Guide to CPython’s Parser

Author

Pablo Galindo Salgado

Abstract

The Parser in CPython is currently a PEG (Parser Expression Grammar) parser. The first version of the parser used to be an LL(1) based parser that was one of the oldest parts of CPython implemented before it was replaced by PEP 617. In particular, both the current parser and the old LL(1) parser are the output of a parser generator. This means that the way the parser is written is by feeding a description of the Grammar of the Python language to a special program (the parser generator) which outputs the parser. The way the Python language is changed is therefore by modifying the grammar file and developers rarely need to interact with the parser generator itself other than use it to generate the parser.

How PEG Parsers Work

A PEG (Parsing Expression Grammar) grammar (like the current one) differs from a context-free grammar in that the way it is written more closely reflects how the parser will operate when parsing it. The fundamental technical difference is that the choice operator is ordered. This means that when writing:

rule: A | B | C

a context-free-grammar parser (like an LL(1) parser) will generate constructions that given an input string will deduce which alternative (A, B or C) must be expanded, while a PEG parser will check if the first alternative succeeds and only if it fails, will it continue with the second or the third one in the order in which they are written. This makes the choice operator not commutative.

Unlike LL(1) parsers, PEG-based parsers cannot be ambiguous: if a string parses, it has exactly one valid parse tree. This means that a PEG-based parser cannot suffer from the ambiguity problems that can arise with LL(1) parsers and with context-free grammars in general.

PEG parsers are usually constructed as a recursive descent parser in which every rule in the grammar corresponds to a function in the program implementing the parser and the parsing expression (the “expansion” or “definition” of the rule) represents the “code” in said function. Each parsing function conceptually takes an input string as its argument, and yields one of the following results:

  • A “success” result. This result indicates that the expression can be parsed by that rule and the function may optionally move forward or consume one or more characters of the input string supplied to it.

  • A “failure” result, in which case no input is consumed.

Notice that “failure” results do not imply that the program is incorrect, nor do they necessarily mean that the parsing has failed. Since the choice operator is ordered, a failure very often merely indicates “try the following option”. A direct implementation of a PEG parser as a recursive descent parser will present exponential time performance in the worst case, because PEG parsers have infinite lookahead (this means that they can consider an arbitrary number of tokens before deciding for a rule). Usually, PEG parsers avoid this exponential time complexity with a technique called “packrat parsing” 1 which not only loads the entire program in memory before parsing it but also allows the parser to backtrack arbitrarily. This is made efficient by memoizing the rules already matched for each position. The cost of the memoization cache is that the parser will naturally use more memory than a simple LL(1) parser, which normally are table-based.

Key ideas

Important

Don’t try to reason about a PEG grammar in the same way you would to with an EBNF or context free grammar. PEG is optimized to describe how input strings will be parsed, while context-free grammars are optimized to generate strings of the language they describe (in EBNF, to know if a given string is in the language, you need to do work to find out as it is not immediately obvious from the grammar).

  • Alternatives are ordered ( A | B is not the same as B | A ).

  • If a rule returns a failure, it doesn’t mean that the parsing has failed, it just means “try something else”.

  • By default PEG parsers run in exponential time, which can be optimized to linear by using memoization.

  • If parsing fails completely (no rule succeeds in parsing all the input text), the PEG parser doesn’t have a concept of “where the SyntaxError is”.

Consequences or the ordered choice operator

Although PEG may look like EBNF, its meaning is quite different. The fact that in PEG parsers alternatives are ordered (which is at the core of how PEG parsers work) has deep consequences, other than removing ambiguity.

If a rule has two alternatives and the first of them succeeds, the second one is not attempted even if the caller rule fails to parse the rest of the input. Thus the parser is said to be “eager”. To illustrate this, consider the following two rules (in these examples, a token is an individual character):

first_rule:  ( 'a' | 'aa' ) 'a'
second_rule: ('aa' | 'a'  ) 'a'

In a regular EBNF grammar, both rules specify the language {aa, aaa} but in PEG, one of these two rules accepts the string aaa but not the string aa. The other does the opposite – it accepts the string aa but not the string aaa. The rule ('a'|'aa')'a' does not accept aaa because 'a'|'aa' consumes the first a, letting the final a in the rule consume the second, and leaving out the third a. As the rule has succeeded, no attempt is ever made to go back and let 'a'|'aa' try the second alternative. The expression ('aa'|'a')'a' does not accept aa because 'aa'|'a' accepts all of aa, leaving nothing for the final a. Again, the second alternative of 'aa'|'a' is not tried.

Caution

The effects of ordered choice, such as the ones illustrated above, may be hidden by many levels of rules.

For this reason, writing rules where an alternative is contained in the next one is in almost all cases a mistake, for example:

my_rule:
    | 'if' expression 'then' block
    | 'if' expression 'then' block 'else' block

In this example, the second alternative will never be tried because the first one will succeed first (even if the input string has an 'else' block that follows). To correctly write this rule you can simply alter the order:

my_rule:
    | 'if' expression 'then' block 'else' block
    | 'if' expression 'then' block

In this case, if the input string doesn’t have an 'else' block, the first alternative will fail and the second will be attempted without said part.

Syntax

The grammar consists of a sequence of rules of the form:

rule_name: expression

Optionally, a type can be included right after the rule name, which specifies the return type of the C or Python function corresponding to the rule:

rule_name[return_type]: expression

If the return type is omitted, then a void * is returned in C and an Any in Python.

Grammar Expressions

# comment

Python-style comments.

e1 e2

Match e1, then match e2.

rule_name: first_rule second_rule

e1 | e2

Match e1 or e2.

The first alternative can also appear on the line after the rule name for formatting purposes. In that case, a | must be used before the first alternative, like so:

rule_name[return_type]:
    | first_alt
    | second_alt

( e )

Match e.

rule_name: (e)

A slightly more complex and useful example includes using the grouping operator together with the repeat operators:

rule_name: (e1 e2)*

[ e ] or e?

Optionally match e.

rule_name: [e]

A more useful example includes defining that a trailing comma is optional:

rule_name: e (',' e)* [',']

e*

Match zero or more occurrences of e.

rule_name: (e1 e2)*

e+

Match one or more occurrences of e.

rule_name: (e1 e2)+

s.e+

Match one or more occurrences of e, separated by s. The generated parse tree does not include the separator. This is otherwise identical to (e (s e)*).

rule_name: ','.e+

&e

Succeed if e can be parsed, without consuming any input.

!e

Fail if e can be parsed, without consuming any input.

An example taken from the Python grammar specifies that a primary consists of an atom, which is not followed by a . or a ( or a [:

primary: atom !'.' !'(' !'['

~

Commit to the current alternative, even if it fails to parse (this is called the “cut”).

rule_name: '(' ~ some_rule ')' | some_alt

In this example, if a left parenthesis is parsed, then the other alternative won’t be considered, even if some_rule or ) fail to be parsed.

Left recursion

PEG parsers normally do not support left recursion but CPython’s parser generator implements a technique similar to the one described in Medeiros et al. 2 but using the memoization cache instead of static variables. This approach is closer to the one described in Warth et al. 3. This allows us to write not only simple left-recursive rules but also more complicated rules that involve indirect left-recursion like:

rule1: rule2 | 'a'
rule2: rule3 | 'b'
rule3: rule1 | 'c'

and “hidden left-recursion” like:

rule: 'optional'? rule '@' some_other_rule

Variables in the Grammar

A sub-expression can be named by preceding it with an identifier and an = sign. The name can then be used in the action (see below), like this:

rule_name[return_type]: '(' a=some_other_rule ')' { a }

Grammar actions

To avoid the intermediate steps that obscure the relationship between the grammar and the AST generation the PEG parser allows directly generating AST nodes for a rule via grammar actions. Grammar actions are language-specific expressions that are evaluated when a grammar rule is successfully parsed. These expressions can be written in Python or C depending on the desired output of the parser generator. This means that if one would want to generate a parser in Python and another in C, two grammar files should be written, each one with a different set of actions, keeping everything else apart from said actions identical in both files. As an example of a grammar with Python actions, the piece of the parser generator that parses grammar files is bootstrapped from a meta-grammar file with Python actions that generate the grammar tree as a result of the parsing.

In the specific case of the PEG grammar for Python, having actions allows directly describing how the AST is composed in the grammar itself, making it more clear and maintainable. This AST generation process is supported by the use of some helper functions that factor out common AST object manipulations and some other required operations that are not directly related to the grammar.

To indicate these actions each alternative can be followed by the action code inside curly-braces, which specifies the return value of the alternative:

rule_name[return_type]:
    | first_alt1 first_alt2 { first_alt1 }
    | second_alt1 second_alt2 { second_alt1 }

If the action is omitted, a default action is generated:

  • If there’s a single name in the rule, it gets returned.

  • If there is more than one name in the rule, a collection with all parsed expressions gets returned (the type of the collection will be different in C and Python).

This default behaviour is primarily made for very simple situations and for debugging purposes.

Warning

It’s important that the actions don’t mutate any AST nodes that are passed into them via variables referring to other rules. The reason for mutation being not allowed is that the AST nodes are cached by memoization and could potentially be reused in a different context, where the mutation would be invalid. If an action needs to change an AST node, it should instead make a new copy of the node and change that.

The full meta-grammar for the grammars supported by the PEG generator is:

start[Grammar]: grammar ENDMARKER { grammar }

grammar[Grammar]:
    | metas rules { Grammar(rules, metas) }
    | rules { Grammar(rules, []) }

metas[MetaList]:
    | meta metas { [meta] + metas }
    | meta { [meta] }

meta[MetaTuple]:
    | "@" NAME NEWLINE { (name.string, None) }
    | "@" a=NAME b=NAME NEWLINE { (a.string, b.string) }
    | "@" NAME STRING NEWLINE { (name.string, literal_eval(string.string)) }

rules[RuleList]:
    | rule rules { [rule] + rules }
    | rule { [rule] }

rule[Rule]:
    | rulename ":" alts NEWLINE INDENT more_alts DEDENT {
            Rule(rulename[0], rulename[1], Rhs(alts.alts + more_alts.alts)) }
    | rulename ":" NEWLINE INDENT more_alts DEDENT { Rule(rulename[0], rulename[1], more_alts) }
    | rulename ":" alts NEWLINE { Rule(rulename[0], rulename[1], alts) }

rulename[RuleName]:
    | NAME '[' type=NAME '*' ']' {(name.string, type.string+"*")}
    | NAME '[' type=NAME ']' {(name.string, type.string)}
    | NAME {(name.string, None)}

alts[Rhs]:
    | alt "|" alts { Rhs([alt] + alts.alts)}
    | alt { Rhs([alt]) }

more_alts[Rhs]:
    | "|" alts NEWLINE more_alts { Rhs(alts.alts + more_alts.alts) }
    | "|" alts NEWLINE { Rhs(alts.alts) }

alt[Alt]:
    | items '$' action { Alt(items + [NamedItem(None, NameLeaf('ENDMARKER'))], action=action) }
    | items '$' { Alt(items + [NamedItem(None, NameLeaf('ENDMARKER'))], action=None) }
    | items action { Alt(items, action=action) }
    | items { Alt(items, action=None) }

items[NamedItemList]:
    | named_item items { [named_item] + items }
    | named_item { [named_item] }

named_item[NamedItem]:
    | NAME '=' ~ item {NamedItem(name.string, item)}
    | item {NamedItem(None, item)}
    | it=lookahead {NamedItem(None, it)}

lookahead[LookaheadOrCut]:
    | '&' ~ atom {PositiveLookahead(atom)}
    | '!' ~ atom {NegativeLookahead(atom)}
    | '~' {Cut()}

item[Item]:
    | '[' ~ alts ']' {Opt(alts)}
    |  atom '?' {Opt(atom)}
    |  atom '*' {Repeat0(atom)}
    |  atom '+' {Repeat1(atom)}
    |  sep=atom '.' node=atom '+' {Gather(sep, node)}
    |  atom {atom}

atom[Plain]:
    | '(' ~ alts ')' {Group(alts)}
    | NAME {NameLeaf(name.string) }
    | STRING {StringLeaf(string.string)}

# Mini-grammar for the actions

action[str]: "{" ~ target_atoms "}" { target_atoms }

target_atoms[str]:
    | target_atom target_atoms { target_atom + " " + target_atoms }
    | target_atom { target_atom }

target_atom[str]:
    | "{" ~ target_atoms "}" { "{" + target_atoms + "}" }
    | NAME { name.string }
    | NUMBER { number.string }
    | STRING { string.string }
    | "?" { "?" }
    | ":" { ":" }

As an illustrative example this simple grammar file allows directly generating a full parser that can parse simple arithmetic expressions and that returns a valid C-based Python AST:

start[mod_ty]: a=expr_stmt* ENDMARKER { _PyAST_Module(a, NULL, p->arena) }
expr_stmt[stmt_ty]: a=expr NEWLINE { _PyAST_Expr(a, EXTRA) }

expr[expr_ty]:
    | l=expr '+' r=term { _PyAST_BinOp(l, Add, r, EXTRA) }
    | l=expr '-' r=term { _PyAST_BinOp(l, Sub, r, EXTRA) }
    | term

term[expr_ty]:
    | l=term '*' r=factor { _PyAST_BinOp(l, Mult, r, EXTRA) }
    | l=term '/' r=factor { _PyAST_BinOp(l, Div, r, EXTRA) }
    | factor

factor[expr_ty]:
    | '(' e=expr ')' { e }
    | atom

atom[expr_ty]:
    | NAME
    | NUMBER

Here EXTRA is a macro that expands to start_lineno, start_col_offset, end_lineno, end_col_offset, p->arena, those being variables automatically injected by the parser; p points to an object that holds on to all state for the parser.

A similar grammar written to target Python AST objects:

start[ast.Module]: a=expr_stmt* ENDMARKER { ast.Module(body=a or [] }
expr_stmt: a=expr NEWLINE { ast.Expr(value=a, EXTRA) }

expr:
    | l=expr '+' r=term { ast.BinOp(left=l, op=ast.Add(), right=r, EXTRA) }
    | l=expr '-' r=term { ast.BinOp(left=l, op=ast.Sub(), right=r, EXTRA) }
    | term

term:
    | l=term '*' r=factor { ast.BinOp(left=l, op=ast.Mult(), right=r, EXTRA) }
    | l=term '/' r=factor { ast.BinOp(left=l, op=ast.Div(), right=r, EXTRA) }
    | factor

factor:
    | '(' e=expr ')' { e }
    | atom

atom:
    | NAME
    | NUMBER

Pegen

Pegen is the parser generator used in CPython to produce the final PEG parser used by the interpreter. It is the program that can be used to read the python grammar located in Grammar/Python.gram and produce the final C parser. It contains the following pieces:

  • A parser generator that can read a grammar file and produce a PEG parser written in Python or C that can parse said grammar. The generator is located at Tools/peg_generator/pegen.

  • A PEG meta-grammar that automatically generates a Python parser that is used for the parser generator itself (this means that there are no manually-written parsers). The meta-grammar is located at Tools/peg_generator/pegen/metagrammar.gram.

  • A generated parser (using the parser generator) that can directly produce C and Python AST objects.

The source code for Pegen lives at Tools/peg_generator/pegen but normally all typical commands to interact with the parser generator are executed from the main makefile.

How to regenerate the parser

Once you have made the changes to the grammar files, to regenerate the C parser (the one used by the interpreter) just execute:

make regen-pegen

using the Makefile in the main directory. If you are on Windows you can use the Visual Studio project files to regenerate the parser or to execute:

./PCbuild/build.bat --regen

The generated parser file is located at Parser/parser.c.

How to regenerate the meta-parser

The meta-grammar (the grammar that describes the grammar for the grammar files themselves) is located at Tools/peg_generator/pegen/metagrammar.gram. Although it is very unlikely that you will ever need to modify it, if you make any modifications to this file (in order to implement new Pegen features) you will need to regenerate the meta-parser (the parser that parses the grammar files). To do so just execute:

make regen-pegen-metaparser

If you are on Windows you can use the Visual Studio project files to regenerate the parser or to execute:

./PCbuild/build.bat --regen

Grammatical elements and rules

Pegen has some special grammatical elements and rules:

  • Strings with single quotes (’) (e.g. 'class') denote KEYWORDS.

  • Strings with double quotes (”) (e.g. "match") denote SOFT KEYWORDS.

  • Upper case names (e.g. NAME) denote tokens in the Grammar/Tokens file.

  • Rule names starting with invalid_ are used for specialized syntax errors.

    • These rules are NOT used in the first pass of the parser.

    • Only if the first pass fails to parse, a second pass including the invalid rules will be executed.

    • If the parser fails in the second phase with a generic syntax error, the location of the generic failure of the first pass will be used (this avoids reporting incorrect locations due to the invalid rules).

    • The order of the alternatives involving invalid rules matter (like any rule in PEG).

Tokenization

It is common among PEG parser frameworks that the parser does both the parsing and the tokenization, but this does not happen in Pegen. The reason is that the Python language needs a custom tokenizer to handle things like indentation boundaries, some special keywords like ASYNC and AWAIT (for compatibility purposes), backtracking errors (such as unclosed parenthesis), dealing with encoding, interactive mode and much more. Some of these reasons are also there for historical purposes, and some others are useful even today.

The list of tokens (all uppercase names in the grammar) that you can use can be found in the Grammar/Tokens file. If you change this file to add new tokens, make sure to regenerate the files by executing:

make regen-token

If you are on Windows you can use the Visual Studio project files to regenerate the tokens or to execute:

./PCbuild/build.bat --regen

How tokens are generated and the rules governing this is completely up to the tokenizer (Parser/tokenizer.c) and the parser just receives tokens from it.

Memoization

As described previously, to avoid exponential time complexity in the parser, memoization is used.

The C parser used by Python is highly optimized and memoization can be expensive both in memory and time. Although the memory cost is obvious (the parser needs memory for storing previous results in the cache) the execution time cost comes for continuously checking if the given rule has a cache hit or not. In many situations, just parsing it again can be faster. Pegen disables memoization by default except for rules with the special marker memo after the rule name (and type, if present):

rule_name[typr] (memo):
    ...

By selectively turning on memoization for a handful of rules, the parser becomes faster and uses less memory.

Note

Left-recursive rules always use memoization, since the implementation of left-recursion depends on it.

To know if a new rule needs memoization or not, benchmarking is required (comparing execution times and memory usage of some considerably big files with and without memoization). There is a very simple instrumentation API available in the generated C parse code that allows to measure how much each rule uses memoization (check the Parser/pegen.c file for more information) but it needs to be manually activated.

Automatic variables

To make writing actions easier, Pegen injects some automatic variables in the namespace available when writing actions. In the C parser, some of these automatic variable names are:

  • p: The parser structure.

  • EXTRA: This is a macro that expands to (_start_lineno, _start_col_offset, _end_lineno, _end_col_offset, p->arena), which is normally used to create AST nodes as almost all constructors need these attributes to be provided. All of the location variables are taken from the location information of the current token.

Hard and Soft keywords

Note

In the grammar files, keywords are defined using single quotes (e.g. ‘class’) while soft keywords are defined using double quotes (e.g. “match”).

There are two kinds of keywords allowed in pegen grammars: hard and soft keywords. The difference between hard and soft keywords is that hard keywords are always reserved words, even in positions where they make no sense (e.g. x = class + 1), while soft keywords only get a special meaning in context. Trying to use a hard keyword as a variable will always fail:

>>> class = 3
File "<stdin>", line 1
    class = 3
        ^
SyntaxError: invalid syntax
>>> foo(class=3)
File "<stdin>", line 1
    foo(class=3)
        ^^^^^
SyntaxError: invalid syntax

While soft keywords don’t have this limitation if used in a context other the one where they are defined as keywords:

>>> match = 45
>>> foo(match="Yeah!")

The match and case keywords are soft keywords, so that they are recognized as keywords at the beginning of a match statement or case block respectively, but are allowed to be used in other places as variable or argument names.

You can get a list of all keywords defined in the grammar from Python:

>>> import keyword
>>> keyword.kwlist
['False', 'None', 'True', 'and', 'as', 'assert', 'async', 'await', 'break',
'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'finally', 'for',
'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'nonlocal', 'not', 'or',
'pass', 'raise', 'return', 'try', 'while', 'with', 'yield']

as well as soft keywords:

>>> import keyword
>>> keyword.softkwlist
['_', 'case', 'match']

Caution

Soft keywords can be a bit challenging to manage as they can be accepted in places you don’t intend to, given how the order alternatives behave in PEG parsers (see consequences of ordered choice section for some background on this). In general, try to define them in places where there is not a lot of alternatives.

Error handling

When a pegen-generated parser detects that an exception is raised, it will automatically stop parsing, no matter what the current state of the parser is and it will unwind the stack and report the exception. This means that if a rule action raises an exception all parsing will stop at that exact point. This is done to allow to correctly propagate any exception set by calling Python C-API functions. This also includes SyntaxError exceptions and this is the main mechanism the parser uses to report custom syntax error messages.

Note

Tokenizer errors are normally reported by raising exceptions but some special tokenizer errors such as unclosed parenthesis will be reported only after the parser finishes without returning anything.

How Syntax errors are reported

As described previously in the how PEG parsers work section, PEG parsers don’t have a defined concept of where errors happened in the grammar, because a rule failure doesn’t imply a parsing failure like in context free grammars. This means that some heuristic has to be used to report generic errors unless something is explicitly declared as an error in the grammar.

To report generic syntax errors, pegen uses a common heuristic in PEG parsers: the location of generic syntax errors is reported in the furthest token that was attempted to be matched but failed. This is only done if parsing has failed (the parser returns NULL in C or None in Python) but no exception has been raised.

Caution

Positive and negative lookaheads will try to match a token so they will affect the location of generic syntax errors. Use them carefully at boundaries between rules.

As the Python grammar was primordially written as an LL(1) grammar, this heuristic has an extremely high success rate, but some PEG features can have small effects, such as positive lookaheads and negative lookaheads.

To generate more precise syntax errors, custom rules are used. This is a common practice also in context free grammars: the parser will try to accept some construct that is known to be incorrect just to report a specific syntax error for that construct. In pegen grammars, these rules start with the invalid_ prefix. This is because trying to match these rules normally has a performance impact on parsing (and can also affect the ‘correct’ grammar itself in some tricky cases, depending on the ordering of the rules) so the generated parser acts in two phases:

  1. The first phase will try to parse the input stream without taking into account rules that start with the invalid_ prefix. If the parsing succeeds it will return the generated AST and the second phase will not be attempted.

  2. If the first phase failed, a second parsing attempt is done including the rules that start with an invalid_ prefix. By design this attempt cannot succeed and is only executed to give to the invalid rules a chance to detect specific situations where custom, more precise, syntax errors can be raised. This also allows to trade a bit of performance for precision reporting errors: given that we know that the input text is invalid, there is no need to be fast because the interpreter is going to stop anyway.

Important

When defining invalid rules:

  • Make sure all custom invalid rules raise SyntaxError exceptions (or a subclass of it).

  • Make sure all invalid rules start with the invalid_ prefix to not impact performance of parsing correct Python code.

  • Make sure the parser doesn’t behave differently for regular rules when you introduce invalid rules (see the how PEG parsers work section for more information).

You can find a collection of macros to raise specialized syntax errors in the Parser/pegen.h header file. These macros allow also to report ranges for the custom errors that will be highlighted in the tracebacks that will be displayed when the error is reported.

Tip

A good way to test if an invalid rule will be triggered when you expect is to test if introducing a syntax error after valid code triggers the rule or not. For example:

<valid python code> $ 42

Should trigger the syntax error in the $ character. If your rule is not correctly defined this won’t happen. For example, if you try to define a rule to match Python 2 style print statements to make a better error message and you define it as:

invalid_print: "print" expression

This will seem to work because the parser will correctly parse print(something) because it is valid code and the second phase will never execute but if you try to parse print(something) $ 3 the first pass of the parser will fail (because of the $) and in the second phase, the rule will match the print(something) as print followed by the variable something between parentheses and the error will be reported there instead of the $ character.

Generating AST objects

The output of the C parser used by CPython that is generated by the Grammar/Python.gram grammar file is a Python AST object (using C structures). This means that the actions in the grammar file generate AST objects when they succeed. Constructing these objects can be quite cumbersome (see the AST compiler section for more information on how these objects are constructed and how they are used by the compiler) so special helper functions are used. These functions are declared in the Parser/pegen.h header file and defined in the Parser/action_helpers.c file. These functions allow you to join AST sequences, get specific elements from them or to do extra processing on the generated tree.

Caution

Actions must never be used to accept or reject rules. It may be tempting in some situations to write a very generic rule and then check the generated AST to decide if is valid or not but this will render the official grammar partially incorrect (because actions are not included) and will make it more difficult for other Python implementations to adapt the grammar to their own needs.

As a general rule, if an action spawns multiple lines or requires something more complicated than a single expression of C code, is normally better to create a custom helper in Parser/action_helpers.c and expose it in the Parser/pegen.h header file so it can be used from the grammar.

If the parsing succeeds, the parser must return a valid AST object.

Testing

There are three files that contain tests for the grammar and the parser:

  • Lib/test/test_grammar.py.

  • Lib/test/test_syntax.py.

  • Lib/test/test_exceptions.py.

Check the contents of these files to know which is the best place to place new tests depending on the nature of the new feature you are adding.

Tests for the parser generator itself can be found in the Lib/test/test_peg_generator directory.

Debugging generated parsers

Making experiments

As the generated C parser is the one used by Python, this means that if something goes wrong when adding some new rules to the grammar you cannot correctly compile and execute Python anymore. This makes it a bit challenging to debug when something goes wrong, especially when making experiments.

For this reason it is a good idea to experiment first by generating a Python parser. To do this, you can go to the Tools/peg_generator/ directory on the CPython repository and manually call the parser generator by executing:

$ python -m pegen python <PATH TO YOUR GRAMMAR FILE>

This will generate a file called parse.py in the same directory that you can use to parse some input:

$ python parse.py file_with_source_code_to_test.py

As the generated parse.py file is just Python code, you can modify it and add breakpoints to debug or better understand some complex situations.

Verbose mode

When Python is compiled in debug mode (by adding --with-pydebug when running the configure step in Linux or by adding -d when calling the PCbuild/python.bat script in Windows), it is possible to activate a very verbose mode in the generated parser. This is very useful to debug the generated parser and to understand how it works, but it can be a bit hard to understand at first.

Note

When activating verbose mode in the Python parser, it is better to not use interactive mode as it can be much harder to understand, because interactive mode involves some special steps compared to regular parsing.

To activate verbose mode you can add the -d flag when executing Python:

$ python -d file_to_test.py

This will print a lot of output to stderr so is probably better to dump it to a file for further analysis. The output consists of trace lines with the following structure:

<indentation> (‘>’|’-‘|’+’|’!’) <rule_name>[<token_location>]: <alternative> …

Every line is indented by a different amount (<indentation>) depending on how deep the call stack is. The next character marks the type of the trace:

  • > indicates that a rule is going to be attempted to be parsed.

  • - indicates that a rule has failed to be parsed.

  • + indicates that a rule has been parsed correctly.

  • ! indicates that an exception or an error has been detected and the parser is unwinding.

The <token_location> part indicates the current index in the token array, the <rule_name> part indicates what rule is being parsed and the <alternative> part indicates what alternative within that rule is being attempted.

References

1

Ford, Bryan http://pdos.csail.mit.edu/~baford/packrat/thesis

2

Medeiros et al. https://arxiv.org/pdf/1207.0443.pdf

3

Warth et al. http://web.cs.ucla.edu/~todd/research/pepm08.pdf