fix issues with ddp/hydra and add tests

src/fairchem/core/models/equiformer_v2/equiformer_v2.py

@@ -61,6 +61,7 @@ class EquiformerV2(nn.Module, GraphModelMixin):
 
     Args:
         use_pbc (bool):         Use periodic boundary conditions
+        use_pbc_single (bool):         Process batch PBC graphs one at a time
         regress_forces (bool):  Compute forces
         otf_graph (bool):       Compute graph On The Fly (OTF)
         max_neighbors (int):    Maximum number of neighbors per atom
@@ -683,6 +684,12 @@ def no_weight_decay(self) -> set:
 
 @registry.register_model("equiformer_v2_backbone")
 class EquiformerV2Backbone(EquiformerV2, BackboneInterface):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # TODO remove these once we deprecate/stop-inheriting EquiformerV2 class
+        self.energy_block = None
+        self.force_block = None
+
     @conditional_grad(torch.enable_grad())
     def forward(self, data: Batch) -> dict[str, torch.Tensor]:
         self.batch_size = len(data.natoms)
@@ -815,6 +822,7 @@ def forward(self, data: Batch) -> dict[str, torch.Tensor]:
 class EquiformerV2EnergyHead(nn.Module, HeadInterface):
     def __init__(self, backbone):
         super().__init__()
+
         self.avg_num_nodes = backbone.avg_num_nodes
         self.energy_block = FeedForwardNetwork(
             backbone.sphere_channels,
@@ -828,6 +836,8 @@ def __init__(self, backbone):
             backbone.use_grid_mlp,
             backbone.use_sep_s2_act,
         )
+        self.apply(backbone._init_weights)
+        self.apply(backbone._uniform_init_rad_func_linear_weights)
 
     def forward(self, data: Batch, emb: dict[str, torch.Tensor | GraphData]):
         node_energy = self.energy_block(emb["node_embedding"])
@@ -871,6 +881,8 @@ def __init__(self, backbone):
             backbone.use_sep_s2_act,
             alpha_drop=0.0,
         )
+        self.apply(backbone._init_weights)
+        self.apply(backbone._uniform_init_rad_func_linear_weights)
 
     def forward(self, data: Batch, emb: dict[str, torch.Tensor]):
         forces = self.force_block(

src/fairchem/core/models/escn/escn.py

@@ -530,7 +530,7 @@ def forward(self, data: Batch) -> dict[str, torch.Tensor]:
 class eSCNEnergyHead(nn.Module, HeadInterface):
     def __init__(self, backbone):
         super().__init__()
-
+        backbone.energy_block = None
         # Output blocks for energy and forces
         self.energy_block = EnergyBlock(
             backbone.sphere_channels_all, backbone.num_sphere_samples, backbone.act
@@ -550,7 +550,7 @@ def forward(
 class eSCNForceHead(nn.Module, HeadInterface):
     def __init__(self, backbone):
         super().__init__()
-
+        backbone.force_block = None
         self.force_block = ForceBlock(
             backbone.sphere_channels_all, backbone.num_sphere_samples, backbone.act
         )

src/fairchem/core/models/gemnet_oc/gemnet_oc.py

@@ -1431,6 +1431,9 @@ def __init__(
         self.direct_forces = backbone.direct_forces
         self.force_scaler = backbone.force_scaler
 
+        backbone.out_mlp_E = None
+        backbone.out_energy = None
+
         out_mlp_E = [
             Dense(
                 backbone.atom_emb.emb_size * (len(backbone.int_blocks) + 1),
@@ -1495,6 +1498,8 @@ def __init__(
 
         emb_size_edge = backbone.edge_emb.dense.linear.out_features
         if self.direct_forces:
+            backbone.out_mlp_F = None
+            backbone.out_forces = None
             out_mlp_F = [
                 Dense(
                     emb_size_edge * (len(backbone.int_blocks) + 1),

src/fairchem/core/models/painn/painn.py

@@ -671,7 +671,7 @@ def forward(self, x, v):
 class PaiNNEnergyHead(nn.Module, HeadInterface):
     def __init__(self, backbone):
         super().__init__()
-
+        backbone.out_energy = None
         self.out_energy = nn.Sequential(
             nn.Linear(backbone.hidden_channels, backbone.hidden_channels // 2),
             ScaledSiLU(),
@@ -697,6 +697,7 @@ def __init__(self, backbone):
         self.direct_forces = backbone.direct_forces
 
         if self.direct_forces:
+            backbone.out_forces = None
             self.out_forces = PaiNNOutput(backbone.hidden_channels)
 
     def forward(

tests/core/e2e/test_s2ef.py

@@ -210,7 +210,9 @@ def _run_main(
 
 
 class TestSmoke:
-    def smoke_test_train(self, input_yaml, tutorial_val_src, otf_norms=False):
+    def smoke_test_train(
+        self, input_yaml, tutorial_val_src, world_size, num_workers, otf_norms=False
+    ):
         with tempfile.TemporaryDirectory() as tempdirname:
             # first train a very simple model, checkpoint
             train_rundir = Path(tempdirname) / "train"
@@ -221,7 +223,12 @@ def smoke_test_train(self, input_yaml, tutorial_val_src, otf_norms=False):
                 rundir=str(train_rundir),
                 input_yaml=input_yaml,
                 update_dict_with={
-                    "optim": {"max_epochs": 2, "eval_every": 8, "batch_size": 5},
+                    "optim": {
+                        "max_epochs": 2,
+                        "eval_every": 8,
+                        "batch_size": 5,
+                        "num_workers": num_workers,
+                    },
                     "dataset": oc20_lmdb_train_and_val_from_paths(
                         train_src=str(tutorial_val_src),
                         val_src=str(tutorial_val_src),
@@ -231,6 +238,7 @@ def smoke_test_train(self, input_yaml, tutorial_val_src, otf_norms=False):
                 },
                 save_checkpoint_to=checkpoint_path,
                 save_predictions_to=training_predictions_filename,
+                world_size=world_size,
             )
             assert "train/energy_mae" in acc.Tags()["scalars"]
             assert "val/energy_mae" in acc.Tags()["scalars"]
@@ -313,10 +321,21 @@ def test_train_and_predict(
         configs,
         tutorial_val_src,
     ):
+        # test without ddp
+        self.smoke_test_train(
+            input_yaml=configs[model_name],
+            tutorial_val_src=tutorial_val_src,
+            otf_norms=otf_norms,
+            world_size=0,
+            num_workers=2,
+        )
+        # test with ddp but no wokers
         self.smoke_test_train(
             input_yaml=configs[model_name],
             tutorial_val_src=tutorial_val_src,
             otf_norms=otf_norms,
+            world_size=1,
+            num_workers=0,
         )
 
     def test_use_pbc_single(self, configs, tutorial_val_src, torch_deterministic):
@@ -341,11 +360,21 @@ def test_use_pbc_single(self, configs, tutorial_val_src, torch_deterministic):
     @pytest.mark.parametrize(
         ("world_size", "ddp"),
         [
-            pytest.param(2, True),
+            pytest.param(
+                2,
+                True,
+            ),
             pytest.param(0, False),
         ],
     )
-    def test_ddp(self, world_size, ddp, configs, tutorial_val_src, torch_deterministic):
+    def test_ddp(
+        self,
+        world_size,
+        ddp,
+        configs,
+        tutorial_val_src,
+        torch_deterministic,
+    ):
         with tempfile.TemporaryDirectory() as tempdirname:
             tempdir = Path(tempdirname)
             extra_args = {"seed": 0}

tests/core/models/test_configs/test_dpp.yml

@@ -1,20 +1,39 @@
 trainer: forces
 
-task:
-  dataset: lmdb
-  type: regression
-  metric: mae
-  primary_metric: forces_mae
-  labels:
-    - potential energy
-  grad_input: atomic forces
-  train_on_free_atoms: True
-  eval_on_free_atoms: True
-  prediction_dtype: float32
+outputs:
+  energy:
+    shape: 1
+    level: system
+  forces:
+    irrep_dim: 1
+    level: atom
+    train_on_free_atoms: True
+    eval_on_free_atoms: True
+
+loss_functions:
+  - energy:
+      fn: mae
+      coefficient: 2
+  - forces:
+      fn: l2mae
+      coefficient: 100
 
+evaluation_metrics:
+  metrics:
+    energy:
+      - mae
+    forces:
+      - mae
+      - cosine_similarity
+      - magnitude_error
+    misc:
+      - energy_forces_within_threshold
+  primary_metric: forces_mae
+
 logger:
     name: tensorboard
 
+
 model:
   name: dimenetplusplus #_bbwheads
   hidden_channels: 4

tests/core/models/test_configs/test_dpp_hydra.yml

@@ -1,20 +1,39 @@
 trainer: forces
 
-task:
-  dataset: lmdb
-  type: regression
-  metric: mae
-  primary_metric: forces_mae
-  labels:
-    - potential energy
-  grad_input: atomic forces
-  train_on_free_atoms: True
-  eval_on_free_atoms: True
-  prediction_dtype: float32
+outputs:
+  energy:
+    shape: 1
+    level: system
+  forces:
+    irrep_dim: 1
+    level: atom
+    train_on_free_atoms: True
+    eval_on_free_atoms: True
+
+loss_functions:
+  - energy:
+      fn: mae
+      coefficient: 2
+  - forces:
+      fn: l2mae
+      coefficient: 100
 
+evaluation_metrics:
+  metrics:
+    energy:
+      - mae
+    forces:
+      - mae
+      - cosine_similarity
+      - magnitude_error
+    misc:
+      - energy_forces_within_threshold
+  primary_metric: forces_mae
+
 logger:
     name: tensorboard
 
+
 model:
   name: hydra
   backbone:

tests/core/models/test_configs/test_equiformerv2_hydra.yml

@@ -1,7 +1,38 @@
-
-
 trainer: forces
 
+outputs:
+  energy:
+    shape: 1
+    level: system
+  forces:
+    irrep_dim: 1
+    level: atom
+    train_on_free_atoms: True
+    eval_on_free_atoms: True
+
+loss_functions:
+  - energy:
+      fn: mae
+      coefficient: 2
+  - forces:
+      fn: l2mae
+      coefficient: 100
+
+evaluation_metrics:
+  metrics:
+    energy:
+      - mae
+    forces:
+      - mae
+      - cosine_similarity
+      - magnitude_error
+    misc:
+      - energy_forces_within_threshold
+  primary_metric: forces_mae
+
+logger:
+    name: tensorboard
+
 model:
   name: hydra
   backbone:
@@ -53,34 +84,6 @@ model:
     forces:
       module: equiformer_v2_force_head
 
-dataset:
-  train:
-    src: tutorial_dset/s2ef/train_100/
-    normalize_labels: True
-    target_mean: -0.7554450631141663
-    target_std: 2.887317180633545
-    grad_target_mean: 0.0
-    grad_target_std: 2.887317180633545
-  val:
-    format: lmdb
-    src: tutorial_dset/s2ef/val_20/
-
-logger:
-  name: tensorboard
-
-task:
-  dataset: lmdb
-  type: regression
-  metric: mae
-  primary_metric: forces_mae
-  labels:
-    - potential energy
-  grad_input: atomic forces
-  train_on_free_atoms: True
-  eval_on_free_atoms: True
-  prediction_dtype: float32
-
-
 optim:
   batch_size: 5
   eval_batch_size: 2