Feat: support OOD dual label

lambench/models/ase_models.py

@@ -201,7 +201,16 @@ def evaluate(
             import torch
 
             torch.set_default_dtype(torch.float32)
-            return self.run_ase_dptest(self, task.test_data, task.dispersion_correction)
+            # Use corresponding DFT label for models supporting OMol25 on Molecules tasks
+            if isinstance(task.test_data, dict):
+                if self.supports_omol and self.model_domain == "molecules":
+                    data_path = task.test_data["wB97"]
+                else:
+                    data_path = task.test_data["PBE"]
+            else:
+                data_path = task.test_data
+
+            return self.run_ase_dptest(self, data_path, task.dispersion_correction)
         elif isinstance(task, CalculatorTask):
             if task.task_name == "nve_md":
                 from lambench.tasks.calculator.nve_md.nve_md import (

lambench/tasks/base_task.py

@@ -26,7 +26,7 @@ class BaseTask(BaseModel):
     """
 
     task_name: str
-    test_data: Path
+    test_data: Path | dict[str, Path]
     task_config: ClassVar[Path]
     model_config = ConfigDict(extra="allow")
     workdir: Path = Path(tempfile.gettempdir()) / "lambench"

lambench/tasks/direct/direct_tasks.yml

@@ -1,13 +1,9 @@
-ANI:
-  test_data: "/bohr/lambench-ood-zwtr/v4/LAMBench-TestData-v3/ANI"
 HEA25_S:
   test_data: "/bohr/lambench-ood-zwtr/v4/LAMBench-TestData-v3/HEA25S"
 HEA25_bulk:
   test_data: "/bohr/lambench-ood-zwtr/v4/LAMBench-TestData-v3/HEA25"
 MoS2:
   test_data: "/bohr/lambench-ood-zwtr/v4/LAMBench-TestData-v3/MoS2"
-MD22:
-  test_data: "/bohr/lambench-ood-zwtr/v4/LAMBench-TestData-v3/MD22"
 REANN_CO2_Ni100:
   test_data: "/bohr/lambench-ood-zwtr/v4/LAMBench-TestData-v3/REANN_CO2_Ni100"
 NequIP_NC_2022:
@@ -24,6 +20,10 @@ HPt_NC_2022:
   test_data: "/bohr/lambench-ood-zwtr/v4/LAMBench-TestData-v3/HPt_NC2022"
 Ca_batteries_CM2021:
   test_data: "/bohr/lambench-ood-zwtr/v4/LAMBench-TestData-v3/Ca_batteries"
+AQM:
+  test_data:
+    PBE: "/bohr/temp-lambench-ood-5zz5/v3/AQM-sol-PBE__downsampled_1000"
+    wB97: "/bohr/temp-lambench-ood-5zz5/v3/AQM-sol-PBE__downsampled_1000_OMol-wb97mv-def2tzvpd-ORCA600"
 ## DEPRECATED
 # Collision:
 #   test_data: "/bohr/lambench-ood-zwtr/v2/LAMBench-TestData-v2/Collision"
@@ -39,3 +39,7 @@ Ca_batteries_CM2021:
 #   test_data: "/bohr/lambench-ood-zwtr/v1/OOD_test_data_v2/HEMC_HEMB"
 # Torsionnet500:
 #   test_data: "/bohr/lambench-ood-zwtr/v1/OOD_test_data_v2/raw_torsionnet500"
+# ANI:
+#   test_data: "/bohr/lambench-ood-zwtr/v4/LAMBench-TestData-v3/ANI"
+# MD22:
+#   test_data: "/bohr/lambench-ood-zwtr/v4/LAMBench-TestData-v3/MD22"

lambench/workflow/dflow.py

@@ -53,6 +53,16 @@ def submit_tasks_dflow(
         name = f"{task.task_name}--{model.model_name}"
         # dflow task name should be alphanumeric
         name = "".join([c if c.isalnum() else "-" for c in name])
+        if task.test_data is not None:
+            # handle dict type test_data, NOTE: if the datasets are in the same parent folder, only need to upload the artifact once.
+            task_data = (
+                list(task.test_data.values())[0]
+                if isinstance(task.test_data, dict)
+                else task.test_data
+            )
+        else:
+            task_data = []
+        logging.warning(f"Submitting task {name} with test data paths: {task_data}")
 
         dflow_task = Task(
             name=name,
@@ -69,7 +79,7 @@ def submit_tasks_dflow(
                 "task": task,
                 "model": model,
             },
-            artifacts={"dataset": get_dataset([model.model_path, task.test_data])},
+            artifacts={"dataset": get_dataset([model.model_path, task_data])},
             executor=DispatcherExecutor(
                 machine_dict={
                     "batch_type": "Bohrium",

lambench/workflow/entrypoint.py

@@ -66,7 +66,7 @@ def gather_task_type(
             continue  # Regular ASEModel does not support PropertyFinetuneTask
         for task_name, task_params in task_configs.items():
             if (task_names and task_name not in task_names) or task_class.__name__ in (
-                model_param["skip_tasks"]
+                model_param.get("skip_tasks", [])
             ):
                 continue
             task = task_class(task_name=task_name, **task_params)