Authoring Aware ROO on LTV ReAgent model (#742)

Fangyu Luo · facebook-github-bot · commit deae6c2cc2cd · 2022-10-25T12:58:55.000-07:00
Summary: X-link: meta-pytorch/torchrec#742 Pull Request resolved: #689 [Free] Authoring Aware ROO on LTV ReAgent model Differential Revision: D40456561 fbshipit-source-id: 797f51078b61dda7c0b7bde5fbec475a8e28191b
diff --git a/reagent/core/types.py b/reagent/core/types.py
@@ -313,6 +313,7 @@ class FeatureData(TensorDataClass):
     float_features: torch.Tensor
     # For sparse features saved in KeyedJaggedTensor format
     id_list_features: Optional[KeyedJaggedTensor] = None
+    id_list_features_ro: Optional[KeyedJaggedTensor] = None
     id_score_list_features: Optional[KeyedJaggedTensor] = None
 
     # For sparse features saved in dictionary format
@@ -339,6 +340,7 @@ def __post_init__(self):
     def has_float_features_only(self) -> bool:
         return (
             not self.id_list_features
+            and not self.id_list_features_ro
             and not self.id_score_list_features
             and self.time_since_first is None
             and self.candidate_docs is None
diff --git a/reagent/models/sparse_dqn.py b/reagent/models/sparse_dqn.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
-from typing import List
+from typing import List, Optional, Tuple
 
 import torch
 from reagent.core import types as rlt
@@ -16,20 +16,39 @@
 @torch.fx.wrap
 def fetch_id_list_features(
     state: rlt.FeatureData, action: rlt.FeatureData
-) -> KeyedJaggedTensor:
-    assert state.id_list_features is not None or action.id_list_features is not None
-    if state.id_list_features is not None and action.id_list_features is None:
-        sparse_features = state.id_list_features
-    elif state.id_list_features is None and action.id_list_features is not None:
-        sparse_features = action.id_list_features
-    elif state.id_list_features is not None and action.id_list_features is not None:
-        sparse_features = KeyedJaggedTensor.concat(
-            [state.id_list_features, action.id_list_features]
-        )
-    else:
+) -> Tuple[Optional[KeyedJaggedTensor], Optional[KeyedJaggedTensor]]:
+    assert (
+        state.id_list_features is not None
+        or state.id_list_features_ro is not None
+        or action.id_list_features is not None
+        or action.id_list_features_ro is not None
+    )
+
+    def _get_sparse_features(
+        id_list_features_1, id_list_features_2
+    ) -> Optional[KeyedJaggedTensor]:
+        sparse_features = None
+        if id_list_features_1 is not None and id_list_features_2 is None:
+            sparse_features = id_list_features_1
+        elif id_list_features_1 is None and id_list_features_2 is not None:
+            sparse_features = id_list_features_2
+        elif id_list_features_1 is not None and id_list_features_2 is not None:
+            sparse_features = KeyedJaggedTensor.concat(
+                [id_list_features_1, id_list_features_2]
+            )
+        return sparse_features
+
+    sparse_features = _get_sparse_features(
+        state.id_list_features, action.id_list_features
+    )
+    sparse_features_ro = _get_sparse_features(
+        state.id_list_features_ro, action.id_list_features_ro
+    )
+    if sparse_features is None and sparse_features_ro is None:
         raise ValueError
+
     # TODO: add id_list_score_features
-    return sparse_features
+    return sparse_features, sparse_features_ro
 
 
 class SparseDQN(ModelBase):
@@ -41,7 +60,8 @@ class SparseDQN(ModelBase):
     def __init__(
         self,
         state_dense_dim: int,
-        embedding_bag_collection: EmbeddingBagCollection,
+        embedding_bag_collection: Optional[EmbeddingBagCollection],
+        embedding_bag_collection_ro: Optional[EmbeddingBagCollection],
         action_dense_dim: int,
         overarch_dims: List[int],
         activation: str = "relu",
@@ -51,17 +71,43 @@ def __init__(
         output_dim: int = 1,
     ) -> None:
         super().__init__()
-        self.sparse_arch: SparseArch = SparseArch(embedding_bag_collection)
 
-        self.sparse_embedding_dim: int = sum(
-            [
-                len(embc.feature_names) * embc.embedding_dim
-                for embc in embedding_bag_collection.embedding_bag_configs()
-            ]
+        self.sparse_arch: Optional[SparseArch] = (
+            SparseArch(embedding_bag_collection) if embedding_bag_collection else None
+        )
+
+        self.sparse_arch_ro: Optional[SparseArch] = (
+            SparseArch(embedding_bag_collection_ro)
+            if embedding_bag_collection_ro
+            else None
+        )
+        self.sparse_embedding_dim: int = (
+            sum(
+                [
+                    len(embc.feature_names) * embc.embedding_dim
+                    for embc in embedding_bag_collection.embedding_bag_configs()
+                ]
+            )
+            if embedding_bag_collection is not None
+            else 0
+        )
+
+        self.sparse_embedding_dim_ro: int = (
+            sum(
+                [
+                    len(embc.feature_names) * embc.embedding_dim
+                    for embc in embedding_bag_collection_ro.embedding_bag_configs()
+                ]
+            )
+            if embedding_bag_collection_ro is not None
+            else 0
         )
 
         self.input_dim: int = (
-            state_dense_dim + self.sparse_embedding_dim + action_dense_dim
+            state_dense_dim
+            + self.sparse_embedding_dim
+            + self.sparse_embedding_dim_ro
+            + action_dense_dim
         )
         layers = [self.input_dim] + overarch_dims + [output_dim]
         activations = [activation] * len(overarch_dims) + [final_activation]
@@ -76,11 +122,23 @@ def forward(self, state: rlt.FeatureData, action: rlt.FeatureData) -> torch.Tens
             (state.float_features, action.float_features), dim=-1
         )
         batch_size = dense_features.shape[0]
-        sparse_features = fetch_id_list_features(state, action)
+        sparse_features, sparse_features_ro = fetch_id_list_features(state, action)
         # shape: batch_size, num_sparse_features, embedding_dim
-        embedded_sparse = self.sparse_arch(sparse_features)
-        # shape: batch_size, num_sparse_features * embedding_dim
-        embedded_sparse = embedded_sparse.reshape(batch_size, -1)
-        concatenated_dense = torch.cat((dense_features, embedded_sparse), dim=-1)
+        embedded_sparse = (
+            self.sparse_arch(sparse_features) if self.sparse_arch else None
+        )
+        embedded_sparse_ro = (
+            self.sparse_arch_ro(sparse_features_ro) if self.sparse_arch_ro else None
+        )
+        features_list: List[torch.Tensor] = [dense_features]
+        if embedded_sparse is not None:
+            # shape: batch_size, num_sparse_features * embedding_dim
+            embedded_sparse = embedded_sparse.reshape(batch_size, -1)
+            features_list.append(embedded_sparse)
+        if embedded_sparse_ro is not None:
+            # shape: batch_size, num_sparse_features * embedding_dim
+            embedded_sparse_ro = embedded_sparse_ro.reshape(batch_size, -1)
+            features_list.append(embedded_sparse_ro)
 
+        concatenated_dense = torch.cat(features_list, dim=-1)
         return self.q_network(concatenated_dense)
diff --git a/reagent/test/models/test_sparse_dqn_net.py b/reagent/test/models/test_sparse_dqn_net.py
@@ -24,13 +24,27 @@ def test_single_step_sparse_dqn(self):
                 embedding_dim=embedding_dim,
             )
         ]
+
+        num_sparse_features_ro = 2  # refer to watched_page_ids and liked_ids below
+        embedding_bag_configs_ro = [
+            EmbeddingBagConfig(
+                name="watched_page_ids",
+                feature_names=["watched_page_ids", "liked_ids"],
+                num_embeddings=embedding_table_size,
+                embedding_dim=embedding_dim,
+            )
+        ]
         embedding_bag_col = EmbeddingBagCollection(
             device=torch.device("cpu"), tables=embedding_bag_configs
         )
+        embedding_bag_col_ro = EmbeddingBagCollection(
+            device=torch.device("cpu"), tables=embedding_bag_configs_ro
+        )
 
         net = SparseDQN(
             state_dense_dim=state_dense_dim,
             embedding_bag_collection=embedding_bag_col,
+            embedding_bag_collection_ro=embedding_bag_col_ro,
             action_dense_dim=action_dense_dim,
             overarch_dims=dense_sizes,
             activation=activation,
@@ -42,7 +56,10 @@ def test_single_step_sparse_dqn(self):
         # number of sparse features times embedding dimension for sparse features
         assert (
             net[1].in_features
-            == state_dense_dim + action_dense_dim + num_sparse_features * embedding_dim
+            == state_dense_dim
+            + action_dense_dim
+            + num_sparse_features * embedding_dim
+            + num_sparse_features_ro * embedding_dim
         )
         assert net[1].out_features == dense_sizes[0]
         assert net[4].in_features == dense_sizes[0]
