.github/workflows/pypi.yml

@@ -0,0 +1,41 @@
+name: Upload to PyPI
+
+# Controls when the action will run.
+on:
+  # Triggers the workflow when a release is created
+  release:
+    types: [created]
+
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+# A workflow run is made up of one or more jobs that can run sequentially or in parallel
+jobs:
+  # This workflow contains a single job called "testpypi"
+  testpypi:
+    # The type of runner that the job will run on
+    runs-on: ubuntu-latest
+
+    # Steps represent a sequence of tasks that will be executed as part of the job
+    steps:
+      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
+      - uses: actions/checkout@v2
+
+      # Sets up python3
+      - uses: actions/setup-python@v2
+        with:
+          python-version: 3.8
+
+      - name: "Installs dependencies"
+        run: |
+          python3 -m pip install --upgrade pip
+          python3 -m pip install setuptools wheel twine
+
+      # Upload to TestPyPI
+      - name: Build and upload to PyPI
+        run: |
+          python3 setup.py sdist bdist_wheel
+          python3 -m twine upload dist/*
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.TWINE_TOKEN }}

.github/workflows/pythonapp.yml

@@ -20,7 +20,7 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install flake8 pytest
+        pip install flake8 pytest torch
         pip install .
     - name: Lint with flake8
       run: |

.github/workflows/testpypi.yml

@@ -0,0 +1,38 @@
+name: Upload to TestPyPI
+
+# Controls when the action will run.
+on:
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+# A workflow run is made up of one or more jobs that can run sequentially or in parallel
+jobs:
+  # This workflow contains a single job called "testpypi"
+  testpypi:
+    # The type of runner that the job will run on
+    runs-on: ubuntu-latest
+
+    # Steps represent a sequence of tasks that will be executed as part of the job
+    steps:
+      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
+      - uses: actions/checkout@v2
+
+      # Sets up python3
+      - uses: actions/setup-python@v2
+        with:
+          python-version: 3.8
+
+      - name: "Installs dependencies"
+        run: |
+          python3 -m pip install --upgrade pip
+          python3 -m pip install setuptools wheel twine
+
+      # Upload to TestPyPI
+      - name: Build and upload to TestPyPI
+        run: |
+          python3 setup.py sdist bdist_wheel
+          python3 -m twine upload dist/*
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.TWINE_TEST_TOKEN }}
+          TWINE_REPOSITORY: testpypi

README.md

@@ -12,6 +12,8 @@ Our paper is available at [https://arxiv.org/abs/1906.08230](https://arxiv.org/a
 
 Some documentation is incomplete. We will try to fill it in over time, but if there is something you would like an explanation for, please open an issue so we know where to focus our effort!
 
+**Update 09/26/2020:** We no longer recommend trying to train directly with TAPE's training code. It will likely still work for some time, but will not be updated for future pytorch versions. Internally, we have been working with different frameworks for training (specifically Pytorch Lightning and Fairseq). We strongly recommend using a framework like these, as it offloads the requirement of maintaining compatability with Pytorch versions. TAPE models will continue to be available, and if the code is working for you, feel free to use it. However we will not be fixing issues regarding multi-GPU errors, OOM erros, etc during training.
+
 ## Contents
 
 * [Installation](#installation)
@@ -187,7 +189,7 @@ This will report the overall accuracy, and will also dump a `results.pkl` file i
 
 ### trRosetta
 
-We have recently re-implemented the trRosetta model from Yang et. al. (2020). A link to the original repository, which was used as a basis for this re-implementation, can be found [here](https://github.com/gjoni/trRosetta). We provide a pytorch implementation and dataset to allow you to play around with the model. Data is available [here](http://s3.amazonaws.com/proteindata/data_pytorch/trrosetta.tar.gz). This is the same as the data in the original paper, however we've added train / val split files to allow you to train your own model reproducibly. To use this model
+We have recently re-implemented the trRosetta model from Yang et. al. (2020). A link to the original repository, which was used as a basis for this re-implementation, can be found [here](https://github.com/gjoni/trRosetta). We provide a pytorch implementation and dataset to allow you to play around with the model. Data is available [here](http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/trrosetta.tar.gz). This is the same as the data in the original paper, however we've added train / val split files to allow you to train your own model reproducibly. To use this model
 
 ```python
 from tape import TRRosetta
@@ -243,13 +245,13 @@ The unsupervised Pfam dataset is around 7GB compressed and 19GB uncompressed. Th
 
 ### LMDB Data
 
-[Pretraining Corpus (Pfam)](http://s3.amazonaws.com/proteindata/data_pytorch/pfam.tar.gz) __|__ [Secondary Structure](http://s3.amazonaws.com/proteindata/data_pytorch/secondary_structure.tar.gz) __|__ [Contact (ProteinNet)](http://s3.amazonaws.com/proteindata/data_pytorch/proteinnet.tar.gz) __|__ [Remote Homology](http://s3.amazonaws.com/proteindata/data_pytorch/remote_homology.tar.gz) __|__ [Fluorescence](http://s3.amazonaws.com/proteindata/data_pytorch/fluorescence.tar.gz) __|__ [Stability](http://s3.amazonaws.com/proteindata/data_pytorch/stability.tar.gz)
+[Pretraining Corpus (Pfam)](http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/pfam.tar.gz) __|__ [Secondary Structure](http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/secondary_structure.tar.gz) __|__ [Contact (ProteinNet)](http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/proteinnet.tar.gz) __|__ [Remote Homology](http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/remote_homology.tar.gz) __|__ [Fluorescence](http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/fluorescence.tar.gz) __|__ [Stability](http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/stability.tar.gz)
 
 ### Raw Data
 
 Raw data files are stored in JSON format for maximum portability. This data is JSON-ified, which removes certain constructs (in particular numpy arrays). As a result they cannot be directly loaded into the provided pytorch datasets (although the conversion should be quite easy by simply adding calls to `np.array`).
 
-[Pretraining Corpus (Pfam)](http://s3.amazonaws.com/proteindata/data_raw_pytorch/pfam.tar.gz) __|__ [Secondary Structure](http://s3.amazonaws.com/proteindata/data_raw_pytorch/secondary_structure.tar.gz) __|__ [Contact (ProteinNet)](http://s3.amazonaws.com/proteindata/data_raw_pytorch/proteinnet.tar.gz) __|__ [Remote Homology](http://s3.amazonaws.com/proteindata/data_raw_pytorch/remote_homology.tar.gz) __|__ [Fluorescence](http://s3.amazonaws.com/proteindata/data_raw_pytorch/fluorescence.tar.gz) __|__ [Stability](http://s3.amazonaws.com/proteindata/data_raw_pytorch/stability.tar.gz)
+[Pretraining Corpus (Pfam)](http://s3.amazonaws.com/songlabdata/proteindata/data_raw_pytorch/pfam.tar.gz) __|__ [Secondary Structure](http://s3.amazonaws.com/songlabdata/proteindata/data_raw_pytorch/secondary_structure.tar.gz) __|__ [Contact (ProteinNet)](http://s3.amazonaws.com/songlabdata/proteindata/data_raw_pytorch/proteinnet.tar.gz) __|__ [Remote Homology](http://s3.amazonaws.com/songlabdata/proteindata/data_raw_pytorch/remote_homology.tar.gz) __|__ [Fluorescence](http://s3.amazonaws.com/songlabdata/proteindata/data_raw_pytorch/fluorescence.tar.gz) __|__ [Stability](http://s3.amazonaws.com/songlabdata/proteindata/data_raw_pytorch/stability.tar.gz)
 
 
 ## Leaderboard

download_data.sh

@@ -4,25 +4,25 @@ mkdir -p ./data
 while true; do
      read -p "Do you wish to download and unzip the pretraining corpus? It is 7.7GB compressed and 19GB uncompressed? [y/n]" yn
      case $yn in
-	    [Yy]* ) wget http://s3.amazonaws.com/proteindata/data_pytorch/pfam.tar.gz; tar -xzf pfam.tar.gz -C ./data; rm pfam.tar.gz; break;;
+	    [Yy]* ) wget http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/pfam.tar.gz; tar -xzf pfam.tar.gz -C ./data; rm pfam.tar.gz; break;;
             [Nn]* ) exit;;
 	    * ) echo "Please answer yes (Y/y) or no (N/n).";;
     esac
 done
 
 # Download Vocab/Model files
-wget http://s3.amazonaws.com/proteindata/data_pytorch/pfam.model
-wget http://s3.amazonaws.com/proteindata/data_pytorch/pfam.vocab
+wget http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/pfam.model
+wget http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/pfam.vocab
 
 mv pfam.model data
 mv pfam.vocab data
 
 # Download Data Files
-wget http://s3.amazonaws.com/proteindata/data_pytorch/secondary_structure.tar.gz
-wget http://s3.amazonaws.com/proteindata/data_pytorch/proteinnet.tar.gz
-wget http://s3.amazonaws.com/proteindata/data_pytorch/remote_homology.tar.gz
-wget http://s3.amazonaws.com/proteindata/data_pytorch/fluorescence.tar.gz
-wget http://s3.amazonaws.com/proteindata/data_pytorch/stability.tar.gz
+wget http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/secondary_structure.tar.gz
+wget http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/proteinnet.tar.gz
+wget http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/remote_homology.tar.gz
+wget http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/fluorescence.tar.gz
+wget http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/stability.tar.gz
 
 tar -xzf secondary_structure.tar.gz -C ./data
 tar -xzf proteinnet.tar.gz -C ./data

download_data_aws.sh

@@ -4,30 +4,30 @@ mkdir -p ./data
 while true; do
      read -p "Do you wish to download and unzip the pretraining corpus? It is 7.7GB compressed and 19GB uncompressed. [y/n]" yn
      case $yn in
-	    [Yy]* ) aws s3 cp s3://proteindata/data_pytorch/pfam.tar.gz .; tar -xzf pfam.tar.gz -C ./data; rm pfam.tar.gz; break;;
+	    [Yy]* ) aws s3 cp s3://songlabdata/proteindata/data_pytorch/pfam.tar.gz .; tar -xzf pfam.tar.gz -C ./data; rm pfam.tar.gz; break;;
             [Nn]* ) exit;;
 	    * ) echo "Please answer yes (Y/y) or no (N/n).";;
     esac
 done
 
 echo "Downloading BPE Vocab/Model files"
-aws s3 cp s3://proteindata/data_pytorch/pfam.model . && mv pfam.model data
-aws s3 cp s3://proteindata/data_pytorch/pfam.vocab . && mv pfam.vocab data
+aws s3 cp s3://songlabdata/proteindata/data_pytorch/pfam.model . && mv pfam.model data
+aws s3 cp s3://songlabdata/proteindata/data_pytorch/pfam.vocab . && mv pfam.vocab data
 
 # Download Data Files
 echo "Download TAPE task datasets"
-aws s3 cp s3://proteindata/data_pytorch/secondary_structure.tar.gz . \
+aws s3 cp s3://songlabdata/proteindata/data_pytorch/secondary_structure.tar.gz . \
     && tar -xzf secondary_structure.tar.gz -C ./data \
     && rm secondary_structure.tar.gz
-aws s3 cp s3://proteindata/data_pytorch/proteinnet.tar.gz . \
+aws s3 cp s3://songlabdata/proteindata/data_pytorch/proteinnet.tar.gz . \
     && tar -xzf proteinnet.tar.gz -C ./data \
     && rm proteinnet.tar.gz
-aws s3 cp s3://proteindata/data_pytorch/remote_homology.tar.gz . \
+aws s3 cp s3://songlabdata/proteindata/data_pytorch/remote_homology.tar.gz . \
     && tar -xzf remote_homology.tar.gz -C ./data \
     && rm remote_homology.tar.gz
-aws s3 cp s3://proteindata/data_pytorch/fluorescence.tar.gz . \
+aws s3 cp s3://songlabdata/proteindata/data_pytorch/fluorescence.tar.gz . \
     && tar -xzf fluorescence.tar.gz -C ./data \
     && rm fluorescence.tar.gz
-aws s3 cp s3://proteindata/data_pytorch/stability.tar.gz . \
+aws s3 cp s3://songlabdata/proteindata/data_pytorch/stability.tar.gz . \
     && tar -xzf stability.tar.gz -C ./data \
     && rm stability.tar.gz

requirements.txt

@@ -1,8 +1,8 @@
-torch>=1.0,<1.5
 tqdm
 tensorboardX
 scipy
 lmdb
 boto3
 requests
 biopython
+filelock

tape/__init__.py

@@ -9,7 +9,7 @@
 import importlib
 import pkgutil
 
-__version__ = '0.4'
+__version__ = '0.5'
 
 
 # Import all the models and configs

tape/main.py

@@ -117,7 +117,7 @@ def create_eval_parser(base_parser: argparse.ArgumentParser) -> argparse.Argumen
 
 def create_embed_parser(base_parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser(
-        description='Embed a set of proteins wiht a pretrained model',
+        description='Embed a set of proteins with a pretrained model',
         parents=[base_parser])
     parser.add_argument('data_file', type=str,
                         help='File containing set of proteins to embed')

tape/models/file_utils.py

@@ -4,7 +4,6 @@
 https://github.com/huggingface/transformers, which in turn is adapted from the AllenNLP
 library at https://github.com/allenai/allennlp
 Copyright by the AllenNLP authors.
-
 Note - this file goes to effort to support Python 2, but the rest of this repository does not.
 """
 from __future__ import (absolute_import, division, print_function, unicode_literals)
@@ -14,18 +13,25 @@
 import json
 import logging
 import os
-import shutil
 import tempfile
 import fnmatch
-from functools import wraps
-from hashlib import sha256
 from io import open
 
 import boto3
 import requests
 from botocore.exceptions import ClientError
 from tqdm import tqdm
 
+from contextlib import contextmanager
+from functools import partial, wraps
+from hashlib import sha256
+
+from filelock import FileLock
+# from tqdm.auto import tqdm
+
+logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
+
+
 try:
     from torch.hub import _get_torch_home
     torch_cache_home = _get_torch_home()
@@ -55,6 +61,30 @@
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 
 
+def get_cache():
+    return PROTEIN_MODELS_CACHE
+
+
+def get_etag(url):
+    # Get eTag to add to filename, if it exists.
+    if url.startswith("s3://"):
+        etag = s3_etag(url)
+    else:
+        try:
+            response = requests.head(url, allow_redirects=True)
+            if response.status_code != 200:
+                etag = None
+            else:
+                etag = response.headers.get("ETag")
+        except EnvironmentError:
+            etag = None
+
+    if sys.version_info[0] == 2 and etag is not None:
+        etag = etag.decode('utf-8')
+
+    return etag
+
+
 def url_to_filename(url, etag=None):
     """
     Convert `url` into a hashed filename in a repeatable way.
@@ -99,12 +129,18 @@ def filename_to_url(filename, cache_dir=None):
     return url, etag
 
 
-def cached_path(url_or_filename, cache_dir=None):
+def cached_path(url_or_filename, force_download=False, cache_dir=None):
     """
     Given something that might be a URL (or might be a local path),
     determine which. If it's a URL, download the file and cache it, and
     return the path to the cached file. If it's already a local path,
     make sure the file exists and then return the path.
+
+    Args:
+        cache_dir: specify a cache directory to save the file to
+        (overwrite the default cache dir).
+        force_download: if True, re-dowload the file even if it's
+        already cached in the cache dir.
     """
     if cache_dir is None:
         cache_dir = PROTEIN_MODELS_CACHE
@@ -117,10 +153,10 @@ def cached_path(url_or_filename, cache_dir=None):
 
     if parsed.scheme in ('http', 'https', 's3'):
         # URL, so get it from the cache (downloading if necessary)
-        return get_from_cache(url_or_filename, cache_dir)
+        output_path = get_from_cache(url_or_filename, cache_dir, force_download)
     elif os.path.exists(url_or_filename):
         # File, and it exists.
-        return url_or_filename
+        output_path = url_or_filename
     elif parsed.scheme == '':
         # File, but it doesn't exist.
         raise EnvironmentError("file {} not found".format(url_or_filename))
@@ -129,6 +165,8 @@ def cached_path(url_or_filename, cache_dir=None):
         raise ValueError("unable to parse {} as a URL or as a local path".format(
             url_or_filename))
 
+    return output_path
+
 
 def split_s3_path(url):
     """Split a full s3 path into the bucket name and path."""
@@ -191,7 +229,7 @@ def http_get(url, temp_file):
     progress.close()
 
 
-def get_from_cache(url, cache_dir=None):
+def get_from_cache(url, cache_dir=None, force_download=False, resume_download=False):
     """
     Given a URL, look for the corresponding dataset in the local cache.
     If it's not there, download it. Then return the path to the cached file.
@@ -226,6 +264,9 @@ def get_from_cache(url, cache_dir=None):
     # get cache path to put the file
     cache_path = os.path.join(cache_dir, filename)
 
+    if os.path.exists(cache_path) and etag is None:
+        return cache_path
+
     # If we don't have a connection (etag is None) and can't identify the file
     # try to get the last downloaded one
     if not os.path.exists(cache_path) and etag is None:
@@ -234,6 +275,48 @@ def get_from_cache(url, cache_dir=None):
         if matching_files:
             cache_path = os.path.join(cache_dir, matching_files[-1])
 
+    # From now on, etag is not None
+    if os.path.exists(cache_path) and not force_download:
+        return cache_path
+
+    # Prevent parallel downloads of the same file with a lock.
+    lock_path = cache_path + ".lock"
+    with FileLock(lock_path):
+
+        # If the download just completed while the lock was activated.
+        if os.path.exists(cache_path) and not force_download:
+            # Even if returning early like here, the lock will be released.
+            return cache_path
+
+        if resume_download:
+            incomplete_path = cache_path + ".incomplete"
+
+            @contextmanager
+            def _resumable_file_manager():
+                with open(incomplete_path, "a+b") as f:
+                    yield f
+
+            temp_file_manager = _resumable_file_manager
+        else:
+            temp_file_manager = partial(tempfile.NamedTemporaryFile, dir=cache_dir,
+                                        delete=False)
+        # Download to temporary file, then copy to cache dir once finished.
+        # Otherwise you get corrupt cache entries if the download gets interrupted.
+        with temp_file_manager() as temp_file:
+            logger.info("%s not in cache or force_download=True, download to %s",
+                        url, temp_file.name)
+
+            http_get(url, temp_file)
+
+        logger.info("storing %s in cache at %s", url, cache_path)
+        os.replace(temp_file.name, cache_path)
+
+        logger.info("creating metadata file for %s", cache_path)
+        meta = {"url": url, "etag": etag}
+        meta_path = cache_path + ".json"
+        with open(meta_path, "w") as meta_file:
+            json.dump(meta, meta_file)
+    '''
     if not os.path.exists(cache_path):
         # Download to temporary file, then copy to cache dir once finished.
         # Otherwise you get corrupt cache entries if the download gets interrupted.
@@ -266,5 +349,5 @@ def get_from_cache(url, cache_dir=None):
                 meta_file.write(output_string)
 
             logger.info("removing temp file %s", temp_file.name)
-
+    '''
     return cache_path

tape/models/modeling_bert.py

@@ -39,7 +39,7 @@
 
 logger = logging.getLogger(__name__)
 
-URL_PREFIX = "https://s3.amazonaws.com/proteindata/pytorch-models/"
+URL_PREFIX = "https://s3.amazonaws.com/songlabdata/proteindata/pytorch-models/"
 BERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
     'bert-base': URL_PREFIX + "bert-base-pytorch_model.bin",
 }

tape/models/modeling_lstm.py

@@ -15,7 +15,7 @@
 logger = logging.getLogger(__name__)
 
 
-URL_PREFIX = "https://s3.amazonaws.com/proteindata/pytorch-models/"
+URL_PREFIX = "https://s3.amazonaws.com/songlabdata/proteindata/pytorch-models/"
 LSTM_PRETRAINED_CONFIG_ARCHIVE_MAP: typing.Dict[str, str] = {}
 LSTM_PRETRAINED_MODEL_ARCHIVE_MAP: typing.Dict[str, str] = {}
 

tape/models/modeling_trrosetta.py

@@ -5,7 +5,7 @@
 from .modeling_utils import ProteinConfig
 from .modeling_utils import ProteinModel
 
-URL_PREFIX = "https://s3.amazonaws.com/proteindata/pytorch-models/"
+URL_PREFIX = "https://s3.amazonaws.com/songlabdata/proteindata/pytorch-models/"
 TRROSETTA_PRETRAINED_MODEL_ARCHIVE_MAP = {
     'xaa': URL_PREFIX + "trRosetta-xaa-pytorch_model.bin",
     'xab': URL_PREFIX + "trRosetta-xab-pytorch_model.bin",

tape/models/modeling_unirep.py

@@ -15,7 +15,7 @@
 logger = logging.getLogger(__name__)
 
 
-URL_PREFIX = "https://s3.amazonaws.com/proteindata/pytorch-models/"
+URL_PREFIX = "https://s3.amazonaws.com/songlabdata/proteindata/pytorch-models/"
 UNIREP_PRETRAINED_CONFIG_ARCHIVE_MAP: typing.Dict[str, str] = {
     'babbler-1900': URL_PREFIX + 'unirep-base-config.json'}
 UNIREP_PRETRAINED_MODEL_ARCHIVE_MAP: typing.Dict[str, str] = {

tape/models/modeling_utils.py

@@ -39,7 +39,7 @@
 
 
 class ProteinConfig(object):
-    r""" Base class for all configuration classes.
+    """ Base class for all configuration classes.
         Handles a few parameters common to all models' configurations as well as methods
         for loading/downloading/saving configurations.
 
@@ -426,6 +426,14 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
                 Path to a directory in which a downloaded pre-trained model
                 configuration should be cached if the standard cache should not be used.
 
+            force_download: (`optional`) boolean, default False:
+                Force to (re-)download the model weights and configuration files and override
+                the cached versions if they exists.
+
+            resume_download: (`optional`) boolean, default False:
+                Do not delete incompletely recieved file. Attempt to resume the download if
+                such a file exists.
+
             output_loading_info: (`optional`) boolean:
                 Set to ``True`` to also return a dictionnary containing missing keys,
                 unexpected keys and error messages.
@@ -435,7 +443,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
                 initiate the model. (e.g. ``output_attention=True``). Behave differently
                 depending on whether a `config` is provided or automatically loaded:
 
-                - If a configuration is provided with ``config``, ``**kwargs`` will be
+                - If a configuration is provided with ``config``, ``**kwarg
                   directly passed to the underlying model's ``__init__`` method (we assume
                   all relevant updates to the configuration have already been done)
                 - If a configuration is not provided, ``kwargs`` will be first passed to the
@@ -462,11 +470,16 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
         cache_dir = kwargs.pop('cache_dir', None)
         output_loading_info = kwargs.pop('output_loading_info', False)
 
+        force_download = kwargs.pop("force_download", False)
+        kwargs.pop("resume_download", False)
+
         # Load config
         if config is None:
             config, model_kwargs = cls.config_class.from_pretrained(
                 pretrained_model_name_or_path, *model_args,
                 cache_dir=cache_dir, return_unused_kwargs=True,
+                # force_download=force_download,
+                # resume_download=resume_download,
                 **kwargs
             )
         else:
@@ -481,7 +494,8 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
             archive_file = pretrained_model_name_or_path
         # redirect to the cache, if necessary
         try:
-            resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
+            resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir,
+                                                force_download=force_download)
         except EnvironmentError:
             if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
                 logger.error(

tests/test_forceDownload.py

@@ -0,0 +1,25 @@
+import time
+import os
+from tape.models.file_utils import url_to_filename, get_cache, get_etag
+from tape import ProteinBertModel
+from tape import TAPETokenizer
+from tape.models.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
+import torch
+
+
+def test_forcedownload():
+    model = ProteinBertModel.from_pretrained('bert-base')
+    url = BERT_PRETRAINED_MODEL_ARCHIVE_MAP['bert-base']
+    filename = url_to_filename(url, get_etag(url))
+    wholepath = get_cache()/filename
+    oldtime = time.ctime(os.path.getmtime(wholepath))
+    model = ProteinBertModel.from_pretrained('bert-base', force_download=True)
+    newtime = time.ctime(os.path.getmtime(wholepath))
+    assert(newtime != oldtime)
+    # Deploy model
+    # iupac is the vocab for TAPE models, use unirep for the UniRep model
+    tokenizer = TAPETokenizer(vocab='iupac')
+    # Pfam Family: Hexapep, Clan: CL0536
+    sequence = 'GCTVEDRCLIGMGAILLNGCVIGSGSLVAAGALITQ'
+    token_ids = torch.tensor([tokenizer.encode(sequence)])
+    model(token_ids)