thinc.neural.ops.Ops.allocate MemoryError.!

anubhav0fnu · July 29, 2018, 2:55am

I have 40 text files each having less than 200,000 words, the complete dataset size is 11MB. My task is to train a language model with custom entity types. For it,

created a my_patterns.jsonl which has the examples for each custom entity types, to show the direction to model.
created an empty dataset my_dataset_ner where the new annotation for each text file will be saved.
tool a pre-trained spaCy language Model en_core_web_lg.
written a custom.ner.teach recipe over the ner.teach recipe.

recipe_train.py recipe :

from pathlib import Path
from argparse import ArgumentParser, ArgumentTypeError
import prodigy
from prodigy.components.loaders import TXT
from prodigy.recipes.ner import teach
from prodigy.components.db import connect

@prodigy.recipe('custom.ner.teach',
                dataset=prodigy.recipe_args['dataset'],
                spacy_model=prodigy.recipe_args['spacy_model'],
                file_path=("The path to the CSV file", "positional", None, str),
                label_set= prodigy.recipe_args['label_set'],
                patterns= prodigy.recipe_args['patterns']
                )
def custom_ner_teach(dataset, spacy_model, file_path, label_set, patterns):
    stream = custom_txt_loader(file_path )

    #setting up the ner.teach recipe.
    components = teach( dataset= dataset,
                        spacy_model=spacy_model,
                        source=stream,
                        label=label_set,
                        patterns=patterns )
    return components

def custom_txt_loader(file_path):
    # Loaders recipe allows streaming in data from a text file.
    for stopper, item in enumerate(Path(file_path).iterdir()):
        yield { 'text': list(TXT( str(item) ))[0]['text'] }

my_patterns.jsonl (hand-created with 60 examples of 8 different custom entity types ):

{"label":"LABEL-1","pattern":[{"lower":"%%%%%%%%%"}]}
{"label":"lABEL-2","pattern":[{"lower":"XXXXXXXXXXXXX"}]}
{"label":"lABEL-2","pattern":[{"lower":"^^^^^^^^^^^"}]}
...

Then I used the following command to run this custom.ner.teach recipe

$ prodigy custom.ner.teach     my_dataset_ner     en_core_web_lg     "Path/to/my/40/text/files/"     --label "my_custom_label"     --patterns my_patterns.jsonl     -F     recipe_train.py

After waiting for about 2 mins, the script failed with memory error.
The stack trace is as follow:

Traceback (most recent call last):
  File "/usr/lib64/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/usr/lib64/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/prodigy/__main__.py", line 259, in <module>
    controller = recipe(*args, use_plac=True)
  File "cython_src/prodigy/core.pyx", line 178, in prodigy.core.recipe.recipe_decorator.recipe_proxy
  File "cython_src/prodigy/core.pyx", line 55, in prodigy.core.Controller.__init__
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/toolz/itertoolz.py", line 368, in first
    return next(iter(seq))
  File "cython_src/prodigy/core.pyx", line 84, in iter_tasks
  File "cython_src/prodigy/components/sorters.pyx", line 136, in __iter__
  File "cython_src/prodigy/components/sorters.pyx", line 51, in genexpr
  File "cython_src/prodigy/util.pyx", line 263, in predict
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/toolz/itertoolz.py", line 716, in partition_all
    prev = next(it)
  File "cython_src/prodigy/components/preprocess.pyx", line 36, in split_sentences
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/spacy/language.py", line 554, in pipe
    for doc, context in izip(docs, contexts):
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/spacy/language.py", line 578, in pipe
    for doc in docs:
  File "nn_parser.pyx", line 367, in pipe
  File "cytoolz/itertoolz.pyx", line 1046, in cytoolz.itertoolz.partition_all.__next__ (cytoolz/itertoolz.c:14538)
  File "nn_parser.pyx", line 367, in pipe
  File "cytoolz/itertoolz.pyx", line 1046, in cytoolz.itertoolz.partition_all.__next__ (cytoolz/itertoolz.c:14538)
  File "pipeline.pyx", line 433, in pipe
  File "pipeline.pyx", line 438, in spacy.pipeline.Tagger.predict
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/neural/_classes/model.py", line 161, in __call__
    return self.predict(x)
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/api.py", line 55, in predict
    X = layer(X)
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/neural/_classes/model.py", line 161, in __call__
    return self.predict(x)
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/api.py", line 293, in predict
    X = layer(layer.ops.flatten(seqs_in, pad=pad))
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/neural/_classes/model.py", line 161, in __call__
    return self.predict(x)
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/api.py", line 55, in predict
    X = layer(X)
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/neural/_classes/resnet.py", line 15, in __call__
    return X + self._layers[0](X)
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/neural/_classes/model.py", line 161, in __call__
    return self.predict(x)
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/api.py", line 55, in predict
    X = layer(X)
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/neural/_classes/model.py", line 161, in __call__
    return self.predict(x)
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/neural/_classes/convolution.py", line 25, in predict
    return self.ops.seq2col(X, self.nW)
  File "ops.pyx", line 466, in thinc.neural.ops.NumpyOps.seq2col
  File "ops.pyx", line 162, in thinc.neural.ops.Ops.allocate
MemoryError

Even after reducing the loaded data-size (number of text files) to only 4 files having total 4MB, the script fails.

Traceback (most recent call last):
  File "/usr/lib64/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/usr/lib64/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/prodigy/__main__.py", line 259, in <module>
    controller = recipe(*args, use_plac=True)
  File "cython_src/prodigy/core.pyx", line 178, in prodigy.core.recipe.recipe_decorator.recipe_proxy
  File "cython_src/prodigy/core.pyx", line 55, in prodigy.core.Controller.__init__
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/toolz/itertoolz.py", line 368, in first
    return next(iter(seq))
  File "cython_src/prodigy/core.pyx", line 84, in iter_tasks
  File "cython_src/prodigy/components/sorters.pyx", line 136, in __iter__
  File "cython_src/prodigy/components/sorters.pyx", line 51, in genexpr
  File "cython_src/prodigy/util.pyx", line 263, in predict
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/toolz/itertoolz.py", line 716, in partition_all
    prev = next(it)
  File "cython_src/prodigy/components/preprocess.pyx", line 36, in split_sentences
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/spacy/language.py", line 554, in pipe
    for doc, context in izip(docs, contexts):
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/spacy/language.py", line 578, in pipe
    for doc in docs:
  File "nn_parser.pyx", line 367, in pipe
  File "cytoolz/itertoolz.pyx", line 1046, in cytoolz.itertoolz.partition_all.__next__ (cytoolz/itertoolz.c:14538)
  File "nn_parser.pyx", line 374, in pipe
  File "nn_parser.pyx", line 401, in spacy.syntax.nn_parser.Parser.parse_batch
  File "nn_parser.pyx", line 729, in spacy.syntax.nn_parser.Parser.get_batch_model
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/api.py", line 61, in begin_update
    X, inc_layer_grad = layer.begin_update(X, drop=drop)
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/api.py", line 280, in begin_update
    drop=drop)
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/api.py", line 61, in begin_update
    X, inc_layer_grad = layer.begin_update(X, drop=drop)
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/neural/_classes/resnet.py", line 18, in begin_update
    y, bp_y = self._layers[0].begin_update(X, drop=drop)
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/api.py", line 61, in begin_update
    X, inc_layer_grad = layer.begin_update(X, drop=drop)
  File "/home/vagrant/.virtualenvs/Cognitive/lib64/python3.6/site-packages/thinc/neural/_classes/convolution.py", line 28, in begin_update
    X__bo = self.ops.seq2col(X__bi, self.nW)
  File "ops.pyx", line 462, in thinc.neural.ops.NumpyOps.seq2col
  File "cymem/cymem.pyx", line 42, in cymem.cymem.Pool.alloc (cymem/cymem.cpp:1091)
MemoryError: Error assigning 315124608 bytesError assigning 315124608 bytesError assigning 315124608 bytesError assigning 315124608 bytes

Could you please provide your suggestion on what’s not been done correctly? I wanna move ahead and create more annotation for my dataset and then see how my annotation work on a completely new test set?

@honnibal & @ines , Using the en_core_web_sm instead of en_core_web_lg, I am able to surpass the Memory error, but I would love to use the en_core_web_lg model as it’s predictions are far better. Moreover, Even with en_core_web_sm fails I get a memory error if I used more than 3 text files having size ~300MB.

honnibal · July 30, 2018, 11:36am

One of the disadvantages of the neural network models is that they use a fair amount of intermediate memory per word. Normally this doesn’t matter, but if the documents are long, it gets prohibitive. The problem occurs because we need to predict vector representations for each word in the document, and we do this all at once. There are various intermediate representations that aren’t freed immediately as well — leading to the explosion in memory.

The solution is to cut your documents up into smaller pieces before training. Paragraph units work quite well, or possibly sections.

In future versions of spaCy we’ll have a solution to do this automatically, but for now it’s best to do it yourself, especially since it’s normally easy to figure out a paragraph segmentation strategy for the specific documents you’re working on.

anubhav0fnu · July 30, 2018, 2:55pm

@honnibal, The text I am dealing with is extracted from pdf’s. So, I don’t know where the paragraph or section ends. I could break each text file into sentences and pass it into the stream to teach recipe. Also, Is there a way to roughly estimate memory requirements prodigy needs per document size with each model? It will help me to go either with AWS large instance or staying with my local machine’s memory(16 GB) for training.

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thinc.neural.ops.Ops.allocate MemoryError.!

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