[Multi-task Learning] Add GSgnnMultiTaskSharedEncoderModel #855
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*Issue #, if available:* #789 *Description of changes:* Add support for multi-task learning. Users can define multiple tasks in the same training loop. A task can be a node classification, node regression, edge classification, edge regression or link prediction task. For each node classification or node regression task, it should be defined on a single node type with one label field. But users can define multiple node classification or regression tasks on the same node type. For each edge classification or node regression task, it should be defined on a single edge type with one label field. But users can define multiple edge classification or regression tasks on the same edge type. For link prediction, users can define prediction targets on multiple edge types. ### Graph construction Update GraphStorm input config parsing to support multi-task learning. Allow user to specify multiple training tasks for a training job through yaml file. By providing the `multi_task_learning` configurations in the yaml file, users can define multiple training tasks. The following config defines two training tasks, one for node classification and one for edge classification. ``` --- 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 thoses in single task learning, except that two new configs are required, i.e., mask_fields and task_weight. The mask_fields provides the training, validation and test masks for the task and the task_weight gives its loss weight. ### DataLoader for multi-task learning Add GSgnnMultiTaskDataLoader to support multi-task learning. When initializing a GSgnnMultiTaskDataLoader, users need to provide two inputs: 1) a list of config.TaskInfo objects recording the information of each task and 2) a list of dataloaders corresponding to each training task. During training for each iteration, GSgnnMultiTaskDataLoader will iteratively call each task-dataloader to generate a mini-batch and finally return a list of mini-batches to the trainer. The length of the dataloader (number of batches for an epoch) is determined by the largest task in the GSgnnMultiTaskDataLoader. #834 ### Evaluator for multi-task learning GSgnnMultiTaskEvaluator accepts a set of Evaluators, in the format of dict ({task_id: Evaluator, ...}) as input to initialize the multi-task evaluator. When doing evaluation, it accepts three arguements val_results, test_results and total_iters. The val_results and test_results will be dicts in the format of {task_id_0: reslut, task_id_1: result}. The GSgnnMultiTaskEvaluator will call task specify evaluators for each task to compute the evaluation scores. #837 ### Refactor graphstorm.model for multi-task learning As multi-task learning trainer will invoke edge_mini_batch_predict, lp_mini_batch_predict and node_mini_batch_predict when conducting evaluation or testing, refactor the code to allow the functions to work with different decoders. #843 ### Add GSgnnMultiTaskSharedEncoderModel GSgnnMultiTaskSharedEncoderModel allows multiple tasks to share the same GNN encoder but have separate decoders for each task. #855 ### Add Multi-task entrypoint #849 By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --------- Co-authored-by: Xiang Song <xiangsx@amazon.com>
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Issue #, if available:
#789
Description of changes:
GSgnnMultiTaskSharedEncoderModel allows multiple tasks to share the same GNN encoder but have separate decoders for each task.
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.