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NAME

Marpa::Algorithm - The Marpa Algorithm

DESCRIPTION

This document describes the aspects of the Marpa algorithm which break new ground. When I started writing Marpa I had many expectations. Marpa has exceeded most of them. Marpa will be a fast algorithm for almost every practical grammar. The one respect in which Marpa disappoints me is its complexity. As an algorithm, Marpa has its moments of beauty, but nobody could say it achieves that beauty through simplicity.

Marpa is many different parsing techniques, rolled into one. The way these interact is a major source of the beauty I see in the algorithm. It was also a source of my hope that underlying simplicities would reveal themselves. But perhaps it was wrong of me to expect Marpa to be a late Picasso. Perhaps I should have been looking more for a Hieronymous Bosch.

THE SOURCES OF THE MARPA ALGORITHM

1970

Jay Earley invents the algorithm that now bears his name.

1991

Joop Leo describes a way to modify Earley's algorithm so that it runs in O(n) time for all LR-regular grammars. LR-regular is a vast class of grammars, including all the LR(k) grammars, all grammars parseable with recursive descent, and regular expressions. LR-regular can safely be thought of as including all grammars in practical use today, and then some.

2002

Aycock and Horspool describe a way to do LR(0) precomputation for Earley's algorithm. Their method makes Earley's faster in most practical situations, but not all. In particular, right-recursion remains quadratic in the Aycock and Horspool algorithm. Worst case is no better than Earley's. Leo is unaware of Aycock and Horspool's work and Aycock and Horspool seem unaware of Leo.

2010

... which brings us to Marpa.

ABOUT THE REST OF THIS DOCUMENT

The rest of this document is very difficult and sketchy material. I tried to write these sections for as wide an audience as possible, but in many cases it simply was not possible to keep the sections short without assuming a lot of familiarity with the literature on Earley parsing.

COMBINING LEO 1991 and AYCOCK-HORSPOOL 2002

The algorithm described in Aycock-Horspool 2002 speeds up Earley's algorithm by combining Earley items. Each Earley item in Earley's original algorithm is for a dotted rule -- a pair consisting of a grammar rule and a position in that rule. Dotted rules are so called because the convention when writing them is to designate the distinguished position with a raised dot. Dotted rules are also called LR(0) items.

Aycock and Horspool combined dotted rules into sets, which were also states of a finite automaton. I call this finite automaton, an Aycock-Horspool Finite Automaton (AHFA). Aycock and Horspool changed their Earley items to contain information about AHFA states, instead of about individual dotted rules. This reduced the number of Earley items and improved the speed of Earley's algorithm considerably.

Aycock-Horspool 2002 had other very practical insights. For example, I can't imagine how I could have written Marpa had I not learned how to deal with nullable symbols and rules from Aycock-Horspool 2002 . But Aycock and Horspool could not lay claim to any theoretical milestones. As the theoreticians say, Aycock-Horspool 2002 "improved the constants". Worst case remained the same, and right-recursion was still quadratic.

Leo's algorithm and the Aycock-Horspool algorithm each offered major improvements. Was it possible to get the best of both worlds? The major obstacle was that the algorithms spoke different dialects: Earley items in the Aycock-Horspool algorithm used AHFA states. Leo kept his Earley items in the classic form, one dotted rule per Earley item. To combine the two algorithms, one algorithm would have to learn to speak the other's dialect.

It was always clear that translating from one algorithm to the other would be possible. What was not so clear was whether this translation could be made without doing so much processing that most or all of the benefit of one algorithm or the other would be lost. Leo 1991 , Aycock-Horspool 2002 , and even Earley's original algorithm (after the bug fix suggested by Aho & Ullman in 1972) all do the same job. For decades now, the only question has been speed.

Most important was the translation of "Leo completions" -- those Earley items added during recognition as a result of Leo's logic. Leo completions had to "play nice" with Marpa's AHFA-based recognition engine, or all was lost. Potentially, I might have been required to rewrite the Aycock-Horspool Finite Automaton. This would be difficult. Worse than that, it might be counter-productive. The AHFA was the source of the efficiencies delivered by the Aycock-Horspool algorithm. Rewrite the AHFA enough and I might as well throw it away.

My explorations here had a very happy result. I discovered, and was able to prove that, in the special case of Leo completions, there is a one-to-one correspondence between AHFA states and the original dotted rules as used by Earley and Leo. Leo completions were always AHFA "singletons" -- exactly one dotted rule.

This was very unexpected. The Aycock-Horspool algorithm produced its gains by combining dotted rules into AHFA states, and was aggressive about it. Singletons were infrequent. That a convenient class of the Aycock-Horspool Earley items should turn out always to be AHFA singletons, was a very pleasant surprise. When it most mattered, the Leo logic and the AHFA states spoke the same language.

In almost every other case, as expected, the Leo logic and the Aycock-Horspool logic did not speak the same language. A lot of details had to be worked out, but none of these other details had the potential to derail the project of combining the best of Leo 1991 and Aycock-Horspool 2002 to get the best of both.

Marpa's Changes to the Leo Recognizer Algorithm

Leo 1991 describes an algorithm for a recognizer. Marpa modifies Leo's original recognizer algorithm in two major respects. First, as mentioned above, Leo's recognizer algorithm had to be converted to work with AHFA states, instead of dotted rules.

Second, Leo's algorithm is lazy about computing Leo items. (A Leo item is my term for what Leo 1991 calls "transition items".) Leo's original algorithm does not compute the Leo items until they are actually needed. I chose eager computation -- computing the Leo items as soon as each Earley set is complete.

Eager computation of Leo items greatly simplifies the logic for using the Leo items. The processing for each Earley set can assume that any Leo items of interest in prior Earley sets already exist. A particular advantage of eager computation is that long chains of Leo items never need to be followed in the recognition phase.

Leo Semantics: Indirect and Lazy

Leo's hints about semantic processing, while brief, were insightful. The first decision to make with the Leo semantics was direct versus indirect. The direct approach does the semantic processing with the Leo items themselves, without converting them. This has the usual advantage that costs are incurred only for the Leo items that are actually used by the semantics. It has the very serious disadvantage of intertwining the Leo logic with the semantics. Dealing directly with Leo items would more than double the complexity of the logic for Marpa's semantics. Because of this, Marpa rejected the direct approach.

The indirect approach is to expand the Leo completions into the stacks of Earley items for which Leo completions are a shorthand. Of course, in a naive implementation, this eliminates every advantage of using the Leo speedups -- it merely moves processing from the recognizer into the semantic phase.

Marpa optimizes by using a lazy implementation of the indirect approach. Only those Leo completions useful for the semantics are expanded.

For a lazy and indirect implementation, in all cases, time complexity is the same as with a direct implementation. But for some ambiguous grammars, the space required grows from linear to quadratic with any indirect approach. This does not change the worst case time-complexity -- that was already quadratic. Fortunately, this worst case -- ambiguous parses where all the parse results are demanded -- is of limited practical interest.

The logic for lazy translation of Leo items proved simple. The tradeoffs clearly favored the indirect implementation which Leo 1991 suggests and Marpa uses.

IMPROVEMENTS TO THE AYCOCK-HORSPOOL RECOGNITION ENGINE

Marpa takes the recognition engine as described by Aycock and Horspool and turns it inside out and upside down -- a double inversion. In the Aycock and Horspool version of Earley's, the main loop iterated over each item of an Earley set, first using that item to scan for tokens, then using it to add new items in the set through completion. Marpa turns this into two separate loops over the Earley items, the first of which completes the current earley set, and the second of which scans for tokens.

Prediction-driven Lexing and Earley Parsing

The advantage of this double inversion is that the lexical analysis can be put between the two loops -- after completion, but before scanning. This makes prediction-driven lexing possible. Prediction-driven lexing, when combined with Earley power, turns out to be very useful.

EARLEMES

Marpa allows ambiguous lexing, including recognition of lexemes of different lengths starting at the same lexical position, and recognition of overlapping lexemes. To facilitate this, Marpa introduces the earleme (named after Jay Earley). Previous Earley implementations required the input to be broken up into tokens, typically by lexical analysis of the input using regular expressions. Requiring that the first level of analysis be performed by a regular expression can be viewed as hobbling a general parser like Earley's.

Marpa allows the scanning phase of Earley's algorithm to add items not just to the next Earley set, but to any later one. Alternative scans of the input can be put into the Earley sets, and the power of Earley's algorithm harnessed to deal with the indeterminism.

Marpa does this by allowing each scanned token to have a length in earlemes. The earleme is a unit of distance measured in Earley sets. If the length of a scanned token is L, and the current Earley set is C, a newly scanned Earley item is added to Earley set C+L. For example, if a token is 7 earlemes long, and it is to start at Earley set 35, then it is added to Earley set 42.

Lexing in earlemes is flexible. If an implementation defines an earleme so that the distances in earlemes are the same as distance in a token stream, then lexing in earlemes is exactly equivalent to traditional lexing. But distance in earlemes could also be defined as equivalent to string length, measured either in bytes or Unicode graphemes.

CHOMSKY-HORSPOOL-AYCOCK FORM

Another significant change to the Aycock and Horspool algorithm in Marpa is its internal rewriting of the grammar. Aycock and Horspool rewrote their grammars internally into what they called NNF (Nihilist Normal Form). Earley's original algorithm had serious issues with nullable symbols and productions, and the NNF rewrite fixes almost all of them. (As a reminder, a nullable symbol or production is one which derives the empty string.) Importantly, NNF also allows complete and easy mapping of the semantics of the original grammar to its NNF rewrite, so that NNF and the whole rewrite process can be made invisible to the user.

A problem with NNF is that the rewritten grammar is exponentially larger than the original in the worst case. I think cases would arise in practice where the NNF size explosion is a real problem. For example, any language in which whitespace is significant but optional could cause an NNF size explosion.

Marpa's solution is Chomsky-Horspool-Aycock Form (CHAF). This is Aycock and Horspool's NNF, but with the further restriction that no more than two nullable symbols may appear in any production. (The idea that any context-free grammar can be rewritten into productions of at most a small fixed size appears to originate, like so much else, with Noam Chomsky.) The shortened CHAF production maps back to the original grammar so that like NNF, the CHAF rewrite can be made invisible to the user. With CHAF, the theoretical worst behavior is linear, and in those difficult cases likely to arise in practice the multiplier is much smaller.

AN ALGORITHM TO CONSTRUCT THE SPLIT ε-DFA.

The Aycock-Horspool algorithm requires the grammar to be converted into a "split ε-DFA". But Aycock and Horspool do not give an algorithm for doing this construction. Writing an algorithm for Marpa to do this construction proved non-trivial.

The description of the "split ε-DFA" is on pages 624-627 of Aycock-Horspool 2002. It contained several insights without which Marpa could not have been written. I find the term "split ε-DFA" unwieldy, and prefer to take the opportunity to give credit where it is due. The sets which Aycock and Horspool call split ε-DFA states, I call AHFA states.

ITERATING AYCOCK-HORSPOOL PARSES

Aycock and Horspool give an algorithm for constructing a rightmost derivation from their version of the Earley sets. They suggest that, in the case of multiple parses, their Earley sets could be iterated through. They point out where in their algorithm the decision points would be.

But Aycock and Horspool give no algorithm to do that iteration. Marpa, of course, needed an algorithm to actually evaluate its parse results. Inventing that algorithm was one of the two most difficult parts of writing Marpa -- as difficult as combining the three sets of modifications to Earley's recognizer -- those of Aycock and Horspool, Leo's, and mine.

THE PARSE BOCAGE

Marpa sets itself up to iterate and evaluate parse results by creating a parse bocage from the Earley sets. A Marpa parse is a set of parse trees. It's a trivial set if the parse is unambiguous -- a set of trees that contains only one tree.

If the parse is ambiguous, there is more than one parse tree. In the parsing literature, there is an efficient and compact means of storing a set of closely related parse trees. It is called, aptly, a parse forest.

Nodes in a parse forest are divided into and-nodes and or-nodes. And-nodes are individual pieces of parse trees. In conventional parse forests, each and-node represents a production. And-nodes in a parse forest are just like all the nodes in a parse tree -- they have the lhs as the parent node and the rhs symbols as child nodes.

Or-nodes represent choices among and-nodes. Each child of an or-node is one choice. A parse tree is generated from a parse forest by traversing the forest and selecting one child and-node at every or-node.

Marpa uses a modified form of parse forest, which I call a parse bocage. Marpa could not use standard parse forests for two reasons. First, parse forests not only contain trees, but a parse forest itself must take the form of a tree. However, the data produced by Marpa's recognizer may contain cycles. Therefore, the data structure Marpa needs is not a tree, strictly speaking, but a graph.

Second, the original productions of the grammar are not repesented directly in Marpa's parse bocage. Instead, they are represented in the form in which the Marpa evaluator finds them in the Earley items. In the Earley items, each production is broken up, and represented as links between Earley items.

This system of links comes from Aycock and Horspool. In Marpa's elaboration of the Aycock-Horspool scheme, each Earley item has a list of sources. Each source can have two elements: a predecessor and a cause.

The predecessor is optional. If present, it is a link to an Earley item. It's called a "predecessor link" because both the linking and the linked Earley item are pieces of the same production, and the linked Earley item is the "predecessor" or earlier part.

The cause element of the source is always present. It can be a token or a link to a child Earley item.

Because the Earley items contain their sources in the form of predecessor-cause pairs, they in effect rewrite the original grammar into productions with at most two symbols on the right hand side. This is basically Chomsky Normal Form (CNF).

A CNF grammar can be represented conveniently as a binary tree. Marpa does not recreate and-nodes with the original, non-binary, structure of the grammar when it creates the parse bocage from the Earley items. Marpa could do so, but for two reasons, that doesn't make sense.

  • CNF, combined with Aycock and Horspool's split DFA states, produces a representation even more compact than a conventional parse forest.

  • Code to traverse, iterate and evaluate binary nodes is considerably simpler than code which needs to deal with nodes which have an arbitrary number of children. Rather than rewrite the grammar into a form that's harder to process, it makes sense to leave the grammar the way it is found in the Earley items.

For these reasons, the and-nodes in parse bocages do not directly represent productions of the grammar. Instead productions are broken up, so that every and-node has at most two children.

In some cases, it is useful to know the current parse location in terms of the rules of the original grammar. All of Marpa's grammar rewrites can be efficiently "unwritten" when needed. This "un-rewriting" is done on a "lazy" basis.

The term "bocage" comes from the bocage forests of Normandy and Brittany. These man-made forests are networks of hedgerows, vegetative tangles encouraged to grow denser and denser over the centuries. Their purpose is to fence in livestock and to fence out invading armies. Bocage hedges are serious tactical obstacles even to armies equipped with tanks. The Norman bocage played a major role in the D-Day invasion of World War II.

LICENSE AND COPYRIGHT

Copyright 2007-2010 Jeffrey Kegler, all rights reserved. Marpa is free software under the Perl license. For details see the LICENSE file in the Marpa distribution.

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