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pybindings.cpp
pybindings.cpp
pybindings.pyi
pybindings.pyi

README.md

ExecuTorch Python Module (WIP)

This Python module, named portable_lib, provides a set of functions and classes for loading and executing bundled programs. To install it, run the fullowing command:

CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON" pip install . --no-build-isolation

Or when installing the rest of dependencies:

install_executorch.sh --pybind

Link Backends

You can link the runtime against some backends to make sure a delegated or partitioned model can still run by Python module successfully:

CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_COREML=ON -DEXECUTORCH_BUILD_MPS=ON" \
pip install . --no-build-isolation

Similarly, when installing the rest of dependencies:

install_executorch.sh --pybind xnnpack coreml mps

Functions

  • _load_for_executorch(path: str, enable_etdump: bool = False): Load a module from a file.
  • _load_for_executorch_from_buffer(buffer: str, enable_etdump: bool = False): Load a module from a buffer.
  • _load_for_executorch_from_bundled_program(ptr: str, enable_etdump: bool = False): Load a module from a bundled program.
  • _load_bundled_program_from_buffer(buffer: str, non_const_pool_size: int = kDEFAULT_BUNDLED_INPUT_POOL_SIZE): Load a bundled program from a buffer.
  • _dump_profile_results(): Dump profile results.
  • _get_operator_names(): Get operator names.
  • _create_profile_block(): Create a profile block.
  • _reset_profile_results(): Reset profile results.

Classes

ExecuTorchModule

  • load_bundled_input(): Load bundled input.
  • verify_result_with_bundled_expected_output(bundle: str, method_name: str, testset_idx: int, rtol: float = 1e-5, atol: float = 1e-8): Verify result with bundled expected output.
  • plan_execute(): Plan and execute.
  • run_method(): Run method.
  • forward(): Forward. This takes a pytree-flattend PyTorch-tensor-based input.
  • has_etdump(): Check if etdump is available.
  • write_etdump_result_to_file(): Write etdump result to a file.
  • __call__(): Call method.

BundledModule

This class is currently empty and serves as a placeholder for future methods and attributes.

Note

All functions and methods are guarded by a call guard that redirects cout and cerr to the Python environment.

, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
 blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
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btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
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btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
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codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
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}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
executorch/extension/pybindings at main · per/executorch · GitHub
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portable_lib.py
portable_lib.py
pybindings.cpp
pybindings.cpp
pybindings.pyi
pybindings.pyi
README.md
ExecuTorch Python Module (WIP)
This Python module, named portable_lib, provides a set of functions and classes for loading and executing bundled programs. To install it, run the fullowing command:
CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON" pip install . --no-build-isolation
Or when installing the rest of dependencies:
install_executorch.sh --pybind
Link Backends
You can link the runtime against some backends to make sure a delegated or partitioned model can still run by Python module successfully:
CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_COREML=ON -DEXECUTORCH_BUILD_MPS=ON" \
pip install . --no-build-isolation
Similarly, when installing the rest of dependencies:
install_executorch.sh --pybind xnnpack coreml mps
Functions
  • _load_for_executorch(path: str, enable_etdump: bool = False): Load a module from a file.
  • _load_for_executorch_from_buffer(buffer: str, enable_etdump: bool = False): Load a module from a buffer.
  • _load_for_executorch_from_bundled_program(ptr: str, enable_etdump: bool = False): Load a module from a bundled program.
  • _load_bundled_program_from_buffer(buffer: str, non_const_pool_size: int = kDEFAULT_BUNDLED_INPUT_POOL_SIZE): Load a bundled program from a buffer.
  • _dump_profile_results(): Dump profile results.
  • _get_operator_names(): Get operator names.
  • _create_profile_block(): Create a profile block.
  • _reset_profile_results(): Reset profile results.
Classes
ExecuTorchModule
  • load_bundled_input(): Load bundled input.
  • verify_result_with_bundled_expected_output(bundle: str, method_name: str, testset_idx: int, rtol: float = 1e-5, atol: float = 1e-8): Verify result with bundled expected output.
  • plan_execute(): Plan and execute.
  • run_method(): Run method.
  • forward(): Forward. This takes a pytree-flattend PyTorch-tensor-based input.
  • has_etdump(): Check if etdump is available.
  • write_etdump_result_to_file(): Write etdump result to a file.
  • __call__(): Call method.
BundledModule
This class is currently empty and serves as a placeholder for future methods and attributes.
Note
All functions and methods are guarded by a call guard that redirects cout and cerr to the Python environment.

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
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.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
executorch/extension/pybindings at main · per/executorch · GitHub
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TARGETS
TARGETS
portable_lib.py
portable_lib.py
pybindings.cpp
pybindings.cpp
pybindings.pyi
pybindings.pyi
README.md
ExecuTorch Python Module (WIP)
This Python module, named portable_lib, provides a set of functions and classes for loading and executing bundled programs. To install it, run the fullowing command:
CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON" pip install . --no-build-isolation
Or when installing the rest of dependencies:
install_executorch.sh --pybind
Link Backends
You can link the runtime against some backends to make sure a delegated or partitioned model can still run by Python module successfully:
CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_COREML=ON -DEXECUTORCH_BUILD_MPS=ON" \
pip install . --no-build-isolation
Similarly, when installing the rest of dependencies:
install_executorch.sh --pybind xnnpack coreml mps
Functions
  • _load_for_executorch(path: str, enable_etdump: bool = False): Load a module from a file.
  • _load_for_executorch_from_buffer(buffer: str, enable_etdump: bool = False): Load a module from a buffer.
  • _load_for_executorch_from_bundled_program(ptr: str, enable_etdump: bool = False): Load a module from a bundled program.
  • _load_bundled_program_from_buffer(buffer: str, non_const_pool_size: int = kDEFAULT_BUNDLED_INPUT_POOL_SIZE): Load a bundled program from a buffer.
  • _dump_profile_results(): Dump profile results.
  • _get_operator_names(): Get operator names.
  • _create_profile_block(): Create a profile block.
  • _reset_profile_results(): Reset profile results.
Classes
ExecuTorchModule
  • load_bundled_input(): Load bundled input.
  • verify_result_with_bundled_expected_output(bundle: str, method_name: str, testset_idx: int, rtol: float = 1e-5, atol: float = 1e-8): Verify result with bundled expected output.
  • plan_execute(): Plan and execute.
  • run_method(): Run method.
  • forward(): Forward. This takes a pytree-flattend PyTorch-tensor-based input.
  • has_etdump(): Check if etdump is available.
  • write_etdump_result_to_file(): Write etdump result to a file.
  • __call__(): Call method.
BundledModule
This class is currently empty and serves as a placeholder for future methods and attributes.
Note
All functions and methods are guarded by a call guard that redirects cout and cerr to the Python environment.

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
var frag = document.createDocumentFragment();
var parts = text.split(regex);
parts.forEach(function(part, i) {
if (i % 2 === 0) {
frag.appendChild(document.createTextNode(part));
} else {
var span = document.createElement('span');
span.className = 'userscript-highlight';
span.textContent = part;
frag.appendChild(span);
}
});
node.parentNode.replaceChild(frag, node);
}
});
} else if (node.nodeType === 1 && node.childNodes) { // element
var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
executorch/extension/pybindings at main · per/executorch · GitHub
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TARGETS
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portable_lib.py
portable_lib.py
pybindings.cpp
pybindings.cpp
pybindings.pyi
pybindings.pyi
README.md
ExecuTorch Python Module (WIP)
This Python module, named portable_lib, provides a set of functions and classes for loading and executing bundled programs. To install it, run the fullowing command:
CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON" pip install . --no-build-isolation
Or when installing the rest of dependencies:
install_executorch.sh --pybind
Link Backends
You can link the runtime against some backends to make sure a delegated or partitioned model can still run by Python module successfully:
CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_COREML=ON -DEXECUTORCH_BUILD_MPS=ON" \
pip install . --no-build-isolation
Similarly, when installing the rest of dependencies:
install_executorch.sh --pybind xnnpack coreml mps
Functions
  • _load_for_executorch(path: str, enable_etdump: bool = False): Load a module from a file.
  • _load_for_executorch_from_buffer(buffer: str, enable_etdump: bool = False): Load a module from a buffer.
  • _load_for_executorch_from_bundled_program(ptr: str, enable_etdump: bool = False): Load a module from a bundled program.
  • _load_bundled_program_from_buffer(buffer: str, non_const_pool_size: int = kDEFAULT_BUNDLED_INPUT_POOL_SIZE): Load a bundled program from a buffer.
  • _dump_profile_results(): Dump profile results.
  • _get_operator_names(): Get operator names.
  • _create_profile_block(): Create a profile block.
  • _reset_profile_results(): Reset profile results.
Classes
ExecuTorchModule
  • load_bundled_input(): Load bundled input.
  • verify_result_with_bundled_expected_output(bundle: str, method_name: str, testset_idx: int, rtol: float = 1e-5, atol: float = 1e-8): Verify result with bundled expected output.
  • plan_execute(): Plan and execute.
  • run_method(): Run method.
  • forward(): Forward. This takes a pytree-flattend PyTorch-tensor-based input.
  • has_etdump(): Check if etdump is available.
  • write_etdump_result_to_file(): Write etdump result to a file.
  • __call__(): Call method.
BundledModule
This class is currently empty and serves as a placeholder for future methods and attributes.
Note
All functions and methods are guarded by a call guard that redirects cout and cerr to the Python environment.

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
executorch/extension/pybindings at main · per/executorch · GitHub
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pybindings
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README.md
TARGETS
TARGETS
portable_lib.py
portable_lib.py
pybindings.cpp
pybindings.cpp
pybindings.pyi
pybindings.pyi
README.md
ExecuTorch Python Module (WIP)
This Python module, named portable_lib, provides a set of functions and classes for loading and executing bundled programs. To install it, run the fullowing command:
CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON" pip install . --no-build-isolation
Or when installing the rest of dependencies:
install_executorch.sh --pybind
Link Backends
You can link the runtime against some backends to make sure a delegated or partitioned model can still run by Python module successfully:
CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_COREML=ON -DEXECUTORCH_BUILD_MPS=ON" \
pip install . --no-build-isolation
Similarly, when installing the rest of dependencies:
install_executorch.sh --pybind xnnpack coreml mps
Functions
  • _load_for_executorch(path: str, enable_etdump: bool = False): Load a module from a file.
  • _load_for_executorch_from_buffer(buffer: str, enable_etdump: bool = False): Load a module from a buffer.
  • _load_for_executorch_from_bundled_program(ptr: str, enable_etdump: bool = False): Load a module from a bundled program.
  • _load_bundled_program_from_buffer(buffer: str, non_const_pool_size: int = kDEFAULT_BUNDLED_INPUT_POOL_SIZE): Load a bundled program from a buffer.
  • _dump_profile_results(): Dump profile results.
  • _get_operator_names(): Get operator names.
  • _create_profile_block(): Create a profile block.
  • _reset_profile_results(): Reset profile results.
Classes
ExecuTorchModule
  • load_bundled_input(): Load bundled input.
  • verify_result_with_bundled_expected_output(bundle: str, method_name: str, testset_idx: int, rtol: float = 1e-5, atol: float = 1e-8): Verify result with bundled expected output.
  • plan_execute(): Plan and execute.
  • run_method(): Run method.
  • forward(): Forward. This takes a pytree-flattend PyTorch-tensor-based input.
  • has_etdump(): Check if etdump is available.
  • write_etdump_result_to_file(): Write etdump result to a file.
  • __call__(): Call method.
BundledModule
This class is currently empty and serves as a placeholder for future methods and attributes.
Note
All functions and methods are guarded by a call guard that redirects cout and cerr to the Python environment.

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
executorch/extension/pybindings at main · per/executorch · GitHub
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pybindings
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pybindings
Folders and files
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test
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README.md
TARGETS
TARGETS
portable_lib.py
portable_lib.py
pybindings.cpp
pybindings.cpp
pybindings.pyi
pybindings.pyi
README.md
ExecuTorch Python Module (WIP)
This Python module, named portable_lib, provides a set of functions and classes for loading and executing bundled programs. To install it, run the fullowing command:
CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON" pip install . --no-build-isolation
Or when installing the rest of dependencies:
install_executorch.sh --pybind
Link Backends
You can link the runtime against some backends to make sure a delegated or partitioned model can still run by Python module successfully:
CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_COREML=ON -DEXECUTORCH_BUILD_MPS=ON" \
pip install . --no-build-isolation
Similarly, when installing the rest of dependencies:
install_executorch.sh --pybind xnnpack coreml mps
Functions
  • _load_for_executorch(path: str, enable_etdump: bool = False): Load a module from a file.
  • _load_for_executorch_from_buffer(buffer: str, enable_etdump: bool = False): Load a module from a buffer.
  • _load_for_executorch_from_bundled_program(ptr: str, enable_etdump: bool = False): Load a module from a bundled program.
  • _load_bundled_program_from_buffer(buffer: str, non_const_pool_size: int = kDEFAULT_BUNDLED_INPUT_POOL_SIZE): Load a bundled program from a buffer.
  • _dump_profile_results(): Dump profile results.
  • _get_operator_names(): Get operator names.
  • _create_profile_block(): Create a profile block.
  • _reset_profile_results(): Reset profile results.
Classes
ExecuTorchModule
  • load_bundled_input(): Load bundled input.
  • verify_result_with_bundled_expected_output(bundle: str, method_name: str, testset_idx: int, rtol: float = 1e-5, atol: float = 1e-8): Verify result with bundled expected output.
  • plan_execute(): Plan and execute.
  • run_method(): Run method.
  • forward(): Forward. This takes a pytree-flattend PyTorch-tensor-based input.
  • has_etdump(): Check if etdump is available.
  • write_etdump_result_to_file(): Write etdump result to a file.
  • __call__(): Call method.
BundledModule
This class is currently empty and serves as a placeholder for future methods and attributes.
Note
All functions and methods are guarded by a call guard that redirects cout and cerr to the Python environment.

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
executorch/extension/pybindings at main · per/executorch · GitHub
Skip to content
pybindings
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README.md
TARGETS
TARGETS
portable_lib.py
portable_lib.py
pybindings.cpp
pybindings.cpp
pybindings.pyi
pybindings.pyi
README.md
ExecuTorch Python Module (WIP)
This Python module, named portable_lib, provides a set of functions and classes for loading and executing bundled programs. To install it, run the fullowing command:
CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON" pip install . --no-build-isolation
Or when installing the rest of dependencies:
install_executorch.sh --pybind
Link Backends
You can link the runtime against some backends to make sure a delegated or partitioned model can still run by Python module successfully:
CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_COREML=ON -DEXECUTORCH_BUILD_MPS=ON" \
pip install . --no-build-isolation
Similarly, when installing the rest of dependencies:
install_executorch.sh --pybind xnnpack coreml mps
Functions
  • _load_for_executorch(path: str, enable_etdump: bool = False): Load a module from a file.
  • _load_for_executorch_from_buffer(buffer: str, enable_etdump: bool = False): Load a module from a buffer.
  • _load_for_executorch_from_bundled_program(ptr: str, enable_etdump: bool = False): Load a module from a bundled program.
  • _load_bundled_program_from_buffer(buffer: str, non_const_pool_size: int = kDEFAULT_BUNDLED_INPUT_POOL_SIZE): Load a bundled program from a buffer.
  • _dump_profile_results(): Dump profile results.
  • _get_operator_names(): Get operator names.
  • _create_profile_block(): Create a profile block.
  • _reset_profile_results(): Reset profile results.
Classes
ExecuTorchModule
  • load_bundled_input(): Load bundled input.
  • verify_result_with_bundled_expected_output(bundle: str, method_name: str, testset_idx: int, rtol: float = 1e-5, atol: float = 1e-8): Verify result with bundled expected output.
  • plan_execute(): Plan and execute.
  • run_method(): Run method.
  • forward(): Forward. This takes a pytree-flattend PyTorch-tensor-based input.
  • has_etdump(): Check if etdump is available.
  • write_etdump_result_to_file(): Write etdump result to a file.
  • __call__(): Call method.
BundledModule
This class is currently empty and serves as a placeholder for future methods and attributes.
Note
All functions and methods are guarded by a call guard that redirects cout and cerr to the Python environment.

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
executorch/extension/pybindings at main · per/executorch · GitHub
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README.md
ExecuTorch Python Module (WIP)
This Python module, named portable_lib, provides a set of functions and classes for loading and executing bundled programs. To install it, run the fullowing command:
CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON" pip install . --no-build-isolation
Or when installing the rest of dependencies:
install_executorch.sh --pybind
Link Backends
You can link the runtime against some backends to make sure a delegated or partitioned model can still run by Python module successfully:
CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_COREML=ON -DEXECUTORCH_BUILD_MPS=ON" \
pip install . --no-build-isolation
Similarly, when installing the rest of dependencies:
install_executorch.sh --pybind xnnpack coreml mps
Functions
  • _load_for_executorch(path: str, enable_etdump: bool = False): Load a module from a file.
  • _load_for_executorch_from_buffer(buffer: str, enable_etdump: bool = False): Load a module from a buffer.
  • _load_for_executorch_from_bundled_program(ptr: str, enable_etdump: bool = False): Load a module from a bundled program.
  • _load_bundled_program_from_buffer(buffer: str, non_const_pool_size: int = kDEFAULT_BUNDLED_INPUT_POOL_SIZE): Load a bundled program from a buffer.
  • _dump_profile_results(): Dump profile results.
  • _get_operator_names(): Get operator names.
  • _create_profile_block(): Create a profile block.
  • _reset_profile_results(): Reset profile results.
Classes
ExecuTorchModule
  • load_bundled_input(): Load bundled input.
  • verify_result_with_bundled_expected_output(bundle: str, method_name: str, testset_idx: int, rtol: float = 1e-5, atol: float = 1e-8): Verify result with bundled expected output.
  • plan_execute(): Plan and execute.
  • run_method(): Run method.
  • forward(): Forward. This takes a pytree-flattend PyTorch-tensor-based input.
  • has_etdump(): Check if etdump is available.
  • write_etdump_result_to_file(): Write etdump result to a file.
  • __call__(): Call method.
BundledModule
This class is currently empty and serves as a placeholder for future methods and attributes.
Note
All functions and methods are guarded by a call guard that redirects cout and cerr to the Python environment.