GeezerVison is showing github.com — _decimal: pi benchmark 31-45% slower compared to 3.9 · Issue #114682 · python/cpython
_decimal: pi benchmark 31-45% slower compared to 3.9 #114682DescriptionBug report
Bug description:
The pi benchmark in Modules/_decimal/tests/bench.py is up to 31% slower with --enable-gil and up to 45% slower with --disable-gil compared to Python 3.9 as the baseline.
Change the number of iterations to 100000 for more stable benchmarks.
This is heavily OS and hardware dependent. The worst case cited above was observed on a six core i5-8600K.
CPython versions tested on:
CPython main branch
Operating systems tested on:
Linux
Linked PRs
Reacted by Pieter Eendebak Activitysunmy2019 commented on Jan 29, 2024 Can you try --disable-gil with current main? skrah commented on Jan 29, 2024 ContributorAuthor More actions The figures are almost the same, around 30% (enable) and 43% (disable). skrah commented on Jan 29, 2024 ContributorAuthor More actions The base slowdown of 30% is of course due to the module state changes. For comparison, and to see that the i5-8600K is not inherently underperforming with threads:
The ideal situation is implemented in libmpdec++: It has a C++11 thread local context, which is also called for every inline operation. The C++11 TLS adds just 4% overhead compared to pure C (where the context is passed directly to the functions).
This of course would be quite difficult to achieve in CPython. But the thread local context may not even be the primary culprit. In Martin von Loewis' first module state implementation the basic slowdown for _decimal was 20%, and a good part of that was due to the heap types. skrah commented on Jan 29, 2024 ContributorAuthor More actions
The ideal situation is implemented in libmpdec++: It has a C++11 thread local context, which is also called for every inline operation. The C++11 TLS adds just 4% overhead compared to pure C (where the context is passed directly to the functions).
This of course would be quite difficult to achieve in CPython.
The wording was not ideal: The key point is that C++ achieves this without a cached context, which is of course now in the module state. ericsnowcurrently commented on Feb 1, 2024 neonene commented on Feb 2, 2024 PyModule_GetState()PyType_GetModuleByDef() seems to be expensive for heavy use.
It would be nice if a heap type could own the module state's address at its creation time to recover more than 10%.
Reacted by Erlend E. Aasland erlend-aasland commented on Feb 2, 2024
PyModule_GetState() via PyType_GetModuleByDef() seems to be expensive for heavy use. It would be nice if a heap type could own the module state's address at its creation time to recover more than 10%.
_PyModule_GetState() is used, not PyModule_GetState(); I'd be surprised if this is the culprit:
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static inline void* _PyModule_GetState(PyObject* mod) { |
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assert(PyModule_Check(mod)); |
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return ((PyModuleObject *)mod)->md_state; |
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} |
OTOH, PyType_GetModuleByDef() can be more expensive. Storing the state pointer in the type struct is a possibility. neonene commented on Feb 2, 2024 @ericsnowcurrently Can a module state be accessed correctly with the following?
_Py_thread_local module_state *thread_local_state; // set at the exec phase
If not invalid, it will be useful for the function which currently has no way to get the module state. A _Py_thread_local pointer may not be so fast to access, but it can also bypass a state struct? ericsnowcurrently commented on Feb 2, 2024 Multiple interpreters can run in the same thread, so a thread-local may be ambiguous in that case.
As to storing a reference to the module state on the type object, that shouldn't be necessary. Every heap type as a ht_module field that points to the module, accessible via PyType_GetModule(). From there you call PyModule_GetState(). In fact, we already have PyType_GetModuleState() that combines those two calls. That's fairly efficient, though obviously not as efficient as a static global.
The only caveat is that PyType_GetModule() (and thus PyType_GetModuleState()) operates relative to the given type. Thus subclasses make things trickier. IIRC, when they're involved, you end up having to walk the MRO one way or another, which is what PyType_GetModuleByDef() does.
FWIW, the common case where PyType_GetModuleByDef() is used is either in "tp slot" functions or intermediate static functions that aren't doing the right thing. In both cases the problem lies in throwing away the expensive information we already have. For tp slot functions the API doesn't pass the module in, even though it could. We're stuck with backward compatibility issues there. For intermediate static functions, I expect it's all too common to figure out the module (or module state) at some "leaf node" function but not pass it through the intermediate functions. Thus the relatively expensive PyType_GetModuleByDef() gets called more than it needs to be.
I suppose another problem case is with callbacks that don't provide a way to pass the module through. I'm not sure that's a common issue though.
Back to the _decimal module, I haven't looked at what usage patterns are resulting in such a significant slowdown. erlend-aasland commented on Feb 2, 2024
Thus subclasses make things trickier. IIRC, when they're involved, you end up having to walk the MRO one way or another, which is what PyType_GetModuleByDef() does.
Yes; heap types that are not base classes can go the fast route via PyType_GetModuleState() (and _PyType_GetModuleState()). encukou commented on Feb 4, 2024 hauntsaninja commented on Feb 4, 2024 skrah commented on Feb 4, 2024 ContributorAuthor More actions The float object gets the luxury of its own freelist and avoids the slow PyObject_GC_* functions altogether. The Decimal constructor is a pure function (the context is only used for recording errors) and Decimals are immutable, just like floats.
It is likely that 15 percentage points can be shaved off just by doing the same as in floatobject.c and then focus on the thread local context (indeed, PyType_GetModuleByDef() is called at dozens of places). erlend-aasland commented on Feb 4, 2024 AFAICS, we can trim away a lot of the PyType_GetModuleByDef calls. For example, a lot of them are happening in methods; we can use the METH_METHOD calling convention on practically all of these, so we can access the module state using _PyType_GetModuleState:
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_PyType_GetModuleState(PyTypeObject *type) |
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{ |
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assert(PyType_Check(type)); |
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assert(type->tp_flags & Py_TPFLAGS_HEAPTYPE); |
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PyHeapTypeObject *et = (PyHeapTypeObject *)type; |
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assert(et->ht_module); |
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PyModuleObject *mod = (PyModuleObject *)(et->ht_module); |
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assert(mod != NULL); |
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return mod->md_state; |
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} |
Also (but not performance relevant), the find_state_left_or_right() calls in the Py_tp_richcompare slots can be replaced with get_module_state_by_def() calls. Reacted by neonene and Petr Viktorin skrah commented on Feb 11, 2024 ContributorAuthor More actions I'm getting a smaller speedup by using PyType_GetModuleState() in the number methods and a larger one by using PyObject_New for the decimal constructor.
The _threadmodule itself uses PyObject_New in new_thread_handle.
@ericsnowcurrently, is there a reason why PyObject_New cannot be used for the immutable Decimal type? vstinner commented on Feb 12, 2024 @erlend-aasland:
I withdraw myself from this discussion; I am no longer working on this issue.
@skrah:
That is an extraordinary comment from Mr. Stinner. It is entirely irrelevant to this issue
Well, same than Erlend for me: I unsubscribe. I'm not interested to work on an issue with such tone in the discussion. Reacted by Erlend E. Aasland, Maxim Martynov and Gregory P. Smith ericsnowcurrently commented on Feb 12, 2024
is there a reason why PyObject_New cannot be used for the immutable Decimal type?
I'm not aware of any reason. It isn't a GC type, right? skrah commented on Feb 12, 2024 ContributorAuthor More actions
I'm not aware of any reason. It isn't a GC type, right?
Yes, up to 3.12 it wasn't a GC type. It has been made a GC type in main during the module state changes.
I now found some explanations in PEP-630, but I still do not understand it:
Instances of heap types hold a reference to their type.
This ensures that the type isn't destroyed before all its instances are,
but may result in reference cycles that need to be broken by the
garbage collector.
How would one create a cycle?
>>> x = Decimal("3.222")
>>> x.y = x
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
x.y = x
^^^
AttributeError: 'decimal.Decimal' object has no attribute 'y' and no __dict__ for setting new attributes
>>> x = Decimal("3.222")
>>> t = type(x)
>>> t.y = x
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
t.y = x
^^^
TypeError: cannot set 'y' attribute of immutable type 'decimal.Decimal'
>>> x.adjusted = 10
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
x.adjusted = 10
^^^^^^^^^^
AttributeError: 'decimal.Decimal' object attribute 'adjusted' is read-only
neonene commented on Feb 13, 2024
The strategy suggested earlier of caching the state in the decimal object seems to be the most fruitful (intuitively, the actual patch will require some experimentation).
@skrah I have posted a draft: gh-115401 ericsnowcurrently commented on Feb 13, 2024
Yes, up to 3.12 it wasn't a GC type. It has been made a GC type in main during the module state changes.
Ah, there are definitely some subtleties involved with GC types, especially when switching from a non-GC type. I don't recall the specifics, but I'm sure @encukou does. encukou commented on Feb 14, 2024 It might be more interesting to start with static (non-GC) types: these are “immortal” (statically allocated), but they do have a bunch of dynamically allocated, interpreter-specific data (e.g. subclasses, weakrefs). Getting this data properly cleaned up requires rather special hacks, appropriate for core types that really are created/destroyed with the interpreter itself, rather than extensions loaded on demand (see code around static_types[] in Objects/object.c for details).
On the other hand, GC types (heap types) use reference counting like any other Python object. Not saying that getting rid of all the assumptions of static-ness/immortality is/was smooth, but at least the model is much clearer: we have a good idea of what the details should be, even though getting there is a bumpy road full of backcompat issues.
Instances of heap types hold a reference to their type.
This ensures that the type isn't destroyed before all its instances are,
but may result in reference cycles that need to be broken by the
garbage collector.
This is a generic statement. A class with no __dict__ nor user-settable attributes probably can't create a reference cycle.
While we could omit the instance→type reference only in cases where one can prove this, I don't think such a special case would be worth it. neonene commented on Feb 16, 2024
How would one create a cycle?
PEP 573 and type_traverse() says:
Usually, creating a class with ht_module set will create
a reference cycle involving the class and the module.
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Py_VISIT(type->tp_dict); |
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Py_VISIT(type->tp_cache); |
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Py_VISIT(type->tp_mro); |
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Py_VISIT(type->tp_bases); |
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Py_VISIT(type->tp_base); |
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Py_VISIT(((PyHeapTypeObject *)type)->ht_module); |
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|
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/* There's no need to visit others because they can't be involved |
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in cycles: |
To make Decimal type a non-GC heap type, at least the module arg needs to be NULL in PyType_FromMetaclass(), like thread_handle_type in _threadmodule?
I agree that PyObject_GC_*() has some overhead, but I'm not sure how to hold a module safely outside Decimal type. gpshead commented on Mar 12, 2024 FYI - skrah has been banned from python spaces, quoting details from my message posted on a duplicate-ish issue:
#114922 (comment)
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