Makes RetrievalModelV2 support item tower with transforms (e.g. pre-t…

…rained embeddings) (NVIDIA-Merlin#1198)

* Making retrieval model to_top_k_model(), candidate_embeddings() and batch_predict() support Loader with transforms for pre-trained embeddings in item tower

* Fixing test error and ensuring all batch_predict() with the new API support Loader with transforms (which include pre-trained embeddings)

* Fixing retrieval example, which was using wrong schema to export query and item embeddings

* Added missing importorskip on torch and pytorch_lightning for torch integration tests

* Skiping a test if nvtabular is available

examples/05-Retrieval-Model.ipynb

@@ -1616,7 +1616,8 @@
     }
    ],
    "source": [
-    "queries = model.query_embeddings(Dataset(user_features, schema=schema), batch_size=1024, index=Tags.USER_ID)\n",
+    "queries = model.query_embeddings(Dataset(user_features, schema=schema.select_by_tag(Tags.USER)), \n",
+    "                                 batch_size=1024, index=Tags.USER_ID)\n",
     "query_embs_df = queries.compute(scheduler=\"synchronous\").reset_index()"
    ]
   },
@@ -1996,7 +1997,8 @@
     }
    ],
    "source": [
-    "item_embs = model.candidate_embeddings(Dataset(item_features, schema=schema), batch_size=1024, index=Tags.ITEM_ID)"
+    "item_embs = model.candidate_embeddings(Dataset(item_features, schema=schema.select_by_tag(Tags.ITEM)), \n",
+    "                                       batch_size=1024, index=Tags.ITEM_ID)"
    ]
   },
   {
@@ -2460,7 +2462,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.8.10"
   },
   "merlin": {
    "containers": [

merlin/models/tf/core/encoder.py

@@ -27,6 +27,7 @@
 from merlin.models.tf.core.prediction import TopKPrediction
 from merlin.models.tf.inputs.base import InputBlockV2
 from merlin.models.tf.inputs.embedding import CombinerType, EmbeddingTable
+from merlin.models.tf.loader import Loader
 from merlin.models.tf.models.base import BaseModel, get_output_schema
 from merlin.models.tf.outputs.topk import TopKOutput
 from merlin.models.tf.transforms.features import PrepareFeatures
@@ -84,7 +85,7 @@ def __init__(
 
     def encode(
         self,
-        dataset: merlin.io.Dataset,
+        dataset: Union[merlin.io.Dataset, Loader],
         index: Union[str, ColumnSchema, Schema, Tags],
         batch_size: int,
         **kwargs,
@@ -93,7 +94,7 @@ def encode(
 
         Parameters
         ----------
-        dataset: merlin.io.Dataset
+        dataset: Union[merlin.io.Dataset, merlin.models.tf.loader.Loader]
             The dataset to encode.
         index: Union[str, ColumnSchema, Schema, Tags]
             The index to use for encoding.
@@ -127,7 +128,7 @@ def encode(
 
     def batch_predict(
         self,
-        dataset: merlin.io.Dataset,
+        dataset: Union[merlin.io.Dataset, Loader],
         batch_size: int,
         output_schema: Optional[Schema] = None,
         index: Optional[Union[str, ColumnSchema, Schema, Tags]] = None,
@@ -137,8 +138,8 @@ def batch_predict(
 
         Parameters
         ----------
-        dataset: merlin.io.Dataset
-            Dataset to predict on.
+        dataset: Union[merlin.io.Dataset, merlin.models.tf.loader.Loader]
+            Dataset or Loader to predict on.
         batch_size: int
             Batch size to use for prediction.
 
@@ -161,18 +162,35 @@ def batch_predict(
                 raise ValueError("Only one column can be used as index")
             index = index.first.name
 
+        dataset_schema = None
         if hasattr(dataset, "schema"):
-            if not set(self.schema.column_names).issubset(set(dataset.schema.column_names)):
+            dataset_schema = dataset.schema
+            data_output_schema = dataset_schema
+            if isinstance(dataset, Loader):
+                data_output_schema = dataset.output_schema
+            if not set(self.schema.column_names).issubset(set(data_output_schema.column_names)):
                 raise ValueError(
                     f"Model schema {self.schema.column_names} does not match dataset schema"
-                    + f" {dataset.schema.column_names}"
+                    + f" {data_output_schema.column_names}"
                 )
 
+        loader_transforms = None
+        if isinstance(dataset, Loader):
+            loader_transforms = dataset.transforms
+            batch_size = dataset.batch_size
+            dataset = dataset.dataset
+
         # Check if merlin-dataset is passed
         if hasattr(dataset, "to_ddf"):
             dataset = dataset.to_ddf()
 
-        model_encode = TFModelEncode(self, batch_size=batch_size, **kwargs)
+        model_encode = TFModelEncode(
+            self,
+            batch_size=batch_size,
+            loader_transforms=loader_transforms,
+            schema=dataset_schema,
+            **kwargs,
+        )
 
         encode_kwargs = {}
         if output_schema:
@@ -583,7 +601,7 @@ def encode_candidates(
 
     def batch_predict(
         self,
-        dataset: merlin.io.Dataset,
+        dataset: Union[merlin.io.Dataset, Loader],
         batch_size: int,
         output_schema: Optional[Schema] = None,
         **kwargs,
@@ -592,8 +610,8 @@ def batch_predict(
 
         Parameters
         ----------
-        dataset : merlin.io.Dataset
-            Raw queries features dataset
+        dataset : Union[merlin.io.Dataset, merlin.models.tf.loader.Loader]
+            Raw queries features dataset or Loader
         batch_size : int
             The number of queries to process at each prediction step
         output_schema: Schema, optional
@@ -606,15 +624,24 @@ def batch_predict(
         """
         from merlin.models.tf.utils.batch_utils import TFModelEncode
 
+        loader_transforms = None
+        if isinstance(dataset, Loader):
+            loader_transforms = dataset.transforms
+            batch_size = dataset.batch_size
+            dataset = dataset.dataset
+
+        dataset_schema = dataset.schema
+        dataset = dataset.to_ddf()
+
         model_encode = TFModelEncode(
             model=self,
             batch_size=batch_size,
+            loader_transforms=loader_transforms,
+            schema=dataset_schema,
             output_names=TopKPrediction.output_names(self.k),
             **kwargs,
         )
 
-        dataset = dataset.to_ddf()
-
         encode_kwargs = {}
         if output_schema:
             encode_kwargs["filter_input_columns"] = output_schema.column_names

merlin/models/tf/models/base.py

@@ -1553,7 +1553,7 @@ def predict(
         return out
 
     def batch_predict(
-        self, dataset: merlin.io.Dataset, batch_size: int, **kwargs
+        self, dataset: Union[merlin.io.Dataset, Loader], batch_size: int, **kwargs
     ) -> merlin.io.Dataset:
         """Batched prediction using the Dask.
         Parameters
@@ -1565,20 +1565,38 @@ def batch_predict(
         Returns merlin.io.Dataset
         -------
         """
+        dataset_schema = None
         if hasattr(dataset, "schema"):
-            if not set(self.schema.column_names).issubset(set(dataset.schema.column_names)):
+            dataset_schema = dataset.schema
+            data_output_schema = dataset_schema
+            if isinstance(dataset, Loader):
+                data_output_schema = dataset.output_schema
+
+            if not set(self.schema.column_names).issubset(set(data_output_schema.column_names)):
                 raise ValueError(
                     f"Model schema {self.schema.column_names} does not match dataset schema"
-                    + f" {dataset.schema.column_names}"
+                    + f" {data_output_schema.column_names}"
                 )
 
+        loader_transforms = None
+        if isinstance(dataset, Loader):
+            loader_transforms = dataset.transforms
+            batch_size = dataset.batch_size
+            dataset = dataset.dataset
+
         # Check if merlin-dataset is passed
         if hasattr(dataset, "to_ddf"):
             dataset = dataset.to_ddf()
 
         from merlin.models.tf.utils.batch_utils import TFModelEncode
 
-        model_encode = TFModelEncode(self, batch_size=batch_size, **kwargs)
+        model_encode = TFModelEncode(
+            self,
+            batch_size=batch_size,
+            loader_transforms=loader_transforms,
+            schema=dataset_schema,
+            **kwargs,
+        )
 
         # Processing a sample of the dataset with the model encoder
         # to get the output dataframe dtypes
@@ -2510,20 +2528,20 @@ def query_embeddings(
 
     def candidate_embeddings(
         self,
-        dataset: Optional[merlin.io.Dataset] = None,
+        data: Optional[Union[merlin.io.Dataset, Loader]] = None,
         index: Optional[Union[str, ColumnSchema, Schema, Tags]] = None,
         **kwargs,
     ) -> merlin.io.Dataset:
         if self.has_candidate_encoder:
             candidate = self.candidate_encoder
 
-            if dataset is not None and hasattr(candidate, "encode"):
-                return candidate.encode(dataset, index=index, **kwargs)
+            if data is not None and hasattr(candidate, "encode"):
+                return candidate.encode(data, index=index, **kwargs)
 
             if hasattr(candidate, "to_dataset"):
                 return candidate.to_dataset(**kwargs)
 
-            return candidate.encode(dataset, index=index, **kwargs)
+            return candidate.encode(data, index=index, **kwargs)
 
         if isinstance(self.last, (ContrastiveOutput, CategoricalOutput)):
             return self.last.to_dataset()

merlin/models/tf/utils/batch_utils.py

@@ -74,6 +74,7 @@ def __init__(
         block_load_func: tp.Optional[tp.Callable[[str], Block]] = None,
         schema: tp.Optional[Schema] = None,
         output_concat_func=None,
+        loader_transforms=None,
     ):
         save_path = save_path or tempfile.mkdtemp()
         model.save(save_path)
@@ -95,7 +96,9 @@ def __init__(
         super().__init__(
             save_path,
             output_names,
-            data_iterator_func=data_iterator_func(self.schema, batch_size=batch_size),
+            data_iterator_func=data_iterator_func(
+                self.schema, batch_size=batch_size, loader_transforms=loader_transforms
+            ),
             model_load_func=model_load_func,
             model_encode_func=model_encode,
             output_concat_func=output_concat_func,
@@ -172,14 +175,15 @@ def encode_output(output: tf.Tensor):
     return output.numpy()
 
 
-def data_iterator_func(schema, batch_size: int = 512):
+def data_iterator_func(schema, batch_size: int = 512, loader_transforms=None):
     import merlin.io.dataset
 
     def data_iterator(dataset):
         return Loader(
             merlin.io.dataset.Dataset(dataset, schema=schema),
             batch_size=batch_size,
             shuffle=False,
+            transforms=loader_transforms,
         )
 
     return data_iterator

tests/integration/torch/__init__.py

@@ -0,0 +1,19 @@
+#
+# Copyright (c) 2021, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import pytest
+
+pytest.importorskip("torch")
+pytest.importorskip("pytorch_lightning")

tests/unit/tf/models/test_retrieval.py

@@ -1,11 +1,12 @@
 from pathlib import Path
 
-import nvtabular as nvt
+import numpy as np
 import pytest
 import tensorflow as tf
 
 import merlin.models.tf as mm
 from merlin.core.dispatch import make_df
+from merlin.dataloader.ops.embeddings import EmbeddingOperator
 from merlin.io import Dataset
 from merlin.models.tf.metrics.topk import (
     AvgPrecisionAt,
@@ -24,6 +25,8 @@
 
 
 def test_two_tower_shared_embeddings():
+    nvt = pytest.importorskip("nvtabular")
+
     train = make_df(
         {
             "user_id": [1, 3, 3, 4, 3, 1, 2, 4, 6, 7, 8, 9] * 100,
@@ -435,6 +438,66 @@ def test_two_tower_model_topk_evaluation(ecommerce_data: Dataset, run_eagerly):
     assert all([metric >= 0 for metric in metrics.values()])
 
 
+@pytest.mark.parametrize("run_eagerly", [True, False])
+def test_two_tower_model_topk_evaluation_with_pretrained_emb(music_streaming_data, run_eagerly):
+    music_streaming_data.schema = music_streaming_data.schema.select_by_tag([Tags.USER, Tags.ITEM])
+
+    cardinality = music_streaming_data.schema["item_category"].int_domain.max + 1
+    pretrained_embedding = np.random.rand(cardinality, 12)
+
+    loader_transforms = [
+        EmbeddingOperator(
+            pretrained_embedding,
+            lookup_key="item_category",
+            embedding_name="pretrained_category_embeddings",
+        ),
+    ]
+    loader = mm.Loader(
+        music_streaming_data,
+        schema=music_streaming_data.schema.select_by_tag([Tags.USER, Tags.ITEM]),
+        batch_size=10,
+        transforms=loader_transforms,
+    )
+    schema = loader.output_schema
+
+    pretrained_embeddings = mm.PretrainedEmbeddings(
+        schema.select_by_tag(Tags.EMBEDDING),
+        output_dims=16,
+    )
+
+    schema = loader.output_schema
+
+    query_input = mm.InputBlockV2(schema.select_by_tag(Tags.USER))
+    query = mm.Encoder(query_input, mm.MLPBlock([4], no_activation_last_layer=True))
+    candidate_input = mm.InputBlockV2(
+        schema.select_by_tag(Tags.ITEM), pretrained_embeddings=pretrained_embeddings
+    )
+    candidate = mm.Encoder(candidate_input, mm.MLPBlock([4], no_activation_last_layer=True))
+    model = mm.TwoTowerModelV2(
+        query,
+        candidate,
+        negative_samplers=["in-batch"],
+    )
+    model.compile(optimizer="adam", run_eagerly=run_eagerly)
+    _ = testing_utils.model_test(model, loader)
+
+    # Top-K evaluation
+    candidate_features_data = unique_rows_by_features(music_streaming_data, Tags.ITEM, Tags.ITEM_ID)
+    loader_candidates = mm.Loader(
+        candidate_features_data,
+        batch_size=16,
+        transforms=loader_transforms,
+    )
+
+    topk_model = model.to_top_k_encoder(loader_candidates, k=20, batch_size=16)
+    topk_model.compile(run_eagerly=run_eagerly)
+
+    loader = mm.Loader(music_streaming_data, batch_size=32).map(mm.ToTarget(schema, "item_id"))
+
+    metrics = topk_model.evaluate(loader, return_dict=True)
+    assert all([metric >= 0 for metric in metrics.values()])
+
+
 @pytest.mark.parametrize("run_eagerly", [True, False])
 @pytest.mark.parametrize("logits_pop_logq_correction", [True, False])
 @pytest.mark.parametrize("loss", ["categorical_crossentropy", "bpr-max", "binary_crossentropy"])