…-task learning (awslabs#828)

*Issue #, if available:*
Support multi-task learning. First PR for awslabs#789

*Description of changes:*
Update GraphStorm input config parsing to support multi-task learning.
Allow user to specify 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.


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 <[email protected]>

…ng (awslabs#834)

*Issue #, if available:*
awslabs#789 

*Description of changes:*
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.


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 <[email protected]>

awslabs#837)

*Issue #, if available:*
awslabs#789 

*Description of changes:*
Add GSgnnMultiTaskEvaluator to support multi-task evaluation.

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.


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 <[email protected]>

*Issue #, if available:*
awslabs#789 

*Description of changes:*
Add multi-task learning test dataset based on movielens.


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 <[email protected]>

…ng. (awslabs#852)

*Issue #, if available:*
awslabs#789 

*Description of changes:*
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.

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 <[email protected]>

… in lp and nfeat reconstruct task evaluation.

Previously, in the eval() function of GSgnnMultiTaskLearningTrainer, both link prediction and
node feature reconstruction tasks use the node embeddings computed with the entire graph.
This will cause test edge leakage for link prediction tasks and target node node feature leakage
for node feature reconstruction tasks. This PR fixes this issue.