Add Hugging Face integration tests

.github/workflows/test-hf-integration.yml

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+name: HF integration tests
+
+on:
+  schedule:
+    - cron:  '0 9 * * 0'  # every sunday at 9:00 UTC
+  workflow_dispatch:
+
+jobs:
+  hf-integration-tests:
+
+    runs-on: ubuntu-latest
+    if: "github.repository == 'skorch-dev/skorch'"
+
+    timeout-minutes: 10
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python
+      uses: actions/setup-python@v3
+    - name: Install Requirements
+      run: |
+        python -m pip install -r requirement.txt
+        python -m pip install transformers tokenizers huggingface_hub torch
+        python -m pip install .
+    - name: run HF integration test
+      env:
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+      run: python scripts/hf-integration-tests.py

scripts/hf-integration-tests.py

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+"""Run integration tests with Hugging Face platform
+
+See accompanying test-hf-integration.yml GH workflow file.
+
+Specifically, this tests:
+
+- loading a pre-trained tokenizer
+- loading a pre-trained transformer model
+- saving model (pickle and pytorch) on the model hub
+
+Trigger:
+
+- trigger the action manually at https://github.com/skorch-dev/skorch/actions
+- the action runs automatically according to the cron schedule defined in the GH
+  action
+
+Initial setup (only needs to be done once):
+
+- create a user on https://huggingface.co
+- create the organization 'skorch-tests'
+- add HF_TOKEN as a secret token to the GitHub repo (unfortunately, this has to
+  be a user token, the orga token doesn't have sufficent permissions); now it is
+  available in the GH action as ${{ secrets.HF_TOKEN }}
+
+This script will automatically create a private repo on HF for testing and
+delete it at the end of the tests.
+
+"""
+
+import os
+import pickle
+from contextlib import contextmanager
+
+import numpy as np
+import torch
+from huggingface_hub import HfApi, create_repo, hf_hub_download
+from sklearn.datasets import fetch_20newsgroups, make_classification
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import Pipeline
+from torch import nn
+from transformers import AutoModelForSequenceClassification
+
+from skorch import NeuralNetClassifier
+from skorch.hf import HuggingfacePretrainedTokenizer
+from skorch.callbacks import TrainEndCheckpoint
+from skorch.hf import HfHubStorage
+
+
+# Choose a tokenizer and BERT model that work together
+TOKENIZER = "distilbert-base-uncased"
+PRETRAINED_MODEL = "distilbert-base-uncased"
+
+# bert model hyper-parameters
+OPTMIZER = torch.optim.AdamW
+LR = 5e-5
+MAX_EPOCHS = 2
+CRITERION = nn.CrossEntropyLoss
+BATCH_SIZE = 8
+N_SAMPLES = 100
+
+# device
+DEVICE = 'cpu'
+
+# token with permissions to write to the orga on HF Hub
+TOKEN = os.environ['HF_TOKEN']
+REPO_NAME = 'skorch-tests/test-skorch-hf-ci'
+MODEL_NAME = 'skorch-model.pkl'
+WEIGHTS_NAME = 'weights.pt'
+
+
+#######################################
+# TESTING TRANSFORMERS AND TOKENIZERS #
+#######################################
+
+class BertModule(nn.Module):
+    def __init__(self, name, num_labels):
+        super().__init__()
+        self.name = name
+        self.num_labels = num_labels
+
+        self.reset_weights()
+
+    def reset_weights(self):
+        self.bert = AutoModelForSequenceClassification.from_pretrained(
+            self.name, num_labels=self.num_labels
+        )
+
+    def forward(self, **kwargs):
+        pred = self.bert(**kwargs)
+        return pred.logits
+
+
+def load_20newsgroup_small():
+    dataset = fetch_20newsgroups()
+    y = dataset.target
+    mask_0_or_1 = (y == 0) | (y == 1)
+    X = np.asarray(dataset.data)[mask_0_or_1][:N_SAMPLES]
+    y = dataset.target[mask_0_or_1][:N_SAMPLES]
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, stratify=y, random_state=0
+    )
+    return X_train, X_test, y_train, y_test
+
+
+def get_transformer_model():
+    return Pipeline([
+        ('tokenizer', HuggingfacePretrainedTokenizer(TOKENIZER)),
+        ('net', NeuralNetClassifier(
+            BertModule,
+            module__name=PRETRAINED_MODEL,
+            module__num_labels=2,
+            optimizer=OPTMIZER,
+            lr=LR,
+            max_epochs=MAX_EPOCHS,
+            criterion=CRITERION,
+            batch_size=BATCH_SIZE,
+            iterator_train__shuffle=True,
+            device=DEVICE,
+        )),
+    ])
+
+
+def test_tokenizers_transfomers():
+    print("Testing tokenizers and transfomers started")
+    torch.manual_seed(0)
+    np.random.seed(0)
+    X_train, X_test, y_train, y_test = load_20newsgroup_small()
+
+    pipeline = get_transformer_model()
+    pipeline.fit(X_train, y_train)
+
+    with torch.inference_mode():
+        y_pred = pipeline.predict(X_test)
+
+    assert accuracy_score(y_test, y_pred) > 0.7
+    print("Testing tokenizers and transfomers completed")
+
+
+########################
+# TESTING HF MODEL HUB #
+########################
+
+class ClassifierModule(nn.Module):
+    def __init__(
+            self,
+            num_units=30,
+            nonlin=nn.ReLU(),
+            dropout=0.5,
+    ):
+        super().__init__()
+        self.num_units = num_units
+        self.nonlin = nonlin
+        self.dropout = dropout
+
+        self.dense0 = nn.Linear(20, num_units)
+        self.nonlin = nonlin
+        self.dropout = nn.Dropout(dropout)
+        self.dense1 = nn.Linear(num_units, num_units)
+        self.output = nn.Linear(num_units, 2)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, X, **kwargs):
+        X = self.nonlin(self.dense0(X))
+        X = self.dropout(X)
+        X = self.nonlin(self.dense1(X))
+        X = self.softmax(self.output(X))
+        return X
+
+
+def get_classification_data():
+    X, y = make_classification(1000, 20, n_informative=10, random_state=0)
+    X, y = X.astype(np.float32), y.astype(np.int64)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+    return X_train, X_test, y_train, y_test
+
+
+@contextmanager
+def temporary_hf_repo():
+    print(f"Creating repo '{REPO_NAME}' on HF Hub")
+    create_repo(
+        REPO_NAME,
+        private=True,
+        token=TOKEN,
+        exist_ok=True,
+    )
+    try:
+        yield
+    finally:
+        HfApi().delete_repo(REPO_NAME, token=TOKEN)
+        print(f"Deleted repo '{REPO_NAME}' from HF Hub")
+
+
+def test_hf_model_hub():
+    print("Testing HF model hub started")
+    torch.manual_seed(0)
+    np.random.seed(0)
+    X_train, X_test, y_train, y_test = get_classification_data()
+
+    hf_api = HfApi()
+    hub_pickle_storer = HfHubStorage(
+        hf_api,
+        path_in_repo=MODEL_NAME,
+        repo_id=REPO_NAME,
+        token=TOKEN,
+        verbose=1,
+    )
+    hub_params_storer = HfHubStorage(
+        hf_api,
+        path_in_repo=WEIGHTS_NAME,
+        repo_id=REPO_NAME,
+        token=TOKEN,
+        verbose=1,
+        local_storage='my-model-weights.pt',
+    )
+    checkpoint = TrainEndCheckpoint(
+        f_pickle=hub_pickle_storer,
+        f_params=hub_params_storer,
+        f_optimizer=None,
+        f_criterion=None,
+        f_history=None,
+    )
+
+    net = NeuralNetClassifier(
+        ClassifierModule,
+        lr=0.1,
+        device=DEVICE,
+        iterator_train__shuffle=True,
+        callbacks=[checkpoint],
+    )
+
+    net.fit(X_train, y_train)
+    assert accuracy_score(y_test, net.predict(X_test)) > 0.7
+    print(hub_pickle_storer.latest_url_)
+
+    path = hf_hub_download(REPO_NAME, MODEL_NAME, use_auth_token=TOKEN)
+    with open(path, 'rb') as f:
+        net_loaded = pickle.load(f)
+
+    with torch.inference_mode():
+        assert np.allclose(net.predict_proba(X_test), net_loaded.predict_proba(X_test))
+
+    path = hf_hub_download(REPO_NAME, WEIGHTS_NAME, use_auth_token=TOKEN)
+    with open(path, 'rb') as f:
+        weights_loaded = torch.load(f)
+
+    weights = dict(net.module_.named_parameters())
+    assert weights.keys() == weights_loaded.keys()
+    for key in weights.keys():
+        torch.testing.assert_close(weights[key], weights[key])
+
+    print("Testing HF model hub completed")
+
+
+def main():
+    exceptions = []
+
+    try:
+        with temporary_hf_repo():
+            test_hf_model_hub()
+    except Exception as exc:
+        exceptions.append(exc)
+
+    try:
+        test_tokenizers_transfomers()
+    except Exception as exc:
+        exceptions.append(exc)
+
+    if exceptions:
+        print("encountered the following exceptions:")
+        for exc in exceptions:
+            print(exc)
+        raise RuntimeError(f"{len(exceptions)} of 3 tests failed")
+    print("All tests succeeded")
+
+
+if __name__ == '__main__':
+    main()