This release is a pre-release and may not be stable for production use.
copium
Makes Python deepcopy() fast.
Highlights
- ⚡ 4-28x faster on built-in types
- 🧠 ~30% less memory per copy
- ✨ requires zero code changes
- 🧪 passes Python's test_copy.py
- 📦 pre-built wheels for Python 3.10–3.14 on Linux/macOS/Windows (x64/ARM64)
- 🔓 passes all tests on free-threaded Python builds
Installation
pip install 'copium[autopatch]'
This will effortlessly make copy.deepcopy() fast in current environment.
[!WARNING]
copiumhasn't seen wide production use yet. Expect bugs.
For manual usage
pip install copium
Manual usage
[!TIP] You can skip this section if you depend on
copium[autopatch].
import copium
assert copium.deepcopy(x := []) is not x
The copium module includes all public declarations of stdlib copy module, so it's generally safe
to:
- from copy import copy, deepcopy, Error
+ from copium import copy, deepcopy, Error
[!TIP] Next sections will likely make more sense if you read CPython docs on deepcopy: https://docs.python.org/3/library/copy.html
How is it so fast?
-
Zero interpreter overhead for built-in containers and atomic types
If your data consist only of the types below,
deepcopyoperation won't touch the interpreter:- natively supported containers:
tuple,dict,list,set,frozenset,bytearrayandtypes.MethodType - natively supported atomics:
type(None),int,str,bytes,float,bool,complex,types.EllipsisType,types.NotImplementedType,range,property,weakref.ref,re.Pattern,decimal.Decimal,fractions.Fraction,types.CodeType,types.FunctionType,types.BuiltinFunctionType,types.ModuleType
- natively supported containers:
-
Native memo
- no time spent on creating extra
intobject forid(x) - hash is computed once for lookup and reused to store the copy
- keepalive is a lightweight vector of pointers instead of a
list - memo object is not tracked in GC, unless stolen in custom
__deepcopy__
- no time spent on creating extra
-
Native reduce handling
When type's
__reduce__strictly follows the protocol,copiumhandles returned values natively, without interpreter overhead, the same way CPython pickle implementation does. -
Cached memo
Rather than creating a new memo object for each
deepcopyand discarding it after, copium stores one per thread and reuses it. Referenced objects are cleared, but some amount of memory stays reserved, avoiding malloc/free overhead for typical workloads. -
Zero overhead patch on Python 3.12+
deepcopyfunction object stays the same after patch, only itsvectorcallis changed.
Compatibility notes
copium.deepcopy() designed to be drop-in replacement for copy.deepcopy(),
still there are minor deviations from stdlib you should be aware of.
Pickle protocol
stdlib's copy tolerates some deviations from the pickle protocol that pickle itself reject
(see https://github.com/python/cpython/issues/141757).
copium strictly follows stdlib semantics: if __reduce__
returns a list instead of a tuple for args, or a mapping instead of a dict for kwargs,
copium will coerce them the same way stdlib would
(via *args unpacking, **kwargs merging, .items() iteration, etc.).
Errors from malformed __reduce__ results match what copy.deepcopy produces.
Memo handling
With native memo, custom __deepcopy__ receives a copium.memo,
which is fully compatible with how copy.deepcopy() uses it internally.
Per Python docs, custom __deepcopy__ methods should treat memo as an opaque object and just pass
it through in any subsequent deepcopy calls.
However, some native extensions that implement __deepcopy__ on their objects
may require exact dict object to be passed as memo argument.
Typically, in this case, they raise TypeError or AssertionError.
copium will attempt to recover by calling __deepcopy__ again with dict memo. If that second call
succeeds, a warning with clear suggestions will be emitted, otherwise the error will be raised as
is.
Example
>>> import copium
>>> class CustomType:
... def __deepcopy__(self, memo):
... if not isinstance(memo, dict):
... raise TypeError("I'm enforcing memo to be a dict")
... return self
...
>>> print("Copied successfully: ", copium.deepcopy(CustomType()))
<python-input-2>:1: UserWarning:
Seems like 'copium.memo' was rejected inside '__main__.CustomType.__deepcopy__':
Traceback (most recent call last):
File "<python-input-2>", line 1, in <module>
File "<python-input-1>", line 4, in __deepcopy__
raise TypeError("I'm enforcing memo to be a dict")
TypeError: I'm enforcing memo to be a dict
copium was able to recover from this error, but this is slow and unreliable.
Fix:
Per Python docs, '__main__.CustomType.__deepcopy__' should treat memo as an opaque object.
See: https://docs.python.org/3/library/copy.html#object.__deepcopy__
Workarounds:
local change deepcopy(CustomType()) to deepcopy(CustomType(), {})
-> copium uses dict memo in this call (recommended)
global export COPIUM_USE_DICT_MEMO=1
-> copium uses dict memo everywhere (~1.3-2x slowdown, still faster than stdlib)
silent export COPIUM_NO_MEMO_FALLBACK_WARNING='TypeError: I'm enforcing memo to be a dict'
-> 'deepcopy(CustomType())' stays slow to deepcopy
explosive export COPIUM_NO_MEMO_FALLBACK=1
-> 'deepcopy(CustomType())' raises the error above
Copied successfully: <__main__.CustomType object at 0x104d1cad0>
Credits
- @sobolevn for constructive feedback on C code / tests quality
- @eendebakpt for C implementation of parts of
copy.deepcopyin https://github.com/python/cpython/pull/91610 — used as early reference - @orsinium for svg.py — used to generate main chart
- @provencher for repoprompt.com — used it to build context for LLMs/editing
- Anthropic/OpenAI/xAI for translating my ideas to compilable C code and educating me on the subject
- One special lizard 🦎
1 maintainer
Metadata
1 maintainer
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Transparency logRelease files / copium-0.1.0a1.dev161-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
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| Size | 88.3 kB |
| Tags | CPython 3.10 macOS 10.9+ x86-64 |
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Transparency logSecurity for copium 0.1.0a1.dev161
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