fix: add autocast for float32 only ops

moai/conf/model/parameters/groups/model1.yaml

@@ -0,0 +1,7 @@
+# @package model.parameters.groups.model1
+
+_target_: moai.parameters.selectors.model.ModelParameterSelector
+modules: null # optional
+monads: null # optional
+parameters: null # optional
+force_grad: true

moai/conf/model/parameters/groups/model2.yaml

@@ -0,0 +1,7 @@
+# @package model.parameters.groups.model2
+
+_target_: moai.parameters.selectors.model.ModelParameterSelector
+modules: null # optional
+monads: null # optional
+parameters: null # optional
+force_grad: true

moai/monads/render/nvdiffrast/render.py

@@ -22,6 +22,7 @@ def __init__(
         self.resolution = [height, width]
         self.decomposed = decomposed
 
+    @torch.amp.custom_fwd(device_type="cuda", cast_inputs=torch.float32)
     def forward(
         self,
         ndc_vertices: torch.Tensor,
@@ -90,6 +91,7 @@ def __init__(
     ):
         super().__init__()
 
+    @torch.amp.custom_fwd(device_type="cuda", cast_inputs=torch.float32)
     def forward(
         self,
         attributes: torch.Tensor,
@@ -114,6 +116,7 @@ def __init__(
         super().__init__()
         self.pos_grad_boost = position_gradient_scale
 
+    @torch.amp.custom_fwd(device_type="cuda", cast_inputs=torch.float32)
     def forward(
         self,
         attributes: torch.Tensor,

moai/supervision/objectives/mesh/laplacian.py

@@ -89,6 +89,7 @@ def __init__(self, method: str = "uniform"):
         super().__init__()
         self.method = method
 
+    @torch.amp.custom_fwd(device_type="cuda", cast_inputs=torch.float32)
     def forward(
         self,
         vertices: torch.Tensor,  # [B, V, 3]
@@ -109,9 +110,9 @@ def forward(
             # We don't want to backprop through the computation of the Laplacian;
             # just treat it as a magic constant matrix that is used to transform
             # verts into normals
-            with torch.no_grad():
+            with torch.no_grad(), torch.amp.autocast(device_type="cuda", enabled=False):
                 if self.method == "uniform":
-                    L = laplacian(verts_packed, faces_packed)
+                    L = laplacian(verts_packed.float(), faces_packed)
                 elif self.method in ["cot", "cotcurv"]:
                     L, inv_areas = cot_laplacian(verts_packed, faces_packed)
                     if self.method == "cot":