auto move inputs to same device as model when invoking `forward_with_…

…alphafold3_inputs`

alphafold3_pytorch/alphafold3.py

@@ -10,9 +10,10 @@
 
 import torch
 from torch import nn
-from torch import Tensor, tensor
+from torch import Tensor, tensor, is_tensor
 from torch.amp import autocast
 import torch.nn.functional as F
+from torch.utils._pytree import tree_map
 
 from torch.nn import (
     Module,
@@ -227,6 +228,9 @@ def freeze_(m: Module):
 def max_neg_value(t: Tensor):
     return -torch.finfo(t.dtype).max
 
+def dict_to_device(d, device):
+    return tree_map(lambda t: t.to(device) if is_tensor(t) else t, d)
+
 def pack_one(t, pattern):
     packed, ps = pack([t], pattern)
 
@@ -263,7 +267,7 @@ def should_checkpoint(
     inputs: Tensor | Tuple[Tensor, ...],
     check_instance_variable: str | None = 'checkpoint'
 ) -> bool:
-    if torch.is_tensor(inputs):
+    if is_tensor(inputs):
         inputs = (inputs,)
 
     return (
@@ -6344,7 +6348,11 @@ def forward_with_alphafold3_inputs(
             alphafold3_inputs = [alphafold3_inputs]
 
         batched_atom_inputs = alphafold3_inputs_to_batched_atom_input(alphafold3_inputs, atoms_per_window = self.w)
-        return self.forward(**batched_atom_inputs.model_forward_dict(), **kwargs)
+
+        atom_dict = batched_atom_inputs.model_forward_dict()
+        atom_dict = dict_to_device(atom_dict, device = self.device)
+
+        return self.forward(**atom_dict, **kwargs)
 
     @typecheck
     def forward(

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "alphafold3-pytorch"
-version = "0.5.18"
+version = "0.5.19"
 description = "Alphafold 3 - Pytorch"
 authors = [
     { name = "Phil Wang", email = "[email protected]" },