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Compiler design

Abstract

In CPython, the compilation from source code to bytecode involves several steps:

  1. Tokenize the source code Parser/lexer/ and Parser/tokenizer/.
  2. Parse the stream of tokens into an Abstract Syntax Tree Parser/parser.c.
  3. Transform AST into an instruction sequence Python/compile.c.
  4. Construct a Control Flow Graph and apply optimizations to it Python/flowgraph.c.
  5. Emit bytecode based on the Control Flow Graph Python/assemble.c.

This document outlines how these steps of the process work.

This document only describes parsing in enough depth to explain what is needed for understanding compilation. This document provides a detailed, though not exhaustive, view of the how the entire system works. You will most likely need to read some source code to have an exact understanding of all details.

Parsing

As of Python 3.9, Python's parser is a PEG parser of a somewhat unusual design. It is unusual in the sense that the parser's input is a stream of tokens rather than a stream of characters which is more common with PEG parsers.

The grammar file for Python can be found in Grammar/python.gram. The definitions for literal tokens (such as :, numbers, etc.) can be found in Grammar/Tokens. Various C files, including Parser/parser.c are generated from these.

See Also:

Abstract syntax trees (AST)

The abstract syntax tree (AST) is a high-level representation of the program structure without the necessity of containing the source code; it can be thought of as an abstract representation of the source code. The specification of the AST nodes is specified using the Zephyr Abstract Syntax Definition Language (ASDL) 1, 2.

The definition of the AST nodes for Python is found in the file Parser/Python.asdl.

Each AST node (representing statements, expressions, and several specialized types, like list comprehensions and exception handlers) is defined by the ASDL. Most definitions in the AST correspond to a particular source construct, such as an 'if' statement or an attribute lookup. The definition is independent of its realization in any particular programming language.

The following fragment of the Python ASDL construct demonstrates the approach and syntax:

   module Python
   {
       stmt = FunctionDef(identifier name, arguments args, stmt* body,
                          expr* decorators)
              | Return(expr? value) | Yield(expr? value)
              attributes (int lineno)
   }

The preceding example describes two different kinds of statements and an expression: function definitions, return statements, and yield expressions. All three kinds are considered of type stmt as shown by | separating the various kinds. They all take arguments of various kinds and amounts.

Modifiers on the argument type specify the number of values needed; ? means it is optional, * means 0 or more, while no modifier means only one value for the argument and it is required. FunctionDef, for instance, takes an identifier for the name, arguments for args, zero or more stmt arguments for body, and zero or more expr arguments for decorators.

Do notice that something like 'arguments', which is a node type, is represented as a single AST node and not as a sequence of nodes as with stmt as one might expect.

All three kinds also have an 'attributes' argument; this is shown by the fact that 'attributes' lacks a '|' before it.

The statement definitions above generate the following C structure type:

  typedef struct _stmt *stmt_ty;

  struct _stmt {
        enum { FunctionDef_kind=1, Return_kind=2, Yield_kind=3 } kind;
        union {
                struct {
                        identifier name;
                        arguments_ty args;
                        asdl_seq *body;
                } FunctionDef;

                struct {
                        expr_ty value;
                } Return;

                struct {
                        expr_ty value;
                } Yield;
        } v;
        int lineno;
   }

Also generated are a series of constructor functions that allocate (in this case) a stmt_ty struct with the appropriate initialization. The kind field specifies which component of the union is initialized. The FunctionDef() constructor function sets 'kind' to FunctionDef_kind and initializes the name, args, body, and attributes fields.

See also Green Tree Snakes - The missing Python AST docs by Thomas Kluyver.

Memory management

Before discussing the actual implementation of the compiler, a discussion of how memory is handled is in order. To make memory management simple, an arena is used that pools memory in a single location for easy allocation and removal. This enables the removal of explicit memory deallocation. Because memory allocation for all needed memory in the compiler registers that memory with the arena, a single call to free the arena is all that is needed to completely free all memory used by the compiler.

In general, unless you are working on the critical core of the compiler, memory management can be completely ignored. But if you are working at either the very beginning of the compiler or the end, you need to care about how the arena works. All code relating to the arena is in either Include/internal/pycore_pyarena.h or Python/pyarena.c.

PyArena_New() will create a new arena. The returned PyArena structure will store pointers to all memory given to it. This does the bookkeeping of what memory needs to be freed when the compiler is finished with the memory it used. That freeing is done with PyArena_Free(). This only needs to be called in strategic areas where the compiler exits.

As stated above, in general you should not have to worry about memory management when working on the compiler. The technical details of memory management have been designed to be hidden from you for most cases.

The only exception comes about when managing a PyObject. Since the rest of Python uses reference counting, there is extra support added to the arena to cleanup each PyObject that was allocated. These cases are very rare. However, if you've allocated a PyObject, you must tell the arena about it by calling PyArena_AddPyObject().

Source code to AST

The AST is generated from source code using the function _PyParser_ASTFromString() or _PyParser_ASTFromFile() Parser/peg_api.c.

After some checks, a helper function in Parser/parser.c begins applying production rules on the source code it receives; converting source code to tokens and matching these tokens recursively to their corresponding rule. The production rule's corresponding rule function is called on every match. These rule functions follow the format xx_rule. Where xx is the grammar rule that the function handles and is automatically derived from Grammar/python.gram by Tools/peg_generator/pegen/c_generator.py.

Each rule function in turn creates an AST node as it goes along. It does this by allocating all the new nodes it needs, calling the proper AST node creation functions for any required supporting functions and connecting them as needed. This continues until all nonterminal symbols are replaced with terminals. If an error occurs, the rule functions backtrack and try another rule function. If there are no more rules, an error is set and the parsing ends.

The AST node creation helper functions have the name _PyAST_{xx} where xx is the AST node that the function creates. These are defined by the ASDL grammar and contained in Python/Python-ast.c (which is generated by Parser/asdl_c.py from Parser/Python.asdl). This all leads to a sequence of AST nodes stored in asdl_seq structs.

To demonstrate everything explained so far, here's the rule function responsible for a simple named import statement such as import sys. Note that error-checking and debugging code has been omitted. Removed parts are represented by .... Furthermore, some comments have been added for explanation. These comments may not be present in the actual code.

   // This is the production rule (from python.gram) the rule function
   // corresponds to:
   // import_name: 'import' dotted_as_names
   static stmt_ty
   import_name_rule(Parser *p)
   {
       ...
       stmt_ty _res = NULL;
       { // 'import' dotted_as_names
           ...
           Token * _keyword;
           asdl_alias_seq* a;
           // The tokenizing steps.
           if (
               (_keyword = _PyPegen_expect_token(p, 513))  // token='import'
               &&
               (a = dotted_as_names_rule(p))  // dotted_as_names
           )
           {
               ...
               // Generate an AST for the import statement.
               _res = _PyAST_Import ( a , ...);
               ...
               goto done;
           }
           ...
       }
       _res = NULL;
     done:
       ...
       return _res;
   }

To improve backtracking performance, some rules (chosen by applying a (memo) flag in the grammar file) are memoized. Each rule function checks if a memoized version exists and returns that if so, else it continues in the manner stated in the previous paragraphs.

There are macros for creating and using asdl_xx_seq * types, where xx is a type of the ASDL sequence. Three main types are defined manually -- generic, identifier and int. These types are found in Python/asdl.c and its corresponding header file Include/internal/pycore_asdl.h. Functions and macros for creating asdl_xx_seq * types are as follows:

_Py_asdl_generic_seq_new(Py_ssize_t, PyArena *) Allocate memory for an asdl_generic_seq of the specified length _Py_asdl_identifier_seq_new(Py_ssize_t, PyArena *) Allocate memory for an asdl_identifier_seq of the specified length _Py_asdl_int_seq_new(Py_ssize_t, PyArena *) Allocate memory for an asdl_int_seq of the specified length

In addition to the three types mentioned above, some ASDL sequence types are automatically generated by Parser/asdl_c.py and found in Include/internal/pycore_ast.h. Macros for using both manually defined and automatically generated ASDL sequence types are as follows:

asdl_seq_GET(asdl_xx_seq *, int) Get item held at a specific position in an asdl_xx_seq asdl_seq_SET(asdl_xx_seq *, int, stmt_ty) Set a specific index in an asdl_xx_seq to the specified value

Untyped counterparts exist for some of the typed macros. These are useful when a function needs to manipulate a generic ASDL sequence:

asdl_seq_GET_UNTYPED(asdl_seq *, int) Get item held at a specific position in an asdl_seq asdl_seq_SET_UNTYPED(asdl_seq *, int, stmt_ty) Set a specific index in an asdl_seq to the specified value asdl_seq_LEN(asdl_seq *) Return the length of an asdl_seq or asdl_xx_seq

Note that typed macros and functions are recommended over their untyped counterparts. Typed macros carry out checks in debug mode and aid debugging errors caused by incorrectly casting from void *.

If you are working with statements, you must also worry about keeping track of what line number generated the statement. Currently the line number is passed as the last parameter to each stmt_ty function.

See also PEP 617: New PEG parser for CPython.

Control flow graphs

A control flow graph (often referenced by its acronym, CFG) is a directed graph that models the flow of a program. A node of a CFG is not an individual bytecode instruction, but instead represents a sequence of bytecode instructions that always execute sequentially. Each node is called a basic block and must always execute from start to finish, with a single entry point at the beginning and a single exit point at the end. If some bytecode instruction a needs to jump to some other bytecode instruction b, then a must occur at the end of its basic block, and b must occur at the start of its basic block.

As an example, consider the following code snippet:

if x < 10:
    f1()
    f2()
else:
    g()
end()

The x < 10 guard is represented by its own basic block that compares x with 10 and then ends in a conditional jump based on the result of the comparison. This conditional jump allows the block to point to both the body of the if and the body of the else. The if basic block contains the f1() and f2() calls and points to the end() basic block. The else basic block contains the g() call and similarly points to the end() block.

Note that more complex code in the guard, the if body, or the else body may be represented by multiple basic blocks. For instance, short-circuiting boolean logic in a guard like if x or y: will produce one basic block that tests the truth value of x and then points both (1) to the start of the if body and (2) to a different basic block that tests the truth value of y.

CFGs are useful as an intermediate representation of the code because they are a convenient data structure for optimizations.

AST to CFG to bytecode

The conversion of an AST to bytecode is initiated by a call to the function _PyAST_Compile() in Python/compile.c.

The first step is to construct the symbol table. This is implemented by _PySymtable_Build() in Python/symtable.c. This function begins by entering the starting code block for the AST (passed-in) and then calling the proper symtable_visit_{xx} function (with xx being the AST node type). Next, the AST tree is walked with the various code blocks that delineate the reach of a local variable as blocks are entered and exited using symtable_enter_block() and symtable_exit_block(), respectively.

Once the symbol table is created, the AST is transformed by compiler_codegen() in Python/compile.c into a sequence of pseudo instructions. These are similar to bytecode, but in some cases they are more abstract, and are resolved later into actual bytecode. The construction of this instruction sequence is handled by several functions that break the task down by various AST node types. The functions are all named compiler_visit_{xx} where xx is the name of the node type (such as stmt, expr, etc.). Each function receives a struct compiler * and {xx}_ty where xx is the AST node type. Typically these functions consist of a large 'switch' statement, branching based on the kind of node type passed to it. Simple things are handled inline in the 'switch' statement with more complex transformations farmed out to other functions named compiler_{xx} with xx being a descriptive name of what is being handled.

When transforming an arbitrary AST node, use the VISIT() macro. The appropriate compiler_visit_{xx} function is called, based on the value passed in for (so VISIT({c}, expr, {node}) calls compiler_visit_expr({c}, {node})). The VISIT_SEQ() macro is very similar, but is called on AST node sequences (those values that were created as arguments to a node that used the '*' modifier).

Emission of bytecode is handled by the following macros:

  • ADDOP(struct compiler *, location, int) add a specified opcode
  • ADDOP_IN_SCOPE(struct compiler *, location, int) like ADDOP, but also exits current scope; used for adding return value opcodes in lambdas and closures
  • ADDOP_I(struct compiler *, location, int, Py_ssize_t) add an opcode that takes an integer argument
  • ADDOP_O(struct compiler *, location, int, PyObject *, TYPE) add an opcode with the proper argument based on the position of the specified PyObject in PyObject sequence object, but with no handling of mangled names; used for when you need to do named lookups of objects such as globals, consts, or parameters where name mangling is not possible and the scope of the name is known; TYPE is the name of PyObject sequence (names or varnames)
  • ADDOP_N(struct compiler *, location, int, PyObject *, TYPE) just like ADDOP_O, but steals a reference to PyObject
  • ADDOP_NAME(struct compiler *, location, int, PyObject *, TYPE) just like ADDOP_O, but name mangling is also handled; used for attribute loading or importing based on name
  • ADDOP_LOAD_CONST(struct compiler *, location, PyObject *) add the LOAD_CONST opcode with the proper argument based on the position of the specified PyObject in the consts table.
  • ADDOP_LOAD_CONST_NEW(struct compiler *, location, PyObject *) just like ADDOP_LOAD_CONST_NEW, but steals a reference to PyObject
  • ADDOP_JUMP(struct compiler *, location, int, basicblock *) create a jump to a basic block

The location argument is a struct with the source location to be associated with this instruction. It is typically extracted from an AST node with the LOC macro. The NO_LOCATION can be used for synthetic instructions, which we do not associate with a line number at this stage. For example, the implicit return None which is added at the end of a function is not associated with any line in the source code.

There are several helper functions that will emit pseudo-instructions and are named compiler_{xx}() where xx is what the function helps with (list, boolop, etc.). A rather useful one is compiler_nameop(). This function looks up the scope of a variable and, based on the expression context, emits the proper opcode to load, store, or delete the variable.

Once the instruction sequence is created, it is transformed into a CFG by _PyCfg_FromInstructionSequence(). Then _PyCfg_OptimizeCodeUnit() applies various peephole optimizations, and _PyCfg_OptimizedCfgToInstructionSequence() converts the optimized CFG back into an instruction sequence. These conversions and optimizations are implemented in Python/flowgraph.c.

Finally, the sequence of pseudo-instructions is converted into actual bytecode. This includes transforming pseudo instructions into actual instructions, converting jump targets from logical labels to relative offsets, and construction of the exception table and locations table. The bytecode and tables are then wrapped into a PyCodeObject along with additional metadata, including the consts and names arrays, information about function reference to the source code (filename, etc). All of this is implemented by _PyAssemble_MakeCodeObject() in Python/assemble.c.

Code objects

The result of PyAST_CompileObject() is a PyCodeObject which is defined in Include/cpython/code.h. And with that you now have executable Python bytecode!

The code objects (byte code) are executed in Python/ceval.c. This file will also need a new case statement for the new opcode in the big switch statement in _PyEval_EvalFrameDefault().

Important files

Objects

Specializing Adaptive Interpreter

Adding a specializing, adaptive interpreter to CPython will bring significant performance improvements. These documents provide more information:

References

  1. Daniel C. Wang, Andrew W. Appel, Jeff L. Korn, and Chris S. Serra. The Zephyr Abstract Syntax Description Language._ In Proceedings of the Conference on Domain-Specific Languages, pp. 213--227, 1997. ↩︎

  2. The Zephyr Abstract Syntax Description Language.: https://www.cs.princeton.edu/research/techreps/TR-554-97 ↩︎