[Documentation] Add doc for multi-task learning

docs/source/advanced/multi-task-learning.rst

@@ -0,0 +1,266 @@
+.. _multi_task_learning:
+
+Multi-task Learning in GraphStorm
+=========================================
+In real world graphs, it is common to have multiple tasks defined on the same graph. For example, people
+many want to do link prediction as well as node feature reconstruction at the same time to supervise the
+training of a GNN model. As another example, people may want to do fraud detection on both seller and
+buyer nodes in a seller-product-buyer graph. To support certain scenarios, GraphStorm supports
+multi-task learning, allowing users to define multiple training targets on different nodes and edges
+within a single training loop. The supported training supervisions for multi-task learning include node classification/regression, edge classification/regression, link prediction and node feature reconstruction.
+
+
+Preparing the Training Data
+---------------------------
+You can follow the :ref:`Use Your Own Data tutorial<use-own-data>` to prepare your graph data for
+multi-task learning. You can define multiple tasks on the same node type or edge type as shown below.
+
+.. code-block:: json
+
+    {
+        "version": "gconstruct-v0.1",
+        "nodes": [
+
+            ......
+
+            {
+                "node_type": "paper",
+                "format": {
+                    "name": "parquet"
+                },
+                "files": [
+                    "/tmp/acm_raw/nodes/paper.parquet"
+                ],
+                "node_id_col": "node_id",
+                "features": [
+                    {
+                        "feature_col": "feat",
+                        "feature_name": "feat"
+                    }
+                ],
+                "labels": [
+                    {
+                        "label_col": "label_class",
+                        "task_type": "classification",
+                        "split_pct":	[0.8, 0.1, 0.1],
+                        "mask_field_names": ["train_mask_class",
+                                             "val_mask_class",
+                                             "test_mask_class"]
+                    },
+                    {
+                        "label_col": "label_reg",
+                        "task_type": "regression",
+                        "split_pct":	[0.8, 0.1, 0.1],
+                        "mask_field_names": ["train_mask_reg",
+                                             "val_mask_reg",
+                                             "test_mask_reg"]
+                    }
+                ]
+            },
+
+            ......
+
+        ],
+        ......
+    }
+
+In the above configuration, we define two tasks for the **paper** nodes. One is a classification task
+with the label name of `label_class` and the train/validation/test mask fields as `train_mask_class`,
+`val_mask_class` and `test_mask_class`, respectively. Another one is a regression task with label name of `label_reg`
+and the train/validation/test mask fields as `train_mask_reg`, `val_mask_reg` and `test_mask_reg`, respectively.
+
+You can also define multiple tasks on different node and edge types as show below:
+
+.. code-block:: json
+
+    {
+        "version": "gconstruct-v0.1",
+        "nodes": [
+
+            ......
+
+            {
+                "node_type": "paper",
+                "format": {
+                    "name": "parquet"
+                },
+                "files": [
+                    "/tmp/acm_raw/nodes/paper.parquet"
+                ],
+                "node_id_col": "node_id",
+                "features": [
+                    {
+                        "feature_col": "feat",
+                        "feature_name": "feat"
+                    }
+                ],
+                "labels": [
+                    {
+                        "label_col": "label",
+                        "task_type": "classification",
+                        "split_pct":	[0.8, 0.1, 0.1],
+                        "mask_field_names": ["train_mask_class",
+                                             "val_mask_class",
+                                             "test_mask_class"]
+                    }
+                ]
+            },
+
+                ......
+
+        ],
+        "edges": [
+
+            ......
+
+            {
+                "relation": [
+                    "paper",
+                    "citing",
+                    "paper"
+                ],
+                "format": {
+                    "name": "parquet"
+                },
+                "files": [
+                    "/tmp/acm_raw/edges/paper_citing_paper.parquet"
+                ],
+                "source_id_col": "source_id",
+                "dest_id_col": "dest_id",
+                "labels": [
+                    {
+                        "task_type": "link_prediction",
+                        "split_pct":	[0.8, 0.1, 0.1],
+                        "mask_field_names": ["train_mask_lp",
+                                             "val_mask_lp",
+                                             "test_mask_lp"]
+                    }
+                ]
+            },
+
+        ......
+
+        ]
+    }
+
+In the above configuration, we define one task for the **paper** node and one task for the
+**paper,citing,paper** edge. The node classification task will take the label name of `label_class` and the train/validation/test mask fields as `train_mask_class`,
+`val_mask_class` and `test_mask_class`, respectively. The link prediction task will take the train/validation/test mask fields as `train_mask_lp`, `val_mask_lp` and `test_mask_lp`, respectively.
+
+
+Construct Graph
+~~~~~~~~~~~~~~~~
+You can follow the instructions in :ref:`Run graph construction<run-graph-construction>` to use the
+GraphStorm construction tool for creating partitioned graph data for training. Please ensure you
+customize the command line arguments such as `--conf-file`, `--output-dir`, `--graph-name` to your
+specific values.
+
+
+Run Multi-task Learning Training
+--------------------------------
+Running a multi-task learning training task is similar to running other GraphStorm built-in tasks as
+detailed in :ref:`Launch Training<launch-training>`. The main defines is to define multiple training
+targets in the YAML configuration file.
+
+
+Define Multi-task for training
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+You can specify multiple training tasks for a training job by providing the `multi_task_learning`
+configurations in the yaml file The following config defines two training tasks, one for node
+classification and one for edge classification.
+
+.. code-blocks:: yaml
+
+    ---
+    version: 1.0
+    gsf:
+        basic:
+            ...
+        ...
+        multi_task_learning:
+            - node_classification:
+                target_ntype: "movie"
+                label_field: "label"
+                mask_fields:
+                    - "train_mask_field_nc"
+                    - "val_mask_field_nc"
+                    - "test_mask_field_nc"
+                task_weight: 1.0
+            - edge_classification:
+                target_etype:
+                    - "user,rating,movie"
+                label_field: "rate"
+                mask_fields:
+                    - "train_mask_field_ec"
+                    - "val_mask_field_ec"
+                    - "test_mask_field_ec"
+                task_weight: 0.5 # weight of the task
+
+Task specific hyperparameters in multi-task learning are same as those for single task learning as
+detailed in :ref:`Training and Inference<configurations-run>`, except that two new configs are required,
+i.e., `mask_fields` and `task_weight`. The `mask_fields` provides the specific training, validation and
+test masks for a task and the `task_weight` gives its loss weight.
+
+In multi-task learning, GraphStorm provides a new unsupervised training signal, i.e., node feature
+reconstruction (`BUILTIN_TASK_RECONSTRUCT_NODE_FEAT = "reconstruct_node_feat"`). You can define a
+node feature reconstruction task as following:
+
+.. code-blocks:: yaml
+
+    ---
+    version: 1.0
+    gsf:
+        basic:
+            ...
+        ...
+        multi_task_learning:
+            - node_classification:
+                ...
+            - reconstruct_node_feat:
+                reconstruct_nfeat_name: "title"
+                target_ntype: "movie"
+                batch_size: 128
+                mask_fields:
+                    - "train_mask_c0" # node classification mask 0
+                    - "val_mask_c0"
+                    - "test_mask_c0"
+                task_weight: 1.0
+                eval_metric:
+                    - "mse"
+
+In the configuration, `target_ntype`` defines the target node type, the reconstruct node feature
+learning will be applied. `reconstruct_nfeat_name`` defines the name of the feature to be
+re-construct. The other configs are same as node regression tasks.
+
+
+Run Model Training
+~~~~~~~~~~~~~~~~~~~
+GraphStorm introduces a new command line `graphstorm.run.gs_multi_task_learning` to run multi-task learning tasks. You can use the following command to start a multi-task training
+job:
+
+.. code-block:: bash
+
+    python -m graphstorm.run.gs_multi_task_learning \
+              --workspace <PATH_TO_WORKSPACE> \
+              --num-trainers 1 \
+              --num-servers 1 \
+              --part-config <PATH_TO_GRAPH_DATA> \
+              --cf <PATH_TO_CONFIG> \
+
+Run Model Inference
+~~~~~~~~~~~~~~~~~~~~
+You can use the same command line `graphstorm.run.gs_multi_task_learning` to run inference as following:
+
+.. code-block:: bash
+
+    python -m graphstorm.run.gs_multi_task_learning \
+              --inference \
+              --workspace <PATH_TO_WORKSPACE> \
+              --num-trainers 1 \
+              --num-servers 1 \
+              --part-config <PATH_TO_GRAPH_DATA> \
+              --cf <PATH_TO_CONFIG> \
+              --save-prediction-path <PATH_TO_OUTPUT>
+
+The prediction results of each prediction tasks (node classification, node regression,
+edge classification and edge regression) will be saved into different sub-directories under PATH_TO_OUTPUT. The sub-directories are prefixed with the `<task_type>_<node/edge_type>_<label_name>`.

docs/source/tutorials/own-data.rst

@@ -249,6 +249,9 @@ The raw node and edge data files are both in a parquet format, whose contents ar
 
 In this example, only the ``paper`` nodes have labels and the task is node classification. So, in the JSON file, the ``paper`` node has the ``labels`` field, and the ``task_type`` is specified as ``classification``. Correspondingly, in the paper node parquet file, there is a column, ``label``, stores the label values. All edge types do not have features associated. Therefore, we only have two columns in these parquet files for edges, the ``source_id`` and the ``dest_id``. For the link prediction task, there is no actual labels. Users just need to specify the ``labels`` field in one or more ``edge`` objects of the JSON config file.
 
+
+.. _run-graph-construction:
+
 Run graph construction
 ```````````````````````
 The configuration JSON file along with these node and edge parquet files are the required inputs of the GraphStorm's construction tools. Then we can use the tool to create the partition graph data with the following command.