CE restricted & CCE loss

.github/workflows/deploy_docs.yml

@@ -19,6 +19,9 @@ concurrency:
 
 jobs:
   deploy_docs:
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
     runs-on: ubuntu-latest
     steps:
       - name: Checkout
@@ -41,9 +44,9 @@ jobs:
       - name: Setup Pages
         uses: actions/configure-pages@v4
       - name: Upload artifact
-        uses: actions/upload-pages-artifact@v3
+        uses: actions/upload-pages-artifact@v2
         with:
           path: 'docs/_build/html/'
       - name: Deploy to GitHub Pages
         id: deployment
-        uses: actions/deploy-pages@v4
+        uses: actions/deploy-pages@v3

.github/workflows/main.yml

@@ -51,7 +51,7 @@ jobs:
           toml-sort --check pyproject.toml
           ./poetry_wrapper.sh --experimental --generate
           toml-sort --check pyproject.toml
-
+          
   pytest_core:
     needs: static_tests
     runs-on: ubuntu-20.04
@@ -98,7 +98,7 @@ jobs:
           python -m venv venv
           . ./venv/bin/activate
           pip install --upgrade pip wheel poetry==1.5.1
-          ./poetry_wrapper.sh install -E torch-openvino
+          ./poetry_wrapper.sh install -E torch
       - name: pytest
         run: |
           . ./venv/bin/activate
@@ -299,4 +299,4 @@ jobs:
         run: |
           . ./venv/bin/activate
           export PACKAGE_SUFFIX=.preview
-          ./poetry_wrapper.sh --experimental build
+          ./poetry_wrapper.sh --experimental build

docs/pages/modules/models.rst

@@ -353,7 +353,7 @@ Neural Networks recommenders are Lightning-compatible. They can be trained using
 Bert4Rec
 ````````
 .. autoclass:: replay.models.nn.Bert4Rec
-   :members: __init__, predict
+   :members: __init__, predict_step
 
 SasRec
 ``````
@@ -372,8 +372,7 @@ SasRecCompiled
 
 Bert4RecCompiled
 ~~~~~~~~~~~~~~~~
-.. autoclass:: replay.models.nn.sequential.compiled.Bert4RecCompiled
-   :members: compile, predict
+TODO
 
 Features for easy training and validation with Lightning
 ________________________________________________________

replay/metrics/offline_metrics.py

@@ -156,13 +156,13 @@ def __init__(
     ):
         """
         :param metrics: (list of metrics): List of metrics to be calculated.
-        :param query_column:: (str): The name of the query column.
+        :param user_column: (str): The name of the user column.
             Note that you do not need to specify the value of this parameter for each metric separately.
             It is enough to specify the value of this parameter here once.
         :param item_column: (str): The name of the item column.
             Note that you do not need to specify the value of this parameter for each metric separately.
             It is enough to specify the value of this parameter here once.
-        :param rating_column: (str): The name of the rating column.
+        :param score_column: (str): The name of the score column.
             Note that you do not need to specify the value of this parameter for each metric separately.
             It is enough to specify the value of this parameter here once.
         :param category_column: (str): The name of the category column.

replay/models/base_rec.py

@@ -1162,11 +1162,10 @@ def get_features(
     ) -> Optional[Tuple[SparkDataFrame, int]]:
         """
         Returns query or item feature vectors as a Column with type ArrayType
-        If a model does not have a vector for some ids they are not present in the final result.
-
         :param ids: Spark DataFrame with unique ids
         :param features: Spark DataFrame with features for provided ids
         :return: feature vectors
+            If a model does not have a vector for some ids they are not present in the final result.
         """
         return self._get_features_wrap(ids, features)
 

replay/models/nn/optimizer_utils/__init__.py

@@ -2,3 +2,4 @@
 
 if TORCH_AVAILABLE:
     from .optimizer_factory import FatLRSchedulerFactory, FatOptimizerFactory, LRSchedulerFactory, OptimizerFactory
+    from .fused_linear_ce_loss import LigerFusedLinearCrossEntropyFunction