventListener('mouseup', stopResize, false);
|
||||
body.classList.add('sidebar-resizing');
|
||||
}
|
||||
function resize(e) {
|
||||
let 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() {
|
||||
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;
|
||||
}
|
||||
|
||||
const curX = e.touches[0].clientX;
|
||||
const 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;
|
||||
}
|
||||
const html = document.querySelector('html');
|
||||
|
||||
function next() {
|
||||
const nextButton = document.querySelector('.nav-chapters.next');
|
||||
if (nextButton) {
|
||||
window.location.href = nextButton.href;
|
||||
}
|
||||
}
|
||||
function prev() {
|
||||
const 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() {
|
||||
const clipButtons = document.querySelectorAll('.clip-button');
|
||||
|
||||
function hideTooltip(elem) {
|
||||
elem.firstChild.innerText = '';
|
||||
elem.className = 'clip-button';
|
||||
}
|
||||
|
||||
function showTooltip(elem, msg) {
|
||||
elem.firstChild.innerText = msg;
|
||||
elem.className = 'clip-button tooltipped';
|
||||
}
|
||||
|
||||
const clipboardSnippets = new ClipboardJS('.clip-button', {
|
||||
text: function(trigger) {
|
||||
hideTooltip(trigger);
|
||||
const 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() {
|
||||
const menuTitle = document.querySelector('.menu-title');
|
||||
|
||||
menuTitle.addEventListener('click', function() {
|
||||
document.scrollingElement.scrollTo({ top: 0, behavior: 'smooth' });
|
||||
});
|
||||
})();
|
||||
|
||||
(function controllMenu() {
|
||||
const menu = document.getElementById('menu-bar');
|
||||
|
||||
(function controllPosition() {
|
||||
let scrollTop = document.scrollingElement.scrollTop;
|
||||
let prevScrollTop = scrollTop;
|
||||
const 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
|
||||
let topCache = menu.style.top.slice(0, -2);
|
||||
menu.classList.remove('sticky');
|
||||
let 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
|
||||
let nextSticky = null;
|
||||
let nextTop = null;
|
||||
const scrollDown = scrollTop > prevScrollTop;
|
||||
const 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 });
|
||||
})();
|
||||
})();
|
||||
7
clipboard.min.js
vendored
Normal file
7
clipboard.min.js
vendored
Normal file
File diff suppressed because one or more lines are too long
643
css/chrome.css
Normal file
643
css/chrome.css
Normal file
@ -0,0 +1,643 @@
|
||||
/* CSS for UI elements (a.k.a. chrome) */
|
||||
|
||||
html {
|
||||
scrollbar-color: var(--scrollbar) var(--bg);
|
||||
}
|
||||
#searchresults a,
|
||||
.content a:link,
|
||||
a:visited,
|
||||
a > .hljs {
|
||||
color: var(--links);
|
||||
}
|
||||
|
||||
/*
|
||||
body-container is necessary because mobile browsers don't seem to like
|
||||
overflow-x on the body tag when there is a <meta name="viewport"> tag.
|
||||
*/
|
||||
#body-container {
|
||||
/*
|
||||
This is used when the sidebar pushes the body content off the side of
|
||||
the screen on small screens. 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,
|
||||
#menu-bar-hover-placeholder:hover + #menu-bar,
|
||||
#menu-bar:hover,
|
||||
html.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;
|
||||
}
|
||||
html:not(.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;
|
||||
}
|
||||
.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: 4px 4px 3px 5px;
|
||||
font-size: 23px;
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
pre > .buttons button.clip-button {
|
||||
padding: 2px 4px 0px 6px;
|
||||
}
|
||||
pre > .buttons button.clip-button::before {
|
||||
/* clipboard image from octicons (https://github.com/primer/octicons/tree/v2.0.0) MIT license
|
||||
*/
|
||||
content: url('data:image/svg+xml,<svg width="21" height="20" viewBox="0 0 24 25" \
|
||||
xmlns="http://www.w3.org/2000/svg" aria-label="Copy to clipboard">\
|
||||
<path d="M18 20h2v3c0 1-1 2-2 2H2c-.998 0-2-1-2-2V5c0-.911.755-1.667 1.667-1.667h5A3.323 3.323 0 \
|
||||
0110 0a3.323 3.323 0 013.333 3.333h5C19.245 3.333 20 4.09 20 5v8.333h-2V9H2v14h16v-3zM3 \
|
||||
7h14c0-.911-.793-1.667-1.75-1.667H13.5c-.957 0-1.75-.755-1.75-1.666C11.75 2.755 10.957 2 10 \
|
||||
2s-1.75.755-1.75 1.667c0 .911-.793 1.666-1.75 1.666H4.75C3.793 5.333 3 6.09 3 7z"/>\
|
||||
<path d="M4 19h6v2H4zM12 11H4v2h8zM4 17h4v-2H4zM15 15v-3l-4.5 4.5L15 21v-3l8.027-.032L23 15z"/>\
|
||||
</svg>');
|
||||
filter: var(--copy-button-filter);
|
||||
}
|
||||
pre > .buttons button.clip-button:hover::before {
|
||||
filter: var(--copy-button-filter-hover);
|
||||
}
|
||||
|
||||
@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);
|
||||
}
|
||||
.sidebar-iframe-inner {
|
||||
--padding: 10px;
|
||||
|
||||
background-color: var(--sidebar-bg);
|
||||
padding: var(--padding);
|
||||
margin: 0;
|
||||
font-size: 1.4rem;
|
||||
color: var(--sidebar-fg);
|
||||
min-height: calc(100vh - var(--padding) * 2);
|
||||
}
|
||||
.sidebar-iframe-outer {
|
||||
border: none;
|
||||
height: 100%;
|
||||
position: absolute;
|
||||
top: 0;
|
||||
bottom: 0;
|
||||
left: 0;
|
||||
right: 0;
|
||||
}
|
||||
[dir=rtl] .sidebar { left: unset; right: 0; }
|
||||
.sidebar-resizing {
|
||||
-moz-user-select: none;
|
||||
-webkit-user-select: none;
|
||||
-ms-user-select: none;
|
||||
user-select: none;
|
||||
}
|
||||
html: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;
|
||||
}
|
||||
279
css/general.css
Normal file
279
css/general.css
Normal file
@ -0,0 +1,279 @@
|
||||
/* 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;
|
||||
}
|
||||
|
||||
sup {
|
||||
/* Set the line-height for superscript and footnote references so that there
|
||||
isn't an awkward space appearing above lines that contain the footnote.
|
||||
|
||||
See https://github.com/rust-lang/mdBook/pull/2443#discussion_r1813773583
|
||||
for an explanation.
|
||||
*/
|
||||
line-height: 0;
|
||||
}
|
||||
|
||||
.footnote-definition {
|
||||
font-size: 0.9em;
|
||||
}
|
||||
/* The default spacing for a list is a little too large. */
|
||||
.footnote-definition ul,
|
||||
.footnote-definition ol {
|
||||
padding-left: 20px;
|
||||
}
|
||||
.footnote-definition > li {
|
||||
/* Required to position the ::before target */
|
||||
position: relative;
|
||||
}
|
||||
.footnote-definition > li:target {
|
||||
scroll-margin-top: 50vh;
|
||||
}
|
||||
.footnote-reference:target {
|
||||
scroll-margin-top: 50vh;
|
||||
}
|
||||
/* Draws a border around the footnote (including the marker) when it is selected.
|
||||
TODO: If there are multiple linkbacks, highlight which one you just came
|
||||
from so you know which one to click.
|
||||
*/
|
||||
.footnote-definition > li:target::before {
|
||||
border: 2px solid var(--footnote-highlight);
|
||||
border-radius: 6px;
|
||||
position: absolute;
|
||||
top: -8px;
|
||||
right: -8px;
|
||||
bottom: -8px;
|
||||
left: -32px;
|
||||
pointer-events: none;
|
||||
content: "";
|
||||
}
|
||||
/* Pulses the footnote reference so you can quickly see where you left off reading.
|
||||
This could use some improvement.
|
||||
*/
|
||||
@media not (prefers-reduced-motion) {
|
||||
.footnote-reference:target {
|
||||
animation: fn-highlight 0.8s;
|
||||
border-radius: 2px;
|
||||
}
|
||||
|
||||
@keyframes fn-highlight {
|
||||
from {
|
||||
background-color: var(--footnote-highlight);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
.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;
|
||||
}
|
||||
50
css/print.css
Normal file
50
css/print.css
Normal file
@ -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;
|
||||
}
|
||||
319
css/variables.css
Normal file
319
css/variables.css
Normal file
@ -0,0 +1,319 @@
|
||||
|
||||
/* 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;
|
||||
|
||||
/* Same as `--icons` */
|
||||
--copy-button-filter: invert(45%) sepia(6%) saturate(621%) hue-rotate(198deg) brightness(99%) contrast(85%);
|
||||
/* Same as `--sidebar-active` */
|
||||
--copy-button-filter-hover: invert(68%) sepia(55%) saturate(531%) hue-rotate(341deg) brightness(104%) contrast(101%);
|
||||
|
||||
--footnote-highlight: #2668a6;
|
||||
}
|
||||
|
||||
.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%);
|
||||
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10
elasticlunr.min.js
vendored
Normal file
10
elasticlunr.min.js
vendored
Normal file
File diff suppressed because one or more lines are too long
236
error_manage.html
Normal file
236
error_manage.html
Normal file
@ -0,0 +1,236 @@
|
||||
<!DOCTYPE HTML>
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|
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|
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<main>
|
||||
<h1 id="error-management"><a class="header" href="#error-management">Error management</a></h1>
|
||||
<p>You might have seen in the code base a lot of <code>.unwrap()</code> or <code>?</code>.
|
||||
If you're unfamiliar with Rust check out the <a href="https://doc.rust-lang.org/book/ch09-02-recoverable-errors-with-result.html">Rust book</a>
|
||||
for more information.</p>
|
||||
<p>What's important to know though, is that if you want to know <em>where</em> a particular operation failed
|
||||
You can simply use <code>RUST_BACKTRACE=1</code> to get the location of where the model actually failed.</p>
|
||||
<p>Let's see on failing code:</p>
|
||||
<pre><code class="language-rust ignore">let x = Tensor::zeros((1, 784), DType::F32, &device)?;
|
||||
let y = Tensor::zeros((1, 784), DType::F32, &device)?;
|
||||
let z = x.matmul(&y)?;</code></pre>
|
||||
<p>Will print at runtime:</p>
|
||||
<pre><code class="language-bash">Error: ShapeMismatchBinaryOp { lhs: [1, 784], rhs: [1, 784], op: "matmul" }
|
||||
</code></pre>
|
||||
<p>After adding <code>RUST_BACKTRACE=1</code>:</p>
|
||||
<pre><code class="language-bash">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" }] }
|
||||
</code></pre>
|
||||
<p>Not super pretty at the moment, but we can see error occurred on <code>{ fn: "myapp::main", file: "./src/main.rs", line: 29 }</code></p>
|
||||
<p>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 <a href="https://docs.rs/anyhow/latest/anyhow/"><code>anyhow</code></a> for that.
|
||||
The library is still young, please <a href="https://github.com/LaurentMazare/candle/issues">report</a> any issues detecting where an error is coming from.</p>
|
||||
<h2 id="cuda-error-management"><a class="header" href="#cuda-error-management">Cuda error management</a></h2>
|
||||
<p>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.</p>
|
||||
<p>One way to avoid this is to use <code>CUDA_LAUNCH_BLOCKING=1</code> 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 <code>CudaSlice</code> only.</p>
|
||||
<p>If this occurs, you can use <a href="https://docs.nvidia.com/compute-sanitizer/ComputeSanitizer/index.html"><code>compute-sanitizer</code></a>
|
||||
This tool is like <code>valgrind</code> but for cuda. It will help locate the errors in the kernels.</p>
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93
fonts/SOURCE-CODE-PRO-LICENSE.txt
Normal file
93
fonts/SOURCE-CODE-PRO-LICENSE.txt
Normal file
@ -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.
|
||||
100
fonts/fonts.css
Normal file
100
fonts/fonts.css
Normal file
@ -0,0 +1,100 @@
|
||||
/* Open Sans is licensed under the Apache License, Version 2.0. See http://www.apache.org/licenses/LICENSE-2.0 */
|
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/* Source Code Pro is under the Open Font License. See https://scripts.sil.org/cms/scripts/page.php?site_id=nrsi&id=OFL */
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|
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@font-face {
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font-style: normal;
|
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|
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url('../fonts/open-sans-v17-all-charsets-regular.woff2') format('woff2');
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|
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|
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|
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url('../fonts/open-sans-v17-all-charsets-700.woff2') format('woff2');
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font-style: italic;
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url('../fonts/open-sans-v17-all-charsets-700italic.woff2') format('woff2');
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|
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|
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|
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|
||||
url('../fonts/open-sans-v17-all-charsets-800.woff2') format('woff2');
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}
|
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|
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/* open-sans-800italic - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */
|
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@font-face {
|
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font-family: 'Open Sans';
|
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font-style: italic;
|
||||
font-weight: 800;
|
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src: local('Open Sans ExtraBold Italic'), local('OpenSans-ExtraBoldItalic'),
|
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|
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|
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|
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}
|
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BIN
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223
guide/cheatsheet.html
Normal file
223
guide/cheatsheet.html
Normal file
@ -0,0 +1,223 @@
|
||||
<!DOCTYPE HTML>
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<html lang="en" class="light sidebar-visible" dir="ltr">
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<head>
|
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<!-- Book generated using mdBook -->
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<meta charset="UTF-8">
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<h1 class="menu-title">Candle Documentation</h1>
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<div id="content" class="content">
|
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<main>
|
||||
<h1 id="pytorch-cheatsheet"><a class="header" href="#pytorch-cheatsheet">Pytorch cheatsheet</a></h1>
|
||||
<p>Cheatsheet:</p>
|
||||
<div class="table-wrapper"><table><thead><tr><th></th><th>Using PyTorch</th><th>Using Candle</th></tr></thead><tbody>
|
||||
<tr><td>Creation</td><td><code>torch.Tensor([[1, 2], [3, 4]])</code></td><td><code>Tensor::new(&[[1f32, 2.], [3., 4.]], &Device::Cpu)?</code></td></tr>
|
||||
<tr><td>Creation</td><td><code>torch.zeros((2, 2))</code></td><td><code>Tensor::zeros((2, 2), DType::F32, &Device::Cpu)?</code></td></tr>
|
||||
<tr><td>Indexing</td><td><code>tensor[:, :4]</code></td><td><code>tensor.i((.., ..4))?</code></td></tr>
|
||||
<tr><td>Operations</td><td><code>tensor.view((2, 2))</code></td><td><code>tensor.reshape((2, 2))?</code></td></tr>
|
||||
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|
||||
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|
||||
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|
||||
<tr><td>Dtype</td><td><code>tensor.to(dtype=torch.float16)</code></td><td><code>tensor.to_dtype(&DType::F16)?</code></td></tr>
|
||||
<tr><td>Saving</td><td><code>torch.save({"A": A}, "model.bin")</code></td><td><code>candle::safetensors::save(&HashMap::from([("A", A)]), "model.safetensors")?</code></td></tr>
|
||||
<tr><td>Loading</td><td><code>weights = torch.load("model.bin")</code></td><td><code>candle::safetensors::load("model.safetensors", &device)</code></td></tr>
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<main>
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<h1 id="hello-world"><a class="header" href="#hello-world">Hello world!</a></h1>
|
||||
<p>We will now create the hello world of the ML world, building a model capable of solving MNIST dataset.</p>
|
||||
<p>Open <code>src/main.rs</code> and fill in this content:</p>
|
||||
<pre><pre class="playground"><code class="language-rust"><span class="boring">extern crate candle_core;
|
||||
</span>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(())
|
||||
}</code></pre></pre>
|
||||
<p>Everything should now run with:</p>
|
||||
<pre><code class="language-bash">cargo run --release
|
||||
</code></pre>
|
||||
<h2 id="using-a-linear-layer"><a class="header" href="#using-a-linear-layer">Using a <code>Linear</code> layer.</a></h2>
|
||||
<p>Now that we have this, we might want to complexify things a bit, for instance by adding <code>bias</code> and creating
|
||||
the classical <code>Linear</code> layer. We can do as such</p>
|
||||
<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
|
||||
</span><span class="boring">fn main() {
|
||||
</span><span class="boring">extern crate candle_core;
|
||||
</span><span class="boring">use candle_core::{Device, Result, Tensor};
|
||||
</span>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)
|
||||
}
|
||||
}
|
||||
<span class="boring">}</span></code></pre></pre>
|
||||
<p>This will change the model running code into a new function</p>
|
||||
<pre><pre class="playground"><code class="language-rust"><span class="boring">extern crate candle_core;
|
||||
</span><span class="boring">use candle_core::{Device, Result, Tensor};
|
||||
</span><span class="boring">struct Linear{
|
||||
</span><span class="boring"> weight: Tensor,
|
||||
</span><span class="boring"> bias: Tensor,
|
||||
</span><span class="boring">}
|
||||
</span><span class="boring">impl Linear{
|
||||
</span><span class="boring"> fn forward(&self, x: &Tensor) -> Result<Tensor> {
|
||||
</span><span class="boring"> let x = x.matmul(&self.weight)?;
|
||||
</span><span class="boring"> x.broadcast_add(&self.bias)
|
||||
</span><span class="boring"> }
|
||||
</span><span class="boring">}
|
||||
</span><span class="boring">
|
||||
</span><span class="boring">struct Model {
|
||||
</span><span class="boring"> first: Linear,
|
||||
</span><span class="boring"> second: Linear,
|
||||
</span><span class="boring">}
|
||||
</span><span class="boring">
|
||||
</span><span class="boring">impl Model {
|
||||
</span><span class="boring"> fn forward(&self, image: &Tensor) -> Result<Tensor> {
|
||||
</span><span class="boring"> let x = self.first.forward(image)?;
|
||||
</span><span class="boring"> let x = x.relu()?;
|
||||
</span><span class="boring"> self.second.forward(&x)
|
||||
</span><span class="boring"> }
|
||||
</span><span class="boring">}
|
||||
</span>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(())
|
||||
}</code></pre></pre>
|
||||
<p>Now it works, it is a great way to create your own layers.
|
||||
But most of the classical layers are already implemented in <a href="https://github.com/huggingface/candle/tree/main/candle-nn">candle-nn</a>.</p>
|
||||
<h2 id="using-candle_nn"><a class="header" href="#using-candle_nn">Using <code>candle_nn</code>.</a></h2>
|
||||
<p>For instance <a href="https://github.com/huggingface/candle/blob/main/candle-nn/src/linear.rs">Linear</a> 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.</p>
|
||||
<p>So instead we can simplify our example:</p>
|
||||
<pre><code class="language-bash">cargo add --git https://github.com/huggingface/candle.git candle-nn
|
||||
</code></pre>
|
||||
<p>And rewrite our examples using it</p>
|
||||
<pre><pre class="playground"><code class="language-rust"><span class="boring">extern crate candle_core;
|
||||
</span><span class="boring">extern crate candle_nn;
|
||||
</span>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(())
|
||||
}</code></pre></pre>
|
||||
<p>Feel free to modify this example to use <code>Conv2d</code> to create a classical convnet instead.</p>
|
||||
<p>Now that we have the running dummy code we can get to more advanced topics:</p>
|
||||
<ul>
|
||||
<li><a href="../guide/cheatsheet.html">For PyTorch users</a></li>
|
||||
<li><a href="../inference/inference.html">Running existing models</a></li>
|
||||
<li><a href="../training/training.html">Training models</a></li>
|
||||
</ul>
|
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|
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</main>
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<main>
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<h1 id="installation"><a class="header" href="#installation">Installation</a></h1>
|
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<p><strong>With Cuda support</strong>:</p>
|
||||
<ol>
|
||||
<li>First, make sure that Cuda is correctly installed.</li>
|
||||
</ol>
|
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<ul>
|
||||
<li><code>nvcc --version</code> should print information about your Cuda compiler driver.</li>
|
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<li><code>nvidia-smi --query-gpu=compute_cap --format=csv</code> should print your GPUs compute capability, e.g. something
|
||||
like:</li>
|
||||
</ul>
|
||||
<pre><code class="language-bash">compute_cap
|
||||
8.9
|
||||
</code></pre>
|
||||
<p>You can also compile the Cuda kernels for a specific compute cap using the
|
||||
<code>CUDA_COMPUTE_CAP=<compute cap></code> environment variable.</p>
|
||||
<p>If any of the above commands errors out, please make sure to update your Cuda version.</p>
|
||||
<ol start="2">
|
||||
<li>Create a new app and add <a href="https://github.com/huggingface/candle/tree/main/candle-core"><code>candle-core</code></a> with Cuda support.</li>
|
||||
</ol>
|
||||
<p>Start by creating a new cargo:</p>
|
||||
<pre><code class="language-bash">cargo new myapp
|
||||
cd myapp
|
||||
</code></pre>
|
||||
<p>Make sure to add the <code>candle-core</code> crate with the cuda feature:</p>
|
||||
<pre><code class="language-bash">cargo add --git https://github.com/huggingface/candle.git candle-core --features "cuda"
|
||||
</code></pre>
|
||||
<p>Run <code>cargo build</code> to make sure everything can be correctly built.</p>
|
||||
<pre><code class="language-bash">cargo build
|
||||
</code></pre>
|
||||
<p><strong>Without Cuda support</strong>:</p>
|
||||
<p>Create a new app and add <a href="https://github.com/huggingface/candle/tree/main/candle-core"><code>candle-core</code></a> as follows:</p>
|
||||
<pre><code class="language-bash">cargo new myapp
|
||||
cd myapp
|
||||
cargo add --git https://github.com/huggingface/candle.git candle-core
|
||||
</code></pre>
|
||||
<p>Finally, run <code>cargo build</code> to make sure everything can be correctly built.</p>
|
||||
<pre><code class="language-bash">cargo build
|
||||
</code></pre>
|
||||
<p><strong>With mkl support</strong></p>
|
||||
<p>You can also see the <code>mkl</code> feature which could be interesting to get faster inference on CPU. <a href="./advanced/mkl.html">Using mkl</a></p>
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|
||||
ConvNeXTv2, MobileOne, EfficientVit (MSRA), MobileNetv4, Hiera, FastViT.</li>
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|
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|
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<h1 id="using-the-hub"><a class="header" href="#using-the-hub">Using the hub</a></h1>
|
||||
<p>Install the <a href="https://github.com/huggingface/hf-hub"><code>hf-hub</code></a> crate:</p>
|
||||
<pre><code class="language-bash">cargo add hf-hub
|
||||
</code></pre>
|
||||
<p>Then let's start by downloading the <a href="https://huggingface.co/bert-base-uncased/tree/main">model file</a>.</p>
|
||||
<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
|
||||
</span><span class="boring">fn main() {
|
||||
</span><span class="boring">extern crate candle_core;
|
||||
</span><span class="boring">extern crate hf_hub;
|
||||
</span>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);
|
||||
<span class="boring">}</span></code></pre></pre>
|
||||
<p>We now have access to all the <a href="https://huggingface.co/bert-base-uncased?show_tensors=true">tensors</a> within the file.</p>
|
||||
<p>You can check all the names of the tensors <a href="https://huggingface.co/bert-base-uncased?show_tensors=true">here</a></p>
|
||||
<h2 id="using-async"><a class="header" href="#using-async">Using async</a></h2>
|
||||
<p><code>hf-hub</code> comes with an async API.</p>
|
||||
<pre><code class="language-bash">cargo add hf-hub --features tokio
|
||||
</code></pre>
|
||||
<pre><code class="language-rust ignore"><span class="boring">This is tested directly in examples crate because it needs external dependencies unfortunately:
|
||||
</span><span class="boring">See [this](https://github.com/rust-lang/mdBook/issues/706)
|
||||
</span>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();</code></pre>
|
||||
<h2 id="using-in-a-real-model"><a class="header" href="#using-in-a-real-model">Using in a real model.</a></h2>
|
||||
<p>Now that we have our weights, we can use them in our bert architecture:</p>
|
||||
<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
|
||||
</span><span class="boring">fn main() {
|
||||
</span><span class="boring">extern crate candle_core;
|
||||
</span><span class="boring">extern crate candle_nn;
|
||||
</span><span class="boring">extern crate hf_hub;
|
||||
</span><span class="boring">use hf_hub::api::sync::Api;
|
||||
</span><span class="boring">
|
||||
</span><span class="boring">let api = Api::new().unwrap();
|
||||
</span><span class="boring">let repo = api.model("bert-base-uncased".to_string());
|
||||
</span><span class="boring">
|
||||
</span><span class="boring">let weights = repo.get("model.safetensors").unwrap();
|
||||
</span>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();
|
||||
<span class="boring">}</span></code></pre></pre>
|
||||
<p>For a full reference, you can check out the full <a href="https://github.com/LaurentMazare/candle/tree/main/candle-examples/examples/bert">bert</a> example.</p>
|
||||
<h2 id="memory-mapping"><a class="header" href="#memory-mapping">Memory mapping</a></h2>
|
||||
<p>For more efficient loading, instead of reading the file, you could use <a href="https://docs.rs/memmap2/latest/memmap2/"><code>memmap2</code></a></p>
|
||||
<p><strong>Note</strong>: Be careful about memory mapping it seems to cause issues on <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/5893">Windows, WSL</a>
|
||||
and will definitely be slower on network mounted disk, because it will issue more read calls.</p>
|
||||
<pre><code class="language-rust ignore">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();</code></pre>
|
||||
<p><strong>Note</strong>: This operation is <strong>unsafe</strong>. <a href="https://docs.rs/memmap2/latest/memmap2/struct.Mmap.html#safety">See the safety notice</a>.
|
||||
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.</p>
|
||||
<h2 id="tensor-parallel-sharding"><a class="header" href="#tensor-parallel-sharding">Tensor Parallel Sharding</a></h2>
|
||||
<p>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.</p>
|
||||
<p>For that you need to use <a href="https://crates.io/crates/safetensors"><code>safetensors</code></a> directly.</p>
|
||||
<pre><code class="language-bash">cargo add safetensors
|
||||
</code></pre>
|
||||
<pre><code class="language-rust ignore">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();</code></pre>
|
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<main>
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<h1 id="running-a-model"><a class="header" href="#running-a-model">Running a model</a></h1>
|
||||
<p>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 <a href="https://github.com/huggingface/safetensors"><code>safetensors</code></a> format.</p>
|
||||
<p>Let's get started by running an old model : <code>bert-base-uncased</code>.</p>
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199
lib.rs
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199
lib.rs
Normal file
@ -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<u8> = 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<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,
|
||||
};
|
||||
|
||||
// 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(())
|
||||
}
|
||||
}
|
||||
7
mark.min.js
vendored
Normal file
7
mark.min.js
vendored
Normal file
File diff suppressed because one or more lines are too long
937
print.html
Normal file
937
print.html
Normal file
@ -0,0 +1,937 @@
|
||||
<!DOCTYPE HTML>
|
||||
<html lang="en" class="light sidebar-visible" dir="ltr">
|
||||
<head>
|
||||
<!-- Book generated using mdBook -->
|
||||
<meta charset="UTF-8">
|
||||
<title>Candle Documentation</title>
|
||||
<meta name="robots" content="noindex">
|
||||
|
||||
|
||||
<!-- Custom HTML head -->
|
||||
|
||||
<meta name="description" content="">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1">
|
||||
<meta name="theme-color" content="#ffffff">
|
||||
|
||||
<link rel="icon" href="favicon.svg">
|
||||
<link rel="shortcut icon" href="favicon.png">
|
||||
<link rel="stylesheet" href="css/variables.css">
|
||||
<link rel="stylesheet" href="css/general.css">
|
||||
<link rel="stylesheet" href="css/chrome.css">
|
||||
<link rel="stylesheet" href="css/print.css" media="print">
|
||||
|
||||
<!-- Fonts -->
|
||||
<link rel="stylesheet" href="FontAwesome/css/font-awesome.css">
|
||||
<link rel="stylesheet" href="fonts/fonts.css">
|
||||
|
||||
<!-- Highlight.js Stylesheets -->
|
||||
<link rel="stylesheet" id="highlight-css" href="highlight.css">
|
||||
<link rel="stylesheet" id="tomorrow-night-css" href="tomorrow-night.css">
|
||||
<link rel="stylesheet" id="ayu-highlight-css" href="ayu-highlight.css">
|
||||
|
||||
<!-- Custom theme stylesheets -->
|
||||
|
||||
|
||||
<!-- Provide site root and default themes to javascript -->
|
||||
<script>
|
||||
const path_to_root = "";
|
||||
const default_light_theme = "light";
|
||||
const default_dark_theme = "navy";
|
||||
</script>
|
||||
<!-- Start loading toc.js asap -->
|
||||
<script src="toc.js"></script>
|
||||
</head>
|
||||
<body>
|
||||
<div id="body-container">
|
||||
<!-- Work around some values being stored in localStorage wrapped in quotes -->
|
||||
<script>
|
||||
try {
|
||||
let theme = localStorage.getItem('mdbook-theme');
|
||||
let sidebar = localStorage.getItem('mdbook-sidebar');
|
||||
|
||||
if (theme.startsWith('"') && theme.endsWith('"')) {
|
||||
localStorage.setItem('mdbook-theme', theme.slice(1, theme.length - 1));
|
||||
}
|
||||
|
||||
if (sidebar.startsWith('"') && sidebar.endsWith('"')) {
|
||||
localStorage.setItem('mdbook-sidebar', sidebar.slice(1, sidebar.length - 1));
|
||||
}
|
||||
} catch (e) { }
|
||||
</script>
|
||||
|
||||
<!-- Set the theme before any content is loaded, prevents flash -->
|
||||
<script>
|
||||
const default_theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? default_dark_theme : default_light_theme;
|
||||
let theme;
|
||||
try { theme = localStorage.getItem('mdbook-theme'); } catch(e) { }
|
||||
if (theme === null || theme === undefined) { theme = default_theme; }
|
||||
const html = document.documentElement;
|
||||
html.classList.remove('light')
|
||||
html.classList.add(theme);
|
||||
html.classList.add("js");
|
||||
</script>
|
||||
|
||||
<input type="checkbox" id="sidebar-toggle-anchor" class="hidden">
|
||||
|
||||
<!-- Hide / unhide sidebar before it is displayed -->
|
||||
<script>
|
||||
let sidebar = null;
|
||||
const sidebar_toggle = document.getElementById("sidebar-toggle-anchor");
|
||||
if (document.body.clientWidth >= 1080) {
|
||||
try { sidebar = localStorage.getItem('mdbook-sidebar'); } catch(e) { }
|
||||
sidebar = sidebar || 'visible';
|
||||
} else {
|
||||
sidebar = 'hidden';
|
||||
}
|
||||
sidebar_toggle.checked = sidebar === 'visible';
|
||||
html.classList.remove('sidebar-visible');
|
||||
html.classList.add("sidebar-" + sidebar);
|
||||
</script>
|
||||
|
||||
<nav id="sidebar" class="sidebar" aria-label="Table of contents">
|
||||
<!-- populated by js -->
|
||||
<mdbook-sidebar-scrollbox class="sidebar-scrollbox"></mdbook-sidebar-scrollbox>
|
||||
<noscript>
|
||||
<iframe class="sidebar-iframe-outer" src="toc.html"></iframe>
|
||||
</noscript>
|
||||
<div id="sidebar-resize-handle" class="sidebar-resize-handle">
|
||||
<div class="sidebar-resize-indicator"></div>
|
||||
</div>
|
||||
</nav>
|
||||
|
||||
<div id="page-wrapper" class="page-wrapper">
|
||||
|
||||
<div class="page">
|
||||
<div id="menu-bar-hover-placeholder"></div>
|
||||
<div id="menu-bar" class="menu-bar sticky">
|
||||
<div class="left-buttons">
|
||||
<label id="sidebar-toggle" class="icon-button" for="sidebar-toggle-anchor" title="Toggle Table of Contents" aria-label="Toggle Table of Contents" aria-controls="sidebar">
|
||||
<i class="fa fa-bars"></i>
|
||||
</label>
|
||||
<button id="theme-toggle" class="icon-button" type="button" title="Change theme" aria-label="Change theme" aria-haspopup="true" aria-expanded="false" aria-controls="theme-list">
|
||||
<i class="fa fa-paint-brush"></i>
|
||||
</button>
|
||||
<ul id="theme-list" class="theme-popup" aria-label="Themes" role="menu">
|
||||
<li role="none"><button role="menuitem" class="theme" id="default_theme">Auto</button></li>
|
||||
<li role="none"><button role="menuitem" class="theme" id="light">Light</button></li>
|
||||
<li role="none"><button role="menuitem" class="theme" id="rust">Rust</button></li>
|
||||
<li role="none"><button role="menuitem" class="theme" id="coal">Coal</button></li>
|
||||
<li role="none"><button role="menuitem" class="theme" id="navy">Navy</button></li>
|
||||
<li role="none"><button role="menuitem" class="theme" id="ayu">Ayu</button></li>
|
||||
</ul>
|
||||
<button id="search-toggle" class="icon-button" type="button" title="Search. (Shortkey: s)" aria-label="Toggle Searchbar" aria-expanded="false" aria-keyshortcuts="S" aria-controls="searchbar">
|
||||
<i class="fa fa-search"></i>
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<h1 class="menu-title">Candle Documentation</h1>
|
||||
|
||||
<div class="right-buttons">
|
||||
<a href="print.html" title="Print this book" aria-label="Print this book">
|
||||
<i id="print-button" class="fa fa-print"></i>
|
||||
</a>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="search-wrapper" class="hidden">
|
||||
<form id="searchbar-outer" class="searchbar-outer">
|
||||
<input type="search" id="searchbar" name="searchbar" placeholder="Search this book ..." aria-controls="searchresults-outer" aria-describedby="searchresults-header">
|
||||
</form>
|
||||
<div id="searchresults-outer" class="searchresults-outer hidden">
|
||||
<div id="searchresults-header" class="searchresults-header"></div>
|
||||
<ul id="searchresults">
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Apply ARIA attributes after the sidebar and the sidebar toggle button are added to the DOM -->
|
||||
<script>
|
||||
document.getElementById('sidebar-toggle').setAttribute('aria-expanded', sidebar === 'visible');
|
||||
document.getElementById('sidebar').setAttribute('aria-hidden', sidebar !== 'visible');
|
||||
Array.from(document.querySelectorAll('#sidebar a')).forEach(function(link) {
|
||||
link.setAttribute('tabIndex', sidebar === 'visible' ? 0 : -1);
|
||||
});
|
||||
</script>
|
||||
|
||||
<div id="content" class="content">
|
||||
<main>
|
||||
<h1 id="introduction"><a class="header" href="#introduction">Introduction</a></h1>
|
||||
<h2 id="features"><a class="header" href="#features">Features</a></h2>
|
||||
<ul>
|
||||
<li>Simple syntax, looks and feels like PyTorch.
|
||||
<ul>
|
||||
<li>Model training.</li>
|
||||
<li>Embed user-defined ops/kernels, such as <a href="https://github.com/huggingface/candle/blob/89ba005962495f2bfbda286e185e9c3c7f5300a3/candle-flash-attn/src/lib.rs#L152">flash-attention v2</a>.</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>Backends.
|
||||
<ul>
|
||||
<li>Optimized CPU backend with optional MKL support for x86 and Accelerate for macs.</li>
|
||||
<li>CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL.</li>
|
||||
<li>WASM support, run your models in a browser.</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>Included models.
|
||||
<ul>
|
||||
<li>Language Models.
|
||||
<ul>
|
||||
<li>LLaMA v1, v2, and v3 with variants such as SOLAR-10.7B.</li>
|
||||
<li>Falcon.</li>
|
||||
<li>StarCoder, StarCoder2.</li>
|
||||
<li>Phi 1, 1.5, 2, and 3.</li>
|
||||
<li>Mamba, Minimal Mamba</li>
|
||||
<li>Gemma v1 2b and 7b+, v2 2b and 9b.</li>
|
||||
<li>Mistral 7b v0.1.</li>
|
||||
<li>Mixtral 8x7b v0.1.</li>
|
||||
<li>StableLM-3B-4E1T, StableLM-2-1.6B, Stable-Code-3B.</li>
|
||||
<li>Replit-code-v1.5-3B.</li>
|
||||
<li>Bert.</li>
|
||||
<li>Yi-6B and Yi-34B.</li>
|
||||
<li>Qwen1.5, Qwen1.5 MoE.</li>
|
||||
<li>RWKV v5 and v6.</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>Quantized LLMs.
|
||||
<ul>
|
||||
<li>Llama 7b, 13b, 70b, as well as the chat and code variants.</li>
|
||||
<li>Mistral 7b, and 7b instruct.</li>
|
||||
<li>Mixtral 8x7b.</li>
|
||||
<li>Zephyr 7b a and b (Mistral-7b based).</li>
|
||||
<li>OpenChat 3.5 (Mistral-7b based).</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>Text to text.
|
||||
<ul>
|
||||
<li>T5 and its variants: FlanT5, UL2, MADLAD400 (translation), CoEdit (Grammar correction).</li>
|
||||
<li>Marian MT (Machine Translation).</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>Text to image.
|
||||
<ul>
|
||||
<li>Stable Diffusion v1.5, v2.1, XL v1.0.</li>
|
||||
<li>Wurstchen v2.</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>Image to text.
|
||||
<ul>
|
||||
<li>BLIP.</li>
|
||||
<li>TrOCR.</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>Audio.
|
||||
<ul>
|
||||
<li>Whisper, multi-lingual speech-to-text.</li>
|
||||
<li>EnCodec, audio compression model.</li>
|
||||
<li>MetaVoice-1B, text-to-speech model.</li>
|
||||
<li>Parler-TTS, text-to-speech model.</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>Computer Vision Models.
|
||||
<ul>
|
||||
<li>DINOv2, ConvMixer, EfficientNet, ResNet, ViT, VGG, RepVGG, ConvNeXT,
|
||||
ConvNeXTv2, MobileOne, EfficientVit (MSRA), MobileNetv4, Hiera, FastViT.</li>
|
||||
<li>yolo-v3, yolo-v8.</li>
|
||||
<li>Segment-Anything Model (SAM).</li>
|
||||
<li>SegFormer.</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>File formats: load models from safetensors, npz, ggml, or PyTorch files.</li>
|
||||
<li>Serverless (on CPU), small and fast deployments.</li>
|
||||
<li>Quantization support using the llama.cpp quantized types.</li>
|
||||
</ul>
|
||||
<p>This book will introduce step by step how to use <code>candle</code>.</p>
|
||||
<div style="break-before: page; page-break-before: always;"></div><h1 id="installation"><a class="header" href="#installation">Installation</a></h1>
|
||||
<p><strong>With Cuda support</strong>:</p>
|
||||
<ol>
|
||||
<li>First, make sure that Cuda is correctly installed.</li>
|
||||
</ol>
|
||||
<ul>
|
||||
<li><code>nvcc --version</code> should print information about your Cuda compiler driver.</li>
|
||||
<li><code>nvidia-smi --query-gpu=compute_cap --format=csv</code> should print your GPUs compute capability, e.g. something
|
||||
like:</li>
|
||||
</ul>
|
||||
<pre><code class="language-bash">compute_cap
|
||||
8.9
|
||||
</code></pre>
|
||||
<p>You can also compile the Cuda kernels for a specific compute cap using the
|
||||
<code>CUDA_COMPUTE_CAP=<compute cap></code> environment variable.</p>
|
||||
<p>If any of the above commands errors out, please make sure to update your Cuda version.</p>
|
||||
<ol start="2">
|
||||
<li>Create a new app and add <a href="https://github.com/huggingface/candle/tree/main/candle-core"><code>candle-core</code></a> with Cuda support.</li>
|
||||
</ol>
|
||||
<p>Start by creating a new cargo:</p>
|
||||
<pre><code class="language-bash">cargo new myapp
|
||||
cd myapp
|
||||
</code></pre>
|
||||
<p>Make sure to add the <code>candle-core</code> crate with the cuda feature:</p>
|
||||
<pre><code class="language-bash">cargo add --git https://github.com/huggingface/candle.git candle-core --features "cuda"
|
||||
</code></pre>
|
||||
<p>Run <code>cargo build</code> to make sure everything can be correctly built.</p>
|
||||
<pre><code class="language-bash">cargo build
|
||||
</code></pre>
|
||||
<p><strong>Without Cuda support</strong>:</p>
|
||||
<p>Create a new app and add <a href="https://github.com/huggingface/candle/tree/main/candle-core"><code>candle-core</code></a> as follows:</p>
|
||||
<pre><code class="language-bash">cargo new myapp
|
||||
cd myapp
|
||||
cargo add --git https://github.com/huggingface/candle.git candle-core
|
||||
</code></pre>
|
||||
<p>Finally, run <code>cargo build</code> to make sure everything can be correctly built.</p>
|
||||
<pre><code class="language-bash">cargo build
|
||||
</code></pre>
|
||||
<p><strong>With mkl support</strong></p>
|
||||
<p>You can also see the <code>mkl</code> feature which could be interesting to get faster inference on CPU. <a href="guide/./advanced/mkl.html">Using mkl</a></p>
|
||||
<div style="break-before: page; page-break-before: always;"></div><h1 id="hello-world"><a class="header" href="#hello-world">Hello world!</a></h1>
|
||||
<p>We will now create the hello world of the ML world, building a model capable of solving MNIST dataset.</p>
|
||||
<p>Open <code>src/main.rs</code> and fill in this content:</p>
|
||||
<pre><pre class="playground"><code class="language-rust"><span class="boring">extern crate candle_core;
|
||||
</span>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(())
|
||||
}</code></pre></pre>
|
||||
<p>Everything should now run with:</p>
|
||||
<pre><code class="language-bash">cargo run --release
|
||||
</code></pre>
|
||||
<h2 id="using-a-linear-layer"><a class="header" href="#using-a-linear-layer">Using a <code>Linear</code> layer.</a></h2>
|
||||
<p>Now that we have this, we might want to complexify things a bit, for instance by adding <code>bias</code> and creating
|
||||
the classical <code>Linear</code> layer. We can do as such</p>
|
||||
<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
|
||||
</span><span class="boring">fn main() {
|
||||
</span><span class="boring">extern crate candle_core;
|
||||
</span><span class="boring">use candle_core::{Device, Result, Tensor};
|
||||
</span>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)
|
||||
}
|
||||
}
|
||||
<span class="boring">}</span></code></pre></pre>
|
||||
<p>This will change the model running code into a new function</p>
|
||||
<pre><pre class="playground"><code class="language-rust"><span class="boring">extern crate candle_core;
|
||||
</span><span class="boring">use candle_core::{Device, Result, Tensor};
|
||||
</span><span class="boring">struct Linear{
|
||||
</span><span class="boring"> weight: Tensor,
|
||||
</span><span class="boring"> bias: Tensor,
|
||||
</span><span class="boring">}
|
||||
</span><span class="boring">impl Linear{
|
||||
</span><span class="boring"> fn forward(&self, x: &Tensor) -> Result<Tensor> {
|
||||
</span><span class="boring"> let x = x.matmul(&self.weight)?;
|
||||
</span><span class="boring"> x.broadcast_add(&self.bias)
|
||||
</span><span class="boring"> }
|
||||
</span><span class="boring">}
|
||||
</span><span class="boring">
|
||||
</span><span class="boring">struct Model {
|
||||
</span><span class="boring"> first: Linear,
|
||||
</span><span class="boring"> second: Linear,
|
||||
</span><span class="boring">}
|
||||
</span><span class="boring">
|
||||
</span><span class="boring">impl Model {
|
||||
</span><span class="boring"> fn forward(&self, image: &Tensor) -> Result<Tensor> {
|
||||
</span><span class="boring"> let x = self.first.forward(image)?;
|
||||
</span><span class="boring"> let x = x.relu()?;
|
||||
</span><span class="boring"> self.second.forward(&x)
|
||||
</span><span class="boring"> }
|
||||
</span><span class="boring">}
|
||||
</span>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(())
|
||||
}</code></pre></pre>
|
||||
<p>Now it works, it is a great way to create your own layers.
|
||||
But most of the classical layers are already implemented in <a href="https://github.com/huggingface/candle/tree/main/candle-nn">candle-nn</a>.</p>
|
||||
<h2 id="using-candle_nn"><a class="header" href="#using-candle_nn">Using <code>candle_nn</code>.</a></h2>
|
||||
<p>For instance <a href="https://github.com/huggingface/candle/blob/main/candle-nn/src/linear.rs">Linear</a> 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.</p>
|
||||
<p>So instead we can simplify our example:</p>
|
||||
<pre><code class="language-bash">cargo add --git https://github.com/huggingface/candle.git candle-nn
|
||||
</code></pre>
|
||||
<p>And rewrite our examples using it</p>
|
||||
<pre><pre class="playground"><code class="language-rust"><span class="boring">extern crate candle_core;
|
||||
</span><span class="boring">extern crate candle_nn;
|
||||
</span>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(())
|
||||
}</code></pre></pre>
|
||||
<p>Feel free to modify this example to use <code>Conv2d</code> to create a classical convnet instead.</p>
|
||||
<p>Now that we have the running dummy code we can get to more advanced topics:</p>
|
||||
<ul>
|
||||
<li><a href="guide/../guide/cheatsheet.html">For PyTorch users</a></li>
|
||||
<li><a href="guide/../inference/inference.html">Running existing models</a></li>
|
||||
<li><a href="guide/../training/training.html">Training models</a></li>
|
||||
</ul>
|
||||
<div style="break-before: page; page-break-before: always;"></div><h1 id="pytorch-cheatsheet"><a class="header" href="#pytorch-cheatsheet">Pytorch cheatsheet</a></h1>
|
||||
<p>Cheatsheet:</p>
|
||||
<div class="table-wrapper"><table><thead><tr><th></th><th>Using PyTorch</th><th>Using Candle</th></tr></thead><tbody>
|
||||
<tr><td>Creation</td><td><code>torch.Tensor([[1, 2], [3, 4]])</code></td><td><code>Tensor::new(&[[1f32, 2.], [3., 4.]], &Device::Cpu)?</code></td></tr>
|
||||
<tr><td>Creation</td><td><code>torch.zeros((2, 2))</code></td><td><code>Tensor::zeros((2, 2), DType::F32, &Device::Cpu)?</code></td></tr>
|
||||
<tr><td>Indexing</td><td><code>tensor[:, :4]</code></td><td><code>tensor.i((.., ..4))?</code></td></tr>
|
||||
<tr><td>Operations</td><td><code>tensor.view((2, 2))</code></td><td><code>tensor.reshape((2, 2))?</code></td></tr>
|
||||
<tr><td>Operations</td><td><code>a.matmul(b)</code></td><td><code>a.matmul(&b)?</code></td></tr>
|
||||
<tr><td>Arithmetic</td><td><code>a + b</code></td><td><code>&a + &b</code></td></tr>
|
||||
<tr><td>Device</td><td><code>tensor.to(device="cuda")</code></td><td><code>tensor.to_device(&Device::new_cuda(0)?)?</code></td></tr>
|
||||
<tr><td>Dtype</td><td><code>tensor.to(dtype=torch.float16)</code></td><td><code>tensor.to_dtype(&DType::F16)?</code></td></tr>
|
||||
<tr><td>Saving</td><td><code>torch.save({"A": A}, "model.bin")</code></td><td><code>candle::safetensors::save(&HashMap::from([("A", A)]), "model.safetensors")?</code></td></tr>
|
||||
<tr><td>Loading</td><td><code>weights = torch.load("model.bin")</code></td><td><code>candle::safetensors::load("model.safetensors", &device)</code></td></tr>
|
||||
</tbody></table>
|
||||
</div><div style="break-before: page; page-break-before: always;"></div><h1 id="running-a-model"><a class="header" href="#running-a-model">Running a model</a></h1>
|
||||
<p>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 <a href="https://github.com/huggingface/safetensors"><code>safetensors</code></a> format.</p>
|
||||
<p>Let's get started by running an old model : <code>bert-base-uncased</code>.</p>
|
||||
<div style="break-before: page; page-break-before: always;"></div><h1 id="using-the-hub"><a class="header" href="#using-the-hub">Using the hub</a></h1>
|
||||
<p>Install the <a href="https://github.com/huggingface/hf-hub"><code>hf-hub</code></a> crate:</p>
|
||||
<pre><code class="language-bash">cargo add hf-hub
|
||||
</code></pre>
|
||||
<p>Then let's start by downloading the <a href="https://huggingface.co/bert-base-uncased/tree/main">model file</a>.</p>
|
||||
<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
|
||||
</span><span class="boring">fn main() {
|
||||
</span><span class="boring">extern crate candle_core;
|
||||
</span><span class="boring">extern crate hf_hub;
|
||||
</span>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);
|
||||
<span class="boring">}</span></code></pre></pre>
|
||||
<p>We now have access to all the <a href="https://huggingface.co/bert-base-uncased?show_tensors=true">tensors</a> within the file.</p>
|
||||
<p>You can check all the names of the tensors <a href="https://huggingface.co/bert-base-uncased?show_tensors=true">here</a></p>
|
||||
<h2 id="using-async"><a class="header" href="#using-async">Using async</a></h2>
|
||||
<p><code>hf-hub</code> comes with an async API.</p>
|
||||
<pre><code class="language-bash">cargo add hf-hub --features tokio
|
||||
</code></pre>
|
||||
<pre><code class="language-rust ignore"><span class="boring">This is tested directly in examples crate because it needs external dependencies unfortunately:
|
||||
</span><span class="boring">See [this](https://github.com/rust-lang/mdBook/issues/706)
|
||||
</span>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();</code></pre>
|
||||
<h2 id="using-in-a-real-model"><a class="header" href="#using-in-a-real-model">Using in a real model.</a></h2>
|
||||
<p>Now that we have our weights, we can use them in our bert architecture:</p>
|
||||
<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
|
||||
</span><span class="boring">fn main() {
|
||||
</span><span class="boring">extern crate candle_core;
|
||||
</span><span class="boring">extern crate candle_nn;
|
||||
</span><span class="boring">extern crate hf_hub;
|
||||
</span><span class="boring">use hf_hub::api::sync::Api;
|
||||
</span><span class="boring">
|
||||
</span><span class="boring">let api = Api::new().unwrap();
|
||||
</span><span class="boring">let repo = api.model("bert-base-uncased".to_string());
|
||||
</span><span class="boring">
|
||||
</span><span class="boring">let weights = repo.get("model.safetensors").unwrap();
|
||||
</span>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();
|
||||
<span class="boring">}</span></code></pre></pre>
|
||||
<p>For a full reference, you can check out the full <a href="https://github.com/LaurentMazare/candle/tree/main/candle-examples/examples/bert">bert</a> example.</p>
|
||||
<h2 id="memory-mapping"><a class="header" href="#memory-mapping">Memory mapping</a></h2>
|
||||
<p>For more efficient loading, instead of reading the file, you could use <a href="https://docs.rs/memmap2/latest/memmap2/"><code>memmap2</code></a></p>
|
||||
<p><strong>Note</strong>: Be careful about memory mapping it seems to cause issues on <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/5893">Windows, WSL</a>
|
||||
and will definitely be slower on network mounted disk, because it will issue more read calls.</p>
|
||||
<pre><code class="language-rust ignore">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();</code></pre>
|
||||
<p><strong>Note</strong>: This operation is <strong>unsafe</strong>. <a href="https://docs.rs/memmap2/latest/memmap2/struct.Mmap.html#safety">See the safety notice</a>.
|
||||
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.</p>
|
||||
<h2 id="tensor-parallel-sharding"><a class="header" href="#tensor-parallel-sharding">Tensor Parallel Sharding</a></h2>
|
||||
<p>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.</p>
|
||||
<p>For that you need to use <a href="https://crates.io/crates/safetensors"><code>safetensors</code></a> directly.</p>
|
||||
<pre><code class="language-bash">cargo add safetensors
|
||||
</code></pre>
|
||||
<pre><code class="language-rust ignore">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();</code></pre>
|
||||
<div style="break-before: page; page-break-before: always;"></div><h1 id="error-management"><a class="header" href="#error-management">Error management</a></h1>
|
||||
<p>You might have seen in the code base a lot of <code>.unwrap()</code> or <code>?</code>.
|
||||
If you're unfamiliar with Rust check out the <a href="https://doc.rust-lang.org/book/ch09-02-recoverable-errors-with-result.html">Rust book</a>
|
||||
for more information.</p>
|
||||
<p>What's important to know though, is that if you want to know <em>where</em> a particular operation failed
|
||||
You can simply use <code>RUST_BACKTRACE=1</code> to get the location of where the model actually failed.</p>
|
||||
<p>Let's see on failing code:</p>
|
||||
<pre><code class="language-rust ignore">let x = Tensor::zeros((1, 784), DType::F32, &device)?;
|
||||
let y = Tensor::zeros((1, 784), DType::F32, &device)?;
|
||||
let z = x.matmul(&y)?;</code></pre>
|
||||
<p>Will print at runtime:</p>
|
||||
<pre><code class="language-bash">Error: ShapeMismatchBinaryOp { lhs: [1, 784], rhs: [1, 784], op: "matmul" }
|
||||
</code></pre>
|
||||
<p>After adding <code>RUST_BACKTRACE=1</code>:</p>
|
||||
<pre><code class="language-bash">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" }] }
|
||||
</code></pre>
|
||||
<p>Not super pretty at the moment, but we can see error occurred on <code>{ fn: "myapp::main", file: "./src/main.rs", line: 29 }</code></p>
|
||||
<p>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 <a href="https://docs.rs/anyhow/latest/anyhow/"><code>anyhow</code></a> for that.
|
||||
The library is still young, please <a href="https://github.com/LaurentMazare/candle/issues">report</a> any issues detecting where an error is coming from.</p>
|
||||
<h2 id="cuda-error-management"><a class="header" href="#cuda-error-management">Cuda error management</a></h2>
|
||||
<p>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.</p>
|
||||
<p>One way to avoid this is to use <code>CUDA_LAUNCH_BLOCKING=1</code> 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 <code>CudaSlice</code> only.</p>
|
||||
<p>If this occurs, you can use <a href="https://docs.nvidia.com/compute-sanitizer/ComputeSanitizer/index.html"><code>compute-sanitizer</code></a>
|
||||
This tool is like <code>valgrind</code> but for cuda. It will help locate the errors in the kernels.</p>
|
||||
<div style="break-before: page; page-break-before: always;"></div><h1 id="training"><a class="header" href="#training">Training</a></h1>
|
||||
<p>Training starts with data. We're going to use the huggingface hub and
|
||||
start with the Hello world dataset of machine learning, MNIST.</p>
|
||||
<p>Let's start with downloading <code>MNIST</code> from <a href="https://huggingface.co/datasets/mnist">huggingface</a>.</p>
|
||||
<p>This requires <a href="https://github.com/huggingface/hf-hub"><code>hf-hub</code></a>.</p>
|
||||
<pre><code class="language-bash">cargo add hf-hub
|
||||
</code></pre>
|
||||
<p>This is going to be very hands-on for now.</p>
|
||||
<pre><code class="language-rust ignore"></code></pre>
|
||||
<p>This uses the standardized <code>parquet</code> files from the <code>refs/convert/parquet</code> branch on every dataset.
|
||||
Our handles are now [<code>parquet::file::serialized_reader::SerializedFileReader</code>].</p>
|
||||
<p>We can inspect the content of the files with:</p>
|
||||
<pre><code class="language-rust ignore"></code></pre>
|
||||
<p>You should see something like:</p>
|
||||
<pre><code class="language-bash">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, ....]
|
||||
</code></pre>
|
||||
<p>So each row contains 2 columns (image, label) with image being saved as bytes.
|
||||
Let's put them into a useful struct.</p>
|
||||
<div style="break-before: page; page-break-before: always;"></div><h1 id="simplified"><a class="header" href="#simplified">Simplified</a></h1>
|
||||
<h2 id="how-its-works"><a class="header" href="#how-its-works">How its works</a></h2>
|
||||
<p>This program implements a neural network to predict the winner of the second round of elections based on the results of the first round.</p>
|
||||
<p>Basic moments:</p>
|
||||
<ol>
|
||||
<li>A multilayer perceptron with two hidden layers is used. The first hidden layer has 4 neurons, the second has 2 neurons.</li>
|
||||
<li>The input is a vector of 2 numbers - the percentage of votes for the first and second candidates in the first stage.</li>
|
||||
<li>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.</li>
|
||||
<li>For training, samples with real data on the results of the first and second stages of different elections are used.</li>
|
||||
<li>The model is trained by backpropagation using gradient descent and the cross-entropy loss function.</li>
|
||||
<li>Model parameters (weights of neurons) are initialized randomly, then optimized during training.</li>
|
||||
<li>After training, the model is tested on a deferred sample to evaluate the accuracy.</li>
|
||||
<li>If the accuracy on the test set is below 100%, the model is considered underfit and the learning process is repeated.</li>
|
||||
</ol>
|
||||
<p>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.</p>
|
||||
<pre><code class="language-rust ignore">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)
|
||||
}
|
||||
}
|
||||
</code></pre>
|
||||
<pre><code class="language-rust ignore">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)
|
||||
}
|
||||
}</code></pre>
|
||||
<pre><code class="language-rust ignore">#[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(())
|
||||
|
||||
}</code></pre>
|
||||
<h2 id="example-output"><a class="header" href="#example-output">Example output</a></h2>
|
||||
<pre><code class="language-bash">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
|
||||
</code></pre>
|
||||
<div style="break-before: page; page-break-before: always;"></div><h1 id="mnist"><a class="header" href="#mnist">MNIST</a></h1>
|
||||
<p>So we now have downloaded the MNIST parquet files, let's put them in a simple struct.</p>
|
||||
<pre><code class="language-rust ignore">
|
||||
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,
|
||||
};
|
||||
</code></pre>
|
||||
<p>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.</p>
|
||||
|
||||
</main>
|
||||
|
||||
<nav class="nav-wrapper" aria-label="Page navigation">
|
||||
<!-- Mobile navigation buttons -->
|
||||
|
||||
|
||||
<div style="clear: both"></div>
|
||||
</nav>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<nav class="nav-wide-wrapper" aria-label="Page navigation">
|
||||
|
||||
</nav>
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
<script>
|
||||
window.playground_copyable = true;
|
||||
</script>
|
||||
|
||||
|
||||
<script src="elasticlunr.min.js"></script>
|
||||
<script src="mark.min.js"></script>
|
||||
<script src="searcher.js"></script>
|
||||
|
||||
<script src="clipboard.min.js"></script>
|
||||
<script src="highlight.js"></script>
|
||||
<script src="book.js"></script>
|
||||
|
||||
<!-- Custom JS scripts -->
|
||||
|
||||
<script>
|
||||
window.addEventListener('load', function() {
|
||||
window.setTimeout(window.print, 100);
|
||||
});
|
||||
</script>
|
||||
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
526
searcher.js
Normal file
526
searcher.js
Normal file
@ -0,0 +1,526 @@
|
||||
'use strict';
|
||||
|
||||
/* global Mark, elasticlunr, path_to_root */
|
||||
|
||||
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;
|
||||
}
|
||||
|
||||
// eslint-disable-next-line max-len
|
||||
// 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;
|
||||
};
|
||||
}
|
||||
|
||||
const search_wrap = document.getElementById('search-wrapper'),
|
||||
searchbar = document.getElementById('searchbar'),
|
||||
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'),
|
||||
|
||||
mark_exclude = [],
|
||||
marker = new Mark(content),
|
||||
URL_SEARCH_PARAM = 'search',
|
||||
URL_MARK_PARAM = 'highlight',
|
||||
|
||||
SEARCH_HOTKEY_KEYCODE = 83,
|
||||
ESCAPE_KEYCODE = 27,
|
||||
DOWN_KEYCODE = 40,
|
||||
UP_KEYCODE = 38,
|
||||
SELECT_KEYCODE = 13;
|
||||
|
||||
let current_searchterm = '',
|
||||
doc_urls = [],
|
||||
search_options = {
|
||||
bool: 'AND',
|
||||
expand: true,
|
||||
fields: {
|
||||
title: {boost: 1},
|
||||
body: {boost: 1},
|
||||
breadcrumbs: {boost: 0},
|
||||
},
|
||||
},
|
||||
searchindex = null,
|
||||
results_options = {
|
||||
teaser_word_count: 30,
|
||||
limit_results: 30,
|
||||
},
|
||||
teaser_count = 0;
|
||||
|
||||
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) {
|
||||
const a = document.createElement('a');
|
||||
a.href = url;
|
||||
return {
|
||||
source: url,
|
||||
protocol: a.protocol.replace(':', ''),
|
||||
host: a.hostname,
|
||||
port: a.port,
|
||||
params: (function() {
|
||||
const ret = {};
|
||||
const seg = a.search.replace(/^\?/, '').split('&');
|
||||
for (const part of seg) {
|
||||
if (!part) {
|
||||
continue;
|
||||
}
|
||||
const s = part.split('=');
|
||||
ret[s[0]] = s[1];
|
||||
}
|
||||
return ret;
|
||||
})(),
|
||||
file: (a.pathname.match(/\/([^/?#]+)$/i) || ['', ''])[1],
|
||||
hash: a.hash.replace('#', ''),
|
||||
path: a.pathname.replace(/^([^/])/, '/$1'),
|
||||
};
|
||||
}
|
||||
|
||||
// Helper to recreate a url string from its building blocks.
|
||||
function renderURL(urlobject) {
|
||||
let url = urlobject.protocol + '://' + urlobject.host;
|
||||
if (urlobject.port !== '') {
|
||||
url += ':' + urlobject.port;
|
||||
}
|
||||
url += urlobject.path;
|
||||
let joiner = '?';
|
||||
for (const prop in urlobject.params) {
|
||||
if (Object.prototype.hasOwnProperty.call(urlobject.params, prop)) {
|
||||
url += joiner + prop + '=' + urlobject.params[prop];
|
||||
joiner = '&';
|
||||
}
|
||||
}
|
||||
if (urlobject.hash !== '') {
|
||||
url += '#' + urlobject.hash;
|
||||
}
|
||||
return url;
|
||||
}
|
||||
|
||||
// Helper to escape html special chars for displaying the teasers
|
||||
const escapeHTML = (function() {
|
||||
const MAP = {
|
||||
'&': '&',
|
||||
'<': '<',
|
||||
'>': '>',
|
||||
'"': '"',
|
||||
'\'': ''',
|
||||
};
|
||||
const 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) {
|
||||
const teaser = makeTeaser(escapeHTML(result.doc.body), searchterms);
|
||||
teaser_count++;
|
||||
|
||||
// The ?URL_MARK_PARAM= parameter belongs inbetween the page and the #heading-anchor
|
||||
const 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).
|
||||
const encoded_search = encodeURIComponent(searchterms.join(' ')).replace(/'/g, '%27');
|
||||
|
||||
return '<a href="' + path_to_root + url[0] + '?' + URL_MARK_PARAM + '=' + encoded_search
|
||||
+ '#' + url[1] + '" aria-details="teaser_' + teaser_count + '">'
|
||||
+ result.doc.breadcrumbs + '</a>' + '<span class="teaser" id="teaser_' + teaser_count
|
||||
+ '" aria-label="Search Result Teaser">' + teaser + '</span>';
|
||||
}
|
||||
|
||||
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 <em>.
|
||||
const stemmed_searchterms = searchterms.map(function(w) {
|
||||
return elasticlunr.stemmer(w.toLowerCase());
|
||||
});
|
||||
const searchterm_weight = 40;
|
||||
const weighted = []; // contains elements of ["word", weight, index_in_document]
|
||||
// split in sentences, then words
|
||||
const sentences = body.toLowerCase().split('. ');
|
||||
let index = 0;
|
||||
let value = 0;
|
||||
let searchterm_found = false;
|
||||
for (const sentenceindex in sentences) {
|
||||
const words = sentences[sentenceindex].split(' ');
|
||||
value = 8;
|
||||
for (const wordindex in words) {
|
||||
const word = words[wordindex];
|
||||
if (word.length > 0) {
|
||||
for (const 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;
|
||||
}
|
||||
|
||||
const window_weight = [];
|
||||
const window_size = Math.min(weighted.length, results_options.teaser_word_count);
|
||||
|
||||
let cur_sum = 0;
|
||||
for (let wordindex = 0; wordindex < window_size; wordindex++) {
|
||||
cur_sum += weighted[wordindex][1];
|
||||
}
|
||||
window_weight.push(cur_sum);
|
||||
for (let 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);
|
||||
}
|
||||
|
||||
let max_sum_window_index = 0;
|
||||
if (searchterm_found) {
|
||||
let max_sum = 0;
|
||||
// backwards
|
||||
for (let 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 <em/> around searchterms
|
||||
const teaser_split = [];
|
||||
index = weighted[max_sum_window_index][2];
|
||||
for (let i = max_sum_window_index; i < max_sum_window_index + window_size; i++) {
|
||||
const 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('<em>');
|
||||
}
|
||||
index = word[2] + word[0].length;
|
||||
teaser_split.push(body.substring(word[2], index));
|
||||
if (word[1] === searchterm_weight) {
|
||||
teaser_split.push('</em>');
|
||||
}
|
||||
}
|
||||
|
||||
return teaser_split.join('');
|
||||
}
|
||||
|
||||
function init(config) {
|
||||
results_options = config.results_options;
|
||||
search_options = config.search_options;
|
||||
doc_urls = config.doc_urls;
|
||||
searchindex = elasticlunr.Index.load(config.index);
|
||||
|
||||
// Set up events
|
||||
searchicon.addEventListener('click', () => {
|
||||
searchIconClickHandler();
|
||||
}, false);
|
||||
searchbar.addEventListener('keyup', () => {
|
||||
searchbarKeyUpHandler();
|
||||
}, false);
|
||||
document.addEventListener('keydown', e => {
|
||||
globalKeyHandler(e);
|
||||
}, false);
|
||||
// If the user uses the browser buttons, do the same as if a reload happened
|
||||
window.onpopstate = () => {
|
||||
doSearchOrMarkFromUrl();
|
||||
};
|
||||
// Suppress "submit" events so the page doesn't reload when the user presses Enter
|
||||
document.addEventListener('submit', 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
|
||||
const 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
|
||||
const url = parseURL(window.location.href);
|
||||
if (Object.prototype.hasOwnProperty.call(url.params, 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 (Object.prototype.hasOwnProperty.call(url.params, URL_MARK_PARAM)) {
|
||||
const words = decodeURIComponent(url.params[URL_MARK_PARAM]).split(' ');
|
||||
marker.mark(words, {
|
||||
exclude: mark_exclude,
|
||||
});
|
||||
|
||||
const markers = document.querySelectorAll('mark');
|
||||
const hide = () => {
|
||||
for (let i = 0; i < markers.length; i++) {
|
||||
markers[i].classList.add('fade-out');
|
||||
window.setTimeout(() => {
|
||||
marker.unmark();
|
||||
}, 300);
|
||||
}
|
||||
};
|
||||
|
||||
for (let 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
|
||||
const focused = searchresults.querySelector('li.focus');
|
||||
if (!focused) {
|
||||
return;
|
||||
}
|
||||
e.preventDefault();
|
||||
if (e.keyCode === DOWN_KEYCODE) {
|
||||
const next = focused.nextElementSibling;
|
||||
if (next) {
|
||||
focused.classList.remove('focus');
|
||||
next.classList.add('focus');
|
||||
}
|
||||
} else if (e.keyCode === UP_KEYCODE) {
|
||||
focused.classList.remove('focus');
|
||||
const 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');
|
||||
const results = searchresults.children;
|
||||
for (let 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() {
|
||||
const 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) {
|
||||
const url = parseURL(window.location.href);
|
||||
const first_search = !Object.prototype.hasOwnProperty.call(url.params, 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
|
||||
const results = searchindex.search(searchterm, search_options);
|
||||
const resultcount = Math.min(results.length, results_options.limit_results);
|
||||
|
||||
// Display search metrics
|
||||
searchresults_header.innerText = formatSearchMetric(resultcount, searchterm);
|
||||
|
||||
// Clear and insert results
|
||||
const searchterms = searchterm.split(' ');
|
||||
removeChildren(searchresults);
|
||||
for (let i = 0; i < resultcount ; i++) {
|
||||
const resultElem = document.createElement('li');
|
||||
resultElem.innerHTML = formatSearchResult(results[i], searchterms);
|
||||
searchresults.appendChild(resultElem);
|
||||
}
|
||||
|
||||
// Display results
|
||||
showResults(true);
|
||||
}
|
||||
|
||||
function loadScript(url, id) {
|
||||
const script = document.createElement('script');
|
||||
script.src = url;
|
||||
script.id = id;
|
||||
script.onload = () => init(window.search);
|
||||
script.onerror = error => {
|
||||
console.error(`Failed to load \`${url}\`: ${error}`);
|
||||
};
|
||||
document.head.append(script);
|
||||
}
|
||||
|
||||
loadScript(path_to_root + 'searchindex.js', 'search-index');
|
||||
|
||||
// Exported functions
|
||||
search.hasFocus = hasFocus;
|
||||
})(window.search);
|
||||
1
searchindex.js
Normal file
1
searchindex.js
Normal file
File diff suppressed because one or more lines are too long
196
simplified.rs
Normal file
196
simplified.rs
Normal file
@ -0,0 +1,196 @@
|
||||
//! #A simplified example in Rust of training a neural network and then using it based on the Candle Framework by Hugging Face.
|
||||
//! 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<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)
|
||||
}
|
||||
}
|
||||
|
||||
// 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<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(())
|
||||
|
||||
}
|
||||
// ANCHOR_END: book_training_simplified3
|
||||
|
||||
// ANCHOR: book_training_simplified2
|
||||
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)
|
||||
}
|
||||
}
|
||||
// ANCHOR_END: book_training_simplified2
|
||||
|
||||
|
||||
}
|
||||
32
toc.html
Normal file
32
toc.html
Normal file
@ -0,0 +1,32 @@
|
||||
<!DOCTYPE HTML>
|
||||
<html lang="en" class="light" dir="ltr">
|
||||
<head>
|
||||
<!-- sidebar iframe generated using mdBook
|
||||
|
||||
This is a frame, and not included directly in the page, to control the total size of the
|
||||
book. The TOC contains an entry for each page, so if each page includes a copy of the TOC,
|
||||
the total size of the page becomes O(n**2).
|
||||
|
||||
The frame is only used as a fallback when JS is turned off. When it's on, the sidebar is
|
||||
instead added to the main page by `toc.js` instead. The JavaScript mode is better
|
||||
because, when running in a `file:///` URL, the iframed page would not be Same-Origin as
|
||||
the rest of the page, so the sidebar and the main page theme would fall out of sync.
|
||||
-->
|
||||
<meta charset="UTF-8">
|
||||
<meta name="robots" content="noindex">
|
||||
<!-- Custom HTML head -->
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1">
|
||||
<meta name="theme-color" content="#ffffff">
|
||||
<link rel="stylesheet" href="css/variables.css">
|
||||
<link rel="stylesheet" href="css/general.css">
|
||||
<link rel="stylesheet" href="css/chrome.css">
|
||||
<link rel="stylesheet" href="css/print.css" media="print">
|
||||
<!-- Fonts -->
|
||||
<link rel="stylesheet" href="FontAwesome/css/font-awesome.css">
|
||||
<link rel="stylesheet" href="fonts/fonts.css">
|
||||
<!-- Custom theme stylesheets -->
|
||||
</head>
|
||||
<body class="sidebar-iframe-inner">
|
||||
<ol class="chapter"><li class="chapter-item expanded affix "><a href="index.html" target="_parent">Introduction</a></li><li class="chapter-item expanded affix "><li class="part-title">User Guide</li><li class="chapter-item expanded "><a href="guide/installation.html" target="_parent"><strong aria-hidden="true">1.</strong> Installation</a></li><li class="chapter-item expanded "><a href="guide/hello_world.html" target="_parent"><strong aria-hidden="true">2.</strong> Hello World - MNIST</a></li><li class="chapter-item expanded "><a href="guide/cheatsheet.html" target="_parent"><strong aria-hidden="true">3.</strong> PyTorch cheatsheet</a></li><li class="chapter-item expanded affix "><li class="part-title">Reference Guide</li><li class="chapter-item expanded "><a href="inference/inference.html" target="_parent"><strong aria-hidden="true">4.</strong> Running a model</a></li><li><ol class="section"><li class="chapter-item expanded "><a href="inference/hub.html" target="_parent"><strong aria-hidden="true">4.1.</strong> Using the hub</a></li></ol></li><li class="chapter-item expanded "><a href="error_manage.html" target="_parent"><strong aria-hidden="true">5.</strong> Error management</a></li><li class="chapter-item expanded "><a href="training/training.html" target="_parent"><strong aria-hidden="true">6.</strong> Training</a></li><li><ol class="section"><li class="chapter-item expanded "><a href="training/simplified.html" target="_parent"><strong aria-hidden="true">6.1.</strong> Simplified</a></li><li class="chapter-item expanded "><a href="training/mnist.html" target="_parent"><strong aria-hidden="true">6.2.</strong> MNIST</a></li><li class="chapter-item expanded "><div><strong aria-hidden="true">6.3.</strong> Fine-tuning</div></li><li class="chapter-item expanded "><div><strong aria-hidden="true">6.4.</strong> Serialization</div></li></ol></li><li class="chapter-item expanded "><div><strong aria-hidden="true">7.</strong> Advanced Cuda usage</div></li><li><ol class="section"><li class="chapter-item expanded "><div><strong aria-hidden="true">7.1.</strong> Writing a custom kernel</div></li><li class="chapter-item expanded "><div><strong aria-hidden="true">7.2.</strong> Porting a custom kernel</div></li></ol></li><li class="chapter-item expanded "><div><strong aria-hidden="true">8.</strong> Using MKL</div></li><li class="chapter-item expanded "><div><strong aria-hidden="true">9.</strong> Creating apps</div></li><li><ol class="section"><li class="chapter-item expanded "><div><strong aria-hidden="true">9.1.</strong> Creating a WASM app</div></li><li class="chapter-item expanded "><div><strong aria-hidden="true">9.2.</strong> Creating a REST api webserver</div></li><li class="chapter-item expanded "><div><strong aria-hidden="true">9.3.</strong> Creating a desktop Tauri app</div></li></ol></li></ol>
|
||||
</body>
|
||||
</html>
|
||||
70
toc.js
Normal file
70
toc.js
Normal file
@ -0,0 +1,70 @@
|
||||
// Populate the sidebar
|
||||
//
|
||||
// This is a script, and not included directly in the page, to control the total size of the book.
|
||||
// The TOC contains an entry for each page, so if each page includes a copy of the TOC,
|
||||
// the total size of the page becomes O(n**2).
|
||||
class MDBookSidebarScrollbox extends HTMLElement {
|
||||
constructor() {
|
||||
super();
|
||||
}
|
||||
connectedCallback() {
|
||||
this.innerHTML = '<ol class="chapter"><li class="chapter-item expanded affix "><a href="index.html">Introduction</a></li><li class="chapter-item expanded affix "><li class="part-title">User Guide</li><li class="chapter-item expanded "><a href="guide/installation.html"><strong aria-hidden="true">1.</strong> Installation</a></li><li class="chapter-item expanded "><a href="guide/hello_world.html"><strong aria-hidden="true">2.</strong> Hello World - MNIST</a></li><li class="chapter-item expanded "><a href="guide/cheatsheet.html"><strong aria-hidden="true">3.</strong> PyTorch cheatsheet</a></li><li class="chapter-item expanded affix "><li class="part-title">Reference Guide</li><li class="chapter-item expanded "><a href="inference/inference.html"><strong aria-hidden="true">4.</strong> Running a model</a></li><li><ol class="section"><li class="chapter-item expanded "><a href="inference/hub.html"><strong aria-hidden="true">4.1.</strong> Using the hub</a></li></ol></li><li class="chapter-item expanded "><a href="error_manage.html"><strong aria-hidden="true">5.</strong> Error management</a></li><li class="chapter-item expanded "><a href="training/training.html"><strong aria-hidden="true">6.</strong> Training</a></li><li><ol class="section"><li class="chapter-item expanded "><a href="training/simplified.html"><strong aria-hidden="true">6.1.</strong> Simplified</a></li><li class="chapter-item expanded "><a href="training/mnist.html"><strong aria-hidden="true">6.2.</strong> MNIST</a></li><li class="chapter-item expanded "><div><strong aria-hidden="true">6.3.</strong> Fine-tuning</div></li><li class="chapter-item expanded "><div><strong aria-hidden="true">6.4.</strong> Serialization</div></li></ol></li><li class="chapter-item expanded "><div><strong aria-hidden="true">7.</strong> Advanced Cuda usage</div></li><li><ol class="section"><li class="chapter-item expanded "><div><strong aria-hidden="true">7.1.</strong> Writing a custom kernel</div></li><li class="chapter-item expanded "><div><strong aria-hidden="true">7.2.</strong> Porting a custom kernel</div></li></ol></li><li class="chapter-item expanded "><div><strong aria-hidden="true">8.</strong> Using MKL</div></li><li class="chapter-item expanded "><div><strong aria-hidden="true">9.</strong> Creating apps</div></li><li><ol class="section"><li class="chapter-item expanded "><div><strong aria-hidden="true">9.1.</strong> Creating a WASM app</div></li><li class="chapter-item expanded "><div><strong aria-hidden="true">9.2.</strong> Creating a REST api webserver</div></li><li class="chapter-item expanded "><div><strong aria-hidden="true">9.3.</strong> Creating a desktop Tauri app</div></li></ol></li></ol>';
|
||||
// Set the current, active page, and reveal it if it's hidden
|
||||
let current_page = document.location.href.toString().split("#")[0];
|
||||
if (current_page.endsWith("/")) {
|
||||
current_page += "index.html";
|
||||
}
|
||||
var links = Array.prototype.slice.call(this.querySelectorAll("a"));
|
||||
var l = links.length;
|
||||
for (var i = 0; i < l; ++i) {
|
||||
var link = links[i];
|
||||
var href = link.getAttribute("href");
|
||||
if (href && !href.startsWith("#") && !/^(?:[a-z+]+:)?\/\//.test(href)) {
|
||||
link.href = path_to_root + href;
|
||||
}
|
||||
// The "index" page is supposed to alias the first chapter in the book.
|
||||
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opacity: 0.5;
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}
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<h1 id="mnist"><a class="header" href="#mnist">MNIST</a></h1>
|
||||
<p>So we now have downloaded the MNIST parquet files, let's put them in a simple struct.</p>
|
||||
<pre><code class="language-rust ignore">
|
||||
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,
|
||||
};
|
||||
</code></pre>
|
||||
<p>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.</p>
|
||||
|
||||
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395
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Normal file
395
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Normal file
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<h1 id="simplified"><a class="header" href="#simplified">Simplified</a></h1>
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<h2 id="how-its-works"><a class="header" href="#how-its-works">How its works</a></h2>
|
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<p>This program implements a neural network to predict the winner of the second round of elections based on the results of the first round.</p>
|
||||
<p>Basic moments:</p>
|
||||
<ol>
|
||||
<li>A multilayer perceptron with two hidden layers is used. The first hidden layer has 4 neurons, the second has 2 neurons.</li>
|
||||
<li>The input is a vector of 2 numbers - the percentage of votes for the first and second candidates in the first stage.</li>
|
||||
<li>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.</li>
|
||||
<li>For training, samples with real data on the results of the first and second stages of different elections are used.</li>
|
||||
<li>The model is trained by backpropagation using gradient descent and the cross-entropy loss function.</li>
|
||||
<li>Model parameters (weights of neurons) are initialized randomly, then optimized during training.</li>
|
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<li>After training, the model is tested on a deferred sample to evaluate the accuracy.</li>
|
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<li>If the accuracy on the test set is below 100%, the model is considered underfit and the learning process is repeated.</li>
|
||||
</ol>
|
||||
<p>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.</p>
|
||||
<pre><code class="language-rust ignore">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)
|
||||
}
|
||||
}
|
||||
</code></pre>
|
||||
<pre><code class="language-rust ignore">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)
|
||||
}
|
||||
}</code></pre>
|
||||
<pre><code class="language-rust ignore">#[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(())
|
||||
|
||||
}</code></pre>
|
||||
<h2 id="example-output"><a class="header" href="#example-output">Example output</a></h2>
|
||||
<pre><code class="language-bash">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
|
||||
</code></pre>
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<main>
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<h1 id="training"><a class="header" href="#training">Training</a></h1>
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<p>Training starts with data. We're going to use the huggingface hub and
|
||||
start with the Hello world dataset of machine learning, MNIST.</p>
|
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<p>Let's start with downloading <code>MNIST</code> from <a href="https://huggingface.co/datasets/mnist">huggingface</a>.</p>
|
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<p>This requires <a href="https://github.com/huggingface/hf-hub"><code>hf-hub</code></a>.</p>
|
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<pre><code class="language-bash">cargo add hf-hub
|
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</code></pre>
|
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<p>This is going to be very hands-on for now.</p>
|
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<pre><code class="language-rust ignore"></code></pre>
|
||||
<p>This uses the standardized <code>parquet</code> files from the <code>refs/convert/parquet</code> branch on every dataset.
|
||||
Our handles are now [<code>parquet::file::serialized_reader::SerializedFileReader</code>].</p>
|
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<p>We can inspect the content of the files with:</p>
|
||||
<pre><code class="language-rust ignore"></code></pre>
|
||||
<p>You should see something like:</p>
|
||||
<pre><code class="language-bash">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, ....]
|
||||
</code></pre>
|
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<p>So each row contains 2 columns (image, label) with image being saved as bytes.
|
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Let's put them into a useful struct.</p>
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