copium 0.1.0a1.dev158

Fast drop-in replacement for copy.deepcopy()

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] copium hasn'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, deepcopy operation won't touch the interpreter:

  • natively supported containers: tuple, dict, list, set, frozenset, bytearray and types.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

• Native memo

  • no time spent on creating extra int object for id(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__

• Native reduce handling

  When type's __reduce__ strictly follows the protocol, copium handles returned values natively, without interpreter overhead, the same way CPython pickle implementation does.

  What if there's type mismatch?

• Cached memo

  Rather than creating a new memo object for each deepcopy and 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+

  deepcopy function object stays the same after patch, only its vectorcall is 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.

Tracking issue

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.deepcopy in 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 🦎