scikit-learn / scikit-learn/scikit-learn

EFF Reduce the size of shared objects of the C-extensions generated by Cython

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Build / CI C/C++ cython
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Description

Context

scikit-learn uses C-extensions in critical part of its implementations via Cython.

Each C-entension is build from one or several Cython translation unit (a .pyx file with a potential .pxd companion file).

In scikit-learn, each C-extension build consists of a single Cython translation which is transpilled to a C or C++ translation unit, which is then compiled to a shared object file.

The resulting C or C++ translation unit contains the code translation from Cython to C and large preambule and epylogue of macros, functions, structs, global variables such as virtual tables, Python module definition, etc.

For instance, while the code of sklearn/utils/_heap.pyx only consists of less than 100 lines for a single function, the resulting sklearn/utils/heap.c file consists of more than 3500 lines, most of being the preambule's and the epilogue's injected by Cython:

Content of the generated sklearn/utils/heap.c 

▾ macros
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   -CYTHON_ASSUME_SAFE_MACROS
   -CYTHON_ASSUME_SAFE_MACROS
   -CYTHON_ASSUME_SAFE_MACROS
   -CYTHON_AVOID_BORROWED_REFS
   -CYTHON_AVOID_BORROWED_REFS
   -CYTHON_AVOID_BORROWED_REFS
   -CYTHON_AVOID_BORROWED_REFS
   -CYTHON_COMPILING_IN_CPYTHON
   -CYTHON_COMPILING_IN_CPYTHON
   -CYTHON_COMPILING_IN_CPYTHON
   -CYTHON_COMPILING_IN_CPYTHON
   -CYTHON_COMPILING_IN_NOGIL
   -CYTHON_COMPILING_IN_NOGIL
   -CYTHON_COMPILING_IN_NOGIL
   -CYTHON_COMPILING_IN_NOGIL
   -CYTHON_COMPILING_IN_PYPY
   -CYTHON_COMPILING_IN_PYPY
   -CYTHON_COMPILING_IN_PYPY
   -CYTHON_COMPILING_IN_PYPY
   -CYTHON_COMPILING_IN_PYSTON
   -CYTHON_COMPILING_IN_PYSTON
   -CYTHON_COMPILING_IN_PYSTON
   -CYTHON_COMPILING_IN_PYSTON
   -CYTHON_FALLTHROUGH
   -CYTHON_FALLTHROUGH
   -CYTHON_FALLTHROUGH
   -CYTHON_FALLTHROUGH
   -CYTHON_FALLTHROUGH
   -CYTHON_FALLTHROUGH
   -CYTHON_FAST_PYCALL
   -CYTHON_FAST_PYCALL
   -CYTHON_FAST_PYCALL
   -CYTHON_FAST_PYCALL
   -CYTHON_FAST_PYCCALL
   -CYTHON_FAST_THREAD_STATE
   -CYTHON_FAST_THREAD_STATE
   -CYTHON_FAST_THREAD_STATE
   -CYTHON_FAST_THREAD_STATE
   -CYTHON_FAST_THREAD_STATE
   -CYTHON_FORMAT_SSIZE_T
   -CYTHON_FUTURE_DIVISION
   -CYTHON_HEX_VERSION
   -CYTHON_INLINE
   -CYTHON_INLINE
   -CYTHON_INLINE
   -CYTHON_INLINE
   -CYTHON_INLINE
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   -CYTHON_PEP393_ENABLED
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   -CYTHON_PEP489_MULTI_PHASE_INIT
   -CYTHON_PEP489_MULTI_PHASE_INIT
   -CYTHON_PEP489_MULTI_PHASE_INIT
   -CYTHON_PEP489_MULTI_PHASE_INIT
   -CYTHON_PEP489_MULTI_PHASE_INIT
   -CYTHON_REFNANNY
   -CYTHON_RESTRICT
   -CYTHON_RESTRICT
   -CYTHON_RESTRICT
   -CYTHON_RESTRICT
   -CYTHON_SMALL_CODE
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   -CYTHON_UNPACK_METHODS
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Problem

Currently the uncompressed size of scikit-learn is around 48.8MB, 20MB of which are shared object files. As reported by @rth in https://github.com/pyodide/pyodide/issues/4289, while shared object files are optimized for Emscripten quite heavily, they still accounts for most of the size of scikit-learn on this stack.

Extensions' shared object sizes on Linux 
find . -name \*.so -exec du -h {} \; | sort -h --reverse

2,1M	./sklearn/_loss/_loss.cpython-312-x86_64-linux-gnu.so
712K	./sklearn/utils/sparsefuncs_fast.cpython-312-x86_64-linux-gnu.so
672K	./sklearn/neighbors/_kd_tree.cpython-312-x86_64-linux-gnu.so
672K	./sklearn/neighbors/_ball_tree.cpython-312-x86_64-linux-gnu.so
624K	./sklearn/tree/_tree.cpython-312-x86_64-linux-gnu.so
608K	./sklearn/metrics/_dist_metrics.cpython-312-x86_64-linux-gnu.so
520K	./sklearn/datasets/_svmlight_format_fast.cpython-312-x86_64-linux-gnu.so
504K	./sklearn/preprocessing/_target_encoder_fast.cpython-312-x86_64-linux-gnu.so
476K	./sklearn/svm/_libsvm.cpython-312-x86_64-linux-gnu.so
448K	./sklearn/svm/_libsvm_sparse.cpython-312-x86_64-linux-gnu.so
448K	./sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer.cpython-312-x86_64-linux-gnu.so
440K	./sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.cpython-312-x86_64-linux-gnu.so
440K	./sklearn/linear_model/_cd_fast.cpython-312-x86_64-linux-gnu.so
404K	./sklearn/cluster/_k_means_elkan.cpython-312-x86_64-linux-gnu.so
396K	./sklearn/utils/_cython_blas.cpython-312-x86_64-linux-gnu.so
396K	./sklearn/cluster/_k_means_common.cpython-312-x86_64-linux-gnu.so
384K	./sklearn/preprocessing/_csr_polynomial_expansion.cpython-312-x86_64-linux-gnu.so
352K	./sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.cpython-312-x86_64-linux-gnu.so
340K	./sklearn/tree/_splitter.cpython-312-x86_64-linux-gnu.so
340K	./sklearn/cluster/_hdbscan/_tree.cpython-312-x86_64-linux-gnu.so
324K	./sklearn/metrics/_pairwise_distances_reduction/_argkmin.cpython-312-x86_64-linux-gnu.so
324K	./sklearn/linear_model/_sgd_fast.cpython-312-x86_64-linux-gnu.so
312K	./sklearn/tree/_criterion.cpython-312-x86_64-linux-gnu.so
312K	./sklearn/cluster/_k_means_lloyd.cpython-312-x86_64-linux-gnu.so
308K	./sklearn/ensemble/_hist_gradient_boosting/splitting.cpython-312-x86_64-linux-gnu.so
308K	./sklearn/cluster/_hdbscan/_reachability.cpython-312-x86_64-linux-gnu.so
304K	./sklearn/cluster/_hierarchical_fast.cpython-312-x86_64-linux-gnu.so
292K	./sklearn/metrics/_pairwise_distances_reduction/_base.cpython-312-x86_64-linux-gnu.so
280K	./sklearn/linear_model/_sag_fast.cpython-312-x86_64-linux-gnu.so
276K	./sklearn/ensemble/_hist_gradient_boosting/histogram.cpython-312-x86_64-linux-gnu.so
272K	./sklearn/svm/_liblinear.cpython-312-x86_64-linux-gnu.so
268K	./sklearn/utils/_seq_dataset.cpython-312-x86_64-linux-gnu.so
264K	./sklearn/neighbors/_quad_tree.cpython-312-x86_64-linux-gnu.so
260K	./sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_classmode.cpython-312-x86_64-linux-gnu.so
256K	./sklearn/_isotonic.cpython-312-x86_64-linux-gnu.so
252K	./sklearn/cluster/_k_means_minibatch.cpython-312-x86_64-linux-gnu.so
248K	./sklearn/utils/_fast_dict.cpython-312-x86_64-linux-gnu.so
248K	./sklearn/tree/_utils.cpython-312-x86_64-linux-gnu.so
248K	./sklearn/metrics/_pairwise_fast.cpython-312-x86_64-linux-gnu.so
244K	./sklearn/decomposition/_online_lda_fast.cpython-312-x86_64-linux-gnu.so
240K	./sklearn/metrics/_pairwise_distances_reduction/_argkmin_classmode.cpython-312-x86_64-linux-gnu.so
232K	./sklearn/utils/_typedefs.cpython-312-x86_64-linux-gnu.so
232K	./sklearn/utils/_isfinite.cpython-312-x86_64-linux-gnu.so
220K	./sklearn/utils/arrayfuncs.cpython-312-x86_64-linux-gnu.so
220K	./sklearn/ensemble/_gradient_boosting.cpython-312-x86_64-linux-gnu.so
216K	./sklearn/ensemble/_hist_gradient_boosting/utils.cpython-312-x86_64-linux-gnu.so
212K	./sklearn/utils/_random.cpython-312-x86_64-linux-gnu.so
212K	./sklearn/utils/murmurhash.cpython-312-x86_64-linux-gnu.so
212K	./sklearn/ensemble/_hist_gradient_boosting/_predictor.cpython-312-x86_64-linux-gnu.so
212K	./sklearn/decomposition/_cdnmf_fast.cpython-312-x86_64-linux-gnu.so
212K	./sklearn/cluster/_hdbscan/_linkage.cpython-312-x86_64-linux-gnu.so
204K	./sklearn/metrics/cluster/_expected_mutual_info_fast.cpython-312-x86_64-linux-gnu.so
204K	./sklearn/manifold/_barnes_hut_tsne.cpython-312-x86_64-linux-gnu.so
192K	./sklearn/utils/_weight_vector.cpython-312-x86_64-linux-gnu.so
184K	./sklearn/manifold/_utils.cpython-312-x86_64-linux-gnu.so
184K	./sklearn/ensemble/_hist_gradient_boosting/_gradient_boosting.cpython-312-x86_64-linux-gnu.so
184K	./sklearn/cluster/_dbscan_inner.cpython-312-x86_64-linux-gnu.so
180K	./sklearn/ensemble/_hist_gradient_boosting/_bitset.cpython-312-x86_64-linux-gnu.so
180K	./sklearn/ensemble/_hist_gradient_boosting/_binning.cpython-312-x86_64-linux-gnu.so
128K	./sklearn/utils/_vector_sentinel.cpython-312-x86_64-linux-gnu.so
112K	./sklearn/ensemble/_hist_gradient_boosting/common.cpython-312-x86_64-linux-gnu.so
80K	    ./sklearn/feature_extraction/_hashing_fast.cpython-312-x86_64-linux-gnu.so
48K	    ./sklearn/utils/_openmp_helpers.cpython-312-x86_64-linux-gnu.so
32K	    ./sklearn/svm/_newrand.cpython-312-x86_64-linux-gnu.so
28K	    ./sklearn/utils/_sorting.cpython-312-x86_64-linux-gnu.so
28K	    ./sklearn/neighbors/_partition_nodes.cpython-312-x86_64-linux-gnu.so
24K	    ./sklearn/utils/_heap.cpython-312-x86_64-linux-gnu.so
24K	    ./sklearn/__check_build/_check_build.cpython-312-x86_64-linux-gnu.so
Possible solutions
Strip all symbols and optimize for size

This can be done by adding -Wl,--strip-all to extra_link_args and -Os -g0 to extra_compile_args.

In practice, it can significantly shrink shared object (up to nearly 50% size reduction):

Extensions' shared object sizes on Linux after striping all symbols and optimizing for size 
find . -name \*.so -exec du -h {} \; | sort -h --reverse

1,2M	sklearn/_loss/_loss.cpython-312-x86_64-linux-gnu.so
560K	sklearn/tree/_tree.cpython-312-x86_64-linux-gnu.so
468K	sklearn/neighbors/_kd_tree.cpython-312-x86_64-linux-gnu.so
468K	sklearn/neighbors/_ball_tree.cpython-312-x86_64-linux-gnu.so
460K	sklearn/utils/sparsefuncs_fast.cpython-312-x86_64-linux-gnu.so
460K	sklearn/metrics/_dist_metrics.cpython-312-x86_64-linux-gnu.so
380K	sklearn/datasets/_svmlight_format_fast.cpython-312-x86_64-linux-gnu.so
376K	sklearn/svm/_libsvm.cpython-312-x86_64-linux-gnu.so
352K	sklearn/svm/_libsvm_sparse.cpython-312-x86_64-linux-gnu.so
336K	sklearn/tree/_splitter.cpython-312-x86_64-linux-gnu.so
320K	sklearn/preprocessing/_target_encoder_fast.cpython-312-x86_64-linux-gnu.so
320K	sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer.cpython-312-x86_64-linux-gnu.so
316K	sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.cpython-312-x86_64-linux-gnu.so
312K	sklearn/utils/_cython_blas.cpython-312-x86_64-linux-gnu.so
308K	sklearn/linear_model/_cd_fast.cpython-312-x86_64-linux-gnu.so
300K	sklearn/tree/_criterion.cpython-312-x86_64-linux-gnu.so
280K	sklearn/preprocessing/_csr_polynomial_expansion.cpython-312-x86_64-linux-gnu.so
280K	sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.cpython-312-x86_64-linux-gnu.so
280K	sklearn/cluster/_k_means_elkan.cpython-312-x86_64-linux-gnu.so
276K	sklearn/cluster/_k_means_common.cpython-312-x86_64-linux-gnu.so
252K	sklearn/linear_model/_sgd_fast.cpython-312-x86_64-linux-gnu.so
248K	sklearn/cluster/_hdbscan/_tree.cpython-312-x86_64-linux-gnu.so
244K	sklearn/metrics/_pairwise_distances_reduction/_argkmin.cpython-312-x86_64-linux-gnu.so
240K	sklearn/tree/_utils.cpython-312-x86_64-linux-gnu.so
240K	sklearn/ensemble/_hist_gradient_boosting/splitting.cpython-312-x86_64-linux-gnu.so
236K	sklearn/cluster/_hierarchical_fast.cpython-312-x86_64-linux-gnu.so
236K	sklearn/cluster/_hdbscan/_reachability.cpython-312-x86_64-linux-gnu.so
232K	sklearn/svm/_liblinear.cpython-312-x86_64-linux-gnu.so
228K	sklearn/cluster/_k_means_lloyd.cpython-312-x86_64-linux-gnu.so
224K	sklearn/metrics/_pairwise_distances_reduction/_base.cpython-312-x86_64-linux-gnu.so
212K	sklearn/neighbors/_quad_tree.cpython-312-x86_64-linux-gnu.so
208K	sklearn/linear_model/_sag_fast.cpython-312-x86_64-linux-gnu.so
204K	sklearn/ensemble/_hist_gradient_boosting/histogram.cpython-312-x86_64-linux-gnu.so
200K	sklearn/utils/_seq_dataset.cpython-312-x86_64-linux-gnu.so
196K	sklearn/utils/_fast_dict.cpython-312-x86_64-linux-gnu.so
196K	sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_classmode.cpython-312-x86_64-linux-gnu.so
196K	sklearn/_isotonic.cpython-312-x86_64-linux-gnu.so
196K	sklearn/decomposition/_online_lda_fast.cpython-312-x86_64-linux-gnu.so
196K	sklearn/cluster/_k_means_minibatch.cpython-312-x86_64-linux-gnu.so
192K	sklearn/utils/_isfinite.cpython-312-x86_64-linux-gnu.so
192K	sklearn/metrics/_pairwise_fast.cpython-312-x86_64-linux-gnu.so
192K	sklearn/metrics/_pairwise_distances_reduction/_argkmin_classmode.cpython-312-x86_64-linux-gnu.so
184K	sklearn/utils/_typedefs.cpython-312-x86_64-linux-gnu.so
180K	sklearn/utils/arrayfuncs.cpython-312-x86_64-linux-gnu.so
180K	sklearn/ensemble/_hist_gradient_boosting/utils.cpython-312-x86_64-linux-gnu.so
180K	sklearn/ensemble/_gradient_boosting.cpython-312-x86_64-linux-gnu.so
172K	sklearn/utils/_random.cpython-312-x86_64-linux-gnu.so
168K	sklearn/utils/murmurhash.cpython-312-x86_64-linux-gnu.so
168K	sklearn/ensemble/_hist_gradient_boosting/_predictor.cpython-312-x86_64-linux-gnu.so
168K	sklearn/decomposition/_cdnmf_fast.cpython-312-x86_64-linux-gnu.so
168K	sklearn/cluster/_hdbscan/_linkage.cpython-312-x86_64-linux-gnu.so
160K	sklearn/metrics/cluster/_expected_mutual_info_fast.cpython-312-x86_64-linux-gnu.so
160K	sklearn/manifold/_barnes_hut_tsne.cpython-312-x86_64-linux-gnu.so
152K	sklearn/utils/_weight_vector.cpython-312-x86_64-linux-gnu.so
152K	sklearn/manifold/_utils.cpython-312-x86_64-linux-gnu.so
152K	sklearn/ensemble/_hist_gradient_boosting/_gradient_boosting.cpython-312-x86_64-linux-gnu.so
152K	sklearn/cluster/_dbscan_inner.cpython-312-x86_64-linux-gnu.so
148K	sklearn/ensemble/_hist_gradient_boosting/_bitset.cpython-312-x86_64-linux-gnu.so
144K	sklearn/ensemble/_hist_gradient_boosting/_binning.cpython-312-x86_64-linux-gnu.so
112K	sklearn/utils/_vector_sentinel.cpython-312-x86_64-linux-gnu.so
96K	sklearn/ensemble/_hist_gradient_boosting/common.cpython-312-x86_64-linux-gnu.so
68K	sklearn/feature_extraction/_hashing_fast.cpython-312-x86_64-linux-gnu.so
44K	sklearn/utils/_openmp_helpers.cpython-312-x86_64-linux-gnu.so
32K	sklearn/svm/_newrand.cpython-312-x86_64-linux-gnu.so
28K	sklearn/utils/_sorting.cpython-312-x86_64-linux-gnu.so
28K	sklearn/neighbors/_partition_nodes.cpython-312-x86_64-linux-gnu.so
24K	sklearn/utils/_heap.cpython-312-x86_64-linux-gnu.so
24K	sklearn/__check_build/_check_build.cpython-312-x86_64-linux-gnu.so
Group several translation units within C extensions (and use interprocedural optimization)

So as to reuse duplicated symbols in shared objects and perform optimization over several translation units (such as inlining functions, etc.)

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with sklearn/utils/_heap.pyx and its generated sklearn/utils/heap.c, then inspect how scikit-learn builds Cython translation units into shared objects. Measure the generated shared-object size and identify which Cython-generated preamble or epilogue contributes to it; done should be a validated reduction without changing extension behavior.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
build-system, performance
Issue type
Refactor
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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