Sample Colab Notebook seems to have a bug
Closed this issue · 10 comments
Hi Jerin,
Is the first cell needed for anything other than storage/ vscode linkage?
If not, the sample Colab Notebook seems to have some bug. Have a look at this when you get the time : https://colab.research.google.com/gist/rahulraj80/2f45c7ab1b44c616b12917f5211c51d3/ilmulti-sample-run-notebook.ipynb
It complaints:
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
/content/ilmulti/ilmulti/translator/translator.py in <module>()
5 try:
----> 6 import fairseq
7 import torch
ModuleNotFoundError: No module named 'fairseq'
During handling of the above exception, another exception occurred:
NameError Traceback (most recent call last)
2 frames
<ipython-input-8-e2b51881e661> in <module>()
----> 1 from ilmulti.translator import from_pretrained
2
3 translator = from_pretrained(tag='mm-all-iter0')
4
5 sample = translator("The quick brown fox jumps over the lazy dog", tgt_lang='hi')
/content/ilmulti/ilmulti/translator/__init__.py in <module>()
1
----> 2 from .translator import FairseqTranslator
3 from .mt_engine import MTEngine
4 from .pretrained import from_pretrained, mm_all
/content/ilmulti/ilmulti/translator/translator.py in <module>()
9 from fairseq import data, options, tasks, tokenizer, utils
10 except ImportError:
---> 11 warnings.warn(
12 """
13 Please check if you have installed specified versions of torch,
NameError: name 'warnings' is not defined
The last torch version error seems to be erroneous as a few cells up, it said:
Requirement already satisfied: torch==1.0.0 in /usr/local/lib/python3.6/dist-packages (1.0.0)
Requirement already satisfied: torchvision==0.2.1 in /usr/local/lib/python3.6/dist-packages (0.2.1)
Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.2.1) (7.0.0)
Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision==0.2.1) (1.16.0)
Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision==0.2.1) (1.15.0)
Cheers,
Rahul
@rahulraj80 See if it's fixed now? I had modified the source so that someone (@Nimishasri) without torch/fairseq on a lesser system. can run the library locally. So the bug is possibly my bad, sorry. I should be testing this to avoid unnecessary bugs among a lot of other things.
- The warnings
ImportErrorshould be resolved by now. I have correctly importedwarnings - Alternatively you can simply install
torch==1.0.0andfairseq==0.8.0, which will prevent theexceptblock from triggering, which will fix the case anyway. Your logs don't indicate the fairseq requirement to be satisfied?
Maybe @Nimishasri can help you with the Colab notebook, I shared it as she supplied one. I don't work with Colab Notebooks much.
Thanks Jerin.
Will try out and revert.
Will be happy to help on the Colab side if it helps you focus on the more important stuff.
@jerinphilip :
So the earlier issues seem to have sorted out. It is currently getting stuck at:
Runnable Gist : Just press Ctrl-F9 (or Runtime->Run All) after opening the link to replicate.
| [src] dictionary: 40897 types
| [tgt] dictionary: 40897 types
/content/ilmulti/ilmulti/translator/translator.py:37: UserWarning: utils.load_ensemble_for_inference is deprecated. Please use checkpoint_utils.load_model_ensemble instead.
self.models, model_args = fairseq.utils.load_ensemble_for_inference(model_paths, self.task, model_arg_overrides=eval(args.model_overrides))
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-12-e2b51881e661> in <module>()
1 from ilmulti.translator import from_pretrained
2
----> 3 translator = from_pretrained(tag='mm-all-iter0')
4
5 sample = translator("The quick brown fox jumps over the lazy dog", tgt_lang='hi')
7 frames
/content/ilmulti/ilmulti/translator/pretrained.py in from_pretrained(tag, use_cuda)
60 from .mt_engine import MTEngine
61
---> 62 translator = build_translator(config['model'], use_cuda=use_cuda)
63 segmenter = build_segmenter(config['segmenter'])
64 tokenizer = build_tokenizer(config['tokenizer'])
/content/ilmulti/ilmulti/translator/translator.py in build_translator(model, use_cuda)
169 args.enhance(**keyword_arguments)
170
--> 171 fseq_translator = FairseqTranslator(args, use_cuda=use_cuda)
172 return fseq_translator
173
/content/ilmulti/ilmulti/translator/translator.py in __init__(self, args, use_cuda)
35 # print('| loading model(s) from {}'.format(args.path))
36 model_paths = args.path.split(':')
---> 37 self.models, model_args = fairseq.utils.load_ensemble_for_inference(model_paths, self.task, model_arg_overrides=eval(args.model_overrides))
38 self.tgt_dict = self.task.target_dictionary
39
/usr/local/lib/python3.6/dist-packages/fairseq/utils.py in load_ensemble_for_inference(filenames, task, model_arg_overrides)
27 )
28 return checkpoint_utils.load_model_ensemble(
---> 29 filenames, arg_overrides=model_arg_overrides, task=task,
30 )
31
/usr/local/lib/python3.6/dist-packages/fairseq/checkpoint_utils.py in load_model_ensemble(filenames, arg_overrides, task)
153 task (fairseq.tasks.FairseqTask, optional): task to use for loading
154 """
--> 155 ensemble, args, _task = load_model_ensemble_and_task(filenames, arg_overrides, task)
156 return ensemble, args
157
/usr/local/lib/python3.6/dist-packages/fairseq/checkpoint_utils.py in load_model_ensemble_and_task(filenames, arg_overrides, task)
164 if not os.path.exists(filename):
165 raise IOError('Model file not found: {}'.format(filename))
--> 166 state = load_checkpoint_to_cpu(filename, arg_overrides)
167
168 args = state['args']
/usr/local/lib/python3.6/dist-packages/fairseq/checkpoint_utils.py in load_checkpoint_to_cpu(path, arg_overrides)
140 for arg_name, arg_val in arg_overrides.items():
141 setattr(args, arg_name, arg_val)
--> 142 state = _upgrade_state_dict(state)
143 return state
144
/usr/local/lib/python3.6/dist-packages/fairseq/checkpoint_utils.py in _upgrade_state_dict(state)
302
303 # set any missing default values in the task, model or other registries
--> 304 registry.set_defaults(state['args'], tasks.TASK_REGISTRY[state['args'].task])
305 registry.set_defaults(state['args'], models.ARCH_MODEL_REGISTRY[state['args'].arch])
306 for registry_name, REGISTRY in registry.REGISTRIES.items():
KeyError: 'shared-multilingual-translation'
If you see the gist above, all installations went through without issues.
-
If you want this only for inference, edit the checkpoint to change the task to 'translation'. I overrode tasks to easily plug unplug datasets and tokenizer nothing else, this is an artifact coming out of the same. This will allow you to use fairseq-generate as well, which is batched properly and much faster. You just have to use the tokenizer for preprocessing and dictionaries here with 'translation' for models to be compatible.
-
If you want training as well as inference on the datasets used in this work, install fairseq-ilmt instead of fairseq==0.8.0. There is some hardcode which @shashanksiripragada did which will have to be written around to ensure compatibility.
Sorry this is complicated, we customized the fork to make certain things feasible without maintaining backwards compatibility etc. @Nimishasri seems to have gotten this (example) to work without issues, the colab using fairseq-ilmt. So I'd say it's worth a try to replace with 0.8.0 with fairseq-ilmt instead. I've corresponded with someone who had the same issue as you and instead chose to go with 1 for faster throughput.
Makes sense.
I changed over to fairseq-ilmt, but the exact same bug reappears. I also tried checking out a previous version but I am getting the same error. (Cuda version is 10.0, and pytorch==1.0.0) Sharing the output I have of the current environment below.
Maybe the environment is playing the spoilsport. To debug, can you share the cuda version on your test setup and the output of pip freeze for your environment for which the minimal startup script of the README is working.
Let's say, starting from a fresh install of Ubuntu 20.04 with a supported Cuda card, what all does one need to do to get the first output if the ILMulti translator.
If we can get a fresh-install-to-output steps nailed down, I am sure we will be able to debug the failure and have a working Notebook for anyone who uses the repository.
Also @Nimishasri : Can you share if we are going about this all wrong? Does the notebook still work for you? What is the environment you have?
Output:
$ pip freeze --all
absl-py==0.10.0
alabaster==0.7.12
albumentations==0.1.12
altair==4.1.0
argon2-cffi==20.1.0
asgiref==3.2.10
astor==0.8.1
astropy==4.0.1.post1
astunparse==1.6.3
async-generator==1.10
atari-py==0.2.6
atomicwrites==1.4.0
attrs==20.2.0
audioread==2.1.8
autograd==1.3
Babel==2.8.0
backcall==0.2.0
beautifulsoup4==4.6.3
bleach==3.1.5
blis==0.4.1
bokeh==2.1.1
boto==2.49.0
boto3==1.14.59
botocore==1.17.59
Bottleneck==1.3.2
branca==0.4.1
bs4==0.0.1
CacheControl==0.12.6
cachetools==4.1.1
catalogue==1.0.0
certifi==2020.6.20
cffi==1.14.2
chainer==7.4.0
chardet==3.0.4
click==7.1.2
cloudpickle==1.3.0
cmake==3.12.0
cmdstanpy==0.9.5
colorlover==0.3.0
community==1.0.0b1
contextlib2==0.5.5
convertdate==2.2.2
coverage==3.7.1
coveralls==0.5
crcmod==1.7
cufflinks==0.17.3
cupy-cuda101==7.4.0
cvxopt==1.2.5
cvxpy==1.0.31
cycler==0.10.0
cymem==2.0.3
Cython==0.29.21
daft==0.0.4
dask==2.12.0
dataclasses==0.7
datascience==0.10.6
debugpy==1.0.0rc2
decorator==4.4.2
defusedxml==0.6.0
descartes==1.1.0
dill==0.3.2
distributed==1.25.3
Django==3.1.1
dlib==19.18.0
dm-tree==0.1.5
docopt==0.6.2
docutils==0.15.2
dopamine-rl==1.0.5
earthengine-api==0.1.234
easydict==1.9
ecos==2.0.7.post1
editdistance==0.5.3
en-core-web-sm==2.2.5
entrypoints==0.3
ephem==3.7.7.1
et-xmlfile==1.0.1
fa2==0.3.5
-e git+https://github.com/rahulraj80/fairseq-ilmt.git@42a628b59b3e37431e6d4de79313fe6107873e87#egg=fairseq
fancyimpute==0.4.3
fastai==1.0.61
fastBPE==0.1.0
fastdtw==0.3.4
fastprogress==1.0.0
fastrlock==0.5
fbprophet==0.7.1
feather-format==0.4.1
filelock==3.0.12
firebase-admin==4.1.0
fix-yahoo-finance==0.0.22
Flask==1.1.2
folium==0.8.3
future==0.16.0
gast==0.3.3
GDAL==2.2.2
gdown==3.6.4
gensim==3.6.0
geographiclib==1.50
geopy==1.17.0
gin-config==0.3.0
glob2==0.7
google==2.0.3
google-api-core==1.16.0
google-api-python-client==1.7.12
google-auth==1.17.2
google-auth-httplib2==0.0.4
google-auth-oauthlib==0.4.1
google-cloud-bigquery==1.21.0
google-cloud-core==1.0.3
google-cloud-datastore==1.8.0
google-cloud-firestore==1.7.0
google-cloud-language==1.2.0
google-cloud-storage==1.18.1
google-cloud-translate==1.5.0
google-colab==1.0.0
google-pasta==0.2.0
google-resumable-media==0.4.1
googleapis-common-protos==1.52.0
googledrivedownloader==0.4
graphviz==0.10.1
grpcio==1.32.0
gspread==3.0.1
gspread-dataframe==3.0.8
gym==0.17.2
h5py==2.10.0
HeapDict==1.0.1
holidays==0.10.3
holoviews==1.13.3
html5lib==1.0.1
httpimport==0.5.18
httplib2==0.17.4
httplib2shim==0.0.3
humanize==0.5.1
hyperopt==0.1.2
ideep4py==2.0.0.post3
idna==2.10
ilmulti==0.0.1
image==1.5.32
imageio==2.4.1
imagesize==1.2.0
imbalanced-learn==0.4.3
imblearn==0.0
imgaug==0.2.9
importlib-metadata==1.7.0
imutils==0.5.3
inflect==2.1.0
iniconfig==1.0.1
intel-openmp==2020.0.133
intervaltree==2.1.0
ipykernel==4.10.1
ipython==5.5.0
ipython-genutils==0.2.0
ipython-sql==0.3.9
ipywidgets==7.5.1
itsdangerous==1.1.0
jax==0.1.75
jaxlib==0.1.52
jdcal==1.4.1
jedi==0.17.2
jieba==0.42.1
Jinja2==2.11.2
jmespath==0.10.0
joblib==0.16.0
jpeg4py==0.1.4
jsonschema==2.6.0
jupyter==1.0.0
jupyter-client==5.3.5
jupyter-console==5.2.0
jupyter-core==4.6.3
jupyterlab-pygments==0.1.1
kaggle==1.5.8
kapre==0.1.3.1
Keras==2.4.3
Keras-Preprocessing==1.1.2
keras-vis==0.4.1
kiwisolver==1.2.0
knnimpute==0.1.0
korean-lunar-calendar==0.2.1
langdetect==1.0.8
langid==1.1.6
librosa==0.6.3
lightgbm==2.2.3
llvmlite==0.31.0
lmdb==0.99
lucid==0.3.8
LunarCalendar==0.0.9
lxml==4.2.6
Markdown==3.2.2
MarkupSafe==1.1.1
matplotlib==3.2.2
matplotlib-venn==0.11.5
missingno==0.4.2
mistune==0.8.4
mizani==0.6.0
mkl==2019.0
mlxtend==0.14.0
more-itertools==8.5.0
moviepy==0.2.3.5
mpmath==1.1.0
msgpack==1.0.0
multiprocess==0.70.10
multitasking==0.0.9
murmurhash==1.0.2
music21==5.5.0
natsort==5.5.0
nbclient==0.5.0
nbconvert==5.6.1
nbformat==5.0.7
nest-asyncio==1.4.0
networkx==2.5
nibabel==3.0.2
nltk==3.2.5
notebook==5.3.1
np-utils==0.5.12.1
numba==0.48.0
numexpr==2.7.1
numpy==1.18.5
nvidia-ml-py3==7.352.0
oauth2client==4.1.3
oauthlib==3.1.0
okgrade==0.4.3
opencv-contrib-python==4.1.2.30
opencv-python==4.1.2.30
openpyxl==2.5.9
opt-einsum==3.3.0
osqp==0.6.1
packaging==20.4
palettable==3.3.0
pandas==1.0.5
pandas-datareader==0.8.1
pandas-gbq==0.11.0
pandas-profiling==1.4.1
pandocfilters==1.4.2
panel==0.9.7
param==1.9.3
parso==0.7.1
pathlib==1.0.1
patsy==0.5.1
pexpect==4.8.0
pickleshare==0.7.5
Pillow==7.0.0
pip==19.3.1
pip-tools==4.5.1
plac==1.1.3
plotly==4.4.1
plotnine==0.6.0
pluggy==0.7.1
portalocker==2.0.0
portpicker==1.3.1
prefetch-generator==1.0.1
preshed==3.0.2
prettytable==0.7.2
progressbar2==3.38.0
prometheus-client==0.8.0
promise==2.3
prompt-toolkit==1.0.18
protobuf==3.12.4
psutil==5.4.8
psycopg2==2.7.6.1
ptyprocess==0.6.0
py==1.9.0
pyarrow==0.14.1
pyasn1==0.4.8
pyasn1-modules==0.2.8
pycocotools==2.0.2
pycparser==2.20
pyct==0.4.7
pydata-google-auth==1.1.0
pydot==1.3.0
pydot-ng==2.0.0
pydotplus==2.0.2
PyDrive==1.3.1
pyemd==0.5.1
pyglet==1.5.0
Pygments==2.6.1
pygobject==3.26.1
pymc3==3.7
PyMeeus==0.3.7
pymongo==3.11.0
pymystem3==0.2.0
PyOpenGL==3.1.5
pyparsing==2.4.7
pyrsistent==0.16.0
pysndfile==1.3.8
PySocks==1.7.1
pystan==2.19.1.1
pytest==3.6.4
python-apt==1.6.5+ubuntu0.3
python-chess==0.23.11
python-dateutil==2.8.1
python-louvain==0.14
python-slugify==4.0.1
python-utils==2.4.0
pytz==2018.9
pyviz-comms==0.7.6
PyWavelets==1.1.1
PyYAML==3.13
pyzmq==19.0.2
qtconsole==4.7.7
QtPy==1.9.0
regex==2019.12.20
requests==2.23.0
requests-oauthlib==1.3.0
resampy==0.2.2
retrying==1.3.3
rpy2==3.2.7
rsa==4.6
s3transfer==0.3.3
sacrebleu==1.4.14
scikit-image==0.16.2
scikit-learn==0.22.2.post1
scipy==1.4.1
scs==2.1.2
seaborn==0.10.1
Send2Trash==1.5.0
sentencepiece==0.1.91
setuptools==50.3.0
setuptools-git==1.2
Shapely==1.7.1
simplegeneric==0.8.1
six==1.15.0
sklearn==0.0
sklearn-pandas==1.8.0
slugify==0.0.1
smart-open==2.1.1
snowballstemmer==2.0.0
sortedcontainers==2.2.2
spacy==2.2.4
Sphinx==1.8.5
sphinxcontrib-serializinghtml==1.1.4
sphinxcontrib-websupport==1.2.4
SQLAlchemy==1.3.19
sqlparse==0.3.1
srsly==1.0.2
statsmodels==0.10.2
sympy==1.1.1
tables==3.4.4
tabulate==0.8.7
tblib==1.7.0
tensorboard==2.3.0
tensorboard-plugin-wit==1.7.0
tensorboardcolab==0.0.22
tensorflow==2.3.0
tensorflow-addons==0.8.3
tensorflow-datasets==2.1.0
tensorflow-estimator==2.3.0
tensorflow-gcs-config==2.3.0
tensorflow-hub==0.9.0
tensorflow-metadata==0.24.0
tensorflow-privacy==0.2.2
tensorflow-probability==0.11.0
termcolor==1.1.0
terminado==0.8.3
testpath==0.4.4
text-unidecode==1.3
textblob==0.15.3
textgenrnn==1.4.1
Theano==1.0.5
thinc==7.4.0
tifffile==2020.9.3
toml==0.10.1
toolz==0.10.0
torch==1.0.0
torchsummary==1.5.1
torchtext==0.3.1
torchvision==0.3.0
tornado==5.1.1
tqdm==4.41.1
traitlets==4.3.3
tweepy==3.6.0
typeguard==2.7.1
typing-extensions==3.7.4.3
tzlocal==1.5.1
umap-learn==0.4.6
uritemplate==3.0.1
urllib3==1.24.3
vega-datasets==0.8.0
wasabi==0.8.0
wcwidth==0.2.5
webencodings==0.5.1
Werkzeug==1.0.1
wheel==0.35.1
widgetsnbextension==3.5.1
wordcloud==1.5.0
wrapt==1.12.1
xarray==0.15.1
xgboost==0.90
xlrd==1.1.0
xlwt==1.3.0
yellowbrick==0.9.1
zict==2.0.0
zipp==3.1.0
@rahulraj80 The same error can't appear with fairseq-ilmt, as 'shared-multilingual-translation' is a defined task? Can you check once again?
Sorry - My bad. The error is very different from earlier - I missed attaching that in the last message.
I should have said that same error message (different from the earlier one) comes in spite of any change that I do to the configuration of the environment and torch/cuda versions. cuda=False gives the same (following) error as well.
When translator attempts to _make_batches, it tries to iterate over the itr object, which calls the _ getitem _ of langauage_pair_dataset, but it fails as self.src is a list of Tensors instead of a ConcatDataset (??) that it probably needs to be.
The exact error log is (please ignore the autolog/print statements):
| [src] dictionary: 40897 types
| [tgt] dictionary: 40897 types
./ilmulti/translator/translator.py:38: UserWarning: utils.load_ensemble_for_inference is deprecated. Please use checkpoint_utils.load_model_ensemble instead.
self.models, model_args = fairseq.utils.load_ensemble_for_inference(model_paths, self.task, model_arg_overrides=eval(args.model_overrides))
./ilmulti/utils/language_utils.py:47: UserWarning: Detect segmented is not recommended.This might lead to large slowdowns.
"Detect segmented is not recommended."
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-28-82060a75cda2> in <module>()
---> 36 result = translator("நாளைக்கு என்ன எக்ஸாம். நாளைக்கு என்ன எக்ஸாம்.", tgt_lang="en")
9 frames
/content/ilmulti/ilmulti/translator/mt_engine.py in __call__(self, source, tgt_lang, src_lang, detokenize)
47
48 autolog(f":Sources::T:{str(type(sources))}:len:{len(sources)}:::Sources::T:{sources}:::")
---> 49 export = self.translator(sources)
50 export = self._handle_empty_lines_noise(export)
51 if detokenize:
/content/ilmulti/ilmulti/translator/translator.py in __call__(self, lines, attention)
69 autolog(f":src_dict:T:{str(type(src_dict))}:L::tgt:T:{str(type(tgt_dict))}:L::Align:T:{str(type(align_dict))}:L::")
70 autolog(f"::Lines:T:{str(type(lines))}:L:{len(lines)}:lines:{lines}:")
---> 71 for batch, idx in self._make_batches(lines):
72 src_tokens = batch.src_tokens
73 src_lengths = batch.src_lengths
/content/ilmulti/ilmulti/translator/translator.py in _make_batches(self, lines)
145 autolog(f"lengths:T:{str(type(lengths))}:L::{len(lengths)}::_:{lengths}:")
146 autolog(f"itr:T:{str(type(itr))}:L::{len(itr)}::_:{itr}:")
--> 147 for batch in itr:
148 yield Batch(
149 ids=batch['id'],
/content/fairseq-ilmt/fairseq/data/iterators.py in __iter__(self)
33
34 def __iter__(self):
---> 35 for x in self.iterable:
36 self.count += 1
37 yield x
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __next__(self)
361
362 def __next__(self):
--> 363 data = self._next_data()
364 self._num_yielded += 1
365 if self._dataset_kind == _DatasetKind.Iterable and \
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in _next_data(self)
401 def _next_data(self):
402 index = self._next_index() # may raise StopIteration
--> 403 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
404 if self._pin_memory:
405 data = _utils.pin_memory.pin_memory(data)
/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
42 def fetch(self, possibly_batched_index):
43 if self.auto_collation:
---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]
45 else:
46 data = self.dataset[possibly_batched_index]
/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py in <listcomp>(.0)
42 def fetch(self, possibly_batched_index):
43 if self.auto_collation:
---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]
45 else:
46 data = self.dataset[possibly_batched_index]
/content/fairseq-ilmt/fairseq/data/language_pair_dataset.py in __getitem__(self, index)
135 print(">"*3, f"calling get corpus for Self:{self}:t:{str(type(self))}:") ####Self[0]:{self.src[0]}:t:{str(type(self.src[0]))}:Self[0][0]:{self.src[0][0]}:t:{str(type(self.src[0][0]))}:::{index}:::")
136 print(">"*3, f" Self src:{self.src}:t:{str(type(self.src))}:Self[0]:{self.src[0]}:t:{str(type(self.src[0]))}:Self[0][0]:{self.src[0][0]}:t:{str(type(self.src[0][0]))}:::{index}:::")
137
--> 138 src_id = self.src.get_corpus_id(index)
139 tgt_id = self.tgt.get_corpus_id(index)
AttributeError: 'list' object has no attribute 'get_corpus_id'
The line numbers have shifted down a bit as I added a few logging (autolog) and print statements to debug the data type issue.
The self.src object at this stage is a list of Tensors (output of the two print statements above in /content/fairseq-ilmt/fairseq/data/language_pair_dataset.py in getitem(self, index)):
>>> calling get corpus for Self:<fairseq.data.language_pair_dataset.LanguagePairDataset object at 0x7f71aed93630>:t:<class 'fairseq.data.language_pair_dataset.LanguagePairDataset'>:
>>> Self src:[tensor([ 262, 35292, 6460, 5802, 34390, 34356, 5775, 6554, 6299, 2])]:t:<class 'list'>:Self[0]:tensor([ 262, 35292, 6460, 5802, 34390, 34356, 5775, 6554, 6299, 2]):t:<class 'torch.Tensor'>:Self[0][0]:262:t:<class 'torch.Tensor'>:::0:::
Do let me know if I can share something else.
So, some breaking code was added a month back, which I don't think will be modded soon. Can you try the following tag, so your life becomes easier than to edit this code?
Thanks @jerinphilip 👍 : That solves it. I used the June versions for both ilmulti and fairseq-ilmt as the latest ilmulti did not play well with the older fairseq-ilmt.
Confirming that it works Working Colab Notebook : Press Ctrl-F9 or Runtime-> Run All
With a small patch in fairseq-ilmt/fairseq/search.py, it works with the latest pytorch versions without the need to downgrade torch on Colab.
YMMV for other integrations, but IMHO replacing torch.div with torch.floor_divide should not break any applications.
In any case, people should first follow the suggested versions as per the README.md before trying these stunts.
Let's hope this is future proof.
https://colab.research.google.com/drive/1KOvjawhzPXOQ6RLlFBFeInkuuR0QAWTK?usp=sharing