Using the message "maximum recursion depth exceeded" for when the pystack
runs out of memory can be misleading because the pystack can run out for
reasons other than deep recursion (although in most cases pystack
exhaustion is probably indirectly related to deep recursion). And it's
important to give the user more precise feedback as to the reason for the
error: if they know precisely that the pystack was exhausted then they have
a chance to increase the amount of memory available to the pystack (as
opposed to not knowing if it was the C stack or pystack that ran out).
Also, C stack exhaustion is more serious than pystack exhaustion because it
could have been that the C stack overflowed and overwrote/corrupted some
data and so the system must be restarted. The pystack can never corrupt
data in this way so pystack exhaustion does not require a system restart.
Knowing the difference between these two cases is therefore important.
The actual exception type for pystack exhaustion remains as RuntimeError so
that programatically it behaves the same as a C stack exhaustion.
This patch introduces the MICROPY_ENABLE_PYSTACK option (disabled by
default) which enables a "Python stack" that allows to allocate and free
memory in a scoped, or Last-In-First-Out (LIFO) way, similar to alloca().
A new memory allocation API is introduced along with this Py-stack. It
includes both "local" and "nonlocal" LIFO allocation. Local allocation is
intended to be equivalent to using alloca(), whereby the same function must
free the memory. Nonlocal allocation is where another function may free
the memory, so long as it's still LIFO.
Follow-up patches will convert all uses of alloca() and VLA to the new
scoped allocation API. The old behaviour (using alloca()) will still be
available, but when MICROPY_ENABLE_PYSTACK is enabled then alloca() is no
longer required or used.
The benefits of enabling this option are (or will be once subsequent
patches are made to convert alloca()/VLA):
- Toolchains without alloca() can use this feature to obtain correct and
efficient scoped memory allocation (compared to using the heap instead
of alloca(), which is slower).
- Even if alloca() is available, enabling the Py-stack gives slightly more
efficient use of stack space when calling nested Python functions, due to
the way that compilers implement alloca().
- Enabling the Py-stack with the stackless mode allows for even more
efficient stack usage, as well as retaining high performance (because the
heap is no longer used to build and destroy stackless code states).
- With Py-stack and stackless enabled, Python-calling-Python is no longer
recursive in the C mp_execute_bytecode function.
The micropython.pystack_use() function is included to measure usage of the
Python stack.
Damien George