Add Model class

merlin/models/torch/__init__.py

@@ -16,6 +16,8 @@
 
 from merlin.models.torch.batch import Batch, Sequence
 from merlin.models.torch.block import Block, ParallelBlock
+from merlin.models.torch.blocks.mlp import MLPBlock
+from merlin.models.torch.models.base import Model
 from merlin.models.torch.outputs.base import ModelOutput
 from merlin.models.torch.outputs.classification import BinaryOutput
 from merlin.models.torch.outputs.regression import RegressionOutput
@@ -26,8 +28,10 @@
     "Batch",
     "BinaryOutput",
     "Block",
-    "ParallelBlock",
+    "MLPBlock",
+    "Model",
     "ModelOutput",
+    "ParallelBlock",
     "Sequence",
     "RegressionOutput",
     "RouterBlock",

merlin/models/torch/block.py

@@ -29,7 +29,7 @@
 
 
 class Block(BlockContainer, SchemaTrackingMixin, RegistryMixin):
-    """A base-class that calls it's modules sequentially.
+    """A base-class that calls its modules sequentially.
 
     Parameters
     ----------
@@ -114,7 +114,7 @@ def copy(self) -> "Block":
 
 
 class ParallelBlock(Block):
-    """A base-class that calls it's modules in parallel.
+    """A base-class that calls its modules in parallel.
 
     A ParallelBlock contains multiple branches that will be executed
     in parallel. Each branch can contain multiple modules, and

merlin/models/torch/models/__init__.py

@@ -0,0 +1,15 @@
+#
+# Copyright (c) 2023, 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.
+#

merlin/models/torch/models/base.py

@@ -0,0 +1,238 @@
+#
+# Copyright (c) 2023, 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.
+#
+from functools import reduce
+from typing import Dict, List, Optional, Sequence, Union
+
+import torch
+from pytorch_lightning import LightningModule
+from torch import nn
+
+from merlin.dataloader.torch import Loader
+from merlin.io import Dataset
+from merlin.models.torch.batch import Batch
+from merlin.models.torch.block import Block
+from merlin.models.torch.container import BlockContainer
+from merlin.models.torch.outputs.base import ModelOutput
+from merlin.models.torch.utils import module_utils
+from merlin.models.utils.registry import camelcase_to_snakecase
+from merlin.schema import Schema
+
+
+class Model(Block, LightningModule):
+    """
+    Merlin Model class.
+
+    The Model class extends from both the Block and LightningModule classes. It
+    allows for easy construction of models using pre-defined blocks.
+
+    Parameters
+    ----------
+    *blocks: nn.Module
+        One or more blocks that make up the core functionality of the model.
+    schema: Schema, optional
+        A Merlin schema. Default is None.
+    optimizer: torch.optim.Optimizer, optional
+        A PyTorch optimizer from the PyTorch library (or any custom optimizer
+        that follows the same API). Default is Adam optimizer.
+
+    Example usage
+    -------------
+    >>> model = Model(
+    ...    TabularInputBlock(schema),
+    ...    MLPBlock([32, 16]),
+    ...    BinaryOutput(schema.select_by_tag(Tags.TARGET).first),
+    ... )
+    ... trainer = Trainer(max_epochs=1)
+    ... with Loader(dataset, batch_size=16) as loader:
+    ...     model.initialize(loader)
+    ...     trainer.fit(model, loader)
+    """
+
+    def __init__(
+        self,
+        *blocks: nn.Module,
+        schema: Optional[Schema] = None,
+        optimizer=torch.optim.Adam,
+    ):
+        """Initializes `Model` class"""
+        super().__init__()
+        self.schema = schema
+
+        self.pre = BlockContainer(name="pre")
+        self.blocks = BlockContainer(name="blocks")
+        for block in blocks:
+            self.blocks.append(block)
+        self.post = BlockContainer(name="post")
+
+        self.optimizer = optimizer
+
+    def initialize(self, data: Union[Dataset, Loader, Batch]):
+        """Initializes the model based on a given data set."""
+        return module_utils.initialize(self, data)
+
+    def forward(
+        self, inputs: Union[torch.Tensor, Dict[str, torch.Tensor]], batch: Optional[Batch] = None
+    ):
+        """Performs a forward pass through the model."""
+        outputs = inputs
+        for pre in self.pre.values:
+            outputs = pre(outputs, batch=batch)
+        for block in self.blocks.values:
+            outputs = block(outputs, batch=batch)
+        for post in self.post.values:
+            outputs = post(outputs, batch=batch)
+        return outputs
+
+    def training_step(self, batch, batch_idx):
+        """Performs a training step with a single batch."""
+        del batch_idx
+        if isinstance(batch, Batch):
+            features = batch.features
+            targets = batch.targets
+        else:
+            features, targets = batch
+
+        predictions = self(features, batch=Batch(features, targets))
+
+        loss_and_metrics = compute_loss(predictions, targets, self.model_outputs())
+        for name, value in loss_and_metrics.items():
+            self.log(f"train_{name}", value)
+
+        return loss_and_metrics["loss"]
+
+    def configure_optimizers(self):
+        """Configures the optimizer for the model."""
+        return self.optimizer(self.parameters())
+
+    def model_outputs(self) -> List[ModelOutput]:
+        """Finds all instances of `ModelOutput` in the model."""
+        return module_utils.find_all_instances(self, ModelOutput)
+
+    def first(self) -> nn.Module:
+        """Returns the first block in the model."""
+        return self.blocks.values[0]
+
+    def last(self) -> nn.Module:
+        """Returns the last block in the model."""
+        return self.blocks.values[-1]
+
+    def input_schema(self) -> Schema:
+        """Returns the input schema of the model."""
+        if self.schema:
+            return self.schema
+        # TODO: Implement logic when TabularInputBlock is available.
+        return Schema([])
+
+    def output_schema(self) -> Schema:
+        output_schemas = []
+        for child in module_utils.get_all_children(self):
+            if hasattr(child, "output_schema"):
+                output_schemas.append(child.output_schema())
+
+        if not output_schemas:
+            raise RuntimeError("No output schema found")
+
+        return reduce(lambda a, b: a + b, output_schemas)  # type: ignore
+
+
+def compute_loss(
+    predictions: Union[torch.Tensor, Dict[str, torch.Tensor]],
+    targets: Optional[Union[torch.Tensor, Dict[str, torch.Tensor]]],
+    model_outputs: Sequence[ModelOutput],
+    compute_metrics: bool = True,
+) -> Dict[str, torch.Tensor]:
+    """Compute the loss and metrics for the given model outputs.
+
+    This function takes in predictions and targets, and a list of model
+    outputs. It computes the loss using the loss function of each model output
+    and averages it. If `compute_metrics` is set to True, it also computes the
+    metrics defined in each model output.
+
+    Parameters
+    ----------
+    predictions: Union[torch.Tensor, Dict[str, torch.Tensor]]
+        The predictions from the model.
+    targets: Optional[Union[torch.Tensor, Dict[str, torch.Tensor]]]
+        The ground truth targets.
+    model_outputs: Sequence[ModelOutput]
+        A list of model outputs. Each model output must have a defined loss
+        function.
+    compute_metrics: bool, optional
+        Whether to compute metrics defined in each model output. Default: True.
+
+    Returns
+    -------
+    Dict[str, torch.Tensor]
+        A dictionary containing the loss and the computed metrics (if any).
+
+    Raises
+    ------
+    RuntimeError: If no model outputs are provided, or if multiple model
+        outputs are provided but only one set of targets is given.
+
+    Example usage
+    -------------
+    >>> predictions = torch.tensor([0.2, 0.3, 0.6, 0.8])
+    >>> targets = torch.tensor([1.0, 0.0, 1.0, 0.0], dtype=torch.float32)
+    >>> binary_output = mm.BinaryOutput(ColumnSchema("target"))
+    >>> results = compute_loss(predictions, targets, [binary_output])
+    >>> results["loss"]
+    tensor(0.7653)
+    >>> results["binary_accuracy"]
+    tensor(0.5000)
+    """
+    if len(model_outputs) < 1:
+        raise RuntimeError("No model outputs found.")
+
+    results = {"loss": torch.tensor(0.0)}
+    for model_out in model_outputs:
+        name = model_out.output_schema.first.name
+
+        if targets is None or (isinstance(targets, dict) and name not in targets):
+            if not hasattr(model_out, "target"):
+                raise ValueError(f"'{model_out.__class__.__name__}' has no target.")
+            if isinstance(predictions, dict):
+                pred_col = predictions[name]
+            else:
+                pred_col = predictions
+            _targets = torch.ones_like(pred_col) * model_out.target
+        elif isinstance(targets, dict):
+            _targets = targets[name]
+        elif isinstance(targets, torch.Tensor):
+            _targets = targets
+        else:
+            raise ValueError(f"Unknown 'targets' type: {type(targets)}")
+
+        if isinstance(predictions, dict):
+            if name not in predictions:
+                raise RuntimeError(f"Column '{name}' not found in predictions")
+            _predictions = predictions[name]
+        elif isinstance(predictions, torch.Tensor):
+            _predictions = predictions
+        else:
+            raise ValueError(f"Unknown 'predictions' type: {type(predictions)}")
+
+        results["loss"] = results["loss"] + model_out.loss(_predictions, _targets) / len(
+            model_outputs
+        )
+
+        if not compute_metrics:
+            continue
+
+        for metric in model_out.metrics:
+            metric_name = camelcase_to_snakecase(metric.__class__.__name__)
+            results[metric_name] = metric(_predictions, _targets)
+    return results