update snapshot; fix test seed and change tolerance

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

@@ -424,7 +424,6 @@ def __init__(
             )
 
         self.apply(partial(eqv2_init_weights, weight_init=self.weight_init))
-        # self.apply(eqv2_uniform_init_rad_func_linear_weights)
 
     def _init_gp_partitions(
         self,

tests/core/models/__snapshots__/test_equiformer_v2.ambr

@@ -56,7 +56,7 @@
 # ---
 # name: TestEquiformerV2.test_gp.1
   Approx(
-  	array([-0.03269595], dtype=float32), 
+  	array([0.12408739], dtype=float32), 
   	rtol=0.001, 
   	atol=0.001
   )
@@ -69,7 +69,7 @@
 # ---
 # name: TestEquiformerV2.test_gp.3
   Approx(
-  	array([ 0.00208857, -0.00017979, -0.0028318 ], dtype=float32), 
+  	array([ 1.4928661e-03, -7.4134863e-05,  2.9909245e-03], dtype=float32), 
   	rtol=0.001, 
   	atol=0.001
   )

tests/core/models/test_rank2_head.py

@@ -33,6 +33,7 @@ def batch():
 def test_rank2_head(
     batch, decompose, edge_level_mlp, use_source_target_embedding, extensive
 ):
+    torch.manual_seed(100)  # fix network initialization
     backbone = EquiformerV2Backbone(
         num_layers=2,
         sphere_channels=8,
@@ -58,7 +59,7 @@ def test_rank2_head(
         assert r2_out["out_isotropic"].shape[1] == 1
         tensor = _reshape_tensor(r2_out["out_isotropic"])
         # anisotropic must be traceless
-        assert torch.diagonal(tensor).sum().item() == pytest.approx(0.0, abs=1e-8)
+        assert torch.diagonal(tensor).sum().item() == pytest.approx(0.0, abs=2e-8)
     else:
         assert "out" in r2_out
         tensor = r2_out["out"].view(3, 3)