diff --git a/_static/alabaster.css b/_static/alabaster.css index 4c500fb..fc325ec 100644 --- a/_static/alabaster.css +++ b/_static/alabaster.css @@ -419,7 +419,9 @@ table.footnote td { } dl { - margin: 0; + margin-left: 0; + margin-right: 0; + margin-top: 0; padding: 0; } diff --git a/_static/doctools.js b/_static/doctools.js index c3db08d..527b876 100644 --- a/_static/doctools.js +++ b/_static/doctools.js @@ -10,6 +10,13 @@ */ "use strict"; +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + const _ready = (callback) => { if (document.readyState !== "loading") { callback(); @@ -18,73 +25,11 @@ const _ready = (callback) => { } }; -/** - * highlight a given string on a node by wrapping it in - * span elements with the given class name. - */ -const _highlight = (node, addItems, text, className) => { - if (node.nodeType === Node.TEXT_NODE) { - const val = node.nodeValue; - const parent = node.parentNode; - const pos = val.toLowerCase().indexOf(text); - if ( - pos >= 0 && - !parent.classList.contains(className) && - !parent.classList.contains("nohighlight") - ) { - let span; - - const closestNode = parent.closest("body, svg, foreignObject"); - const isInSVG = closestNode && closestNode.matches("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.classList.add(className); - } - - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - parent.insertBefore( - span, - parent.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling - ) - ); - node.nodeValue = val.substr(0, pos); - - if (isInSVG) { - const rect = document.createElementNS( - "http://www.w3.org/2000/svg", - "rect" - ); - const bbox = parent.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute("class", className); - addItems.push({ parent: parent, target: rect }); - } - } - } else if (node.matches && !node.matches("button, select, textarea")) { - node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); - } -}; -const _highlightText = (thisNode, text, className) => { - let addItems = []; - _highlight(thisNode, addItems, text, className); - addItems.forEach((obj) => - obj.parent.insertAdjacentElement("beforebegin", obj.target) - ); -}; - /** * Small JavaScript module for the documentation. */ const Documentation = { init: () => { - Documentation.highlightSearchWords(); Documentation.initDomainIndexTable(); Documentation.initOnKeyListeners(); }, @@ -126,51 +71,6 @@ const Documentation = { Documentation.LOCALE = catalog.locale; }, - /** - * highlight the search words provided in the url in the text - */ - highlightSearchWords: () => { - const highlight = - new URLSearchParams(window.location.search).get("highlight") || ""; - const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); - if (terms.length === 0) return; // nothing to do - - // There should never be more than one element matching "div.body" - const divBody = document.querySelectorAll("div.body"); - const body = divBody.length ? divBody[0] : document.querySelector("body"); - window.setTimeout(() => { - terms.forEach((term) => _highlightText(body, term, "highlighted")); - }, 10); - - const searchBox = document.getElementById("searchbox"); - if (searchBox === null) return; - searchBox.appendChild( - document - .createRange() - .createContextualFragment( - '

' + - '' + - Documentation.gettext("Hide Search Matches") + - "

" - ) - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - const url = new URL(window.location); - url.searchParams.delete("highlight"); - window.history.replaceState({}, "", url); - }, - /** * helper function to focus on search bar */ @@ -210,15 +110,11 @@ const Documentation = { ) return; - const blacklistedElements = new Set([ - "TEXTAREA", - "INPUT", - "SELECT", - "BUTTON", - ]); document.addEventListener("keydown", (event) => { - if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements - if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; if (!event.shiftKey) { switch (event.key) { @@ -240,10 +136,6 @@ const Documentation = { event.preventDefault(); } break; - case "Escape": - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; - Documentation.hideSearchWords(); - event.preventDefault(); } } diff --git a/_static/searchtools.js b/_static/searchtools.js index f2fb7d5..e89e34d 100644 --- a/_static/searchtools.js +++ b/_static/searchtools.js @@ -57,14 +57,14 @@ const _removeChildren = (element) => { const _escapeRegExp = (string) => string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string -const _displayItem = (item, highlightTerms, searchTerms) => { +const _displayItem = (item, searchTerms) => { const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT; const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; - const [docName, title, anchor, descr] = item; + const [docName, title, anchor, descr, score, _filename] = item; let listItem = document.createElement("li"); let requestUrl; @@ -82,10 +82,9 @@ const _displayItem = (item, highlightTerms, searchTerms) => { requestUrl = docUrlRoot + docName + docFileSuffix; linkUrl = docName + docLinkSuffix; } - const params = new URLSearchParams(); - params.set("highlight", [...highlightTerms].join(" ")); let linkEl = listItem.appendChild(document.createElement("a")); - linkEl.href = linkUrl + "?" + params.toString() + anchor; + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; linkEl.innerHTML = title; if (descr) listItem.appendChild(document.createElement("span")).innerHTML = @@ -96,7 +95,7 @@ const _displayItem = (item, highlightTerms, searchTerms) => { .then((data) => { if (data) listItem.appendChild( - Search.makeSearchSummary(data, searchTerms, highlightTerms) + Search.makeSearchSummary(data, searchTerms) ); }); Search.output.appendChild(listItem); @@ -116,15 +115,14 @@ const _finishSearch = (resultCount) => { const _displayNextItem = ( results, resultCount, - highlightTerms, searchTerms ) => { // results left, load the summary and display it // this is intended to be dynamic (don't sub resultsCount) if (results.length) { - _displayItem(results.pop(), highlightTerms, searchTerms); + _displayItem(results.pop(), searchTerms); setTimeout( - () => _displayNextItem(results, resultCount, highlightTerms, searchTerms), + () => _displayNextItem(results, resultCount, searchTerms), 5 ); } @@ -237,6 +235,12 @@ const Search = { * execute search (requires search index to be loaded) */ query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + // stem the search terms and add them to the correct list const stemmer = new Stemmer(); const searchTerms = new Set(); @@ -264,6 +268,10 @@ const Search = { } }); + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + // console.debug("SEARCH: searching for:"); // console.info("required: ", [...searchTerms]); // console.info("excluded: ", [...excludedTerms]); @@ -272,6 +280,40 @@ const Search = { let results = []; _removeChildren(document.getElementById("search-progress")); + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + // lookup as object objectTerms.forEach((term) => results.push(...Search.performObjectSearch(term, objectTerms)) @@ -318,7 +360,7 @@ const Search = { // console.info("search results:", Search.lastresults); // print the results - _displayNextItem(results, results.length, highlightTerms, searchTerms); + _displayNextItem(results, results.length, searchTerms); }, /** @@ -399,8 +441,8 @@ const Search = { // prepare search const terms = Search._index.terms; const titleTerms = Search._index.titleterms; - const docNames = Search._index.docnames; const filenames = Search._index.filenames; + const docNames = Search._index.docnames; const titles = Search._index.titles; const scoreMap = new Map(); @@ -497,11 +539,9 @@ const Search = { /** * helper function to return a node containing the * search summary for a given text. keywords is a list - * of stemmed words, highlightWords is the list of normal, unstemmed - * words. the first one is used to find the occurrence, the - * latter for highlighting it. + * of stemmed words. */ - makeSearchSummary: (htmlText, keywords, highlightWords) => { + makeSearchSummary: (htmlText, keywords) => { const text = Search.htmlToText(htmlText); if (text === "") return null; @@ -519,10 +559,6 @@ const Search = { summary.classList.add("context"); summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; - highlightWords.forEach((highlightWord) => - _highlightText(summary, highlightWord, "highlighted") - ); - return summary; }, }; diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 0000000..aae669d --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,144 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + parent.insertBefore( + span, + parent.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(SphinxHighlight.highlightSearchWords); +_ready(SphinxHighlight.initEscapeListener); diff --git a/api.html b/api.html index 0b7d1ad..37aa0f1 100644 --- a/api.html +++ b/api.html @@ -14,6 +14,7 @@ + @@ -58,7 +59,22 @@

Semiring Einsum

Navigation

Contents:

@@ -104,7 +120,7 @@

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this package.

Parameters:
-

equation (str) – An equation in einsum syntax.

+

equation (str) – An equation in einsum syntax.

Return type:

Equation

@@ -140,23 +156,23 @@

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torch_semiring_einsum.real_einsum_forward(equation, *args, block_size=AUTOMATIC_BLOCK_SIZE)

Einsum where addition and multiplication have their usual meanings.

This function has different memory and runtime characteristics than -torch.einsum(), which can be tuned with block_size. Higher +torch.einsum(), which can be tuned with block_size. Higher values of block_size result in faster runtime and higher memory usage.

In some cases, when dealing with summations over more than two input tensors at once, this implementation can have better space complexity than -torch.einsum(), because it does not create intermediate tensors +torch.einsum(), because it does not create intermediate tensors whose sizes are proportional to the dimensions being summed over.

Parameters:

  • equation (Equation) – A pre-compiled equation.

    • -

  • args (Tensor) – Input tensors. The number of input tensors must be compatible +

  • args (Tensor) – Input tensors. The number of input tensors must be compatible with equation.

    • -

  • block_size (Union[int, AutomaticBlockSize]) – Block size used to control memory usage.

    • +

  • block_size (Union[int, AutomaticBlockSize]) – Block size used to control memory usage.

Return type:
-

Tensor

+

Tensor

Returns:

Output of einsum.

@@ -176,16 +192,16 @@

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  • equation (Equation) – Pre-compiled einsum equation. The derivative of the einsum operation specified by this equation will be computed.

    • -

  • args (Sequence[Tensor]) – The inputs to the einsum operation whose derivative +

  • args (Sequence[Tensor]) – The inputs to the einsum operation whose derivative is being computed.

    • -

  • needs_grad (Sequence[bool]) – Indicates which inputs in args require gradient.

    • -

  • grad (Tensor) – The gradient of the loss function with respect to the output +

  • needs_grad (Sequence[bool]) – Indicates which inputs in args require gradient.

    • +

  • grad (Tensor) – The gradient of the loss function with respect to the output of the einsum operation.

    • -

  • block_size (Union[int, AutomaticBlockSize]) – Block size used to control memory usage.

    • +

  • block_size (Union[int, AutomaticBlockSize]) – Block size used to control memory usage.

Return type:
-

List[Optional[Tensor]]

+

List[Optional[Tensor]]

Returns:

The gradients with respect to each of the inputs to the @@ -204,18 +220,18 @@

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Parameters:
    -

  • save_max (bool) – If true, save the tensor of maximum terms computed in the +

  • save_max (bool) – If true, save the tensor of maximum terms computed in the forward pass and reuse it in the backward pass. This tensor has the same size as the output tensor. Setting this to false will save memory but increase runtime.

    • -

  • save_sumexpsub (bool) – If true, save the tensor of sums of terms computed +

  • save_sumexpsub (bool) – If true, save the tensor of sums of terms computed in the forward pass and reuse it in the backward pass. This tensor has the same size as the output tensor. Setting this to false will save memory but increase runtime.

Return type:
-

Tensor

+

Tensor

@@ -230,18 +246,18 @@

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Parameters:

  • equation (Equation) – A pre-compiled equation.

    • -

  • args (Tensor) – Input tensors. The number of input tensors must be compatible +

  • args (Tensor) – Input tensors. The number of input tensors must be compatible with equation.

    • -

  • block_size (Union[int, AutomaticBlockSize]) – Block size used to control memory usage.

    • -

  • return_max (bool) – If true, also return the tensor of maximum terms, which +

  • block_size (Union[int, AutomaticBlockSize]) – Block size used to control memory usage.

    • +

  • return_max (bool) – If true, also return the tensor of maximum terms, which can be reused when computing the gradient.

    • -

  • return_sumexpsub (bool) – If true, also return the tensor of sums of terms +

  • return_sumexpsub (bool) – If true, also return the tensor of sums of terms (where the maximum term has been subtracted from each term), which can be reused when computing the gradient.

Return type:
-

Union[Tensor, Tuple[Tensor]]

+

Union[Tensor, Tuple[Tensor]]

Returns:

Output of einsum. If return_max or return_sumexpsub is @@ -262,17 +278,17 @@

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  • equation (Equation) – Pre-compiled einsum equation. The derivative of the log-space einsum operation specified by this equation will be computed.

    • -

  • args (Sequence[Tensor]) – The inputs to the log-space einsum operation whose derivative +

  • args (Sequence[Tensor]) – The inputs to the log-space einsum operation whose derivative is being computed.

    • -

  • needs_grad (Sequence[bool]) – Indicates which inputs in args require gradient.

    • -

  • grad (Tensor) – The gradient of the loss function with respect to the output +

  • needs_grad (Sequence[bool]) – Indicates which inputs in args require gradient.

    • +

  • grad (Tensor) – The gradient of the loss function with respect to the output of the log-space einsum operation.

    • -

  • block_size (Union[int, AutomaticBlockSize]) – Block size used to control memory usage.

    • -

  • grad_of_neg_inf (Union[float, Literal[‘uniform’]]) – How to handle the gradient of cases where all +

  • block_size (Union[int, AutomaticBlockSize]) – Block size used to control memory usage.

    • +

  • grad_of_neg_inf (Union[float, Literal['uniform']]) – How to handle the gradient of cases where all inputs to a logsumexp are \(-\infty\), which results in an output of \(-\infty\). The default behavior is to output NaN, which -matches the behavior of PyTorch’s logsumexp(), but -sometimes this is not desired. If a float is provided, all +matches the behavior of PyTorch’s logsumexp(), but +sometimes this is not desired. If a float is provided, all gradients will be set to that value. A value of 0, which causes the inputs not to change, may be appropriate. For example, if one input is a parameter and another is a constant \(-\infty\), it may not make @@ -284,14 +300,14 @@

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    softmax, and in this case it will attempt to increase the inputs to the logsumexp above \(-\infty\). NOTE: Only NaN and 0 are currently implemented.

    • -

  • saved_max (Optional[Tensor]) – See return_max in +

  • saved_max (Optional[Tensor]) – See return_max in log_einsum_forward().

    • -

  • saved_sumexpsub (Optional[Tensor]) – See return_sumexpsub in +

  • saved_sumexpsub (Optional[Tensor]) – See return_sumexpsub in log_einsum_forward().

Return type:
-

List[Optional[Tensor]]

+

List[Optional[Tensor]]

Returns:

The gradients with respect to each of the inputs to the log-space @@ -312,13 +328,13 @@

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Parameters:

  • equation (Equation) – A pre-compiled equation.

    • -

  • args (Tensor) – Input tensors. The number of input tensors must be compatible +

  • args (Tensor) – Input tensors. The number of input tensors must be compatible with equation.

    • -

  • block_size (Union[int, AutomaticBlockSize]) – Block size used to control memory usage.

    • +

  • block_size (Union[int, AutomaticBlockSize]) – Block size used to control memory usage.

Return type:
-

Tuple[Tensor, LongTensor]

+

Tuple[Tensor, LongTensor]

Returns:

A tuple containing the max and argmax of the einsum operation. @@ -361,14 +377,14 @@

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Parameters:
    -

  • max_cpu_bytes (int) – The maximum amount of memory (in bytes) to use +

  • max_cpu_bytes (int) – The maximum amount of memory (in bytes) to use when the device is cpu. By default, this is set to 1 GiB.

    • -

  • max_cuda_bytes (Optional[int]) – The maximum amount of memory (in bytes) to use +

  • max_cuda_bytes (Optional[int]) – The maximum amount of memory (in bytes) to use when the device is cuda. If None, then the amount of memory used will be determined based on the amount of free CUDA memory. Note that specifying an explicit memory limit is much faster than querying the amount of free CUDA memory.

    • -

  • cache_available_cuda_memory (bool) – Only applies when +

  • cache_available_cuda_memory (bool) – Only applies when max_cuda_bytes is None. When true, the amount of available CUDA memory is only queried the first time einsum is called with this object as block_size, and it is reused on subsequent @@ -376,7 +392,7 @@

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    available memory every time. To account for future decreases in the amount of available memory, only a portion of the available memory is used, as determined by cuda_memory_proportion.

    • -

  • cuda_memory_proportion (float) – Determines the proportion of available +

  • cuda_memory_proportion (float) – Determines the proportion of available memory used when cache_available_cuda_memory is true. This should be a number between 0 and 1.

@@ -401,7 +417,7 @@

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Note that this function only implements the forward aspect of einsum and is not differentiable. To turn your instantiation of einsum in a particular semiring into a differentiable PyTorch -Function, implement its derivative and use +Function, implement its derivative and use combine() to combine the forward and backward functions into one function. Odds are, semiring_einsum_forward() can be used to implement the derivative @@ -420,7 +436,7 @@

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implementation, one must first compute maximum values and then use them for a subsequent logsumexp step).

Here is a quick example that implements the equivalent of -torch.einsum():

+torch.einsum():

def regular_einsum(equation, *args, block_size):
     def func(compute_sum):
         def add_in_place(a, b):
@@ -448,16 +464,16 @@ 
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 Likewise, multiply_in_place(a, b) must implement a *= b for the
 desired definition of *. The arguments a and b are values
 returned from sum_block (see below) and are usually of type
-Tensor, although they can be something fancier for
+Tensor, although they can be something fancier for
 cases like Viterbi (which involves a pair of tensors: max and argmax).
 These functions must modify the object a in-place; the return value
 is ignored.

 
sum_block(a, dims) should be a function that “sums” over multiple
 dimensions in a tensor at once. It must return its result. a is always
-a Tensor. dims is a tuple of
-ints representing the dimensions in a to sum out. Take
+a Tensor. dims is a tuple of
+ints representing the dimensions in a to sum out. Take
 special care to handle the case where dims is an empty tuple –
-in particular, keep in mind that torch.sum() returns a scalar
+in particular, keep in mind that torch.sum() returns a scalar
 when dims is an empty tuple. Simply returning a is sufficient to
 handle this edge case. Note that it is always safe to return a view of
 a from sum_block, since a is itself never a view of the input
@@ -466,7 +482,7 @@ 
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 third argument, var_values, which contains the current indexes of the
 parts of the input tensors being summed over (sum_block is called
 multiple times on different slices of the inputs). var_values is a
-tuple of range objects representing the ranges
+tuple of range objects representing the ranges
 of indexes representing the current slice. var_values contains an
 entry for each summed variable, in order of first appearance in the
 equation.

@@ -483,15 +499,15 @@ 
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Parameters:

 
    
 
  • equation (Equation) – A pre-compiled equation.
    • 
-
  • args (Sequence[Tensor]) – A list of input tensors.
    • 
-
  • block_size (Union[int, AutomaticBlockSize]) – To keep memory usage in check, the einsum summation is
+
  • args (Sequence[Tensor]) – A list of input tensors.
    • 
+
  • block_size (Union[int, AutomaticBlockSize]) – To keep memory usage in check, the einsum summation is
 done over multiple “windows” or “blocks” of bounded size. This
 parameter sets the maximum size of these windows. More precisely, it
 defines the maximum size of the range of values of each summed
 variable that is included in a single window. If there are n
 summed variables, the size of the window tensor is proportional to
 block_size ** n.
    • 
-
  • func (Callable) – A callback of the form described above.
    • 
+
  • func (Callable) – A callback of the form described above.
    •
@@ -507,20 +523,20 @@

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Parameters:
    -

  • forward (Callable) – The forward implementation.

    • -

  • backward (Callable) – The backward implementation. Its signature must be +

  • forward (Callable) – The forward implementation.

    • +

  • backward (Callable) – The backward implementation. Its signature must be backward(equation, args, needs_grad, grad, block_size), and it -must return a tuple of Tensor +must return a tuple of Tensor containing the gradients with respect to args. The \(i\)th gradient may be None if needs_grad[i] is False.

    • -

  • forward_options (Tuple[str]) – A list of optional keyword arguments that should +

  • forward_options (Tuple[str]) – A list of optional keyword arguments that should be passed to the forward function.

    • -

  • backward_options (Tuple[str]) – A list of optional keyword arguments that should +

  • backward_options (Tuple[str]) – A list of optional keyword arguments that should be passed to the backward function.

Return type:
-

Callable

+

Callable

Returns:

A function whose return value is compatible with PyTorch’s @@ -542,8 +558,8 @@

Quick search

©2019-2022, Brian DuSell. | - Powered by Sphinx 5.1.1 - & Alabaster 0.7.12 + Powered by Sphinx 5.3.0 + & Alabaster 0.7.13 | + @@ -228,8 +229,8 @@

T

©2019-2022, Brian DuSell. | - Powered by Sphinx 5.1.1 - & Alabaster 0.7.12 + Powered by Sphinx 5.3.0 + & Alabaster 0.7.13
diff --git a/index.html b/index.html index 3dfd1da..c04dc5b 100644 --- a/index.html +++ b/index.html @@ -14,6 +14,7 @@ + @@ -106,7 +107,7 @@

Semiring Einsum (semirings. It includes implementations for the real, log, and Viterbi semirings out of the box and can be extended to support additional semirings. It can also offer -better performance than the built-in torch.einsum() function and +better performance than the built-in torch.einsum() function and makes the memory-execution time tradeoff configurable, allowing you to run large einsum operations that might otherwise be impossible given typical hardware constraints.

@@ -121,30 +122,45 @@

Semiring Einsum (torch.einsum() function, +better space complexity than the built-in torch.einsum() function, because it does not need to create intermediate tensors whose sizes are proportional to the dimensions being summed over.

Contents:

Installation

You can install torch_semiring_einsum from PyPI using pip:

-
pip install torch-semiring-einsum
+
pip install torch-semiring-einsum
 

 

 
or a package manager like Poetry:

-
poetry add torch-semiring-einsum
+
poetry add torch-semiring-einsum
 

 

 
You can also install it directly from GitHub:

-
pip install git+git://github.com/bdusell/semiring-einsum.git
+
pip install git+git://github.com/bdusell/semiring-einsum.git
 

 

-
poetry add git+https://github.com/bdusell/semiring-einsum@master
+
poetry add git+https://github.com/bdusell/semiring-einsum@master
@@ -220,7 +236,7 @@

Semirings

Einsum Syntax

This package supports the same einsum equation syntax as -torch.einsum(), except it does not support ellipses (...) syntax.

+torch.einsum(), except it does not support ellipses (...) syntax.

Time and Space Complexity

@@ -230,7 +246,7 @@

Time and Space Complexity\(A \times K\). There is even a routine in NumPy, -numpy.einsum_path(), which figures out the best contraction order. +numpy.einsum_path(), which figures out the best contraction order. However, it should, in principle, be possible to avoid this by summing over all tensors at the same time. This is exactly what torch_semiring_einsum does, and as a result the amount of scratch space the forward pass of einsum requires @@ -260,7 +276,7 @@

Time and Space Complexity _images/time-complexity-2.png

As we can see, execution time gets dramatically better even with small -increases in block size. The built-in torch.einsum() function is still +increases in block size. The built-in torch.einsum() function is still much faster than the blocked versions, but when the block size is unbounded and the summation is fully parallel, it is even faster.

_images/space-complexity.png @@ -302,8 +318,8 @@

BibliographySphinx 5.1.1 - & Alabaster 0.7.12 + Powered by Sphinx 5.3.0 + & Alabaster 0.7.13 | + @@ -127,8 +128,8 @@

Python Module Index

©2019-2022, Brian DuSell. | - Powered by Sphinx 5.1.1 - & Alabaster 0.7.12 + Powered by Sphinx 5.3.0 + & Alabaster 0.7.13 diff --git a/search.html b/search.html index 5e661ae..6af46f4 100644 --- a/search.html +++ b/search.html @@ -14,6 +14,7 @@ + @@ -128,8 +129,8 @@

Search

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