Add DLRM block

merlin/models/torch/__init__.py

@@ -17,6 +17,7 @@
 from merlin.models.torch import schema
 from merlin.models.torch.batch import Batch, Sequence
 from merlin.models.torch.block import Block, ParallelBlock
+from merlin.models.torch.blocks.dlrm import DLRMBlock
 from merlin.models.torch.blocks.mlp import MLPBlock
 from merlin.models.torch.inputs.embedding import EmbeddingTable, EmbeddingTables
 from merlin.models.torch.inputs.select import SelectFeatures, SelectKeys
@@ -51,4 +52,5 @@
     "Concat",
     "Stack",
     "schema",
+    "DLRMBlock",
 ]

merlin/models/torch/blocks/dlrm.py

@@ -0,0 +1,141 @@
+from typing import Dict, Optional
+
+import torch
+from torch import nn
+
+from merlin.models.torch.block import Block
+from merlin.models.torch.inputs.embedding import EmbeddingTables
+from merlin.models.torch.inputs.tabular import TabularInputBlock
+from merlin.models.torch.link import Link
+from merlin.models.torch.transforms.agg import MaybeAgg, Stack
+from merlin.models.utils.doc_utils import docstring_parameter
+from merlin.schema import Schema, Tags
+
+_DLRM_REF = """
+    References
+    ----------
+    ..  [1] Naumov, Maxim, et al. "Deep learning recommendation model for
+        personalization and recommendation systems." arXiv preprint arXiv:1906.00091 (2019).
+"""
+
+
+@docstring_parameter(dlrm_reference=_DLRM_REF)
+class DLRMInputBlock(TabularInputBlock):
+    """Input block for DLRM model.
+
+    Parameters
+    ----------
+    schema : Schema, optional
+        The schema to use for selection. Default is None.
+    dim : int
+        The dimensionality of the output vectors.
+    bottom_block : Block
+        Block to pass the continuous features to.
+        Note that, the output dimensionality of this block must be equal to ``dim``.
+
+    {dlrm_reference}
+
+    Raises
+    ------
+    ValueError
+        If no categorical input is provided in the schema.
+
+    """
+
+    def __init__(self, schema: Schema, dim: int, bottom_block: Block):
+        super().__init__(schema)
+        self.add_route(Tags.CATEGORICAL, EmbeddingTables(dim, seq_combiner="mean"))
+        self.add_route(Tags.CONTINUOUS, bottom_block)
+
+        if "categorical" not in self:
+            raise ValueError("DLRMInputBlock must have a categorical input")
+
+
+@docstring_parameter(dlrm_reference=_DLRM_REF)
+class DLRMInteraction(nn.Module):
+    """
+    This class defines the forward interaction operation as proposed
+    in the DLRM
+     `paper https://arxiv.org/pdf/1906.00091.pdf`_ [1]_.
+
+    This forward operation performs elementwise multiplication
+    followed by a reduction sum (equivalent to a dot product) of all embedding pairs.
+
+    {dlrm_reference}
+
+    """
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        if not hasattr(self, "triu_indices"):
+            self.register_buffer(
+                "triu_indices", torch.triu_indices(inputs.shape[1], inputs.shape[1], offset=1)
+            )
+
+        interactions = torch.bmm(inputs, torch.transpose(inputs, 1, 2))
+        interactions_flat = interactions[:, self.triu_indices[0], self.triu_indices[1]]
+
+        return interactions_flat
+
+
+class ShortcutConcatContinuous(Link):
+    """
+    A shortcut connection that concatenates
+    continuous input features and intermediate outputs.
+
+    When there's no continuous input, the intermediate output is returned.
+    """
+
+    def forward(self, inputs: Dict[str, torch.Tensor]) -> torch.Tensor:
+        intermediate_output = self.output(inputs)
+
+        if "continuous" in inputs:
+            return torch.cat((inputs["continuous"], intermediate_output), dim=1)
+
+        return intermediate_output
+
+
+@docstring_parameter(dlrm_reference=_DLRM_REF)
+class DLRMBlock(Block):
+    """Builds the DLRM architecture, as proposed in the following
+     `paper https://arxiv.org/pdf/1906.00091.pdf`_ [1]_.
+
+    Parameters
+    ----------
+    schema : Schema, optional
+        The schema to use for selection. Default is None.
+    dim : int
+        The dimensionality of the output vectors.
+    bottom_block : Block
+        Block to pass the continuous features to.
+        Note that, the output dimensionality of this block must be equal to ``dim``.
+    top_block : Block, optional
+        An optional upper-level block of the model.
+    interaction : nn.Module, optional
+        Interaction module for DLRM.
+        If not provided, DLRMInteraction will be used by default.
+
+    {dlrm_reference}
+
+    Raises
+    ------
+    ValueError
+        If no categorical input is provided in the schema.
+    """
+
+    def __init__(
+        self,
+        schema: Schema,
+        dim: int,
+        bottom_block: Block,
+        top_block: Optional[Block] = None,
+        interaction: Optional[nn.Module] = None,
+    ):
+        super().__init__(DLRMInputBlock(schema, dim, bottom_block))
+
+        self.append(
+            Block(MaybeAgg(Stack(dim=1)), interaction or DLRMInteraction()),
+            link=ShortcutConcatContinuous(),
+        )
+
+        if top_block:
+            self.append(top_block)

merlin/models/torch/router.py

@@ -88,6 +88,9 @@ def add_route(
         """
 
         routing_module = schema.select(self.selectable, selection)
+        if not routing_module:
+            return self
+
         if module is not None:
             schema.setup_schema(module, routing_module.schema)
 

merlin/models/torch/transforms/agg.py

@@ -104,6 +104,9 @@ def forward(self, inputs: Dict[str, torch.Tensor]) -> torch.Tensor:
 
         if self.align_dims:
             max_dims = max(tensor.dim() for tensor in sorted_tensors)
+            max_dims = max(
+                max_dims, 2
+            )  # assume first dimension is batch size + at least one feature
             _sorted_tensors = []
             for tensor in sorted_tensors:
                 if tensor.dim() < max_dims:

tests/unit/torch/blocks/test_dlrm.py

@@ -0,0 +1,112 @@
+import math
+
+import pytest
+import torch
+
+import merlin.models.torch as mm
+from merlin.models.torch.batch import sample_batch
+from merlin.models.torch.blocks.dlrm import DLRMInputBlock, DLRMInteraction
+from merlin.models.torch.utils import module_utils
+from merlin.schema import Tags
+
+
+class TestDLRMInputBlock:
+    def test_routes_and_output_shapes(self, testing_data):
+        schema = testing_data.schema
+        embedding_dim = 64
+        block = DLRMInputBlock(schema, embedding_dim, mm.MLPBlock([embedding_dim]))
+
+        assert isinstance(block["categorical"], mm.EmbeddingTables)
+        assert len(block["categorical"]) == len(schema.select_by_tag(Tags.CATEGORICAL))
+
+        assert isinstance(block["continuous"][0], mm.SelectKeys)
+        assert isinstance(block["continuous"][1], mm.MLPBlock)
+
+        batch_size = 16
+        batch = sample_batch(testing_data, batch_size=batch_size)
+
+        outputs = module_utils.module_test(block, batch)
+
+        for col in schema.select_by_tag(Tags.CATEGORICAL):
+            assert outputs[col.name].shape == (batch_size, embedding_dim)
+        assert outputs["continuous"].shape == (batch_size, embedding_dim)
+
+
+class TestDLRMInteraction:
+    @pytest.mark.parametrize(
+        "batch_size,num_features,dim",
+        [(16, 3, 3), (32, 5, 8), (64, 5, 4)],
+    )
+    def test_output_shape(self, batch_size, num_features, dim):
+        module = DLRMInteraction()
+        inputs = torch.rand((batch_size, num_features, dim))
+        outputs = module_utils.module_test(module, inputs)
+
+        assert outputs.shape == (batch_size, num_features - 1 + math.comb(num_features - 1, 2))
+
+
+class TestDLRMBlock:
+    @pytest.fixture(autouse=True)
+    def setup_method(self, testing_data):
+        self.schema = testing_data.schema
+        self.batch_size = 16
+        self.batch = sample_batch(testing_data, batch_size=self.batch_size)
+
+    def test_dlrm_output_shape(self):
+        embedding_dim = 64
+        block = mm.DLRMBlock(
+            self.schema,
+            dim=embedding_dim,
+            bottom_block=mm.MLPBlock([embedding_dim]),
+        )
+
+        outputs = module_utils.module_test(block, self.batch)
+
+        num_features = len(self.schema.select_by_tag(Tags.CATEGORICAL)) + 1
+        dot_product_dim = (num_features - 1) * num_features // 2
+        assert list(outputs.shape) == [self.batch_size, dot_product_dim + embedding_dim]
+
+    def test_dlrm_with_top_block(self):
+        embedding_dim = 32
+        top_block_dim = 8
+        block = mm.DLRMBlock(
+            self.schema,
+            dim=embedding_dim,
+            bottom_block=mm.MLPBlock([embedding_dim]),
+            top_block=mm.MLPBlock([top_block_dim]),
+        )
+
+        outputs = module_utils.module_test(block, self.batch)
+
+        assert list(outputs.shape) == [self.batch_size, top_block_dim]
+
+    def test_dlrm_block_no_categorical_features(self):
+        schema = self.schema.remove_by_tag(Tags.CATEGORICAL)
+        embedding_dim = 32
+
+        with pytest.raises(ValueError, match="must have a categorical input"):
+            _ = mm.DLRMBlock(
+                schema,
+                dim=embedding_dim,
+                bottom_block=mm.MLPBlock([embedding_dim]),
+            )
+
+    def test_dlrm_block_no_continuous_features(self, testing_data):
+        schema = testing_data.schema.remove_by_tag(Tags.CONTINUOUS)
+        testing_data.schema = schema
+
+        embedding_dim = 32
+        block = mm.DLRMBlock(
+            schema,
+            dim=embedding_dim,
+            bottom_block=mm.MLPBlock([embedding_dim]),
+        )
+
+        batch_size = 16
+        batch = sample_batch(testing_data, batch_size=batch_size)
+
+        outputs = module_utils.module_test(block, batch)
+
+        num_features = len(schema.select_by_tag(Tags.CATEGORICAL))
+        dot_product_dim = (num_features - 1) * num_features // 2
+        assert list(outputs.shape) == [batch_size, dot_product_dim]

tests/unit/torch/models/test_base.py

@@ -133,11 +133,11 @@ def test_training_step_with_dataloader(self):
             mm.BinaryOutput(ColumnSchema("target")),
         )
 
-        feature = [[1.0, 2.0], [3.0, 4.0]]
-        target = [[0.0], [1.0]]
+        feature = [2.0, 3.0]
+        target = [0.0, 1.0]
         dataset = Dataset(pd.DataFrame({"feature": feature, "target": target}))
 
-        with Loader(dataset, batch_size=1) as loader:
+        with Loader(dataset, batch_size=2) as loader:
             model.initialize(loader)
             batch = loader.peek()
 

tests/unit/torch/outputs/test_tabular.py

@@ -29,6 +29,8 @@ def test_exceptions(self):
         with pytest.raises(ValueError, match="not found"):
             mm.TabularOutputBlock(self.schema, init="not_found")
 
+    def test_no_route_for_non_existent_tag(self):
         outputs = mm.TabularOutputBlock(self.schema)
-        with pytest.raises(ValueError):
-            outputs.add_route(Tags.CATEGORICAL)
+        outputs.add_route(Tags.CATEGORICAL)
+
+        assert not outputs