Edit: After test on different machines it is Apple M1 M2 specific bug. I am trying to run a Flask application inside a Docker container on Apple Silicon M2 (could be an issue), where I use the SentenceTransformer model to encode sentences. However, when I call the encode method on the model, the application crashes with a segmentation fault. Here's the relevant code: from sentence_transformers import SentenceTransformer model = SentenceTransformer('all-MiniLM-L6-v2') #Our sentences we like to encode sentences = ['This framework generates embeddings for each input sentence', 'Sentences are passed as a list of string.', 'The quick brown fox jumps over the lazy dog.'] #Sentences are encoded by calling model.encode() sentence_embeddings = model.encode(sentences) Source: https://www.sbert.net/docs/quickstart.html after using import faulthandler and faulthandler.enable() The error traceback is: Fatal Python error: Segmentation fault Thread 0x0000ffff640ff1a0 (most recent call first): File "/usr/local/lib/python3.11/threading.py", line 331 in wait File "/usr/local/lib/python3.11/threading.py", line 629 in wait File "/usr/local/lib/python3.11/site-packages/tqdm/_monitor.py", line 60 in run File "/usr/local/lib/python3.11/threading.py", line 1045 in _bootstrap_inner File "/usr/local/lib/python3.11/threading.py", line 1002 in _bootstrap Current thread 0x0000ffffa6814020 (most recent call first): File "/usr/local/lib/python3.11/site-packages/transformers/activations.py", line 78 in forward File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1527 in _call_impl File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1518 in _wrapped_call_impl File "/usr/local/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py", line 452 in forward File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1527 in _call_impl File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1518 in _wrapped_call_impl File "/usr/local/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py", line 551 in feed_forward_chunk File "/usr/local/lib/python3.11/site-packages/transformers/pytorch_utils.py", line 240 in apply_chunking_to_forward File "/usr/local/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py", line 539 in forward File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1527 in _call_impl File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1518 in _wrapped_call_impl File "/usr/local/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py", line 612 in forward File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1527 in _call_impl File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1518 in _wrapped_call_impl File "/usr/local/lib/python3.11/site-packages/transformers/models/bert/modeling_bert.py", line 1022 in forward File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1527 in _call_impl File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1518 in _wrapped_call_impl File "/usr/local/lib/python3.11/site-packages/sentence_transformers/models/Transformer.py", line 66 in forward File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1527 in _call_impl File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1518 in _wrapped_call_impl File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/container.py", line 215 in forward File "/usr/local/lib/python3.11/site-packages/sentence_transformers/SentenceTransformer.py", line 165 in encode File "<stdin>", line 1 in <module> Extension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, torch._C, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, scipy._lib._ccallback_c, scipy.sparse._sparsetools, _csparsetools, scipy.sparse._csparsetools, scipy.sparse.linalg._isolve._iterative, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg.cython_lapack, scipy.linalg._cythonized_array_utils, scipy.linalg._solve_toeplitz, scipy.linalg._decomp_lu_cython, scipy.linalg._matfuncs_sqrtm_triu, scipy.linalg.cython_blas, scipy.linalg._matfuncs_expm, scipy.linalg._decomp_update, scipy.linalg._flinalg, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpack, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flow, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, scipy.spatial._ckdtree, scipy._lib.messagestream, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._distance_wrap, scipy.spatial._hausdorff, scipy.special._ufuncs_cxx, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.special._ellip_harm_2, scipy.spatial.transform._rotation, scipy.ndimage._nd_image, _ni_label, scipy.ndimage._ni_label, scipy.optimize._minpack2, scipy.optimize._group_columns, scipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._cobyla, scipy.optimize._slsqp, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, scipy.optimize._zeros, scipy.optimize.__nnls, scipy.optimize._highs.cython.src._highs_wrapper, scipy.optimize._highs._highs_wrapper, scipy.optimize._highs.cython.src._highs_constants, scipy.optimize._highs._highs_constants, scipy.linalg._interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.optimize._direct, scipy.integrate._odepack, scipy.integrate._quadpack, scipy.integrate._vode, scipy.integrate._dop, scipy.integrate._lsoda, scipy.special.cython_special, scipy.stats._stats, scipy.stats.beta_ufunc, scipy.stats._boost.beta_ufunc, scipy.stats.binom_ufunc, scipy.stats._boost.binom_ufunc, scipy.stats.nbinom_ufunc, scipy.stats._boost.nbinom_ufunc, scipy.stats.hypergeom_ufunc, scipy.stats._boost.hypergeom_ufunc, scipy.stats.ncf_ufunc, scipy.stats._boost.ncf_ufunc, scipy.stats.ncx2_ufunc, scipy.stats._boost.ncx2_ufunc, scipy.stats.nct_ufunc, scipy.stats._boost.nct_ufunc, scipy.stats.skewnorm_ufunc, scipy.stats._boost.skewnorm_ufunc, scipy.stats.invgauss_ufunc, scipy.stats._boost.invgauss_ufunc, scipy.interpolate._fitpack, scipy.interpolate.dfitpack, scipy.interpolate._bspl, scipy.interpolate._ppoly, scipy.interpolate.interpnd, scipy.interpolate._rbfinterp_pythran, scipy.interpolate._rgi_cython, scipy.stats._biasedurn, scipy.stats._levy_stable.levyst, scipy.stats._stats_pythran, scipy._lib._uarray._uarray, scipy.stats._statlib, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._mvn, scipy.stats._rcont.rcont, regex._regex, sklearn.__check_build._check_build, sklearn.utils._isfinite, sklearn.utils.murmurhash, sklearn.utils._openmp_helpers, sklearn.utils._logistic_sigmoid, sklearn.utils.sparsefuncs_fast, sklearn.preprocessing._csr_polynomial_expansion, sklearn.preprocessing._target_encoder_fast, sklearn.utils._vector_sentinel, sklearn.feature_extraction._hashing_fast, sklearn.utils._random, sklearn.utils._seq_dataset, sklearn.metrics.cluster._expected_mutual_info_fast, sklearn.metrics._dist_metrics, sklearn.metrics._pairwise_distances_reduction._datasets_pair, sklearn.utils._cython_blas, sklearn.metrics._pairwise_distances_reduction._base, sklearn.metrics._pairwise_distances_reduction._middle_term_computer, sklearn.utils._heap, sklearn.utils._sorting, sklearn.metrics._pairwise_distances_reduction._argkmin, sklearn.metrics._pairwise_distances_reduction._argkmin_classmode, sklearn.metrics._pairwise_distances_reduction._radius_neighbors, sklearn.metrics._pairwise_fast, sklearn.linear_model._cd_fast, sklearn._loss._loss, sklearn.utils.arrayfuncs, sklearn.svm._liblinear, sklearn.svm._libsvm, sklearn.svm._libsvm_sparse, sklearn.utils._weight_vector, sklearn.linear_model._sgd_fast, sklearn.linear_model._sag_fast, scipy.io.matlab._mio_utils, scipy.io.matlab._streams, scipy.io.matlab._mio5_utils, sklearn.datasets._svmlight_format_fast, charset_normalizer.md, yaml._yaml, sentencepiece._sentencepiece, PIL._imaging (total: 163) Segmentation fault Some points: The Docker container has ample memory allocated. I've tried updating the libraries (torch, transformers, and sentence-transformers). The same code works perfectly outside the Docker environment. How can I resolve this segmentation fault when running the code inside Docker? Here is a pip list of actual versions. Package Version --------------------- --------- blinker 1.6.3 certifi 2023.7.22 charset-normalizer 3.3.0 click 8.1.7 filelock 3.12.4 Flask 3.0.0 fsspec 2023.9.2 huggingface-hub 0.17.3 idna 3.4 itsdangerous 2.1.2 Jinja2 3.1.2 joblib 1.3.2 MarkupSafe 2.1.3 mpmath 1.3.0 networkx 3.1 nltk 3.8.1 numpy 1.26.0 packaging 23.2 Pillow 10.0.1 pip 23.2.1 PyYAML 6.0.1 regex 2023.10.3 requests 2.31.0 safetensors 0.4.0 scikit-learn 1.3.1 scipy 1.11.3 sentence-transformers 2.2.2 sentencepiece 0.1.99 setuptools 65.5.1 sympy 1.12 threadpoolctl 3.2.0 tokenizers 0.14.1 torch 2.1.0 torchvision 0.16.0 tqdm 4.66.1 transformers 4.34.0 typing_extensions 4.8.0 urllib3 2.0.6 Werkzeug 3.0.0 wheel 0.41.2 For more clarification, here is the most important part of my Dockerfile. FROM python:3.11 RUN pip install --upgrade pip RUN pip install Flask==3.0.0 sentence-transformers==2.2.2 Continue reading...
