make hydra compat with multitask

src/fairchem/core/models/base.py

@@ -264,6 +264,7 @@ def __init__(
         self.output_heads: dict[str, HeadInterface] = {}
 
         head_names_sorted = sorted(heads.keys())
+        assert len(set(head_names_sorted)) == len(head_names_sorted), "Head names must be unique!"
         for head_name in head_names_sorted:
             head_config = heads[head_name]
             if "module" not in head_config:
@@ -284,8 +285,7 @@ def forward(self, data: Batch):
         # Predict all output properties for all structures in the batch for now.
         out = {}
         for k in self.output_heads:
-            out.update(self.output_heads[k](data, emb))
-
+            out[k] = self.output_heads[k](data, emb)
         return out
 
     def get_backbone(self) -> BackboneInterface:

src/fairchem/core/trainers/ocp_trainer.py

@@ -251,9 +251,18 @@ def _forward(self, batch):
         for target_key in self.output_targets:
             ### Target property is a direct output of the model
             if target_key in out:
-                pred = out[target_key]
-            ## Target property is a derived output of the model. Construct the
-            ## parent property
+                if isinstance(out[target_key], torch.Tensor):
+                    pred = out[target_key]
+                elif isinstance(out[target_key], dict):
+                    # if output is a nested dictionary (in the case of hydra models), we attempt to retrieve it using the property name
+                    # ie: "output_head_name.property"
+                    assert "property" in self.output_targets[target_key], \
+                        f"we need to know which property to match the target to, please specify the property field in the task config, current config: {self.output_targets[target_key]}"
+                    property = self.output_targets[target_key]["property"]
+                    pred = out[target_key][property]
+
+            ## TODO: deprecate the following logic?
+            ## Otherwise, assume target property is a derived output of the model. Construct the parent property
             else:
                 _max_rank = 0
                 for subtarget_key in self.output_targets[target_key]["decomposition"]: