Dear Mikel,
first of all congratulations on this great piece of work and thank you for sharing it with the community.
I experienced out-of-memory errors when mapping pre-trained fastText embeddings trained on Wikipedia (https://fasttext.cc/docs/en/pretrained-vectors.html). For the EN-DE language pair, the embeddings are quite large, having 300 dimensions and vocabulary sizes of approx 2.2M to 2.5M.
Out-of-memory errors occurred in both the supervised and unsupervised modes, and both with and without --cuda.
In the supervised mode (using the EN-DE training dictionary from your 2017 ACL paper), the following error ocurred:
Call: python3 vecmap/map_embeddings.py --cuda --supervised TRAIN_DICT EMB_SRC EMB_TRG EMB_SRC_MAPPED EMB_TRG_MAPPED --log log.txt --verbose
Output:
tcmalloc: large alloc 3023249408 bytes == 0x2bd6000 @ 0x7fc625b3a1e7 0x7fc6235c3ca1 0x7fc623628778 0x7fc623628d47 0x7fc6236c3038 0x4f8925 0x4fa6c0 0x4f6128 0x4f7d60 0x4f876d 0x4fa6c0 0x4f6128 0x4f7d60 0x4f876d 0x4f98c7 0x4f6128 0x4f9023 0x6415b2 0x64166a 0x643730 0x62b26e 0x4b4cb0 0x7fc625737b97 0x5bdf6a
tcmalloc: large alloc 3023249408 bytes == 0xbb06e000 @ 0x7fc625b3a1e7 0x7fc6235c3ca1 0x7fc623628778 0x7fc623628d47 0x7fc6236c3038 0x4f8925 0x4fa6c0 0x4f6128 0x4f7d60 0x4f876d 0x4fa6c0 0x4f6128 0x4f7d60 0x4f876d 0x4f98c7 0x4f6128 0x4f9023 0x6415b2 0x64166a 0x643730 0x62b26e 0x4b4cb0 0x7fc625737b97 0x5bdf6a
WARNING: OOV dictionary entry (time - fragestunde)
[ ... approx 600 more warnings ommitted ...]
WARNING: OOV dictionary entry (constitutional - verfassungsmäßigen)
Traceback (most recent call last):
File "vecmap/map_embeddings.py", line 422, in <module>
main()
File "vecmap/map_embeddings.py", line 251, in main
simfwd = xp.empty((args.batch_size, trg_size), dtype=dtype)
File "/usr/local/lib/python3.6/dist-packages/cupy/creation/basic.py", line 20, in empty
return cupy.ndarray(shape, dtype, order=order)
File "cupy/core/core.pyx", line 152, in cupy.core.core.ndarray.__init__
File "cupy/cuda/memory.pyx", line 517, in cupy.cuda.memory.alloc
File "cupy/cuda/memory.pyx", line 1076, in cupy.cuda.memory.MemoryPool.malloc
File "cupy/cuda/memory.pyx", line 1097, in cupy.cuda.memory.MemoryPool.malloc
File "cupy/cuda/memory.pyx", line 925, in cupy.cuda.memory.SingleDeviceMemoryPool.malloc
File "cupy/cuda/memory.pyx", line 940, in cupy.cuda.memory.SingleDeviceMemoryPool._malloc
File "cupy/cuda/memory.pyx", line 695, in cupy.cuda.memory._try_malloc
cupy.cuda.memory.OutOfMemoryError: out of memory to allocate 10077480448 bytes (total 22170457600 bytes)
In the unsupervised mode (python3 vecmap/map_embeddings.py --cuda --unsupervised EMB_SRC EMB_TRG EMB_SRC_MAPPED EMB_TRG_MAPPED --log log.txt --verbose), the error was:
tcmalloc: large alloc 3023249408 bytes == 0x3098000 @ 0x7f263a57a1e7 0x7f2638003ca1 0x7f2638068778 0x7f2638068d47 0x7f2638103038 0x4f8925 0x4fa6c0 0x4f6128 0x4f7d60 0x4f876d 0x4fa6c0 0x4f6128 0x4f7d60 0x4f876d 0x4f98c7 0x4f6128 0x4f9023 0x6415b2 0x64166a 0x643730 0x62b26e 0x4b4cb0 0x7f263a177b97 0x5bdf6a
tcmalloc: large alloc 3023249408 bytes == 0xbb530000 @ 0x7f263a57a1e7 0x7f2638003ca1 0x7f2638068778 0x7f2638068d47 0x7f2638103038 0x4f8925 0x4fa6c0 0x4f6128 0x4f7d60 0x4f876d 0x4fa6c0 0x4f6128 0x4f7d60 0x4f876d 0x4f98c7 0x4f6128 0x4f9023 0x6415b2 0x64166a 0x643730 0x62b26e 0x4b4cb0 0x7f263a177b97 0x5bdf6a
Traceback (most recent call last):
File "/vecmap/map_embeddings.py", line 422, in <module>
main()
File "/vecmap/map_embeddings.py", line 349, in main
dropout(simfwd[:j-i], 1 - keep_prob).argmax(axis=1, out=trg_indices_forward[i:j])
File "/vecmap/map_embeddings.py", line 32, in dropout
mask = xp.random.rand(*m.shape) >= p
File "/usr/local/lib/python3.6/dist-packages/cupy/random/sample.py", line 46, in rand
return random_sample(size=size, dtype=dtype)
File "/usr/local/lib/python3.6/dist-packages/cupy/random/sample.py", line 158, in random_sample
return rs.random_sample(size=size, dtype=dtype)
File "/usr/local/lib/python3.6/dist-packages/cupy/random/generator.py", line 382, in random_sample
out = self._random_sample_raw(size, dtype)
File "/usr/local/lib/python3.6/dist-packages/cupy/random/generator.py", line 366, in _random_sample_raw
out = cupy.empty(size, dtype=dtype)
File "/usr/local/lib/python3.6/dist-packages/cupy/creation/basic.py", line 20, in empty
return cupy.ndarray(shape, dtype, order=order)
File "cupy/core/core.pyx", line 152, in cupy.core.core.ndarray.__init__
File "cupy/cuda/memory.pyx", line 517, in cupy.cuda.memory.alloc
File "cupy/cuda/memory.pyx", line 1076, in cupy.cuda.memory.MemoryPool.malloc
File "cupy/cuda/memory.pyx", line 1097, in cupy.cuda.memory.MemoryPool.malloc
File "cupy/cuda/memory.pyx", line 925, in cupy.cuda.memory.SingleDeviceMemoryPool.malloc
File "cupy/cuda/memory.pyx", line 940, in cupy.cuda.memory.SingleDeviceMemoryPool._malloc
File "cupy/cuda/memory.pyx", line 695, in cupy.cuda.memory._try_malloc
cupy.cuda.memory.OutOfMemoryError: out of memory to allocate 1600000000 bytes (total 16726299136 bytes)
I was running vecmap on Google Colab with 12.75 GB RAM and with GPU hardware acceleration activated.
Some more background: Out-of-memory errors occurred even when the target embedding file was much smaller, approx. 0.2 M in size. On the other hand, when both the source and target embeddings were around 0.2 M in size, the mapping worked perfectly fine, both in supervised and unsupervised mode.
What is the recommended way to deal with such memory issues? To limit the vocabulary size of the embedding files? To set the --batch_size parameter, or to set the --vocabulary_cutoff parameter? By the way, when setting the --vocabulary_cutoff parameter, does vecmap draw a random sample of size n from the original vocabulary, or does it limit the vocabulary to the n most frequent entries?
Dear Mikel,
first of all congratulations on this great piece of work and thank you for sharing it with the community.
I experienced out-of-memory errors when mapping pre-trained fastText embeddings trained on Wikipedia (https://fasttext.cc/docs/en/pretrained-vectors.html). For the EN-DE language pair, the embeddings are quite large, having 300 dimensions and vocabulary sizes of approx 2.2M to 2.5M.
Out-of-memory errors occurred in both the supervised and unsupervised modes, and both with and without
--cuda.In the supervised mode (using the EN-DE training dictionary from your 2017 ACL paper), the following error ocurred:
Call:
python3 vecmap/map_embeddings.py --cuda --supervised TRAIN_DICT EMB_SRC EMB_TRG EMB_SRC_MAPPED EMB_TRG_MAPPED --log log.txt --verboseOutput:
In the unsupervised mode (
python3 vecmap/map_embeddings.py --cuda --unsupervised EMB_SRC EMB_TRG EMB_SRC_MAPPED EMB_TRG_MAPPED --log log.txt --verbose), the error was:I was running vecmap on Google Colab with 12.75 GB RAM and with GPU hardware acceleration activated.
Some more background: Out-of-memory errors occurred even when the target embedding file was much smaller, approx. 0.2 M in size. On the other hand, when both the source and target embeddings were around 0.2 M in size, the mapping worked perfectly fine, both in supervised and unsupervised mode.
What is the recommended way to deal with such memory issues? To limit the vocabulary size of the embedding files? To set the
--batch_sizeparameter, or to set the--vocabulary_cutoffparameter? By the way, when setting the--vocabulary_cutoffparameter, does vecmap draw a random sample of size n from the original vocabulary, or does it limit the vocabulary to the n most frequent entries?