Objects/frame_layout.md - platform/external/python/cpython3 - Git at Google

 # The Frame Stack

 Each call to a Python function has an activation record,
 commonly known as a "frame".
 Python semantics allows frames to outlive the activation,
 so they have (before 3.11) been allocated on the heap.
 This is expensive as it requires many allocations and
 results in poor locality of reference.

 In 3.11, rather than have these frames scattered about memory,
 as happens for heap-allocated objects, frames are allocated
 contiguously in a per-thread stack.
 This improves performance significantly for two reasons:
 * It reduces allocation overhead to a pointer comparison and increment.
 * Stack allocated data has the best possible locality and will always be in
   CPU cache.

 Generator and coroutines still need heap allocated activation records, but
 can be linked into the per-thread stack so as to not impact performance too much.

 ## Layout

 Each activation record consists of four conceptual sections:

 * Local variables (including arguments, cells and free variables)
 * Evaluation stack
 * Specials: The per-frame object references needed by the VM: globals dict,
   code object, etc.
 * Linkage: Pointer to the previous activation record, stack depth, etc.

 ### Layout

 The specials and linkage sections are a fixed size, so are grouped together.

 Each activation record is laid out as:
 * Specials and linkage
 * Locals
 * Stack

 This seems to provide the best performance without excessive complexity.
 It needs the interpreter to hold two pointers, a frame pointer and a stack pointer.

 #### Alternative layout

 An alternative layout that was used for part of 3.11 alpha was:

 * Locals
 * Specials and linkage
 * Stack

 This has the advantage that no copying is required when making a call,
 as the arguments on the stack are (usually) already in the correct
 location for the parameters. However, it requires the VM to maintain
 an extra pointer for the locals, which can hurt performance.

 A variant that only needs the need two pointers is to reverse the numbering
 of the locals, so that the last one is numbered `0`, and the first in memory
 is numbered `N-1`.
 This allows the locals, specials and linkage to accessed from the frame pointer.
 We may implement this in the future.

 #### Note:

 > In a contiguous stack, we would need to save one fewer registers, as the
 > top of the caller's activation record would be the same at the base of the
 > callee's. However, since some activation records are kept on the heap we
 > cannot do this.

 ### Generators and Coroutines

 Generators and coroutines contain a `_PyInterpreterFrame`
 The specials sections contains the following pointers:

 * Globals dict
 * Builtins dict
 * Locals dict (not the "fast" locals, but the locals for eval and class creation)
 * Code object
 * Heap allocated `PyFrameObject` for this activation record, if any.
 * The function.

 The pointer to the function is not strictly required, but it is cheaper to
 store a strong reference to the function and borrowed references to the globals
 and builtins, than strong references to both globals and builtins.

 ### Frame objects

 When creating a backtrace or when calling `sys._getframe()` the frame becomes
 visible to Python code. When this happens a new `PyFrameObject` is created
 and a strong reference to it placed in the `frame_obj` field of the specials
 section. The `frame_obj` field is initially `NULL`.

 The `PyFrameObject` may outlive a stack-allocated `_PyInterpreterFrame`.
 If it does then `_PyInterpreterFrame` is copied into the `PyFrameObject`,
 except the evaluation stack which must be empty at this point.
 The linkage section is updated to reflect the new location of the frame.

 This mechanism provides the appearance of persistent, heap-allocated
 frames for each activation, but with low runtime overhead.

 ### Generators and Coroutines


 Generator objects have a `_PyInterpreterFrame` embedded in them.
 This means that creating a generator requires only a single allocation,
 reducing allocation overhead and improving locality of reference.
 The embedded frame is linked into the per-thread frame when iterated or
 awaited.

 If a frame object associated with a generator outlives the generator, then
 the embedded `_PyInterpreterFrame` is copied into the frame object.


 All the above applies to coroutines and async generators as well.

 ### Field names

 Many of the fields in `_PyInterpreterFrame` were copied from the 3.10 `PyFrameObject`.
 Thus, some of the field names may be a bit misleading.

 For example the `f_globals` field has a `f_` prefix implying it belongs to the
 `PyFrameObject` struct, although it belongs to the `_PyInterpreterFrame` struct.
 We may rationalize this naming scheme for 3.12.
	# The Frame Stack

	Each call to a Python function has an activation record,
	commonly known as a "frame".
	Python semantics allows frames to outlive the activation,
	so they have (before 3.11) been allocated on the heap.
	This is expensive as it requires many allocations and
	results in poor locality of reference.

	In 3.11, rather than have these frames scattered about memory,
	as happens for heap-allocated objects, frames are allocated
	contiguously in a per-thread stack.
	This improves performance significantly for two reasons:
	* It reduces allocation overhead to a pointer comparison and increment.
	* Stack allocated data has the best possible locality and will always be in
	CPU cache.

	Generator and coroutines still need heap allocated activation records, but
	can be linked into the per-thread stack so as to not impact performance too much.

	## Layout

	Each activation record consists of four conceptual sections:

	* Local variables (including arguments, cells and free variables)
	* Evaluation stack
	* Specials: The per-frame object references needed by the VM: globals dict,
	code object, etc.
	* Linkage: Pointer to the previous activation record, stack depth, etc.

	### Layout

	The specials and linkage sections are a fixed size, so are grouped together.

	Each activation record is laid out as:
	* Specials and linkage
	* Locals
	* Stack

	This seems to provide the best performance without excessive complexity.
	It needs the interpreter to hold two pointers, a frame pointer and a stack pointer.

	#### Alternative layout

	An alternative layout that was used for part of 3.11 alpha was:

	* Locals
	* Specials and linkage
	* Stack

	This has the advantage that no copying is required when making a call,
	as the arguments on the stack are (usually) already in the correct
	location for the parameters. However, it requires the VM to maintain
	an extra pointer for the locals, which can hurt performance.

	A variant that only needs the need two pointers is to reverse the numbering
	of the locals, so that the last one is numbered `0`, and the first in memory
	is numbered `N-1`.
	This allows the locals, specials and linkage to accessed from the frame pointer.
	We may implement this in the future.

	#### Note:

	> In a contiguous stack, we would need to save one fewer registers, as the
	> top of the caller's activation record would be the same at the base of the
	> callee's. However, since some activation records are kept on the heap we
	> cannot do this.

	### Generators and Coroutines

	Generators and coroutines contain a `_PyInterpreterFrame`
	The specials sections contains the following pointers:

	* Globals dict
	* Builtins dict
	* Locals dict (not the "fast" locals, but the locals for eval and class creation)
	* Code object
	* Heap allocated `PyFrameObject` for this activation record, if any.
	* The function.

	The pointer to the function is not strictly required, but it is cheaper to
	store a strong reference to the function and borrowed references to the globals
	and builtins, than strong references to both globals and builtins.

	### Frame objects

	When creating a backtrace or when calling `sys._getframe()` the frame becomes
	visible to Python code. When this happens a new `PyFrameObject` is created
	and a strong reference to it placed in the `frame_obj` field of the specials
	section. The `frame_obj` field is initially `NULL`.

	The `PyFrameObject` may outlive a stack-allocated `_PyInterpreterFrame`.
	If it does then `_PyInterpreterFrame` is copied into the `PyFrameObject`,
	except the evaluation stack which must be empty at this point.
	The linkage section is updated to reflect the new location of the frame.

	This mechanism provides the appearance of persistent, heap-allocated
	frames for each activation, but with low runtime overhead.

	### Generators and Coroutines


	Generator objects have a `_PyInterpreterFrame` embedded in them.
	This means that creating a generator requires only a single allocation,
	reducing allocation overhead and improving locality of reference.
	The embedded frame is linked into the per-thread frame when iterated or
	awaited.

	If a frame object associated with a generator outlives the generator, then
	the embedded `_PyInterpreterFrame` is copied into the frame object.


	All the above applies to coroutines and async generators as well.

	### Field names

	Many of the fields in `_PyInterpreterFrame` were copied from the 3.10 `PyFrameObject`.
	Thus, some of the field names may be a bit misleading.

	For example the `f_globals` field has a `f_` prefix implying it belongs to the
	`PyFrameObject` struct, although it belongs to the `_PyInterpreterFrame` struct.
	We may rationalize this naming scheme for 3.12.