cache the calculation of has_bond in `molecule_lengthed_molecule_inpu…

…t_to_atom_input`

alphafold3_pytorch/inputs.py

@@ -213,17 +213,18 @@
 # simple caching
 
 ATOMPAIR_IDS_CACHE = dict()
+HAS_BOND_CACHE = dict()
 
 @typecheck
 def maybe_cache(
     fn,
     *,
     cache: dict,
-    key: str,
+    key: str | None,
     should_cache: bool = True
 ) -> Callable:
 
-    if not should_cache:
+    if not should_cache or not exists(key):
         return fn
 
     @wraps(fn)
@@ -246,7 +247,7 @@ def inner(*args, **kwargs):
 def get_atompair_ids(
     mol: Mol,
     directed_bonds: bool
-) -> Tensor | None:
+) -> Int['m m'] | None:
 
     coordinates = []
     updates = []
@@ -304,6 +305,46 @@ def get_atompair_ids(
 
     return mol_atompair_ids
 
+@typecheck
+def get_mol_has_bond(
+    mol: Mol
+) -> Bool['m m'] | None:
+
+    coordinates = []
+
+    bonds = mol.GetBonds()
+    num_bonds = len(bonds)
+
+    for bond in bonds:
+        atom_start_index = bond.GetBeginAtomIdx()
+        atom_end_index = bond.GetEndAtomIdx()
+
+        coordinates.extend(
+            [
+                [atom_start_index, atom_end_index],
+                [atom_end_index, atom_start_index],
+            ]
+        )
+
+    if num_bonds == 0:
+        return None
+
+    num_atoms = mol.GetNumAtoms()
+    has_bond = torch.zeros(num_atoms, num_atoms).bool()
+
+    coordinates = tensor(coordinates).long()
+
+    # has_bond = einx.set_at("[h w], c [2], c -> [h w]", has_bond, coordinates, updates)
+
+    has_bond_stride = tensor(has_bond.stride())
+    flattened_coordinates = (coordinates * has_bond_stride).sum(dim = -1)
+    packed_has_bond, unpack_has_bond = pack_one(has_bond, '*')
+
+    packed_has_bond[flattened_coordinates] = True
+    has_bond = unpack_has_bond(packed_has_bond, '*')
+
+    return has_bond
+
 # functions
 
 
@@ -1182,49 +1223,36 @@ def molecule_lengthed_molecule_input_to_atom_input(
 
     for (
         mol,
+        mol_id,
         mol_is_chainable_biomolecule,
         mol_is_first_mol_in_chain,
         mol_is_one_token_per_atom,
-    ) in zip(molecules, is_chainable_biomolecules, is_first_mol_in_chains, one_token_per_atom):
+    ) in zip(
+        molecules,
+        molecule_ids,
+        is_chainable_biomolecules,
+        is_first_mol_in_chains,
+        one_token_per_atom
+    ):
         num_atoms = mol.GetNumAtoms()
 
         if mol_is_chainable_biomolecule and not mol_is_first_mol_in_chain:
             token_bonds[offset, offset - 1] = True
             token_bonds[offset - 1, offset] = True
 
         if mol_is_one_token_per_atom:
-            coordinates = []
-
-            bonds = mol.GetBonds()
-            num_bonds = len(bonds)
-
-            for bond in bonds:
-                atom_start_index = bond.GetBeginAtomIdx()
-                atom_end_index = bond.GetEndAtomIdx()
 
-                coordinates.extend(
-                    [
-                        [atom_start_index, atom_end_index],
-                        [atom_end_index, atom_start_index],
-                    ]
-                )
-
-            if num_bonds > 0:
-                has_bond = torch.zeros(num_atoms, num_atoms).bool()
-
-                coordinates = tensor(coordinates).long()
-
-                # has_bond = einx.set_at("[h w], c [2], c -> [h w]", has_bond, coordinates, updates)
-
-                has_bond_stride = tensor(has_bond.stride())
-                flattened_coordinates = (coordinates * has_bond_stride).sum(dim = -1)
-                packed_has_bond, unpack_has_bond = pack_one(has_bond, '*')
-
-                packed_has_bond[flattened_coordinates] = True
-                has_bond = unpack_has_bond(packed_has_bond, '*')
+            maybe_cached_get_mol_has_bond = maybe_cache(
+                get_mol_has_bond,
+                cache = HAS_BOND_CACHE,
+                key = str(mol_id),
+                should_cache = mol_is_chainable_biomolecule.item()
+            )
 
-                # / ein.set_at
+            has_bond = maybe_cached_get_mol_has_bond(mol)
 
+            if exists(has_bond) and has_bond.numel() > 0:
+                num_atoms = mol.GetNumAtoms()
                 row_col_slice = slice(offset, offset + num_atoms)
                 token_bonds[row_col_slice, row_col_slice] = has_bond
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "alphafold3-pytorch"
-version = "0.5.37"
+version = "0.5.38"
 description = "Alphafold 3 - Pytorch"
 authors = [
     { name = "Phil Wang", email = "[email protected]" },