Finish multimer inference

openfold/data/input_pipeline_multimer.py

@@ -29,6 +29,7 @@ def nonensembled_transform_fns(common_cfg, mode_cfg):
         data_transforms.cast_to_64bit_ints,
         data_transforms_multimer.make_msa_profile,
         data_transforms_multimer.create_target_feat,
+        data_transforms.make_atom14_masks,
     ]
 
     if(common_cfg.use_templates):

openfold/model/embedders.py

@@ -868,7 +868,7 @@ class TemplateEmbedderMultimer(nn.Module):
             
             raw_atom_pos = single_template_feats["template_all_atom_positions"]
             
-            atom_pos = geometry.Vec3Array.from_tensor(raw_atom_pos)
+            atom_pos = geometry.Vec3Array.from_array(raw_atom_pos)
             rigid, backbone_mask = all_atom_multimer.make_backbone_affine(
                 atom_pos,
                 single_template_feats["template_all_atom_mask"],

openfold/model/structure_module.py

@@ -363,9 +363,6 @@ class InvariantPointAttention(nn.Module):
         a *= math.sqrt(1.0 / (3 * self.c_hidden))
         a += (math.sqrt(1.0 / 3) * permute_final_dims(b, (2, 0, 1)))
 
-        for c in q_pts:
-            print(type(c))
-
         # [*, N_res, N_res, H, P_q, 3]
         pt_att = q_pts[..., None, :, :] - k_pts[..., None, :, :, :]
         
@@ -669,7 +666,7 @@ class StructureModule(nn.Module):
         self, r, f  # [*, N, 8]  # [*, N]
     ):
         # Lazily initialize the residue constants on the correct device
-        self._init_residue_constants(r.get_rots().dtype, r.get_rots().device)
+        self._init_residue_constants(r.dtype, r.device)
         return frames_and_literature_positions_to_atom14_pos(
             r,
             f,
@@ -818,11 +815,11 @@ class StructureModule(nn.Module):
             )
             
             preds = {
-                "frames": rigids.scale_translation(self.trans_scale_factor).to_tensor7(),
+                "frames": rigids.scale_translation(self.trans_scale_factor).to_tensor(),
                 "sidechain_frames": all_frames_to_global.to_tensor_4x4(),
                 "unnormalized_angles": unnormalized_angles,
                 "angles": angles,
-                "positions": pred_xyz,
+                "positions": pred_xyz.to_tensor(),
             }
 
             outputs.append(preds)

openfold/np/protein.py

@@ -205,7 +205,7 @@ def from_proteinnet_string(proteinnet_str: str) -> Protein:
     )
 
 
-def _chain_end(atom_index, end_resname, chain_name, residue_indx) -> str:
+def _chain_end(atom_index, end_resname, chain_name, residue_index) -> str:
     chain_end = 'TER'
     return(
         f'{chain_end:<6}{atom_index:>5}      {end_resname:>3} '

openfold/utils/feats.py

@@ -22,6 +22,7 @@ from typing import Dict
 
 from openfold.np import protein
 import openfold.np.residue_constants as rc
+from openfold.utils.geometry import rigid_matrix_vector, rotation_matrix
 from openfold.utils.rigid_utils import Rotation, Rigid
 from openfold.utils.tensor_utils import (
     batched_gather,
@@ -213,15 +214,14 @@ def torsion_angles_to_frames(
     # This follows the original code rather than the supplement, which uses
     # different indices.
 
-    all_rots = alpha.new_zeros(default_r.get_rots().get_rot_mats().shape)
+    all_rots = alpha.new_zeros(default_r.shape + (3, 3))
     all_rots[..., 0, 0] = 1
     all_rots[..., 1, 1] = alpha[..., 1]
     all_rots[..., 1, 2] = -alpha[..., 0]
     all_rots[..., 2, 1:] = alpha
 
-    all_rots = Rigid(Rotation(rot_mats=all_rots), None)
-
-    all_frames = default_r.compose(all_rots)
+    all_rots = rotation_matrix.Rot3Array.from_array(all_rots)
+    all_frames = default_r.compose_rotation(all_rots)
 
     chi2_frame_to_frame = all_frames[..., 5]
     chi3_frame_to_frame = all_frames[..., 6]
@@ -232,7 +232,7 @@ def torsion_angles_to_frames(
     chi3_frame_to_bb = chi2_frame_to_bb.compose(chi3_frame_to_frame)
     chi4_frame_to_bb = chi3_frame_to_bb.compose(chi4_frame_to_frame)
 
-    all_frames_to_bb = Rigid.cat(
+    all_frames_to_bb = rigid_matrix_vector.Rigid3Array.cat(
         [
             all_frames[..., :5],
             chi2_frame_to_bb.unsqueeze(-1),
@@ -248,7 +248,7 @@ def torsion_angles_to_frames(
 
 
 def frames_and_literature_positions_to_atom14_pos(
-    r: Rigid,
+    r: rigid_matrix_vector.Rigid3Array,
     aatype: torch.Tensor,
     default_frames,
     group_idx,
@@ -275,8 +275,8 @@ def frames_and_literature_positions_to_atom14_pos(
         lambda x: torch.sum(x, dim=-1)
     )
 
-    # [*, N, 14, 1]
-    atom_mask = atom_mask[aatype, ...].unsqueeze(-1)
+    # [*, N, 14]
+    atom_mask = atom_mask[aatype, ...]
 
     # [*, N, 14, 3]
     lit_positions = lit_positions[aatype, ...]

openfold/utils/geometry/quat_rigid.py

@@ -22,8 +22,6 @@ class QuatRigid(nn.Module):
         # NOTE: During training, this needs to be run in higher precision
         rigid_flat = self.linear(activations.to(torch.float32))
         
-        print(rigid_flat.shape)
-
         rigid_flat = torch.unbind(rigid_flat, dim=-1)
         if(self.full_quat):
             qw, qx, qy, qz = rigid_flat[:4]

openfold/utils/geometry/rigid_matrix_vector.py

@@ -67,6 +67,9 @@ class Rigid3Array:
         """Apply Rigid3Array transform to point."""
         return self.rotation.apply_to_point(point) + self.translation
 
+    def apply(self, point: torch.Tensor) -> vector.Vec3Array:
+        return self.apply_to_point(vector.Vec3Array.from_array(point))
+
     def apply_inverse_to_point(self, point: vector.Vec3Array) -> vector.Vec3Array:
         """Apply inverse Rigid3Array transform to point."""
         new_point = point - self.translation
@@ -74,7 +77,28 @@ class Rigid3Array:
 
     def compose_rotation(self, other_rotation):
         rot = self.rotation @ other_rotation
-    return Rigid3Array(rot, trans.clone())
+        return Rigid3Array(rot, self.translation.clone())
+
+    def compose(self, other_rigid):
+        return self @ other_rigid
+
+    def unsqueeze(self, dim: int):
+        return Rigid3Array(
+            self.rotation.unsqueeze(dim),
+            self.translation.unsqueeze(dim),
+        )
+
+    @property
+    def shape(self) -> torch.Size:
+        return self.rotation.xx.shape
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.rotation.xx.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.rotation.xx.device
 
     @classmethod
     def identity(cls, shape, device) -> Rigid3Array:
@@ -87,28 +111,51 @@ class Rigid3Array:
     @classmethod
     def cat(cls, rigids: List[Rigid3Array], dim: int) -> Rigid3Array:
         return cls(
-      Rot3Array.cat([r.rotation for r in rigids], dim=dim),
-      Vec3Array.cat([r.translation for r in rigids], dim=dim),
+            rotation_matrix.Rot3Array.cat(
+                [r.rotation for r in rigids], dim=dim
+            ),
+            vector.Vec3Array.cat(
+                [r.translation for r in rigids], dim=dim
+            ),
         ) 
 
     def scale_translation(self, factor: Float) -> Rigid3Array:
         """Scale translation in Rigid3Array by 'factor'."""
         return Rigid3Array(self.rotation, self.translation * factor)
 
-  def to_array(self):
-    rot_array = self.rotation.to_array()
-    vec_array = self.translation.to_array()
-    return torch.cat([rot_array, vec_array[..., None]], dim=-1)
+    def to_tensor(self) -> torch.Tensor:
+        rot_array = self.rotation.to_tensor()
+        vec_array = self.translation.to_tensor()
+        array = torch.zeros(
+            rot_array.shape[:-2] + (4, 4), 
+            device=rot_array.device, 
+            dtype=rot_array.dtype
+        )
+        array[..., :3, :3] = rot_array
+        array[..., :3, 3] = vec_array
+        array[..., 3, 3] = 1.
+        return array
+
+    def to_tensor_4x4(self) -> torch.Tensor:
+        return self.to_tensor()
 
     def reshape(self, new_shape) -> Rigid3Array:
         rots = self.rotation.reshape(new_shape)
         trans = self.translation.reshape(new_shape)
         return Rigid3Aray(rots, trans)
 
+    def stop_rot_gradient(self) -> Rigid3Array:
+        return Rigid3Array(
+            self.rotation.stop_gradient(),
+            self.translation,
+        )
+
     @classmethod
     def from_array(cls, array):
-    rot = rotation_matrix.Rot3Array.from_array(array[..., :3])
-    vec = vector.Vec3Array.from_array(array[..., -1])
+        rot = rotation_matrix.Rot3Array.from_array(
+            array[..., :3, :3],
+        )
+        vec = vector.Vec3Array.from_array(array[..., :3, 3])
         return cls(rot, vec)
 
     @classmethod
@@ -124,5 +171,6 @@ class Rigid3Array:
             array[..., 2, 0], array[..., 2, 1], array[..., 2, 2]
         )
         translation = vector.Vec3Array(
-        array[..., 0, 3], array[..., 1, 3], array[..., 2, 3])
+            array[..., 0, 3], array[..., 1, 3], array[..., 2, 3]
+        )
         return cls(rotation, translation)

openfold/utils/geometry/rotation_matrix.py

@@ -22,6 +22,7 @@ import numpy as np
 from openfold.utils.geometry import struct_of_array
 from openfold.utils.geometry import utils
 from openfold.utils.geometry import vector
+from openfold.utils.tensor_utils import tensor_tree_map
 
 
 COMPONENTS = ['xx', 'xy', 'xz', 'yx', 'yy', 'yz', 'zx', 'zy', 'zz']
@@ -59,6 +60,13 @@ class Rot3Array:
             }
         )
     
+    def __matmul__(self, other: Rot3Array) -> Rot3Array:
+        """Composes two Rot3Arrays."""
+        c0 = self.apply_to_point(vector.Vec3Array(other.xx, other.yx, other.zx))
+        c1 = self.apply_to_point(vector.Vec3Array(other.xy, other.yy, other.zy))
+        c2 = self.apply_to_point(vector.Vec3Array(other.xz, other.yz, other.zz))
+        return Rot3Array(c0.x, c1.x, c2.x, c0.y, c1.y, c2.y, c0.z, c1.z, c2.z)
+
     def map_tensor_fn(self, fn) -> Rot3Array:
         field_names = utils.get_field_names(Rot3Array) 
         return Rot3Array(
@@ -88,12 +96,19 @@ class Rot3Array:
         """Applies inverse Rot3Array to point."""
         return self.inverse().apply_to_point(point)
 
-    def __matmul__(self, other: Rot3Array) -> Rot3Array:
-        """Composes two Rot3Arrays."""
-        c0 = self.apply_to_point(vector.Vec3Array(other.xx, other.yx, other.zx))
-        c1 = self.apply_to_point(vector.Vec3Array(other.xy, other.yy, other.zy))
-        c2 = self.apply_to_point(vector.Vec3Array(other.xz, other.yz, other.zz))
-        return Rot3Array(c0.x, c1.x, c2.x, c0.y, c1.y, c2.y, c0.z, c1.z, c2.z)
+
+    def unsqueeze(self, dim: int):
+        return Rot3Array(
+            *tensor_tree_map(
+                lambda t: t.unsqueeze(dim), 
+                [getattr(self, c) for c in COMPONENTS]
+            )
+        )
+
+    def stop_gradient(self) -> Rot3Array:
+        return Rot3Array(
+            *[getattr(self, c).detach() for c in COMPONENTS]
+        )
 
     @classmethod
     def identity(cls, shape, device) -> Rot3Array:
@@ -130,9 +145,11 @@ class Rot3Array:
     @classmethod
     def from_array(cls, array: torch.Tensor) -> Rot3Array:
         """Construct Rot3Array Matrix from array of shape. [..., 3, 3]."""
-        return cls(torch.unbind(array, dim=-2))
+        rows = torch.unbind(array, dim=-2)
+        rc = [torch.unbind(e, dim=-1) for e in rows]
+        return cls(*[e for row in rc for e in row])
 
-    def to_array(self) -> torch.Tensor:
+    def to_tensor(self) -> torch.Tensor:
         """Convert Rot3Array to array of shape [..., 3, 3]."""
         return torch.stack(
             [
@@ -140,7 +157,8 @@ class Rot3Array:
                 torch.stack([self.yx, self.yy, self.yz], dim=-1),
                 torch.stack([self.zx, self.zy, self.zz], dim=-1)
             ],
-            dim=-2)
+            dim=-2
+        )
 
     @classmethod
     def from_quaternion(cls,

openfold/utils/geometry/vector.py

@@ -134,13 +134,20 @@ class Vec3Array:
 
         return Vec3Array(x, y, z)
 
-    def sum(self, dim) -> Vec3Array:
+    def sum(self, dim: int) -> Vec3Array:
         return Vec3Array(
             torch.sum(self.x, dim=dim),
             torch.sum(self.y, dim=dim),
             torch.sum(self.z, dim=dim),
         )
 
+    def unsqueeze(self, dim: int):
+        return Vec3Array(
+            self.x.unsqueeze(dim),
+            self.y.unsqueeze(dim),
+            self.z.unsqueeze(dim),
+        )
+
     @classmethod
     def zeros(cls, shape, device="cpu"):
         """Return Vec3Array corresponding to zeros of given shape."""
@@ -150,11 +157,11 @@ class Vec3Array:
             torch.zeros(shape, dtype=torch.float32, device=device)
         )
 
-    def to_array(self) -> torch.Tensor:
+    def to_tensor(self) -> torch.Tensor:
         return torch.stack([self.x, self.y, self.z], dim=-1)
 
     @classmethod
-    def from_tensor(cls, tensor):
+    def from_array(cls, tensor):
         return cls(*torch.unbind(tensor, dim=-1))
 
     @classmethod

openfold/utils/import_weights.py

@@ -623,8 +623,8 @@ def import_jax_weights_(model, npz_path, version="model_1"):
     flat_keys = list(flat.keys())
     incorrect = [k for k in flat_keys if k not in keys]
     missing = [k for k in keys if k not in flat_keys]
-    print(f"Incorrect: {incorrect}")
-    print(f"Missing: {missing}")
+    # print(f"Incorrect: {incorrect}")
+    # print(f"Missing: {missing}")
 
     assert len(incorrect) == 0
     # assert(sorted(list(flat.keys())) == sorted(list(data.keys())))

run_pretrained_openfold.py

@@ -217,7 +217,8 @@ def main(args):
         unrelaxed_protein = protein.from_prediction(
             features=batch,
             result=out,
-            b_factors=plddt_b_factors
+            b_factors=plddt_b_factors,
+            remove_leading_feature_dimension=not is_multimer,
         )
 
         # Save the unrelaxed PDB.
@@ -227,6 +228,9 @@ def main(args):
         with open(unrelaxed_output_path, 'w') as f:
             f.write(protein.to_pdb(unrelaxed_protein))
 
+        print(unrelaxed_output_path)
+        print("asdjfh klasjdhf lkasjdhf lkjasdhflkjasdh fkl jasdhfklj hasdkljf hasldkjfh lkasjdfh lkajsdhflk asd")
+
         amber_relaxer = relax.AmberRelaxation(
             use_gpu=(args.model_device != "cpu"),
             **config.relax,