Updates Models to support new dataloader format for lists (__values and __offsets in dict) and scalar (1D)

.github/workflows/tensorflow.yml

@@ -43,12 +43,14 @@ jobs:
           fi
           pip install "pandas>=1.2.0,<1.4.0dev0"
           pip install "NVTabular@git+https://github.com/NVIDIA-Merlin/NVTabular.git@$branch"
-          pip install "merlin-dataloader@git+https://github.com/NVIDIA-Merlin/dataloader.git@$branch"
+          #pip install "merlin-dataloader@git+https://github.com/NVIDIA-Merlin/dataloader.git@$branch"
+          pip install merlin-dataloader@git+https://github.com/bschifferer/dataloader.git@change_output
           pip install "merlin-core@git+https://github.com/NVIDIA-Merlin/core.git@$branch"
       - name: Install dependencies
         run: |
           python -m pip install "tensorflow${{ matrix.tensorflow-version }}"
           python -m pip install .[tensorflow-dev]
+          pip install merlin-dataloader@git+https://github.com/bschifferer/dataloader.git@change_output
       - name: Build
         run: |
           python setup.py develop
@@ -98,12 +100,14 @@ jobs:
           fi
           pip install "pandas>=1.2.0,<1.4.0dev0"
           pip install "NVTabular@git+https://github.com/NVIDIA-Merlin/NVTabular.git@$branch"
-          pip install "merlin-dataloader@git+https://github.com/NVIDIA-Merlin/dataloader.git@$branch"
+          #pip install "merlin-dataloader@git+https://github.com/NVIDIA-Merlin/dataloader.git@$branch"
+          pip install merlin-dataloader@git+https://github.com/bschifferer/dataloader.git@change_output
           pip install "merlin-core@git+https://github.com/NVIDIA-Merlin/core.git@$branch"
       - name: Install dependencies
         run: |
           python -m pip install "tensorflow${{ matrix.tensorflow-version }}"
           python -m pip install .[tensorflow-dev]
+          pip install merlin-dataloader@git+https://github.com/bschifferer/dataloader.git@change_output
       - name: Build
         run: |
           python setup.py develop

docs/source/api.rst

@@ -145,9 +145,8 @@ Transformation Block Constructors
 
    CategoryEncoding
    MapValues
-   ListToDense
-   ListToRagged
-   ListToSparse
+   PrepareListFeatures
+   PrepareFeatures
    ToSparse
    ToDense
    ToTarget

examples/usecases/ecommerce-session-based-next-item-prediction-for-fashion.ipynb

@@ -1704,7 +1704,7 @@
     }
    ],
    "source": [
-    "batch = mm.sample_batch(train, batch_size=128, include_targets=False, to_ragged=True)"
+    "batch = mm.sample_batch(train, batch_size=128, include_targets=False, prepare_features=True)"
    ]
   },
   {
@@ -2093,7 +2093,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.8.10 ('merlin_22.07_dev')",
    "language": "python",
    "name": "python3"
   },
@@ -2111,7 +2111,7 @@
   },
   "vscode": {
    "interpreter": {
-    "hash": "ab403bb43341787581f43b51cdd291d61392c89ddb0f92179de653921d4e05db"
+    "hash": "67b01b24cb2518309f0749863665ff82dad1ad60adc88cabbb59c99b73117545"
    }
   }
  },

examples/usecases/transformers-next-item-prediction.ipynb

@@ -319,8 +319,11 @@
     "    >> ops.Rename(name=\"weekday_checkout\")\n",
     ")\n",
     "\n",
+    "categorical_features = (['city_id', 'booker_country', 'hotel_country'] +\n",
+    "                         weekday_checkin + weekday_checkout\n",
+    "                       ) >> ops.Categorify(start_index=1)  \n",
     "\n",
-    "groupby_features = ['city_id', 'booker_country', 'utrip_id', 'hotel_country', 'checkin'] + weekday_checkin + weekday_checkout >> ops.Groupby(\n",
+    "groupby_features = categorical_features + ['utrip_id', 'checkin'] >> ops.Groupby(\n",
     "    groupby_cols=['utrip_id'],\n",
     "    aggs={\n",
     "        'city_id': ['list', 'count'],\n",
@@ -332,16 +335,16 @@
     "    sort_cols=\"checkin\"\n",
     ")\n",
     "\n",
-    "groupby_features_city = groupby_features['city_id_list'] >> ops.Categorify() >> ops.AddTags([Tags.SEQUENCE, Tags.ITEM, Tags.ITEM_ID])\n",
-    "groupby_features_country = (\n",
-    "    groupby_features['booker_country_list', 'hotel_country_list', 'weekday_checkin_list', 'weekday_checkout_list']\n",
-    "    >> ops.Categorify() >> ops.AddTags([Tags.SEQUENCE, Tags.ITEM])\n",
+    "list_features = (\n",
+    "            groupby_features['city_id_list', 'booker_country_list', 'hotel_country_list', \n",
+    "                 'weekday_checkin_list', 'weekday_checkout_list'\n",
+    "            ] >> ops.AddTags([Tags.SEQUENCE])\n",
     ")\n",
-    "city_id_count = groupby_features['city_id_count'] >> ops.AddTags([Tags.CONTEXT, Tags.ITEM, Tags.CONTINUOUS])\n",
     "\n",
     "# Filter out sessions with less than 2 interactions \n",
     "MINIMUM_SESSION_LENGTH = 2\n",
-    "filtered_sessions = groupby_features_city + groupby_features_country + city_id_count >> ops.Filter(f=lambda df: df[\"city_id_count\"] >= MINIMUM_SESSION_LENGTH) "
+    "features = list_features + (groupby_features['city_id_count'] >>  ops.AddTags([Tags.CONTINUOUS]))\n",
+    "filtered_sessions = features >> ops.Filter(f=lambda df: df[\"city_id_count\"] >= MINIMUM_SESSION_LENGTH) "
    ]
   },
   {
@@ -806,7 +809,7 @@
      "text": [
       "/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].\n",
       "  warnings.warn(\n",
-      "/usr/local/lib/python3.8/dist-packages/keras/initializers/initializers_v2.py:120: UserWarning: The initializer TruncatedNormal is unseeded and being called multiple times, which will return identical values  each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initalizer instance more than once.\n",
+      "/usr/local/lib/python3.8/dist-packages/keras/initializers/initializers_v2.py:120: UserWarning: The initializer TruncatedNormal is unseeded and being called multiple times, which will return identical values  each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
       "  warnings.warn(\n",
       "2023-02-08 13:17:18.083919: I tensorflow/stream_executor/cuda/cuda_blas.cc:1633] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n",
       "2023-02-08 13:17:18.254522: I tensorflow/stream_executor/cuda/cuda_dnn.cc:424] Loaded cuDNN version 8700\n"
@@ -957,7 +960,7 @@
     "model.evaluate(\n",
     "    validation_set_processed,\n",
     "    batch_size=128,\n",
-    "    pre=mm.SequenceMaskLast(schema=validation_set_processed.schema, target=target),\n",
+    "    pre=mm.SequenceMaskLast(schema=seq_schema, target=target),\n",
     "    return_dict=True\n",
     ")"
    ]

merlin/datasets/entertainment/movielens/1m/schema.pbtxt

@@ -53,8 +53,7 @@ feature {
 feature {
   name: "genres"
   value_count {
-    min: 1
-    max: 6
+    min: 1    
   }
   type: INT
   int_domain {

merlin/datasets/entertainment/music_streaming/schema.json

@@ -10,7 +10,6 @@
       },
       "annotation": {
         "tag": [
-          "categorical",
           "session_id"
         ]
       }
@@ -63,8 +62,7 @@
     {
       "name": "item_genres",
       "valueCount": {
-        "min": "1",
-        "max": "20"
+        "min": "1"
       },
       "type": "INT",
       "intDomain": {
@@ -127,8 +125,7 @@
     {
       "name": "user_genres",
       "valueCount": {
-        "min": "1",
-        "max": "20"
+        "min": "1"
       },
       "type": "INT",
       "intDomain": {

merlin/datasets/synthetic.py

@@ -19,13 +19,13 @@
 import pathlib
 from pathlib import Path
 from random import randint
-from typing import Dict, Optional, Sequence, Tuple, Union
+from typing import Dict, Sequence, Tuple, Union
 
 import numpy as np
 
 import merlin.io
 from merlin.models.utils import schema_utils
-from merlin.schema import ColumnSchema, Schema, Tags
+from merlin.schema import Schema, Tags
 from merlin.schema.io.tensorflow_metadata import TensorflowMetadata
 
 LOG = logging.getLogger("merlin-models")
@@ -92,9 +92,13 @@ def generate_data(
         Example::
             train, valid = generate_data(input, 10000, (0.8, 0.2))
     min_session_length: int
-        The minimum number of events in a session.
+        The minimum number of events in a session. Overrides the
+        min sequence length information from the shape of list columns
+        schema (schema[col].shape.dims[1].min)
     max_session_length: int
-        The maximum number of events in a session.
+        The minimum number of events in a session. Overrides the
+        max sequence length information from the shape of list columns
+        schema (schema[col].shape.dims[1].max)
     device: str
         The device to use for the data generation.
         Supported values: {'cpu', 'gpu'}
@@ -119,23 +123,15 @@ def generate_data(
         raise ValueError(f"Unknown input type: {type(input)}")
 
     for col in schema.column_names:
-        if not schema[col].is_list:
-            continue
-        new_properties = schema[col].properties
-        new_properties["value_count"] = {"min": min_session_length}
-        if max_session_length:
-            new_properties["value_count"]["max"] = max_session_length
-        schema[col] = ColumnSchema(
-            name=schema[col].name,
-            tags=schema[col].tags,
-            properties=new_properties,
-            dtype=schema[col].dtype,
-            is_list=True,
-        )
+        if schema[col].shape.is_list:
+            min_session_length = min_session_length or schema[col].shape.dims[1].min
+            max_session_length = max_session_length or schema[col].shape.dims[1].max
+            # Overriding min and max session length from schema
+            schema[col] = schema[col].with_shape(
+                ((0, None), (min_session_length, max_session_length))
+            )
 
-    df = generate_user_item_interactions(
-        schema, num_rows, min_session_length, max_session_length, device=device
-    )
+    df = generate_user_item_interactions(schema, num_rows, device=device)
 
     if list(set_sizes) != [1.0]:
         num_rows = df.shape[0]
@@ -156,8 +152,6 @@ def generate_data(
 def generate_user_item_interactions(
     schema: Schema,
     num_interactions: int,
-    min_session_length: int = 5,
-    max_session_length: Optional[int] = None,
     device: str = "cpu",
 ):
     """
@@ -177,10 +171,6 @@ def generate_user_item_interactions(
         schema object describing the columns to generate.
     num_interactions: int
         number of interaction rows to generate.
-    max_session_length: Optional[int]
-        The maximum length of the multi-hot/sequence features
-    min_session_length: int
-        The minimum length of the multi-hot/sequence features
     device: str
         device to use for generating data.
 
@@ -215,8 +205,6 @@ def generate_user_item_interactions(
             data,
             features,
             session_id_col,
-            min_session_length=min_session_length,
-            max_session_length=max_session_length,
             device=device,
         )
         processed_cols += [f.name for f in features] + [session_id_col.name]
@@ -235,8 +223,6 @@ def generate_user_item_interactions(
             data,
             features,
             user_id_col,
-            min_session_length=min_session_length,
-            max_session_length=max_session_length,
             device=device,
         )
         processed_cols += [f.name for f in features] + [user_id_col.name]
@@ -247,11 +233,12 @@ def generate_user_item_interactions(
         raise ValueError("Item ID column is required")
     item_id_col = item_schema.first
 
-    is_list_feature = item_id_col.is_list
+    is_list_feature = item_id_col.shape.is_list
     if not is_list_feature:
         shape = num_interactions
     else:
-        shape = (num_interactions, max_session_length or min_session_length)  # type: ignore
+        seq_length = item_id_col.shape.dims[1].max or item_id_col.shape.dims[1].min
+        shape = (num_interactions, seq_length)  # type: ignore
     tmp = _array.clip(
         _array.random.lognormal(3.0, 1.0, shape).astype(_array.int32),
         1,
@@ -262,14 +249,7 @@ def generate_user_item_interactions(
     else:
         data[item_id_col.name] = list(tmp)
     features = list(schema.select_by_tag(Tags.ITEM).remove_by_tag(Tags.ITEM_ID))
-    data = generate_conditional_features(
-        data,
-        features,
-        item_id_col,
-        min_session_length=min_session_length,
-        max_session_length=max_session_length,
-        device=device,
-    )
+    data = generate_conditional_features(data, features, item_id_col, device=device)
     processed_cols += [f.name for f in features] + [item_id_col.name]
 
     # Get remaining features
@@ -284,9 +264,7 @@ def generate_user_item_interactions(
         is_int_feature = feature.dtype and np.issubdtype(feature.dtype.to_numpy, np.integer)
         is_list_feature = feature.is_list
         if is_list_feature:
-            data[feature.name] = generate_random_list_feature(
-                feature, num_interactions, min_session_length, max_session_length, device
-            )
+            data[feature.name] = generate_random_list_feature(feature, num_interactions, device)
 
         elif is_int_feature:
             domain = feature.int_domain
@@ -311,8 +289,6 @@ def generate_conditional_features(
     data,
     features,
     parent_feature,
-    min_session_length: int = 5,
-    max_session_length: Optional[int] = None,
     device="cpu",
 ):
     """
@@ -331,9 +307,7 @@ def generate_conditional_features(
         is_list_feature = feature.is_list
 
         if is_list_feature:
-            data[feature.name] = generate_random_list_feature(
-                feature, num_interactions, min_session_length, max_session_length, device
-            )
+            data[feature.name] = generate_random_list_feature(feature, num_interactions, device)
 
         elif is_int_feature:
             if not feature.int_domain:
@@ -364,15 +338,16 @@ def generate_conditional_features(
 def generate_random_list_feature(
     feature,
     num_interactions,
-    min_session_length: int = 5,
-    max_session_length: Optional[int] = None,
     device="cpu",
 ):
     if device == "cpu":
         import numpy as _array
     else:
         import cupy as _array
 
+    seq_length_dim = feature.shape.dims[1]
+    min_session_length, max_session_length = seq_length_dim.min, seq_length_dim.max
+
     is_int_feature = np.issubdtype(feature.dtype.to_numpy, np.integer)
     if is_int_feature:
         if max_session_length: